ORIGINAL_ARTICLE
Masked Data Analysis based on the Generalized Linear Model
In this paper, we consider the estimation problem in the presence of masked data for series systems. A missing indicator is proposed to describe masked set of each failure time.Moreover, a Generalized Linear model (GLM) with appropriate link function is used to model masked indicator in order to involve masked information into likelihood function. Both maximum likelihood and Bayesian methods were considered.The likelihood function with both missing at random (MAR) and missing not at random (MNAR) mechanismsare derived.Using an auxiliary variable, a Bayesian approach is expanded to obtain posterior estimations of the model parameters.The proposed methods have been illustrated through a real example.
http://www.ijrrs.com/article_120705_94af284a3fe900d9b3d9eeddeacc6219.pdf
2020-12-01
1
7
10.30699/IJRRS.3.2.1
Bayesian Modeling
Markov chain Monte Carlo Method
Masked Data
Non-ignorable Missing Mechanism
Hasan
Misaii
hasanmisaii14@gmail.com
1
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
LEAD_AUTHOR
Firoozeh
Haghighi
fhaghighi@ut.ac.ir
2
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
AUTHOR
Samaneh
Eftekhari Mahabadi
s.eftekhari@khayam.ut.ac.ir
3
School of Mathematics, Statistics and Computer Science, College of Science, University of Tehran, Tehran, Iran
AUTHOR
[1] Miyakawa, M. “Analysis of incomplete data in a competing risks model,” IEEE Trans. Rel. 33(4): pp. 293–296, 1984.
1
[2] Basu, S. Basu, A. P. and Mukhopadhyay, C. “Bayesian analysis for masked system failuredata using nonidenticalweibull models,” J. Statist. Plann. Inference, vol. 78, pp. 255–275, 1999.
2
[3] AziziF. and Haghighi,F. Joint modeling of linear degradation and failure time data with masked causes of failure under simple step-stress test. J Stat Comput Simul. 88(8):1603–1615, 2018. doi: 10.1080/00949655.2018.1442468.
3
[4] Usher, J. S. and Hodgson, T. J. “Maximum likelihood analysis of component reliability usingmasked system life data,” IEEE Trans. Rel., vol. 37, no. 5, pp. 550–555, 1998.
4
[5] Lin, D. K. J. Usher, J. S. and Guess, F. M. “Exact maximum likelihood estimation usingmasked system data”, IEEE Trans. Rel., vol. 42, no. 4, pp. 631–635, 1993.
5
[6] Reiser, B. Guttman, I. Lin, D. K. J. Usher, J. S. and Guess, F. M. “Bayesian inference formasked system lifetime data,” Appl. Statist., vol. 44, pp. 79–90, 1995.
6
[7] Berger, J. O. and Sun, D. “Bayesian analysis for the poly-Weibull distribution,” J. Amer.Statist. Assoc., vol. 88, pp. 1412–1418, 1993.
7
[8] Mukhopadhyay, C. and Basu, A. P. “Bayesian analysis of incomplete time and cause offailure data,” J. Statist. Plann. Inference, vol. 59, pp. 79–100, 1997.
8
[9] Cai, J., Shi, Y., Zhang, Y. Robust Bayesian analysis for parallel system with masked data under inverse Weibull lifetime distribution. Commun Stat-Theory Methods. 49:1–13, 2019.
9
[10] Sen, A. Banerjee, M. and Basu, S. Balakrishnan, N. and Rao, C. R. Eds. “Analysis ofmasked failure data under competing risks,” in Handbook of Statistics. Amsterdam, The Nether-lands: North-Holland, vol. 20, pp. 523–540, 2001.
10
[11] Basu, S. Sen, A. and Banerjee, M. “Bayesian analysis of competing risks with partiallymasked cause of failure,” Appl. Statist., vol. 52, pp. 77–93, 2003.
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[12] Lin D. K. J. and Guess, F. M. “System life data analysis with dependent partial knowledgeon the exact cause of system failure”, Microelectron. Rel., vol. 34, pp. 535–544, 1994.
12
[13] Guttman, I. Lin, D. K. J. Reiser, B. and Usher, J. S. “Dependent masking and systemlife data analysis: Bayesian inference for two-component systems,” Lifetime Data Anal., vol. 1, pp.87–100, 1995.
13
[14] Kuo, L. and Yang, T. E. “Bayesian reliability modeling for masked system lifetime data”,Statist. Probab. Lett., vol. 47, pp. 229–241, 2000.
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[15] Mukhopadhyay, C. and Basu, A. P. “Masking without the symmetry assumption: A Bayesianapproach,” in Proc. Abstract Book 2nd Int. Conf. Math. Methods Rel., Bordeaux, France, vol. 2,pp. 784–787, 2000,Universite Victor Segalen.
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[16] Craiu, R. V. and Duchesne, T. Gelman, A. and. Meng, Eds. X. L. “Using EM and DA for thecompeting risk model,” in Applied Bayesian Modeling and Causal Inference from an Incomplete-Data Perspective. New York, NY, USA: Wiley, pp. 234–245, 2004.
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[17] [17] Mukhopadhyay, C. “Maximum likelihood analysis of masked series system lifetime data,” J.Statist. Plann. Inference, vol. 136, pp. 803–838, 2006.
17
[18] Xu, A. and Tang, Y. “Bayesian analysis of Pareto reliability with dependent masked data,”IEEE Trans. Rel., vol. 58, no. 4, pp. 583–588, 2009.
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[19] Xu, A. Basu, S. and Tang, Y.” A Full Bayesian Approach for Masked Data in Step-StressAccelerated Life Testing,” IEEE Trans. Rel., vol. 63, no. 3, pp. 798-806, 2014.
19
ORIGINAL_ARTICLE
Probabilistic Life Assessment of Gas Turbine Blade Alloys under Creep
Deformations occur gradually in the gas turbine components since they are working under high temperature and stress. In the turbine blade alloys, creep is the most significant failure mechanism. In this research, creep life has been estimated for the blade alloys by considering humidity. A method is proposed to estimate the creep life by direct consideration of humidity on the creep life of the gas turbine blade. In the proposed model, the humidity factor is added to the classic Larson Miller creep life estimation method. This model is capable of predicting creep life with known dry temperature (Water Air Ratio=0), mechanical stress, and humidity. In this approach, there is no need to measure blade temperature variation during operation. As a case study, the creep life of first-stage turbine blade alloy is predicted using the proposed method and benchmarked with published (Finite element analysis) FEA results. The reliability of the blades was estimated by considering different success criteria using Monte Carlo simulation. The reliability of the creep rupture life of Nimonic-90 steel was carried out using SCRI mode based on the Z-parameter. The scattered data has been considered for creep rupture of materials in this part. The results show that creep life increases with humidity increase. It is also shown that with an increase in mechanical stress and temperature fluctuations, the reliability of the turbine blade creep life decreases sharply.
http://www.ijrrs.com/article_120951_a6b40aaa3bd0d798430e1602c8b4df2f.pdf
2020-12-01
9
17
10.30699/IJRRS.3.2.2
Creep life prediction
Failure mechanism
Gas Turbine Blade
Humidity
Nimonic 90
Reliability
SCRI method
Bita
soltan Mohammadlou
sml.bita@gmail.com
1
Mechanical Engineering Faculty, Sahand University of Technology, Tabriz, Iran
AUTHOR
Mohammad
Pourgolmohamad
pourgolmohammad@sut.ac.ir
2
Mechanical Engineering Faculty, Sahand University of Technology, Tabriz, Iran
LEAD_AUTHOR
Mojtaba
yazdani
m.yazdani@sut.ac.ir
3
Mechanical Engineering Faculty, Sahand University of Technology, Tabriz, Iran
AUTHOR
[1] Naeem, M., Singh R., and Probert D., "Consequences of aero-engine deteriorations for military aircraft." Applied energy 70.2 ; pp.103-133, 2001.
1
[2] Yazdanipour, Mahboubeh, et al. "Fatigue life prediction based on probabilistic fracture mechanics: case study of automotive parts." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 2.1 , 2016.
2
[3] Yazdanipour, Mahboubeh, and Mohammad Pourgol-Mohammad. "Stochastic fatigue crack growth analysis of metallic structures under multiple thermal–mechanical stress levels", Materials & Design 95: pp.599-611, 2016.
3
[4] Shiri, Saeed, Mohammad Pourgol Mohammad, and Mojtaba Yazdani. "Prediction of remaining fatigue cycles in composite materials under uncertainty." ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering 2.1 , 2016.
4
[5] Soltan Mohammad Lou, Bita, Mohammad Pourgol-Mohammad, and Mojtaba Yazdani. "Life Assessment of Gas Turbine Blades Under Creep Failure Mechanism Considering Humidity." ASME International Mechanical Engineering Congress and Exposition. Vol. 52187. American Society of Mechanical Engineers, 2018.
5
[6] Eshati, S., et al. "Impact of operating conditions and design parameters on gas turbine hot section creep life." Turbo Expo: Power for Land, Sea, and Air. Vol. 43987, 2010.
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[7] Rabotnov,Y. N, "Creep problems in structural members",1969.
7
[8] Choudhary, B. K., et al. "On the reliability assessment of creep life for grade 91 steel." Procedia Engineering 86: pp.335-341, 2014.
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[9] Zhao, Jie, et al. "Introduction of SCRI model for creep rupture life assessment." International journal of pressure vessels and piping 86.9: pp. 599-603, 2009.
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[10] Szargut, Jan, Janusz Skorek, and Ireneusz Szczygiel. "Influence of blade cooling on the efficiency of humid air turbine." International Journal of Applied Thermodynamics 3.1: pp. 21-26, 2000.
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[11] Brun, Klaus, Rainer Kurz, and Harold R. Simmons. "Aerodynamic instability and life limiting effects of inlet and interstage water injection into gas turbines." Turbo Expo: Power for Land, Sea, and Air. Vol. 47276, 2005.
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[12] Eshati, S., et al. "Impact of operating conditions and design parameters on gas turbine hot section creep life." Turbo Expo: Power for Land, Sea, and Air. Vol. 43987. 2010.
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[13] Prager, Martin. "Development of the MPC omega method for life assessment in the creep range.": pp.95-103, 1995.
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[14] Monkman, Forest C. "An empirical relationship between rupture life and minimum creep rate in creep rupture tests." proc. ASTM. Vol. 56. 1956.
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[15] Chen, H., G. R. Zhu, and J. M. Gong. "Creep Life Prediction for P91/12Cr1MoV Dissimilar Joint Based on the Omega Method." Procedia Engineering 130 , 2015.
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[16] Terada, Yoshihiro, Yoshinori Murata, and Tatsuo Sato. "Creep life assessment of a die-cast Mg–5Al–0.3 Mn alloy." Materials Science and Engineering: A 584: pp.63-66, 2013.
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[17] Aghaie-Khafri, M., and M. Noori. "Life prediction of a Ni-base superalloy." Bulletin of Materials Science 34.2: pp.305-309, 2011.
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[18] Xie, Lin-Jun, Dong Ning, and Yi-zhong Yang. "Experimental study on creep characterization and lifetime estimation of RPV material at 723-1023 K." Journal of Materials Engineering and Performance 26.2: 644-652, 2017.
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[19] Evans, M. "A comparative assessment of creep property predictions for a 1CrMoV rotor steel using the CRISPEN, CDM, Omega and Theta projection techniques." Journal of materials science 39.6: pp.2053-2071, 2004.
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[20] Bagnoli, K. E., Z. D. Cater-Cyker, and B. S. Budiman. "Evaluation of the theta (Θ) projection technique for estimating the remaining creep life of GTD-111DS turbine blades." Turbo Expo: Power for Land, Sea, and Air. Vol. 47942. 2007.
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[21] Baldan, A., and E. Tascioglu. "Assessment of θ-projection concept and fracture cavitation." Journal of materials science 43.13: 4592-4606, 2008.
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[22] Kim, Woo-Gon, et al. "A numerical approach to determine creep constants for Time-Temperature Parametric methods." Metals and Materials International 15.4: pp.559-564, 2009.
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[23] Larson, Frank R. "A time-temperature relationship for rupture and creep stresses." Trans. ASME 74: pp.765-775, 1952.
23
[24] Mehta, M. I., et al. "Estimation of Creep Failure Life of Rotor Grade Steel by Using Time–Temperature Parametric Methods." Transactions of the Indian Institute of Metals 69.2: pp.591-595, 2016.
24
[25] Eshati, S., et al. "The Influence of Humidity on the Creep Life of a High Pressure Gas Turbine Blade: Part II—Case Study." Turbo Expo: Power for Land, Sea, and Air. Vol. 44694. American Society of Mechanical Engineers, 2012.
25
[26] Nimonic Alloy, Special Metals, available at: http://www.specialmetals.com/products/index.php, accessed October, 2008.
26
ORIGINAL_ARTICLE
Application of a Model-Based Fault Detection Approach on a Spacecraft
The model-based fault detection approach is one of the software-based supervision systems monitoring. This method has a marked effect to detect components fault without demanding extra sensors to measure or add redundancy. The extended multiple model’s adaptive estimation method is an online strategy to detect and isolation failure of components. Simple implementation, fast and accurate response, compatibility with nonlinear systems, and the ability to detect different types of faults are the most important features of this method. This method is applied to the faulty spacecraft in terms of actuators and its capability is evaluated. The most probable actuator fault implemented using MATLAB/SIMULINK software. The presented approach successfully detects faulty actuators.
http://www.ijrrs.com/article_133779_87c69007a9ce2be8295bcf9447289fd0.pdf
2020-12-01
19
26
10.30699/IJRRS.3.2.3
fault detection
Spacecraft
model-based
EMMAE Method
Actuator Failure
Alireza
Alikhani
aalikhani@ari.ac.ir
1
Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran
LEAD_AUTHOR
Ghasem
Sharifi
ghsm.sharifi@gmail.com
2
Department of the Aerospace Engineering, K.N. Toosi University of Technology, Tehran, Iran
AUTHOR
[1] Rolf Isermann, Model-based fault-detection, and diagnosis status and applications, Annual Reviews in Control, Volume 29, Issue 1,2005, Pages 71-85, ISSN 1367-5788,
1
[2] X. Yu, J. Jiang, A survey of fault-tolerant controllers based on safety-related issues, Annual Reviews in Control 39 (2015) 46–57.
2
[3] Said, M., Abdellafou, K.b. & Taouali, O. Machine learning technique for data-driven fault detection of nonlinear processes. J Intell Manuf 31, 865–884 (2020).
3
[4] S. Anwar, W. Niu, A nonlinear observer-based analytical redundancy for predictive fault- tolerant control of a steer-by-wire system, Asian Journal of Control 16 (2) (2014) 321–334.
4
[5] Belsak, A., Flasker, J. (2008). Vibration analysis to determine the condition of gear units. Strojniški Vestnik - Journal of Mechanical Engineering, vol. 54, no. 1, p. 11-24
5
[6] Capozzoli, A., F. Lauro, and I. Khan. 2015. “Fault Detection Analysis Using Data MiningTechniques for a Cluster of Smart Office Buildings.” Expert Systems with Applications. 42:4324–4338.
6
[7] Bouallègue,W.; Bouslama Bouabdallah, S.; Tagina, M. Causal approaches and fuzzy logic in FDI of Bond Graph uncertain parameters systems. In Proceedings of the IEEE International Conference on Communications, Computing and Control Applications (CCCA), Hammamet, Tunisia, 3–5 March 2011
7
[8] Thirumarimurugan, M.; Bagyalakshmi, N.; Paarkavi, P. Comparison of fault detection and isolation methods: A review. In Proceedings of the 2016 10th International Conference on Intelligent Systems and Control (ISCO), Coimbatore, India, 7–8 January 2016.
8
[9] Li, Xifeng & Xie, Yongle & Bi, Dongjie & Ao, Yongcai. (2013). Kalman Filter-Based Method for Fault Diagnosis of Analog Circuits. Metrology and Measurement Systems. 20. 10.2478/mms-2013-0027.
9
[10] Skliros, C., Esperon Miguez, M., Fakhre, A., Jennions, I. (2019). A review of model-based and data-driven methods targeting hardware systems diagnostics. Diagnostyka, 20(1), 3-21. https://doi.org/10.29354/diag/99603
10
[11] Geliel, Mostafa & Zakzouk, Sherief & Sengaby, M.. (2012). Application of model-based fault detection for an industrial boiler. 2012 20th Mediterranean Conference on Control and Automation, MED 2012 - Conference Proceedings. 98-103. 10.1109/MED.2012.6265621.
11
[12] E. Khalastchi and M. Kalech. A sensor-based approach for fault detection and diagnosis for robotic systems. Autonomous Robots, 42(6):1231–1248, Aug. 2018.
12
[13] Patton, R.; Uppal, F.; Simani, S.; Polle, B. Robust FDI applied to thruster faults of a satellite system. Control Eng. Pract. 2010, 18, 1093–1109.
13
[14] Falcoz, A.; Henry, D.; Zolghadri, A. Robust Fault Diagnosis for Atmospheric Reentry Vehicles: A Case Study. IEEE Trans. Syst. Man Cybern. Part A Syst. Hum. 2010, 40, 886–899.
14
[15] R. Isermann. Fault-Diagnosis Systems, An Introduction from Fault Detection to Fault Tolerance. Springer-Verlag, Berlin Heidelberg, 2006
15
[16] L. Ni.Fault-Tolerant Control of Unmanned Underwater Vehicles.PhDthesis, VA Tech. Univ., Blacksburg, VA, 2001.
16
[17] M. Azam, K. Pattipati, J. Allanach, S. Poll, and A. Petterson-Hine. In-Flight Fault Detection and Isolation in Aircraft Flight Control Systems. In Proceedings of IEEE Aerospace Conference, 2005. Paper 1429.
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[18] G. Ducard and H. P. Geering, "A reconfigurable flight control system based on the EMMAE method," 2006 American Control Conference, Minneapolis, MN, 2006, pp. 6 pp.-, DOI: 10.1109/ACC.2006.1657599.
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[19] Ducard, G.. (2009). Fault-tolerant Flight Control and Guidance Systems: Practical Methods for Small Unmanned Aerial Vehicles. 10.1007/978-1-84882-561-1.
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[20] M.S. Grewall, A.P.Andrews. Kalman Filtering. Theory and Practice. Prentice-Hall, 1993
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[21] MarcelSidi, “SpacecraftDynamics &Control, “ Cambridge university press, 1997
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[22] R. Cowen, “The wheels come off Kepler,” Nature, vol. 497, no. 7450, pp. 417–418, May2013.
22
[23] Ghasem Sharifi & Ehsan Zabihian (2020) An effective approach to identify the mass properties of a satellite attitude dynamics simulator, Australian Journal of Mechanical Engineering, 18:3, 245254, DOI: 10.1080/14484846.2018.1458811
23
[24] A. Mohammad and S. M. B. Billah, "Analysis of speed control of series DC motor using diverter and observation of speed saturation point," 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, 2015, pp. 1-4, DOI: 10.1109/ICEEICT.2015.7307350.
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[25] A. Adouni, A. Abid and L. Sbita, "A DC motor fault detection, isolation and identification based on a new architecture Artificial Neural Network," 2016 5th International Conference on Systems and Control (ICSC), Marrakesh, 2016, pp. 294-299, DOI: 10.1109/ICoSC.2016.7507054.
25
ORIGINAL_ARTICLE
Reliability and Sensitivity Analysis of a Batch Arrival Retrial Queue with k-Phase Services, Feedback, Vacation, Delay, Repair and Admission
Queueing theory is a way for real-world problems modeling and analyzing. In many processes, the input is converted to the desired output after several successive steps. But usually limitations and conditions such as Lack of space, feedback, vacation, failure, repair, etc. have a great impact on process efficiency. This article deals with the modeling the steady-state behavior of an M^X/G/ 1 retrial queueing system with k phases of service. The arriving batches join the system with dependent admission due to the server state. If the customers find the server busy, they join the orbit to repeat their request. Although, the first phase of service is essential for all customers, any customer has three options after the completion of the i-th phase (i=1,2,…,k). They may take the (i+1)-th phase of service with probability θ_i, otherwise return the orbit with probability p_i or leave the system with probability (〖1-p〗_i-θ_i). Also, after each phase, the probabilistic failure, delay, repair and vacation is considered. In this article, after finding the steady-state distributions, the probability generating functions of the system and orbit size have been found. Then, some important performance measures of the system have been derived. Also, the system reliability has been defined. Eventually, to demonstrate the capability of the proposed model, the sensitivity analysis of performance measures via some model parameters (arrival/retrial/vacation rate) in different reliability levels have been investigated in a specific case of this model. Additionally, for optimizing the performance of system, some technical suggestions are presented.
http://www.ijrrs.com/article_133780_b3953206a08bedac31f1e2fb03913c01.pdf
2020-12-01
27
40
10.30699/IJRRS.3.2.4
Bernoulli vacation
feedback
Performance Measures
Retrial queue
State-dependent admission
Repair
delay
Reliability
Saeedeh
Abdollahi
abdollahi6028@yahoo.com
1
Department of Statistics, Faculty of Statistics, Mathematics and Computer, Allameh Tabataba’i University, Tehran, Iran
LEAD_AUTHOR
Mohammad Reza
Salehi Rad
salehirad@atu.ac.ir
2
Department of Statistics, Faculty of Statistics, Mathematics and Computer, Allameh Tabataba’i University, Tehran, Iran
AUTHOR
[1] John, F.Sh., James, M.Th.,Donald, G.,Carl, M.H.: Fundamentals of Queueing Theory. Wiley Series in Probability and Statistics (2018)
1
[2] Falin, G.I., Templeton, J.G.C.: Retrial queues. London, Chapman and Hall (1997)
2
[3] Artalejo, J.R.: Accessible bibliography on retrial queues. Math. Comput. Model. 30, 1-6 (1999)
3
[4] Falin, G.I.: On a multiclass batch arrival retrial queue. Adv. Appl. Probab. 20, 483–487 (1988)
4
[5] Kulkarni, V.G.: Expected waiting times in a multiclass batch arrival retrial queue. J. Appl. Probab. 23, 144–154 (1986)
5
[6] Yamamuro, K. The queue length in an M/G/1 batch arrival retrial queue. QueueingSyst 70, 187–205 (2012)
6
[7] Kumar, B.K., Kumar, A.V., Arivudainambi, D.: An M/G/1 retrial queueing system with two phase service and preemptive resume. Ann. Oper. Res. 113, 61-79 (2002)
7
[8] Choudhury, G., Deka, K.: An retrial queueing system with two phases of service subject to the server breakdown and repair. Perform. Evaluation. 65(10), 714-724 (2008)
8
[9] Wang, J., Li, J.: A single server retrial queue with general retrial times and two phase service. J. Syst. Sci. Complex. 22, 291–302 (2009)
9
[10] Maurya, V.N.: Sensitivity analysis on significant performance measures of bulk arrival retrial queueing model with second phase optional service and Bernoulli vacation schedule. Int. Open. J.Oper. Res. 1(1), 1 – 15 (2013)
10
[11] Jeganathan, K., Kathiresan, J., Anbazhagan, N.: A retrial inventory system with priority customers and second optional service. Opsearch. 53, 808-834 (2016)
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[12] Rao, S.H., Vemuri, V.K., Kumar B.S., Rao T.S.: Analysis of two-Phase queueing system with impatient customers, server breakdowns and delayed repair. Int. J. Pure. Appl. Math. 115(4), 651-663 (2017)
12
[13] Choudhury, G., Paul, M.: A two phase queueing system with Bernoulli feedback. Inf. Manag. Sci. 16(1), 35-52 (2005)
13
[14] Arivudainambi, D., Godhandaraman, P.: A batch arrival retrial queue with two phases of service, feedback and K optional vacations. Appl. Math. Sci. 6(22), 1071-1087 (2012)
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[15] BadamchiZadeh, A.: A batch arrival multi-phase queueing system with random feedback in service and single vacation policy. Opsearch. 52(4), 617-630 (2015)
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[16] Som, B.K., Seth, S.: queueing systems with encouraged arrivals, reneging, retention and feedback customers. Yugosl. J. Oper. Res. 28(00), 6-6 (2018)
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[17] Rajadurai, P., Chandrasekaran, V.M., Saravanarajan, M.C.: Analysis of an unreliable retrial G-queue with orbital search and feedback under Bernoulli vacation schedule. Opsearch. 53(1), 197-223 (2016)
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[18] Bouchentouf, A.A., Cherfaoui, M., Boualem, M.: Performance and economic analysis of a single server feedback queueing model with vacation and impatient customers. Opsearch. 56, 300-323 (2019)
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[19] Senthikumar, R.,Arumuganathan,M.: On the single server batch arrival queue with general vacation time under Bernoulli schedule and two phases of heterogenous service. Quality Technology and Quantitative Management. 5, 145-160 (2008)
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[20] Jain, M., Bhagat, A.: retrial vacation queue for multi-optional services, phase repair and reneging. Quality Technology and Quantitative Management. 13(3), 63-288 (2016)
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[21] Choudhury, G., Ke,JC.: A batch arrival retrial queue with general retrial times under bernoulli vacation schedule for unreliable server and delaying repair. Applied Mathemathical Modeling. 36, 255-269 (2012)
21
[22] Azhagappan, A.: Transient behavior of a Markovianqueue with working vacation variant reneging and awaiting server. TOP 27,351 (2019)
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[23] Ke,JC.: Operating characteristic analysis on the MX/G/1 system with a variant vacation policy and balking. Applied Mathematical Modeling. 31,1321–1337 (2007)
23
[24] Kulkarni, V.G.,Bong, D. C.: Retrial queues with server subject to breakdowns and repairs. Queueing Systems.7, 191–208(1990)
24
[25] Rajadurai, P., Saravanarajan, M.C., Chandrasekaran, V.M.: A study on M/G/1 feedback retrial queue with subject to server breakdown and repair under multiple working vacation policy.Alexandria Engineering Journal.57(2), 947-962 (2018)
25
[26] Jain, M.,Bhagat, A.: Unreliable bulk retrial queues with delayed repairs and modified vacation policy.Journal of Industrial Engineering International. 10, Article number: 63 (2014)
26
[27] Choudhury, G., Deka, K.: An unreliable retrial queue with two phases of service and Bernoulli admission mechanism. Appl. Math. Comput. 215(3), 936-949 (2009)
27
[28] Choudhury, G., Deka, K.: A batch arrival retrial queueing system with two phases of service and service interruption. Comput. Math. with Appl. 59(1), 437-450 (2010)
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[29] Birnbaun,ZW.,Esary, JD., Saunders, SD.: Multi-component systems and structures and their reliability. Technometrics. 3(1): 55-77 (1961)
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[30] Li, W., Shi, D., Chao, X.: Reliability analysis of M/G/1 queueing system with server breakdowns and vacations. Journal of Applied Probability. 34: 546–555 (1997)
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[31] Tang, Y.: A Single Server M/G/1 Queueing system subject to breakdowns: some reliability and queueing problems. Microelectronics Reliability. 37: 315–321 (1997)
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[32] Wang, J., Cao, J., Li, Q.: Reliability analysis of the retrial queue with server breakdowns and repairs. Queueing Systems 38: 363–380. (2001)
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[33] Achcar,JA.,Piratelli, CL.: Modeling quality control data Weibull distributions in the presence of a change point. The International Journal of Advanced Manufacturing Technology. 66: 1611-1621 (2013)
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[34] Dept. of Def. of USA.: Electronic reliability design handbook. MIL-HDBK-338B, USA (1998)
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[35] Dept. of Def. of USA.: Unmanned aerial vehicle reliability study. Office of the secretary of defence, USA (2003)
35
[36] Dept. of NAVY of USA (NSWC).: Handbook of reliability prediction procedures for mechanical equipment. Bethesda, Maryland, 20817-5700, USA (2006)
36
ORIGINAL_ARTICLE
Parametric Study of Torsional Damper on Crankshaft Life Assessment of an IC Engine
Torsional vibration (TV) is one of the major issues and very important calculation for the safe running of internal combustion engines, specifically crankshaft. The properties of parts connected to the crankshaft have significant effect on vibration of the system as well as the crankshaft life. Initial selection of this part is usually specified based on engine designer experience and also the torsional vibration calculation of the crank train. In this paper, the focus is to find optimum tuned mass to connect to the crankshaft from the damper side using CAE tools. It is a mounting disk at the free end of the crankshaft named tuned mass. Therefore, the effect of tuned mass inertia on design criteria, especially crankshaft life, was investigated. The results show high sensitivity of high cycle fatigue safety factor of crankshaft to tuned mass. Therefore, adding a suitable tuned mass to the system can increase the crankshaft life, when needed. The results were presented in the paper in detail
http://www.ijrrs.com/article_120707_c542070a0d88e2ce3ab111d55825e552.pdf
2020-12-01
41
50
10.30699/IJRRS.3.2.5
Classical Torsional vibration
Cranktrain
Tuned mass
High Cycle Fatigue
Crankshaft life
Hadiseh
Karimaei
karimaei@ari.ac.ir
1
Assistant Professor, Ph. D Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran
LEAD_AUTHOR
Hamidreza
Chamani
h.chamani@gmail.com
2
Ph. D,Fatigue and Fracture lab, Center of Excellence in Experimental Solid Mechanic and Dynamics, School of Mechanical Engineering, Iran University of Science and Technology, Tehran, Iran
AUTHOR
Mendes, A. S., Meirelles, P. S., and Zampieri, D. E., "Analysis of torsional vibration in internal combustion engines: modelling and experimental validation," Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 222(2): pp. 155-178, 2008.
1
Nestorides, E. J., ed. A handbook on torsional vibration. Cambridge University Press, 1958.
2
Wilson, WK., Practical solution of torsional vibration problems: with examples from marine, electrical, aeronautical, and automobile engineering practice., Chapman & Hall; Vol. 2, 1956.
3
Hafner, KE. and Maass, H., Theorie der Triebwerksschwingungen der Brennkraft maschine, Wien-New York; Vol. 3, 1984.
4
Chen, S.K., and Chang, T., "Crankshaft torsional and damping simulation—an update and correlation with test results," SAE transactions, 964-985, 1986.
5
Larmi, M. "Torsional vibration calculation and engine damping," Proceedings of international congress on combustion engines (CIMAC), pp. 96-105, 1988.
6
Boysal, A., and Rahnejat, H., "Torsional vibration analysis of a multi-body single cylinder internal combustion engine model," Applied Mathematical Modelling,21(8): pp. 481-493, 1997.
7
Ma, ZD. and Perkins, NC., "An efficient multibody dynamics model for internal combustion engine systems." Multibody system dynamics,10(4): pp. 363-391, 2003.
8
Desbazeille, M., Randall, RB., Guillet, F., El Badaoui, M. and Hoisnard, C. "Model-based diagnosis of large diesel engines based on angular speed variations of the crankshaft," Mechanical Systems and Signal Processing,24(5): pp. 1529-1541, 2010.
9
Han, HS., Lee, KH. and Park, SH. "Parametric study to identify the cause of high torsional vibration of the propulsion shaft in the ship," Engineering Failure Analysis,59(1), pp. 334-346, 2016.
10
Guo, Y., Li, W., Yu, S., Han, X., Yuan, Y., Wang, Z. and Ma, X., "Diesel engine torsional vibration control coupling with speed control system," Mechanical Systems and Signal Processing,94 (1): pp. 1-13, 2017.
11
Karimaei,H., Mehrgou, , and Chamani, HR. "Optimisation of torsional vibration system for a heavy-duty inline six-cylinder diesel engine, " Proceedings of the Institution of Mechanical Engineers, Part K: Journal of Multi-body Dynamics, 233(1): pp. 642-56, 2019.
12
Jee, J., Kim, C., Kim, Y., "Design Improvement of a Viscous-Spring Damper for Controlling Torsional Vibration in a Propulsion Shafting System with an Engine Acceleration Problem," Journal of Marine Science and Engineering, 8(6): pp. 428-438, 2020.
13
Karimaei,H., "Sensitive Analysis of Tuned Mass on High Cycle Fatigue Safety Factor of Crankshaft," International Journal of Reliability, Risk and Safety: Theory and Application, 2020 Nov; Articles in Press.
14
MSC ADAMS. Documentation and help. Using Adams/Engine. 2005.
15
AVL LIST GmbH. AVL Excite Designer, Theory Manual Version 7.0.3. October 2007.
16
Milasinovic, A., Filipovic, I., and Hribernik, A. "Contribution to the definition of the torsional stiffness of the crankshaft of a diesel engine used in heavy-duty vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering,223(7): pp. 921-930, 2009.
17
Geislinger Damper Catalogue. Geislinger GmbH. 5300 Hallwang/Austria. 2016.
18
Philipp, T., "Parametric Identification of Torsional Vibration by Modern Measurement and Calculation Methods, " CIMAC Congress, Vienna, Paper. 130, pp. 2-13, 2007.
19
Beards, CF., "Introduction to finite element vibration analysis, " Aeronaut J, 103(1): pp. 486–490,1999.
20
IACS UR M53, International Association of Classification Societies (IACS) Unified Rules UR-M53 for the calculation of Crankshaft Design Strength Assessment for I.C. Engines. 1986 (Rev.1, Dec 2004)
21
ORIGINAL_ARTICLE
Life extension for a coherent system through cold standby and minimal repair policies for their independent components
We consider life extension for a class of coherent system consisting of independent components with an increasing failure rate functions. The maintenance action is applied in a fixed component called the target component. To this end, the minimal repair and cold standby actions are provided. We also consider two alternative policies for the target component. A component following a new random variable, and another following the same distributions of the target component. These policies obviously increase the reliability and life of the target component and consequently, the life and reliability of a coherent system are also increased. In this regard, the life of the system is also extended. Some numerical results considering these life extensions are presented.
http://www.ijrrs.com/article_119489_7e0487d58417ff2f4a20660669bd2e46.pdf
2020-12-01
51
54
10.30699/IJRRS.3.2.6
coherent system
Cold standby
minimal repair
preventive maintenance
Seyed Mahmoud
Mirjalili
mirjalili8@yahoo.com
1
Department of Statistics, Faculty of Basic Sciences, Velayat University, Iranshahr, Sistan and Baluchestan, Iran
AUTHOR
Jaber
Kazempoor
kazempoorjaber@gmail.com
2
Department of Statistics, Faculty of Mathematical Sciences, Ferdowsi University of Mashhad, Mashhad, Iran
LEAD_AUTHOR
Kuo, M. J. Zuo, Optimal reliability modeling: principles and applications, John Wiley & Sons, 2003.
1
v. S, P. k. P, Probabilistic assessment of two-unit parallel system with correlated lifetime under inspection using regenerative point technique, International Journal of Reliability, Risk and Safety: Theory and Application 2 (1) (2019) 5–14.
2
Eryilmaz, M. H. Pekalp, On optimal age replacement policy for a class of coherent systems, Journal of Computational and Applied Mathematics(2020) 112888.
3
Navarro, A. Arriaza, A. Su´arez-Llorens, Minimal repair of failed components in coherent systems, European Journal of Operational Research 279 (3) (2019) 951–964.
4
E. Barlow, F. Proschan, Statistical theory of reliability and life testing: probability models, Tech. rep., Florida State Univ Tallahassee (1975).
5
V. Singh, P. K. Poonia, A. H. Adbullahi, Performance analysis of a complex repairable system with two subsystems in series configuration with an imperfect switch, J. Math. Comput. Sci. 10 (2) (2020) 359–383.
6
Pham, Handbook of reliability engineering, Springer Science & Business Media, 2006.
7
-H. Sheu, T.-H. Liu, H.-N. Tsai, Z.-G. Zhang, Optimization issues in k-out-of-n systems, Applied Mathematical Modelling 73 (2019) 563–580.
8
Nakagawa, Maintenance theory of reliability, Springer Science & Business Media, 2006.
9
Aven, U. Jensen, A general minimal repair model, journal of applied probability 37 (1) (2000) 187–197.
10
Natvig, Multistate system reliability, Wiley Encyclopedia of Operations Research and Management Science (2010).
11
Gahlot, V. V. Singh, H. I. Ayagi, I. Abdullahi, Stochastic analysis of a two units’ complex repairable system with switch and human failure using copula approach, Life Cycle Reliability and Safety Engineering 9 (1) (2020) 1–11.
12
Zhang, E. Amini-Seresht, W. Ding, Component and system active redundancies for coherent systems with dependent components, Applied Stochastic Models in Business and Industry 33 (4) (2017) 409–421.
13
S. J. Almalki, S. Nadarajah, Modifications of the weibull distribution: A review, Reliability Engineering & System Safety 124 (2014) 32–55.
14
ORIGINAL_ARTICLE
A Combined AHP-PROMETHEE Approach for Intelligent Risk Prediction of Leak in a Storage Tank
This paper describes the use of the Analytic Hierarchy Process (AHP) and Preference Ranking Organization Method for Enrichment Evaluation (PROMETHEE) method for predicting the risk of leakage in a storage tank. This is the first time AHP and PROMETHEE have been used in this way. Important decisions about day to day operations are continually made in a petroleum environment. Storage tanks in refineries contain large volumes of flammable and hazardous liquids. Decision processes need to evaluate and select alternatives with a higher probability of resulting in a hazard, among many different alternatives. The new model described in this paper will aid decision-makers to predict which tank is likely to develop a leak and determine what criteria (source of risk) could result in a leak. Although the case study deals with a specific risk prediction problem, the combination of AHP and PROMETHEE methods can be applied to other decision problems.
http://www.ijrrs.com/article_119488_24af8b5c6797762756a66c16c5a61af4.pdf
2020-12-01
55
61
10.30699/IJRRS.3.2.7
AHP
Decision Making
Multi-criteria analysis
PROMETHEE
risk prediction
Favour
Ikwan
favour.ikwan@myport.ac.uk
1
Systems Engineering Research Group, University of Portsmouth, United Kingdom
LEAD_AUTHOR
David
Sanders
david.sanders@port.ac.uk
2
Systems Engineering Research Group Anglesea Building Anglesea Road, University of Portsmouth, United Kingdom
AUTHOR
Malik
Haddad
malik.haddad@port.ac.uk
3
Systems Engineering Research Group, University of Portsmouth, United Kingdom
AUTHOR
James, C., and Cheng-Chung, L., “A study of storage tank accidents,” Journal of Loss Prevention in the Process Industries19(1): pp. 51-59, 2009.
1
Ikwan, F., “Reducing energy losses and alleviating risk in petroleum engineering using decision making and alarm systems” Journal of computing in systems and engineering, ISSN 1472-9083: pp. 422-429, 2018.
2
Saaty, T.L., “The analytic hierarchy process—what it is and how it is used,” Mathematical modelling, 9 (3-5): pp. 161-76 (1987).
3
Saaty, T.L., “How to make a decision: the analytic hierarchy process,” Interfaces, 24(6): pp.19-43,1994.
4
Huang, C., Tong, I., Chang, W. and Yeh, C., “A two-phase algorithm for product part change utilizing AHP and PSO,” Expert Systems with Applications: pp 38, 2011.
5
Opricovic, S., Multicriteria optimization of civil engineering systems (in Serbian) Belgrade: PhD Thesis, Faculty of Civil Engineering: pp. 302, 1998.
6
Roy, B., “Classement et choixenprésence de points de vue multiples,” RAIRO-Operations Research-Recherche Opérationnelle, (2): pp. 57–75,1968.
7
Brans, J.P., “Lingenierie de la decision. Elaboration d’instrumentd’aide a la decision. Methode PROMETHEE. Colloqued’Aide a la Decision. Universite Laval, Quebec, Canada; pp.183-213, (1982).
8
Ying, L., Wei, W., BingXin, L. and Xin, Z., “Research on oil spill risk of port tank zone based on a fuzzy comprehensive evaluation,” Aquatic Procedia, (3): pp.216 – 223, 2015.
9
Bing, W., Hong, L. and Hong, Y., “Application of AHP, TOPSIS, and TFNs to plant selection for phytoremediation of petroleum-contaminated soils in shale gas and oil fields,” Journal of Cleaner Production: pp.13-22, 2019.
10
Brans, J. P., Vincke, P. H. and Mareschall, B., “How to select and how to rank projects: The PROMETHEE method,” European Journal of Operational Research, 14: pp. 228-238, 1986.
11
Saaty, T.L., “Decision making with the analytic hierarchy process,” International journal of services sciences, 1(1): pp.83-98, 2008.
12
Saaty, T.L. and Vargas, L.G., “Models, methods, concepts & applications of the analytic hierarchy process,” Springer Science & Business Media, 175(2): pp.1-342, 2012.
13
Ishizaka, A. and Labib, A., “Analytic hierarchy process and expert choice: Benefits and limitations” OR Insight, 22(4), pp.201-220, 2009.
14
Haddad, M. and Sanders, D., “Deep Learning Architecture to Assist with Steering a Powered Wheelchair,” IEEE Transactions on Neural System and Rehabilitation Engineering, Accepted and in Press, 2020.
15
Saaty, T.L., Fundamentals of Decision Making and Priority Theory with the Analytic Hierarchy Process, RWS Publications; 2000.
16
Brans, J.P and Vincke, P.H., “A preference ranking organization method: The PROMETHEE method,” Management Science, 31: pp. 647–65, 1985.
17
Macharis, C., and Springael, J., “PROMETHEE and AHP: The design of operational synergies in multicriteria analysis. Strengthening PROMETHEE with ideas of AHP,” European Journal of Operational Research,153: pp 307-317, 2004.
18
Kasım., B, Sari, T. and Koçdağ, V., “A combined AHP-PROMETHEE approach for project selection and a case study in the Turkish textile industry,” EEuropean Journal of Business and Social Sciences, 5(1): pp. 202 – 216,
19
Ikwan F. et al., Intelligent risk prediction of storage tank leakage using an Ishikawa diagram with probability and impact analysis. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, Springer, Cham; vol 1252, 2021.
20
Tongyuan, L., Chao, Wu. and Lixiang, D., “Fishbone diagram and risk matrix analysis method and its application in the safety assessment of natural gas spherical tank,” Journal of Cleaner Production, 174: pp. 296-304, 2017
21
José, L., Carmen, G-C., Cristina, G-G., and Piedad, B., “Risk analysis of a fuel storage terminal using HAZOP and FTA” International Journal of Environmental research and public health, 14(17): pp. 705, 2017.
22
Saaty, T.L., “Decision Making – The Analytical Hierarchy and Network Processes (AHP/ANP),” Journal of Systems Science and Systems Engineering, 13(1): pp. 1 – 35, 2004.
23
Wang, J.J. and Yang, D.L., “Using a hybrid multi-criteria decision aid method for information systems outsourcing,” Computers & Operation Research, 34(12): pp. 3691-3700,
24
Haddad, M. and Sanders, D., “Artificial Neural Network approach for business decision making applied to a corporate relocation problem”. Archives of Business Research,8(6): pp.180-195, 2020.
25
Omoarebun, P., “Disaster risk reduction in petroleum engineering”. Journal of Computing in Systems Engineering. ISSN 1472-9083. Pp. 499, 2018.
26
Omoarebun, P., Sanders, D., Haddad, M., Hassan, M., Tewkesbury, G., Giasin, K., “An intelligent monitoring system for crude oil distillation column”. 2020 IEEE. 10th International Conference on Intelligent Systems (IS). 159, 2020.
27
Omoarebun P. et al., Intelligent Monitoring Using Hazard Identification Technique and Multi-sensor Data Fusion for Crude Distillation Column, In Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. IntelliSys 2020. Advances in Intelligent Systems and Computing, Springer, Cham; vol 1252, 2021.
28
ORIGINAL_ARTICLE
COVID-19 Infection Risk Index Estimation in Flight Destinations (case study: Kish Air destinations)
This paper proposes a risk assessment method for estimating theCOVID-19 Infection risk index in flight destinations based on the pair wise comparison method to solve the problem of health monitoring devices shortage in airlines. In this research, Kish Airlines flight destinations are considered as a case study. By considering the importance of continuing air travel during COVID-19 pandemic, one of the most effective ways for decreasing the risk of infection to COVID-19 in air travel is establishing health monitoring stations at the airport gates. Because of the enormous number of airports and airline routes, nationwide coverage of them by the establishment of the health monitoring stations is unimaginable. Therefore, in this paper, the pair wise comparison method used for evaluating COVID-19 infection risk index in selected flight destinations and to evaluate the optimal policy for allocation of health monitoring equipment in Kish Airline destinations a geostatistical map is designed based on the calculated infection risk score.
http://www.ijrrs.com/article_122802_7ea70321f292e4ae6677a5922aacf1d7.pdf
2020-12-01
63
70
10.30699/IJRRS.3.2.8
COVID-19
Infection Risk index
Risk Assessment Model
Pair wise comparison
Iman
Shafieenejad
shafieenejad@ari.ac.ir
1
Department of Aeronautic, Aerospace Research Institute, Ministry of Science Research and Technology, Tehran, Iran.
LEAD_AUTHOR
Sharareh
Ghasemi
sharareh.ghasemi@hotmail.com
2
Polytechnic University of Madrid, Madrid, Spain.
AUTHOR
Mohammad
Siami
mohammadsiamiy@gmail.com
3
Department of Aerospace Engineering, Faculty of Engineering, Science and Research Branch, Islamic Azad university, Tehran, Iran
AUTHOR
[1] Yang X, Yu Y, Xu J, Shu H, Xia J, Liu H, et al. Clinical course and outcomes of critically ill patients with SARS-CoV-2 pneumonia in Wuhan, China: a single-centered, retrospective, observational study. Lancet Respir Med. 8(5): 475–481 (2020).
1
[2] Hale T, Angrist N, Cameron-Blake E, Hallas L, Kira B, Majumdar S, et al. Variation in government responses to COVID-19. BSG Work Pap Ser Blavatnik Sch Gov Univ Oxford. 31: Version 8.0 (2020).
2
[3] Hou C, Chen J, Zhou Y, Hua L, Yuan J, He S, et al. The effectiveness of quarantine of Wuhan city against the Corona Virus Disease 2019 (COVID-19): A well-mixed SEIR model analysis. J Med Virol. 92(7): 841–848 (2020).
3
[4] Rodríguez-Urrego D, Rodríguez-Urrego L. Air quality during the COVID-19: PM2.5 analysis in the 50 most polluted capital cities in the world, (2020).
4
[5] Jordan RE, Adab P, Cheng KK. COVID-19: Risk factors for severe disease and death, (2020).
5
[6] Casagrande M, Favieri F, Tambelli R, Forte G. The enemy who sealed the world: effects quarantine due to the COVID-19 on sleep quality, anxiety, and psychological distress in the Italian population. Sleep Med. 75: 12–20 (2020).
6
[7] Ashraf BN. Economic impact of government interventions during the COVID-19 pandemic: International evidence from financial markets. J Behav Exp Financ. 27: 100371 (2020).
7
[8] Chinazzi M, Davis JT, Ajelli M, Gioannini C, Litvinova M, Merler S, et al. The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. Science (80- ). 368(6489): 395–400 (2020).
8
[9] Ranney ML, Griffeth V, Jha AK. Critical Supply Shortages — The Need for Ventilators and Personal Protective Equipment during the COVID-19 Pandemic. N Engl J Med. 382(18): e41 (2020).
9
[10] Beetz C, Skrahina V, Forster TM, Gaber H, Paul JJ, Curado F, et al. Rapid large-scale COVID-19 testing during shortages. Diagnostics. 10(7): 464 (2020).
10
[11] Cotfas LA, Delcea C, Milne RJ, Salari M. Evaluating classical airplane boarding methods considering COVID-19 flying restrictions. Symmetry (Basel). 12(7): 1087 (2020).
11
[12] John Milne R, Delcea C, Cotfas L-A. Airplane Boarding Methods that Reduce Risk from COVID-19. Saf Sci. 105061 (2020).
12
[13] Bielecki M, Patel D, Hinkelbein J, Komorowski M, Kester J, Ebrahim S, et al. Air travel and COVID-19 prevention in the pandemic and peri-pandemic period: A narrative review. Travel Med Infect Dis. 39: 101915 (2021).
13
[14] Berry LL, Danaher TS, Aksoy L, Keiningham TL. Service Safety in the Pandemic Age. J Serv Res. 23(4): 391–395 (2020).
14
[15] Neuburger L, Egger R. Travel risk perception and travel behaviour during the COVID-19 pandemic 2020: a case study of the DACH region. Curr Issues Tour. 1–14 (2020).
15
[16] Sadique MZ, Edmunds WJ, Smith RD, Meerding WJ, De Zwart O, Brug J, et al. Precautionary behavior in response to perceived threat of pandemic influenza. Emerg Infect Dis. 13(9): 1307–1313 (2007).
16
[17] TSA. TSA checkpoint travel numbers for 2020 and 2019, Available from: https://www.tsa.gov/coronavirus/passenger-throughput.
17
[18] Airports Council International Advisory Bulletin: The Impact of COVID- 19 on the Airport Business. Montreal, Canada: Airports Council International. (2020).
18
[19] Air Transport Bureau. Effects of Novel Coronavirus (COVID-19) on Civil Aviation: Economic Impact Analysis. Int Civ Aviat Organ (ICAO), Montréal, Canada. (March): 4 (2020).
19
[20] Iacus SM, Natale F, Satamaria C, Spyratos S, Vespe M. Estimating and projecting air passenger traffic during the COVID-19 coronavirus outbreak and its socio-economic impact, (2020).
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[21] OECD. OECD Economic Outlook, Volume 2020 Issue 2: Preliminary version, No. 108. (2020).
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[22] Quarterly Growth Rates of real GDP, change over previous quarter, Available from: https://stats.oecd.org/index.aspx?queryid=350.
22
[23] Henderson DA, Courtney B, Inglesby T V., Toner E, Nuzzo JB. Public health and medical responses to the 1957-58 influenza pandemic. Biosecurity and Bioterrorism. 7(3): 265–273 (2009).
23
[24] Kilbourne ED. Influenza pandemics of the 20th century, (2006).
24
ORIGINAL_ARTICLE
Use Piecewise Crow-AMSAA Method to Predict Infection and Death of Corona virus in Iran
Reliability growth is the positive improvement in a product’s criteria (or parameter) over a period of time due to changes in the design or product process. By analyzing the growth of reliability in a system, it can be seen that at a certain stage of the epidemic, the growth of the transmission and the rate of infection change over time. During the spread of disease, problem areas are identified and knowledge of the disease increased and then initial treatment and tools may be redesigned or reprocessed to take appropriate corrective action. In other words, each stage of the spread of the disease has a different level of growth transmission depending on appropriate corrective action. Therefore, according to this case, there are conditions under which phenomena can be described by Non-Homogeneous Poisson Process (NHPP). However, traditional epidemiological models based on exponential distribution are not able to predict disease growth during different stages of the outbreak. Therefore, in this paper, the Piecewise Crow-AMSAA (NHPP) model, which is based on the Non-Homogeneous Poisson process, is used to predict the growth of infected cases and deaths of Coronavirus outbreak. Initially, the Iran cumulative confirmed case and death data are divided into several sections based on the manual separation to find out each different infection phase at each different time period. Then Crow-AMSAA (NHPP) model is applied to the segmented data. At each stage of the outbreak, the model parameters are estimated independently using the maximum likelihood estimation (MLE) technique. Finally, the growth parameters in each stage are compared with each other and external and environmental factors are identified and examined.
http://www.ijrrs.com/article_132573_932207370ee12b5ce837bca2e4d68e69.pdf
2020-12-01
71
80
10.30699/IJRRS.3.2.9
Coronavirus
Non-homogeneous Poisson process (NHPP)
Piecewise Crow-AMSAA (NHPP)
Infected cases
Deaths
Peyman
Gholami
p.gholami@ae.sharif.ir
1
Department of Aerospace Engineering, Sharif University of Technology, Tehran, Iran
LEAD_AUTHOR
Samaneh
Elahian
selahian@ut.ac.ir
2
Aerospace Research Institute (Ministry of Science, Research and Technology), Tehran, Iran
AUTHOR
[1] Car, Z., Baressi Šegota, S., Anđelić, N., Lorencin, I. and Mrzljak, V., "Modeling the spread of covid-19 infection using a multilayer perceptron," Computational and Mathematical Methods in Medicine, Vol. 2020, 2020.
1
[2] Li, L., Yang, Z., Dang, Z., Meng, C., Huang, J., Meng, H., Wang, D., Chen, G., Zhang, J. and Peng, H., "Propagation analysis and prediction of the covid-19," Infectious Disease Modelling, Vol. 5, pp. 282-292, 2020.
2
[3] Catala, M., Alonso, S., Lacalle, E.A., Lopez, D., Cardona, P.-J. and Prats, C., "Empiric model for short-time prediction of covid-19 spreading," medRxiv, 2020.
3
[4] Zareie, B., Roshani, A., Mansournia, M.A., Rasouli, M.A. and Moradi, G., "A model for covid-19 prediction in iran based on china parameters," medRxiv, 2020.
4
[5] Duane, J., "Learning curve approach to reliability monitoring," IEEE transactions on Aerospace, Vol. 2, pp. 563-566, 1964.
5
[6] Crow, L.H., "Reliability analysis for complex, repairable systems," Army Material Systems Analysis Activityaberdeen Poving Ground MD, 1975.
6
[7] Crow, L.H., "Confidence interval procedures for reliability growth analysis,"Army Material Systems Analysis Activityaberdeen Poving Ground MD, 1977.
7
[8] Crow, L.H., "Confidence interval procedures for the weibull process with applications to reliability growth," Technometrics, Vol. 24, pp. 67-72, 1982.
8
[9] RANI and Misra, R., "Ml estimates for crow/amsaa reliability growth model for grouped and mixed types of software failure data," International Journal of Reliability, Quality and Safety Engineering, Vol. 11, pp. 329-337, 2004.
9
[10] Sun, A., Kee, E., Yu, W., Popova, E., Grantom, R. and Richards, D., "Application of crow-amsaa analysis to nuclear power plant equipment performance", in 13th International Conference on Nuclear Engineering, pp. 1-6, 2005.
10
[11] Barringer, H.P., "Use crow-amsaa reliability growth plots to forecast future system failures," 2006.
11
[12] Tang, Z., Zhou, W., Zhao, J., Wang, D., Zhang, L., Liu, H., Yang, Y. and Zhou, C., "Comparison of the weibull and the crow-amsaa model in prediction of early cable joint failures," IEEE Transactions on Power Delivery, Vol. 30, pp. 2410-2418, 2015.
12
[13] Nadjafi, M. and Gholami, P., "Bayesian inference of reliability growth-oriented weibull distribution for multiple mechanical stages systems," International Journal of Reliability, Risk and Safety: Theory and Application, 2021.
13
[14] Wang, W., Xu, Y. and Hou, L., "Optimal allocation of test times for reliability growth testing with interval-valued model parameters," Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, Vol. 233, pp. 791-802, 2019.
14
[15] Lee, Y., Ryu, J., Son, K., Song, S., Kim, S. and Park, W., "A study on the reliability growth of multiple launch rocket system using accelerated life testing," Journal of the Korea Institute of Military Science and Technology, Vol. 22, pp. 241-248, 2019.
15
[16] Nadjafi, M. and Gholami, P., "Developing of reliability growth model based on nonhomogeneous poisson process with normal distribution," Journal of Mechanical Engineering, Vol. 51, pp. 239-248, 2021.
16
[17] Ranjan, R., Predictions for covid-19 outbreak in india using epidemiological models. Medrxiv. 2020.
17
[18] Canabarro, A., Tenorio, E., Martins, R., Martins, L., Brito, S. and Chaves, R., "Data-driven study of the covid-19 pandemic via age-structured modelling and prediction of the health system failure in brazil amid diverse intervention strategies," medRxiv, 2020.
18
[19] Liu, Z., Magal, P. and Webb, G., "Predicting the number of reported and unreported cases for the covid-19 epidemics in china, south korea, italy, france, germany and united kingdom," Journal of Theoretical Biology, Vol. 509, p. 110501, 2020.
19
ORIGINAL_ARTICLE
Reducing Risk and Increasing Reliability and Safety of Compressed Air Systems by Detecting Patterns in Pressure Signals
This paper investigates the design of a classifier that effectively identifies undesired events by detecting patterns in the pressure signal of a compressed air system using a continuous wavelet transform. The pressure signal of a compressed air system carries useful information about operational events. These events form patterns that can be used as ‘signatures’ for event detection. Such patterns are not always apparent in the time domain and hence the signal was transformed to the time-frequency domain. Data was collected using an industrial compressed air system with load/unload control. Three different operating modes were considered: idle, tool activation , and faulty. The wavelet transforms of the pressure signal revealed unique features to identify events within each mode. A neural network classifier was created to detect faulty compressed air system behaviourbehaviour. Future work will investigate the detection of more faults and using other classification algorithms.
http://www.ijrrs.com/article_132566_108545da22d4ad4c84f96e871071e9e1.pdf
2020-12-01
81
89
10.30699/IJRRS.3.2.10
Compressed
Air
Systems
Intelligent
Wavelet
David
Sanders
david.sanders@port.ac.uk
1
School of Mechanical & Design Engineering, University of Portsmouth, Portsmouth, PO1 2UP, UK.
LEAD_AUTHOR
Mohamad
Thabet
mohamad.thabet@port.ac.uk
2
School of Mechanical & Design Engineering, University of Portsmouth, Portsmouth, PO1 2UP, UK.
AUTHOR
Victor
Becerra
victor.becerra@port.ac.uk
3
School of Energy & Electronic Engineering, University of Portsmouth, Portsmouth, PO1 2UP, UK.
AUTHOR
[1] H. Fridén, L. Bergfors, A. Björk, and E. Mazharsolook, “Energy and LCC optimised design of compressed air systems: A mixed integer optimisation approach with general applicability,” Proc. - 2012 14th Int. Conf. Model. Simulation, UKSim 2012, no. Lcc, pp. 491–496, 2012, doi: 10.1109/UKSim.2012.74.
1
[2] S. Murphy and K. Kissock, “Simulating Energy Efficient Control of Multiple-Compressor Compressed Air Systems.,” Proc. Ind. Energy Technol. Conf., 2015.
2
[3] T. Nehler, R. Parra, and P. Thollander, “Implementation of energy efficiency measures in compressed air systems: barriers, drivers and non-energy benefits,” Energy Effic., vol. 11, no. 5, pp. 1281–1302, 2018, doi: 10.1007/s12053-018-9647-3.
3
[4] M. Benedetti, V. Cesarotti, V. Introna, and J. Serranti, “Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study,” Appl. Energy, vol. 165, pp. 60–71, 2016, doi: 10.1016/j.apenergy.2015.12.066.
4
[5] M. Thabet, D. Sanders, V. Beccera, G. Tewkesbury, M. Haddad, and T. Barker, “Intelligent Energy Management of Compressed Air Systems,” in 10th IEEE International Conference on Intelligent Systems IS’20, 2020.
5
[6] D. A. Sanders, D. C. Robinson, M. Hassan, M. Haddad, A. Gegov, and N. Ahmed, “Making decisions about saving energy in compressed air systems using ambient intelligence and artificial intelligence,” Adv. Intell. Syst. Comput., vol. 869, no. September, pp. 1229–1236, 2018, doi: 10.1007/978-3-030-01057-7_92.
6
[7] A. Santolamazza, V. Cesarotti, and V. Introna, “Evaluation of Machine Learning techniques to enact energy consumption control of Compressed Air Generation in production plants,” Proc. Summer Sch. Fr. Turco, no. 2004, pp. 79–86, 2018.
7
[8] A. Santolamazza, V. Cesarotti, and V. Introna, “Anomaly detection in energy consumption for Condition-Based maintenance of Compressed Air Generation systems: an approach based on artificial neural networks,” IFAC-PapersOnLine, vol. 51, no. 11, pp. 1131–1136, 2018, doi: 10.1016/j.ifacol.2018.08.439.
8
[9] A. Desmet and M. Delore, “Leak detection in compressed air systems using unsupervised anomaly detection techniques,” Proc. Annu. Conf. Progn. Heal. Manag. Soc. PHM, pp. 211–220, 2017.
9
[10] R. X. Gao and R. Yan, Wavelets: Theory and Applications for Manufacturing. Springer, 2011.
10
[11] P. S. Addison, The illustrated wavelet transform handbook. 2017.
11
[12] “THE WAVELET TUTORIAL SECOND EDITION PART I BY,” pp. 1–67.
12
[13] Y. Lei, Intelligent fault diagnosis and remaining useful life prediction of rotating machinery. 2016.
13
[14] R. Polikar, “Pattern Recognition,” Pattern Recognit., pp. 1–22, 2006, doi: 10.1016/B978-0-12-369531-4.X5000-8.
14
[15] N. Mohd-Safar, D. Ndzi, D. Sanders, H. Noor and L. Kamarudin, Integration of fuzzy c-means and artificial neural network for short-term localized rainfall forecast in tropical climate. Lecture Notes in Networks and Systems, vol. 16, Springer, pp. 499-516,2017.
15
[16] Lawrence Berkeley, “Compressed Air: a sourcebook for industry,” pp. 1–128, 2003.
16
[17] M. Thabet, D. Sanders, M. Haddad, N. Bausch, NG. Tewkesbury, V. Becerra, T. Barker, and J. Piner, Management of compressed air to reduce energy consumption using intelligent systems. Advances in Intelligent Systems and Computing, vol. 1252, Springer, pp. 206-217, 2020. doi10.1007/978-3-030-55190-2_16.
17
[18] D. Robinson, D. Sanders, and E. Mazharsolook, 'Ambient intelligence for optimal manufacturing and energy efficiency', Assembly Automation, vol. 35, no. 3, pp. 234-248. 2018, doi10.1108/AA-11-2014-087.
18
[19] D. Robinson, D. Sanders, and E. Mazharsolook, 'Sensor-based ambient intelligence for optimal energy efficiency', Sensor Review, vol. 34, no. 2, pp. 170 - 181. 2014, doi10.1108/SR-10-2012-667.
19
[20] D. Sanders, 'Artificial intelligence tools can aid sensor systems: original paper based journal publication in Control Engineering', Control Engineering, pp. 44-48, 2013.
20
[21] P. Omoarebun, D. Sanders, F. Ikwan, M. Hassan Sayed, M. Haddad, M. Thabet, J. Piner, and A. Shah, Intelligent monitoring using hazard identification technique and multi-sensor data fusion for crude distillation column. Intelligent Systems and Applications: Volume 3. Advances in Intelligent Systems and Computing, vol. 1252, Springer, pp. 730-741, 2020, doi10.1007/978-3-030-55190-2_61.
21
[22] M. Haddad, D. Sanders, G. Tewkesbury, and N. Bausch, 'Analysing the Behaviour of Three Discrete Multi-Criteria Decision Making Methods in the Presence of Uncertainty', Operational Research Perspectives.
22
[23] Haddad, MJM & Sanders, D 2018, 'Selection of discrete multiple criteria decision making methods in the presence of risk and uncertainty', Operational Research Perspectives, vol. 5, pp. 357-370, 2019, doi10.1016/j.orp.2018.10.003.
23
ORIGINAL_ARTICLE
Determination of Optimum Sample Size for Lot Acceptance Attribute Sampling under Life Tests Based On Rayleigh Distribution Using Graphical Evaluation Review Technique (GERT)
This paper presents the graphical evaluation and review technique (GERT) exploration of performance measures for lot acceptance sampling procedures having attribute characteristics following life tests based on percentiles of Rayleigh Distribution and henceforth determining optimum sampling size. The advantageous implications of GERT analysis in this framework is primarily to visualize the dynamics of the sampling inspection system and secondly, critical analysis of sampling procedure characteristics. The formula of operating characteristics (OC) function and average sample number (ASN) function is derived and illustrated numerically. Lastly, tables have been provided to determine the optimum sample size assuring certain mean life or quality of the product.
http://www.ijrrs.com/article_132565_f1f4acf65632df992d193f38b3d1759d.pdf
2020-12-01
91
98
10.30699/IJRRS.3.2.11
Reliability life test sampling plan
Graphical Evaluation Review Technique (GERT)
Rayleigh Distribution
Life Time Distribution Model
Pradeepa Veerakumari
Kumarasamy
pradeepaveerakumari@buc.edu.in
1
Department of Statistics Bharathiar University, India
LEAD_AUTHOR
Sivakasini
Nagarathinam
sivakasini@gmail.com
2
Department of Statistics Bharathiar University, India
AUTHOR
Thottathil
Asif
asiftthottathil@gmail.com
3
Department of Statistics Bharathiar University, India
AUTHOR
Abubakar M.I and Singh,V.V. (2019). Performance assessment of African textile manufacturers, LTD, in Kano state, Nigeria, through Multi failure and repair using copula. Oper Res Decis 29(4):1–18
1
Anand, J and Malik, S.C. 2012. Analysis of a Computer System with Arbitrary Distributions for H/W and S/W Replacement Time and Priority to Repair Activities of H/W over Replacement of the S/W, International Journal of Systems Assurance Engineering and Management, Vol.3 (3), pp. 230-236.
2
Ashok Kumar, D. Pawar1 & S. C. Malik. (2020). Reliability Analysis of a Redundant System with ‘FCFS’ Repair Policy Subject to Weather Conditions, International Journal of Advanced Science and Technology, 29(3),,7568 – 7578
3
Bisht, S and Singh, S. B. (2019), Reliability analysis od acyclic transmission network based on minimal cuts using copula in repair, Proceedings of the Jangjeon Mathematical Society,22(2019), No. 1, pp. 163-173.
4
Kumar, A. and Malik, S. C. 2012. Stochastic Modeling of a Computer System with Priority to PM over S/W the Replacement Subject to Maximum Operation and Repair Times. International Journal of Computer Applications, Vol.43 (3), pp. 27-34.
5
Kumar, Ashish; Anand, Jyoti and Malik, S.C. 2013. Stochastic Modeling of a Computer System with Priority to Up-gradation of Software over Hardware Repair Activities. International Journal of Agricultural and Statistical Sciences, Vol. 9(1), pp. 117-126.
6
Kumar, A. & Malik, S.C. 2014. Reliability modelling of a computer system with priority to H/w repair over replacement of H/w and up-gradation of S/w subject to MOT and MRT, Jordan Journal of Mechanical and Industrial Engineering, Vol.8 (4), pp. 233-241.
7
Kumar, A., Saini, M. and Malik, S.C. 2015. Performance Analysis of a Computer System with Fault Detection of Hardware, Procedia computer science,45, 602-610.
8
Lado, A.K., Singh, V.V., Ismail K.H and Ibrahim, Y. (2018). Performance and cost assessment of repairable complex system with two subsystems connected in the series configuration. Int J Reliabil Appl 19(1):27–42
9
Lado, A. and Singh, V. (2019), "Cost assessment of complex the repairable system consisting two subsystems in the series configuration using Gumbel Hougaard family copula", International Journal of Quality & Reliability Management, Vol. 36 No. 10, pp. 1683-1698. https://doi.org/10.1108/IJQRM-12-2018-0322
10
Liu, Y and Peng, Y.(2016). The application of computer network and research under the new media environment, evaluation of the computer network technology application tutorial (fourth edition). J Youth Press; 32:104–104. 1007/s11263-011-0495-210.1049/iet-cta.2013.0676
11
Malik, S. C. and Anand, J.2010. Reliability and Economic Analysis of a Computer System with Independent Hardware and Software Failures, Bulletin of Pure and Applied Sciences,Vol.29 E (Math. & Stat.), No. 1, pp.141-153.
12
Malik, S.C. and Sureria, J.K. 2012. Profit Analysis of a Computer System with H/W Repair and S/W Replacement. International Journal of Computer Applications, Vol.47(1), pp.19-26.
13
Malik, S.C. 2013. Reliability Modelling of a computer System with Preventive Maintenance and Priority Subject to Maximum Operation and Repair Times. International Journal of System Assurance Engineering and Management, Vol. 4 (1), pp. 94-100.
14
Malik, S.C. and Munday, V.J. 2014. Stochastic Modeling of a Computer System with Hardware Redundancy, International Journal of Computer Applications, Vol. 89 (7), pp. 26-30.
15
Niwas, R. and Garg, H. (2018), “An approach for analyzing the reliability and profit of an industrial system based on the cost-free warranty policy”, Journal of the Brazilian Society of Mechanics and Engineering, Vol. 40 No. 5, pp. 1-9.
16
Singh, V.V., Poonia, P.K and Abdullahi, A.H. (2020). Performance analysis of a complex repairable system with two subsystems in series configuration with imperfect switch, Journal of Mathematical and Computational Science, 10(2). 359-383.
17
Singh, V.V and Poonia, P. K.. (2019). Probabilistic Assessment of Two-Unit Parallel System with Correlated Lifetime under Inspection Using Regenerative Point Technique, IJRRS,2(1),5-14
18
Temraz, N.S.Y.(2019). Availability and reliability of a parallel system under imperfect repair and replacement: analysis and cost optimization. Int J Syst Assur Eng Manag10, 1002–1009 (2019). https://doi.org/10.1007/s13198-019-00829-2
19
Wang, K,-H and Kuo, C,-C. 2000. Cost and probabilistic analysis of series systems with mixed standby components, Applied Mathematical Modelling, 24: 957-967.
20
Wang,K., Hsieh, C and Liou, C. 2006. Cost benefit analysis of series systems with cold standby components and a repairable service station. Journal of quality technology and quantitative management, 3(1): 77-92.
21
Yang, D. Y and Tsao, C. L. (2019). Reliability and availability analysis of standby systems with working vacations and retrial of failed components. Reliability Engineering & System Safety, 182, 46-55.
22
Yen, T, -C and Wang, K, -H. (2018). Cost-benefit analysis of three systems with imperfect coverage and standby switching failures, IJMOR, 12(2),253-272.DOI: 1504/IJMOR.2018.089680
23
Yang, S.(2019)Analysis for the reliability of the computer network by using intelligent cloud computing method,International Journal of Computers and Applications,41:4,306-311,DOI: 1080/1206212X.2017.1417770
24
Yusuf, I., Yusuf, B., Babagana, M., Sani, B and Lawan, M.A. (2018). Some reliability characteristics of a linear consecutive 2-out-of-4system connected to a 2-out-of-4 supporting device for operation. Int J Eng Technol 7(1):135–139.
25
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26
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27
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28
ORIGINAL_ARTICLE
Computation of Importance Measures Using Bayesian Networks for the Reliability and Safety of Complex Systems
Modern engineering systems have proven to be quite complex due to the involvement of uncertainties and a number of dependencies among the system components. Shortcoming in the inclusion of such complex features results in the wrong assessment of reliability and safety of the system, ultimately to the incorrect engineering decisions. In this paper, the usefulness of Bayesian Networks (BNs) for achieving improved modeling and reliability and risk analysis is investigated. The calculation of a number of Importance Measures with use of Fault Tree Analysis as well as BNs is provided for a complicated railway operation problem. The BNs based safety risk model is investigated in terms of quantitative reliability and safety analysis as well as for multi dependencies and uncertainty modeling.
http://www.ijrrs.com/article_120952_228651b3a275ab8eac46766e036d4190.pdf
2020-12-01
99
111
10.30699/IJRRS.3.2.12
Reliability
safety
Importance Measures
Probabilistic modeling
Syed
Raza
aownraza217@gmail.com
1
Mechanical Department of University of Engineering and Technology (UET), Lahore, Pakistan
LEAD_AUTHOR
Qamar
Mahboob
qamar.mahboob@uet.edu.pk
2
Mechanical Department of University of Engineering and Technology (UET), Lahore, Pakistan
AUTHOR
Awais
Khan
awais211@uet.edu.pk
3
Mechanical Department of University of Engineering and Technology (UET), Lahore, Pakistan
AUTHOR
Tauseef
Khan
tauseef.aized@uet.edu.pk
4
Mechanical Department of University of Engineering and Technology (UET), Lahore, Pakistan
AUTHOR
Jafar
Hussain
jafarhussain@uet.edu.pk
5
Mechanical Department of University of Engineering and Technology (UET), Lahore, Pakistan
AUTHOR
Olde Keizer, M., Flapper, S., Teunter, R., “Condition-based maintenance policies for systems with multiple dependent components: a review, ˮEuropean Journal of Operational Research, 261(2): pp. 405-420, 2017.
1
de Jonge, B., Klingenberg, W., Teunter, R., Tinga, T., “Reducing cost by clustering maintenance activities for multiple critical units, ˮReliability Engineering and System Safety,145: pp. 93-103, 2016.
2
Do, P., Voisin, A., Levrat, E., Iung, B.,“A proactive condition-based maintenance strategy with both perfect and imperfect maintenance actions,ˮ Reliability Engineering and System Safety, 133, pp. 22–32, 2016.
3
Rasay, h., Fallahnezhad, M.S., zaremehjerdi, Y., “Application of multivariate control charts for condition based maintenance, ˮInternational Journal Of Engineering, 31(4): pp. 597-604,2018.
4
Zhang, X., Zeng, J., “A general modeling method for opportunistic maintenance modeling of multi-unit systems, ˮReliability Engineering and System Safety, 140: pp. 176–190, 2015.
5
Ahmadi, R., Newby, M., “Maintenance scheduling of a manufacturing system subjected to deterioration, ˮReliability Engineering & System Safety, 96: pp. 1411-1420, 2011.
6
Zhao, J., Chan, A.H.C., Roberts, C., Madelin, K.B., “Reliability evaluation and optimization of imperfect inspections for a component with multi-defects, ˮ Reliability Engineering & System Safety, 92: pp. 65-73, 2017.
7
Liu, Y., Huang, H.Z.,“Optimal replacement policy for multi-state system under Imperfect Maintenance, ˮIEEE Transactions on Reliability, 59: pp. 483-495, 2011.
8
Zhang, Z., Shen, J., Ma, Y., “Optimal maintenance policy considering imperfect repairs and non-constant probabilities of inspection errors,ˮ Reliability Engineering & System Safety,193: pp. 106-118, 2020.
9
Pham, H., Wang, H.,“Imperfect maintenance, ˮ European journal operation research, 94: pp. 425-38, 1996.
10
Wu, S., Zuo, J.M.,“Linear and nonlinear preventive maintenance models, ˮIEEE Transactions on Reliability, 59(1): pp. 242-9, 2010.
11
Wang, L.,“A survey of maintenance policies of deteriorating systems, ˮ European Journal of Operational Research, 139 (3), pp. 469-489, 2002.
12
Shafiee, M., Finkelstein, M., “A proactive group maintenance policy for continuously monitored deteriorating systems: Application to offshore wind turbines, ˮ Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229 (5): pp. 373-384, 2015.
13
Shafiee, M., Finkelstein, M., Berenguer, C., “ An opportunistic condition-base maintenance policy for offshore wind turbin blades subjected to degradation and environmental shocks, ˮReliability Engineering and System Safety, 142: pp. 463-471, 2015.
14