Industry 4.0: Some Challenges and Opportunities for Reliability Engineering

Document Type : Review Article


1 Aerospace Research Institute, Ministry of Science, Research and Technology, Tehran, Iran

2 Energy Department, Politecnico di Milano, Milan, Italy


According to the development of Industry 4.0 and increase the integration of digital, physical and human worlds, reliability engineering must evolve for addressing the existing and future challenges about that. In this paper, the principle of Industry 4.0 is presented and some of these challenges and opportunities for reliability engineering are discussed. New directions for research in system modeling, big data analysis, health management, cyber-physical system, human-machine interaction, uncertainty, jointly optimization, communication, and interfaces are proposed. Each topic can be investigated individually, but this paper summarizes them and prepared a vision about reliability engineering for consideration and discussion by the interested scientific community.


Main Subjects

[1] Enrico Zio (2016). Some Challenges and Opportunities in Reliability Engineering. IEEE Transactions on Reliability, Institute of Electrical and Electronics Engineers, 65 (4), 1769-1782.
[2] R. Geissbauer, s. Schrauf, v. Koch and s. kuge. Industry 4.0-opportunities and challenges of the industrial internet, 2014, Germany.
[3] S. Weyer, M. Schmitt, M. Ohmer, D. Gorecky (2015). Towards Industry 4.0 – Standardization as the crucial challenge for highly modular, multi-vendor production systems, IFAC-PapersOnLine 48 (3), 579-584, https://doi. org/10.1016/j.ifacol.2015.06.143
[4] Pranab K. Muhuri, Amit K. Shukla, Ajith Abraham (2019). Industry 4.0: A bibliometric analysis and detailed overview, Engineering Applications of Artificial Intelligence,78, 218–235.
[5] Lasi, H., Fettke, P., Kemper, H.G., Feld, T., Hoffmann, M. (2014). Industry 4.0. Bus. Inf. Syst. Eng. 6 (4), 239–242.
[6] Stock, T., Seliger, G. (2016). Opportunities of sustainable manufacturing in industry 4.0. Proc. CIRP 40, 536–541.
[7] Mueller, E., Chen, X.L., Riedel, R. (2017). Challenges and requirements for the application of industry 4.0: A special insight with the usage of cyber-physical system. Chin. J. Mech. Eng. 30 (5), 1050–1057.
[8] Lee, J., Bagheri, B., Kao, H.A. (2015). A cyber-physical systems architecture for industry 4.0-based manufacturing systems. Manuf. Lett. 3, 18–23.
[9] Condry, M.W., Nelson, C.B. (2016). Using smart edge IoT devices for safer, rapid response with industry IoT control operations. Proc. IEEE 104 (5), 938–946.
[10] Wang, S., Wan, J., Zhang, D., Li, D., Zhang, C. (2016). Towards smart factory for industry 4.0: a self-organized multi-agent system with big data-based feedback and coordination. Comput. Netw. 101, 158–168.
[11] Kadera, P., Novák, P. (2017). Performance modeling extension of directory facilitator for enhancing communication in FIPA-compliant multi-agent systems. IEEE Trans. Ind. Inf. 13 (2), 688–695.
[13] Kang, H.S., Lee, J.Y., Choi, S., Kim, H., Park, J.H., Son, J.Y., Do Noh, S. (2016). Smart manufacturing: Past research, present findings, and future directions. Int. J. Precis. Eng. Manuf.-Green Technol. 3 (1), 111–128.
[14] Huang, Z., Yu, H., Peng, Z., Feng, Y. (2017). Planning community energy system in the industry 4.0 era: Achievements, challenges and a potential solution. Renewable Sustainable Energy Rev. 78, 710–721.
 [15] Andrew Kusiak (2019). Fundamentals of smart manufacturing: A multi-thread perspective, AnnualReviews in Control, In press,
[16] Enrico Zio (2018). The future of risk assessment, Reliability Engineering and System Safety, 177, 176–190.
[17] Hervé Panetto, Benoit Iung, Dmitry Ivanov, Georg Weichhart, Xiaofan Wang (2019). Challenges for the cyber-physical manufacturing enterprises of the future, Annual Reviews in Control.
[18] V. Alcácer, V. Cruz-Machado (2019). Scanning the Industry 4.0: A Literature Review on Technologies for Manufacturing Systems, Engineering Science and Technology, an International Journal, In Press.
[19] Saurabh Vaidya, Prashant Ambad, Santosh Bhosle (2018). Industry 4.0 – A Glimpse, Procedia Manufacturing, 20, 233–238.
[20] Erwin Rauch, Christian Linder, Patrick Dallasega (2019). Anthropocentric perspective of production before and within Industry 4.0, Computers & Industrial Engineering, published online,
[21] F. Zezulka, P. Marcon, I. Vesely, O. Sajdl (2016). Industry 4.0 – An Introduction in the phenomenon, IFAC-Papers Online, 49-25, 008–012.
[22] M. Najafi, M A Farsi, H. Jabbari kh. (2018). Dynamic fault tree analysis using fuzzy L-U bounds failure distributions, Journal of Intelligent and Fuzzy Systems 33(6):3275-3286.
[23] Farsi, M. (2018). Fault Analysis of Complex Systems via Dynamic Bayesian Network, AUT Journal of Mechanical Engineering, 2(2), 207-216. Doi: 10.22060/ ajme.2018. 13711. 5692
[24] k. keefe, W.h. Sanders (2015). Reliability Analysis with Dynamic Reliability Block Diagrams in the Möbius Modeling Tool, 9th EAI International Conference on Performance Evaluation Methodologies and Tools, Value Tools, Berlin, Germany
[25] N. Moustafa, E. Adi, B. Turnbull and J. Hu (2018). A New Threat Intelligence Scheme for Safeguarding Industry 4.0 Systems," IEEE Access, 6, 32910-32924,
Doi: 10.1109/ACCESS.2018.2844794
 [27] Md. Abdul Moktadir, Syed Mithun Ali, Simonov Kusi-Sarpong, Md. Aftab Ali Shaikh. (2018).  Assessing challenges for implementing Industry 4.0: Implications for process safety and environmental protection, Process Safety and Environmental Protection, 117,730–741.
[28] Seyed Mohsen Hosseini, Kash Barker, Jose E. Ramirez Marquez (2106). A Review of Definitions and Measures of System Resilience, Reliability Engineering and System Safety,145,47-61,
[29]Dinh, L. T. Pasman, H., Gao, X., Sam Mannan, M. (2012). Resilience engineering of industrial processes: principles and contributing factors. Journal of Loss Prevention in the Process Industries, 25, 233-241.
[30] Jing Wang, Wangda Zuo, Landolf Rhode-Barbarigos, Xing Lu, Jianhui Wang, Yanling Lin (2018). Literature Review on Modeling and Simulation of Energy Infrastructures from a Resilience Perspective, Reliability Engineering and System Safety, Doi: 10.1016/j.ress.2018.11.029
[31] Abimbola, M., Khan, F. (2019). Resilience modeling of engineering systems using dynamic object-oriented Bayesian network approach. Computers & Industrial Engineering.DOI: 10.1016/j.cie.2019.02.022 
[32] Minou C.A. Olde Keizer, Ruud H. Teunter, Jasper Veldman, M. Zied Babai (2018). Condition-based maintenance for systems with economic dependence and load sharing. International Journal of Production Economics, 195, 319–327.
[33] Wang C, Xu J, WAng H, Zhang Z. (2018). A criticality importance-based spare ordering policy for multi-component degraded systems. Eksploatacja iNiezawodnosc – Maintenance and Reliability, 20 (4), 662–670, doi:10.17531/ein.2018.4.17.
[34] Farhad Zahedi-Hosseini, Philip Scarf (2018). Aris Syntetos. Joint maintenance-inventory optimization of parallel production systems. Journal of Manufacturing Systems, 48, 73–86.
[35] M Galagedarage Don, F Khan, (2019). Process fault prognosis using Hidden Markov Model-Bayesian Networks (HMM-BN) hybrid model, Industrial & Engineering Chemistry Research, 582712041-12053. DoI: 10.1021/acs.iecr.9b00524.
[38] Olde Keizer MCA, Flapper SDP, Teunter RH. (2017). Condition-based maintenance policies for systems with multiple dependent components: a review. Eur J Oper Research, 261, 405–20.
[39] Arnesh Telukdarie, Eyad Buhulaiga, Surajit Bag, Shivam Gupta, Zongwei Luo (2018). Industry 4.0 implementation for multinationals, Process Safety and Environmental Protection, 118, 316–329.
[40] M Abimbola, F Khan (2019), Resilience modeling of engineering systems using dynamic object-oriented Bayesian network approach, Computers & Industrial Engineering, 130,108-118.
[41] Fei Tao, Qinglin Qi, Ang Liu, Andrew Kusiak (2018). Data-driven smart manufacturing, Journal of Manufacturing Systems, 48, 157–169
[42] Jinjiang Wang, Yulin Ma, Laibin Zhang, Robert X. Gao, Dazhong Wu (2018). Deep learning for smart manufacturing: Methods and applications, Journal of Manufacturing Systems, 48, 144–156.
[43] Aftab A. Chandio, Nikos Tziritas, Cheng-Zhong Xu (2015), Big-Data Processing Techniques and Their Challenges in Transport Domain, ZTE Communications, 13, 50-59, DOI: 10.3969/j.issn.1673-5188.2015.01.007.
[44]Mohammad Ali Farsi, S. Masood Hosseini (2019), Statistical distributions comparison for remaining useful life prediction of components via ANN, International Journal of System Assurance Engineering and Management,10,  3, 429–436.
[45] Brian Albright (2019). Deep learning and design engineering, addressed by: Https://
[46] Dezhen Xue, P. V. Balachandran, John Hogden, James Theiler, Deqing Xue and Turab Lookman (2016). Accelerated search for materials with targeted properties by adaptive design, NATURE COMMUNICATIONS, 7,11241, DOI: 10.1038/ncomms11241.
[47] Miketen Wolde, Adel A. Ghobbar (2013). Optimizing inspection intervals—Reliability and availability in terms of a cost model: A case study on railway carriers, Reliability Engineering & System Safety, 114, 137-147.
[48] Andika Rachman, R.M. Chandima Ratnayake (2019). Machine learning approach for risk-based inspection screening assessment, Reliability Engineering & System Safety,185, 518-532.
[49] Khanh T.P.Nguyen, Kamal Medjaher (2019). A new dynamic predictive maintenance framework using deep learning for failure prognostics, Reliability Engineering & System Safety, 188, 251-262.
[50] Xian Zhao, Xiaoxin Guo, Xiaoyue Wang (2018). Reliability and maintenance policies for a two-stage shock model with self-healing mechanism, Reliability Engineering & System Safety, 172,185-194.
[52] F. Ahmadzadeh and J. Lundberg (2014). Remaining useful life estimation: review, International Journal of System Assurance Engineering and Management, 5, 4, 461-474.
[53] XiangLi,Wei Zhang, Qian Ding (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction, Reliability Engineering & System Safety, 182, 208-219. 
[54] Liu Yingchao, HuXiaofeng , Wenjuan Zhang (2019). Remaining useful life prediction based on health index similarity, Reliability Engineering & System Safety, 185, 502-510. 
[55] C. Heinrich, M. Khalil, K. Martynov, U. Wever (2019). Online remaining lifetime estimation for structures, Mechanical Systems and Signal Processing, 119, 312-327.
[56] Paulo R.L. Almeida,Luiz S. Oliveira, Alceu S. Britto Jr, Robert Sabourin (2018). Adapting dynamic classifier selection for concept drift, Expert Systems with Applications, 104: 67-85.
[57] Khanh T.P.Nguyen, Mitra Fouladirad, Antoine Grall (2018). Model selection for degradation modeling and prognosis with health monitoring data, 169,105-116.
[58] Qiang Feng, Wenjing Bi, Yiran Chen, Yi Ren, Dezhen Yang (2017). Cooperative game approach based on agent learning for fleet maintenance oriented to mission reliability, Computers & Industrial Engineering, 112, 221-230.
[59] Oveisi, S., Farsi, M. (2018). Software Safety Analysis with UML-Based SRBD and Fuzzy VIKOR- Based FMEA, International Journal of Reliability, Risk and Safety: Theory and Application, 1(1), 35-44. doi: 10.30699/ijrrs.1.35.
[60] Lucas S. Dalenogare, Guilherme B. Benitez, N. Fabián Ayala, Alejandro G. Frank, (2018).The expected contribution of Industry 4.0 technologies for industrial performance, International Journal of Production Economics, 204, 383–394.
[61] Framework for Cyber-Physical Systems, Release 1.0. May 2016, Cyber-Physical Systems Public Working Group.
[62] Cardin Oliver, (2019). Classification of cyber-physical production systems applications: Proposition of an analysis framework, Computers in Industry, 104, 11-21.
[63] L. Monostori, B. Kádár, T. Bauernhansl, S. Kondoh, S. Kumara, G. Reinhart, O. Sauer, G. Schuh, W. Sihn, K. Ueda (2016).  Cyber-physical systems in manufacturing, CIRP Ann. 65, 621–641. DOI: 10.1016/j.cirp.2016.06.005.
[64] Tao, Fei, Qinglin Qi, Andrew nee et al, (2018). Digital twin-driven product design framework. International Journal of Production Research: 1-19. DOI: 10.1080/00207543.2018.1443229
[65] Seshadri B. R., and Krishnamurthy T. (2017). Structural Health Management of Damaged Aircraft Structures Using the Digital Twin Concept. 25th AIAA/AHS Adaptive Structures Conference, DOI: 10.2514/6.2017-1675
[66] Gockel, B. T., A. W. Tudor, M. D. Brandy berry, R. C. Penmetsa, and E. J. Tuegel. (2012). Challenges with Structural Life Forecasting Using Realistic Mission Profiles. 53rd AIAA/ASME/ASCE/AHS/ASC joint conference: Structures, Structural Dynamics and Materials Conference.
[67] Editorial letter (2019). Quality management in the 21st-century enterprises: Research pathway towards Industry 4.0, International Journal of Production Economics, 207, 125–129.
[68] Francesco Longo, Letizia Nicoletti, Antonio Padovano (2017). Smart operators in industry 4.0: A human-centered approach to enhance operators’ capabilities and competencies within the new smart factory context, Computers & Industrial Engineering, 113, 144–159.
[69] Iveta Zolotová, Peter Papcun, Erik Kajáti, Martin Miškuf, Jozef Mocnej (2019). Smart and cognitive solutions for Operator 4.0: Laboratory H-CPPS case studies, Computers & Industrial Engineering, In press, DOI: 10.1016/j.cie.2018.10.032.
[70] Julie Bell and Justin Holroyd (2009). Review of human reliability assessment methods, Health and Safety Laboratory, UK.
[71] Romero, D., Stahre, J., Wuest, T., Noran, O., Bernus, P., Fast-Berglund, Å., and Gorecky, D.(2016). Towards an Operator 4.0 typology: a human-centric perspective onthe fourth industrial revolution technologies. International conference on computers &industrial engineering CIE46) ,pp. 1–11.
[72] Stojkić, Ž.; Veža, I. & Bošnjak, I. (2015). A concept of information system implementation (crmand erp) within industry 4.0, Annals of DAAAM and Proceedings of the International DAAAM Symposium, pp. 912-919.
[74] M. a. Farsi (2016). Principle of reliability engineering, Simayeh danesh, Tehran.
[75] David W. Coit , Enrico Zio. (2019), The Evolution of System Reliability Optimization,Reliability Engineering and System Safety, in press, DOI: 10.1016/ j.ress.2018.09.008.
[76] Sai Zhang, Mengyu Du, Jiejuan Tong, Yan-FuLi. (2019) Multi-objective optimization of maintenance program in multi-unit nuclear power plant sites, Reliability Engineering & System Safety, 188: 532-548.
[77] Ronald M. Martinod,, Olivier Bistorin ,Leonel F. Castañeda ,Nidhal Rezg. (2018), Maintenance policy optimization for multi-component systems considering degradation of components and imperfect maintenance actions, Computers & Industrial Engineering, 124, 100-112.
[78] Carl S. Carlson (2012). Effective FMEAs: achieving safe, reliable, and economical products and processes using failure mode and effects analysis, John Wiley & Sons, Inc., Hoboken, New Jersey.
[79] B. Czerny, G. Khatibi (2016). Interface reliability and lifetime prediction of heavy aluminum wire bonds, Microelectronics Reliability, 58, 65-72.
[80] Jens Due,Anthony J. Robinson. (2013), Reliability of thermal interface materials: A review, Applied Thermal Engineering, 50(1), 455-463.
[81] Sascha Heinssen, Theodor Hillebrand, Maike Taddi, kenKonstantin, Tscher kaschinSteffen, PaulDagmar and Peters-Drolshagen (2018). Design for reliability of generic sensor interface circuits, Microelectronics Reliability, 80, 184-197.
[82] Marc C. Kennedy, Anthony O'Hagan (2001), Bayesian calibration of computer models, Journal of the Royal Statistical Society, Series B, 63(3), 425–464.
[83] Ba Kader, Dellagi Sofiene, Rezg Nidhal, Erray Walid (2016).Joint optimization of preventive maintenance and spare parts inventory for an optimal production plan with consideration of CO2 emission, Reliability Engineering and System Safety, 149, 172–186.
[84] Chiara Franciosi, Alfredo Lambiase, Salvatore Miranda(2017). Sustainable Maintenance: A Periodic Preventive Maintenance Model with Sustainable Spare Parts Management, IFAC, 50(1),13692-13697, DOI: 10.1016/j.ifacol.2017.08.2536
[85] Ya-Ju Chang, S. Neugebauer, A. Lehmann, R. Scheumann and M. Finkbeiner (2107). Life Cycle Sustainability Assessment Approaches for Manufacturing, the chapter of Sustainable Manufacturing, Editors; Stark, Rainer, Seliger, Günther, Bonvoisin, Jérémy, Springer.
[86] Munsamy, M., A. Telukdarie (2019). Application of Industry 4.0 towards Achieving Business Sustainability, IEEE International Conference on Industrial Engineering and Engineering Management,
[87] Varela, L., Araújo, A., Ávila, P., Castro, H., Putnik (2109). G. Evaluation of the Relation between Lean Manufacturing, Industry 4.0, and Sustainability,Sustainability, 11(5), 1439, DOI: 10.3390/su11051439.
[88] Birkel, H.S., Veile, J.W., Müller, J.M., Hartmann, E., Voigt, K. I. (2019). Development of a risk framework for Industry 4.0 in the context of sustainability for established manufacturers, Sustainability,11,1-27, DOI:10.3390/su11020384.
[89] Surajit Bag, Arnesh Telukdarie, J.H.C. Pretorius, Shivam Gupta (2018). Industry 4.0 and supply chain sustainability: framework and future research directions, Benchmarking: An International Journal, In Press, DOI: 10.1108/BIJ-03-2018-0056.