Prediction of Failure Time and Remaining Useful Life in Aviation Systems: Predictors, models, and challenges

Document Type : Review Article


1 Faculty of Management and Industrial Engineering, Malek Ashtar University of Technology, Iran

2 Faculty of Aerospace, Malek Ashtar University of Technology, Iran


In many important industries, such as aerial transportation, offshore wind turbine (OWT) structures, and nuclear power plants that reached or are near the end of their useful life, the structural conditions for continued usage are acceptable. Thus, safe continued operation with required modifications and assessment is more cost-effective than replacing them with a new system. To achieve this goal, many studies have been performed on predicting failure time and remaining useful life, especially in systems that require a very high level of reliability. The present review investigates the articles that predict the remaining useful life or failure time in aviation systems, from three perspectives: 1. Methods and algorithms, especially Machine Learning algorithms, which are growing in recent years in the field of Prognosis and Health Management. 2. Historical predictors such as working life history, environmental conditions, mechanical loads, failure records, asset age, maintenance information, or sensor variables and indicators that can be continuously controlled in each system, such as noise, temperature, vibration, and pressure.3. Challenges of researches on prediction of the failure time of flying systems. The literature assessment in this field shows that using diagnostic and prognostic outputs to identify possible defects and their origin, checking the system's health, and predicting the remaining useful life (RUL) is increasing due to market needs.


Main Subjects

  1. Xia, Y. Dong, L. Xiao, S.H. Du, E. Pan, and L. Xi, “Recent advances in prognostics and health management for advanced manufacturing paradigms”, Reliability Engineering and System Safety Monthly, October., pp. 255-268, 2018.
  2. Brandt, and S. M. Mohd Sarif, “Life extension of offshore assets - balancing safety & project economics”, In proc.SPE Asia Pacific Oil & Gas Conference and Exhibition, 2013, pp. 1–9.
  3. Fink, Q. Wang, M. Svensén, P. Dersin, W.J. Lee, and M. Ducoffe, “Potential, challenges and future directions for deep learning in prognostics and health management applications”, Engineering Applications of Artificial Intelligence, Vol.92,Article 103678, 2020.
  4. L. Rosero, C. Silva, and B. Ribeiro, “Remaining Useful Life Estimation in Aircraft Components with Federated Learning”, In proc. European Conference of the Prognostics and Health Management Society, 2020, pp.9.
  5. Jiao, K. Penga, J. Donga, and C. Zhanga, “Fault monitoring and remaining useful life prediction framework for multiple fault models in prognostics”, Reliability Engineering & System Safety Monthly, November., Article 107028, 2020.
  6. K.S. Jardine, D. Lin, and D. Banjevic, “A review on machinery diagnostics and prognostics implementing condition-based maintenance”, Mechanical Systems and Signal Processing, Vol.20, no.7, pp. 1483–1510, 2006.
  7. M. Mirmohammadi, M. Khazaee, A.M. Shahverdi, M. Vaseli Khabbaz, andM.J. Pourebrahim, “Sensitivity analysis of the monitoring characteristics to determine the location of mass imbalance fault on the helicopter blade”, presented at 20th International Conference of Iranian Aerospace Society, Tehran, Iran, 2022.
  8. Rezaeian Jouybari, and Y. Shang, “Deep Learning for Prognostics and Health Management: State of the Art, Challenges, and Opportunities”, Measurement, Vol.163, no.15, 2020.
  9. Vaidya, and M. Rausand, “Remaining useful life, technical health, and life extension”, Journal of Risk and Reliability, Vol. 225, no.2, pp. 219–231, 2011.
  10. Vaidya, “Prognosis - subsea oil and gas industry”, In proc. Annual Conference of the Prognostics and Health Management Society, 2010, pp. 1–10.
  11. Animah, and M. Shafiee, “Condition assessment, remaining useful life prediction and life extension decision making for offshore oil and gas assets”, Journal of Loss Prevention in the Process Industries Monthly, May., pp.17-28,2018.
  12. Forman, “An extensive empirical study of feature selection metrics for text classification”, Journal of Machine Learning Research, Vol.3,March., pp.1289–1305, 2003.
  13. Guyon, and A. Elisseeff, “An Introduction to Variable and Feature Selection”, Journal of Machine Learning Research,Vol.3, March., pp. 1157–1182, 2003.
  14. Bengio, A. Courville, and P. Vincent, “Representation learning: A review and new perspectives”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35 no. 8, pp. 1798–1828, 2013.
  15. Maksimović, Z. Vasić, and R. Došić, “Service Life Extension Program for Aircraft Structures”, Scientific Technical Review, Vol. 65, no.3, pp.46-54, 2015.
  16. H. Saleh, A. Tikayat Ray, K.S. Zhang, and J.S. Churchwell, “Maintenance and inspection as risk factors in helicopter accidents: Analysis and recommendations”, PLoS One, Vol.14, no. 2, e0211424, 2019.
  17. Benny, M. Johny, and L.S. Mathew, “Prediction of Aviation Accidents using Logistic Regression Model”, International Journal of Innovative Research in Technology, Vol. 7, no.6,pp. 2349-6002, 2020.
  18. Xu, J.S. Saleh, and R. Subagia, “Machine learning for helicopter accident analysis using supervised classification: Inference, prediction, and implications”, Reliability Engineering and System Safety Monthly, December., Article 107210, 2020.
  19. S. Churchwell, K.S. Zhang, and J.H. Saleh, “Epidemiology of helicopter accidents: Trends, rates, and covariates”, Reliability Engineering & System Safety Monthly, December., pp. 373-384, 2018.
  20. Peiravi, M. Karbasian, M. AboueiArdakan, and D.W. Coit, “Reliability optimization of series-parallel systems with K-mixed redundancy strategy”, Reliability Engineering & System Safety Monthly, March., pp. 17-28, 2019.
  21. M. Mortazavi, M. Karbasian, and S. Goli, “Evaluating MTTF of 2-out-of-3 redundant systems with common cause failure and load share based on alpha factor and capacity flow models”, International Journal of System Assurance Engineering and Management, Vol. 8, no.3,pp. 542-552,2017.
  22. A. Tobon-Mejia, K. Medjaher, and N. Zerhouni, “CNC machine tool's wear diagnostic and prognostic by using dynamic Bayesian networks”, Mechanical Systems and Signal Processing, Vol. 28, April., pp.167-82, 2012.
  23. Fink, Data-driven intelligent predictive maintenance of industrial assets, Part of the Women in Industrial and Systems Engineering book series, pp. 589–605, 2020.
  24. An, N.H. Kim, and J.H. Choi, “Practical options for selecting data-driven or physics-based prognostics algorithms with reviews”, Reliability Engineering &System Safety Monthly, January., pp.223-36, 2015.
  25. A. Chao, C. Kulkarni, K. Goebel, and O. Fink, “Hybrid deep fault detection and isolation: Combining deep neural networks and system performance models”, International Journal of Prognostics and Health Management, Vol.10, no. 4, 2019.
  26. Chen, S. Cao, and Z. Mao, “Remaining Useful Life Estimation of Aircraft Engines Using a Modified Similarity and Supporting Vector Machine (SVM) Approach”, Energies,Vol.11, no.1, pp. 28, 2017.
  27. Javed, R. Gouriveau, and N. Zerhouni, “State of the art and taxonomy of prognostics approaches, trends of prognostics applications and open issues towards maturity at different technology readiness levels”, Mechanical Systems and Signal Processing, Vol. 94, pp.214-36, 2017.
  28. Zhao, J. Kim, K. Warns, and X. Wang, P. Ramuhalli, S. Cetiner, H.G. Kang, and M. Golay, “Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review with Special Focus on Data-Driven Methods”, Frontiers in Energy Research, Vol. 9, Article 696785, 2021.
  29. P. Murphy, Machine learning: a probabilistic perspective, MIT Press, 2012.
  30. Sutharssan, S. Stoyanov, C.Bailey, and C. Yin, “Prognostic and Health, Management for Engineering Systems: A Review of the Data-driven Approach and Algorithms”, Journal of Engineering, Vol.7, pp.215–222, 2015.
  31. Atamuradov, K. Medjaher, P. Dersin, B. Lamoureux, and N. Zerhouni, “Prognostics and Health Management for Maintenance Practitioners - Review, Implementation and Tools Evaluation”, International Journal of Prognostics and Health Management,Vol.8, no. 3, 2017.
  32. Xu, and J.H. Saleh, “Machine learning for reliability engineering and safety applications: Review of current status and future opportunities”, Reliability Engineering and System Safety Monthly, July.,Article 107530, 2021.
  33. A. Freedman, Statistical models: theory and practice, Cambridge university press, 2009.
  34. R. Edwards, “Polynomial regression and response surface methodology”, in Perspectives on organizational fit, C. Ostroff and T. A. Judge, Eds. San Francisco: Jossey-Bass,2007, pp.361–72.
  35. Liaw, and M. Wiener, “Classification and regression by random Forest”, R News, Vol.2/3,pp.18–22, 2002.
  36. J. Lewis, “An introduction to classification and regression tree (CART) analysis”, In proc. Annual meeting of the society for academic emergency medicine,2000, pp.419-608.
  37. A. Jimenez, C.Q.G. Mu˜noz, and F. P.G. Marquez, “Dirt and mud detection and diagnosis on a wind turbine blade employing guided waves and supervised learning classifiers”, Reliability Engineering & System Safety Monthly, April., pp.2–12,2019.
  38. Naderpour, H. Mojaddadi Rizeei, N. Khakzad, and B. Pradhan, “Forest fire-induced Natech risk assessment: a survey of geospatial technologies”, Reliability Engineering System Safety Monthly, November., Article 106558, 2019.
  39. J. Myles, R.N. Feudale, Y. Liu, N.A. Woody, and S.D. Brown, “An introduction to decision tree modeling”, Chemometrics, Vol.18, no.6, pp.275–85,2004.
  40. Friedman, D. Geiger, and M. Goldszmidt, “Bayesian Network Classifiers”, Machine Learning,Vol.29, no 2-3, pp.131–163,1997.
  41. Gehl, F. Cavalieri, and P. Franchin,” Approximate Bayesian network formulation for the rapid loss assessment of real-world infrastructure systems”, Reliability Engineering System Safety Monthly, September., pp.80–93, 2018.
  42. Xiang, J. Chen, Y. Bao, and H. Li,” An active learning method combining DNN and weighted sampling for structural reliability analysis”, Mechanical Systems and Signal Processing, Vol.140, June., Article 106684,2020.
  43. A. Chojaczyk, A.P. Teixeira, L.C. Neves, J.B. Cardoso, and C. Guedes Soares, "Review and application of Artificial Neural Networks models in reliability analysis of steel structures", Structural Safety Monthly, January., pp.78–89, 2015.
  44. Fink, E. Zio, andU. Weidmann, “Predicting component reliability and level of degradation with complex-valued neural networks”. Reliability Engineering and System Safety Monthly, January., pp. 198–206,2014.
  45. T.P. Nguyen, and K. Medjaher, “A new dynamic predictive maintenance framework using deep learning for failure prognostics”, Reliability Engineering and System Safety Monthly, August., pp.251–62,2019.
  46. Wang, G. We, S. Yang, and Y. Liu, “Remaining Useful Life Estimation in Prognostics Using Deep Bidirectional LSTM Neural Network”, In proc. IEEE International Conference on Prognostics and Health Management,2018, pp. 1037–42.
  47. O. Heimes, “Recurrent neural networks for remaining useful life estimation”, In proc. international conference on prognostics and health management, 2008, pp. 1-6.
  48. Krizhevsky, I. Sutskever, and G.E. Hinton, “Imagenet classification with deep Convolutional Neural Networks”, Advances in Neural Information Processing Systems, Vol.25, no. 2, 2012.
  49. Le Cun, L. Bottou, Y. Bengio, and P. Haffiner,” Gradient-based learning applied to document recognition”, Proceedings of the IEEE, Vol.86, no. 11, pp. 2278–2324, 1998.
  50. P. Kingma, and Welling, M., An Introduction to Variational Auto encoders, Now Publishers, 2019.
  51. Liu, X. Hu, and W. Zhang, “Remaining useful life prediction based on health index similarity”, Reliability Engineering System Safety Monthly, May., pp.502–10, 2019.
  52. G.J. Nieto, E. Garcia-Gonzalo, F.S. Lasheras, and F.J. de Cos Juez, “Hybrid PSO–SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability”, Reliability Engineering System Safety Monthly, June.,pp. 219–31,2015.
  53. Li, Q. Ding, and J. Q. Sun, “Remaining useful life estimation in prognostics using deep convolution neural networks”, Reliability Engineering and System Safety Monthly, April., pp.1–11, 2018.
  54. Khelif, B. Chebel-Morello, S. Malinowski, E. Laajili, F. Fnaiech, and N. Zerhouni, “Direct Remaining Useful Life Estimation Based on Support Vector Regression”, IEEE Transactions on Industrial Electronics, Vol. 64, no. 3, pp.2276 - 2285, 2017.
  55. Jun, M. Ling, Z. Lixin, and W. Chunhui, “A Concept for PHM System for Storage and Life Extension of Tactical Missile”, In proc. Prognostics and System Health Management Conference, 2014, pp. 689-695.
  56. Ma, H. Su, W.L. Zhao, and B. Liu, “Predicting the Remaining Useful Life of an Aircraft Engine Using a Stacked Sparse Auto encoder with Multilayer Self-Learning”, Complexity, Article 3813029,2018.
  57. Liu, X. Song, and Z. Zhoua, “Aircraft engine remaining useful life estimation via a double attention-based data-driven architecture”, Reliability Engineering & System Safety Monthly, May., Article 108330, 2022.
  58. A. Farsi, “Identification of Size and Location of Bearing Damage via Deep Learning”, International Journal of Reliability, Risk, Safety: Theory and Applications, Vol.4, no.1,pp. 69-74, 2021.
  59. De Pater, A. Reijns, and M. Mitici, “Alarm-based predictive maintenance scheduling for aircraft engines with imperfect Remaining Useful Life prognostics”, Reliability Engineering & System Safety Monthly, May., Article 108341, 2022.
  60. Lee, and M. Mitici, “Multi-objective design of aircraft maintenance using Gaussian process learning and adaptive sampling”, Reliability Engineering & System Safety Monthly, February., Part A, Article 108123, 2022.
  61. Subagia, J.H. Saleh, J.S. Churchwell, and K.S. Zhang, “Statistical learning for turbo shaft helicopter accidents using logistic regression” PLoS One, Vol.15, no.1, e0227334,2020.
  62. S. Sampaio, A. Rabello de Aguiar Vallim Filho, L. Santos da Silva, and L. Augusto da Silva, “Prediction of Motor Failure Time Using an Artificial Neural Network”, Sensors Monthly,Vol.19, no.19, 4342, 2019.
  63. Zhao, B. Liang, X. Wang, and W. Lu, “Remaining useful life prediction of aircraft engine based on degradation pattern learning”, Reliability Engineering & System Safety Monthly, August., pp. 74-83, 2017.
  64. Celikmih, O. Inan, and H. Uguz, “Failure Prediction of Aircraft Equipment Using Machine Learning with a Hybrid Data Preparation Method”, Scientific Programming, Article 8616039, 2020.
  65. Liu, F. Lei, C. Pan, D. Hu, and H. Zuo, “Prediction of remaining useful life of multi-stage aero-engine based on clustering and LSTM fusion”, Reliability Engineering and System Safety Monthly, October., Article 107807, 2021.
  66. Zhang, P. Srinivasan, and S. Mahadevan, “Sequential deep learning from NTSB reports for aviation safety prognosis”, Safety Science Monthly, October., Article 105390,2021.
  67. Zhang, and S. Mahadevan, “Bayesian network modeling of accident investigation reports for aviation safety assessment”, Reliability Engineering & System Safety Monthly, May., Article 107371, 2021.