Direct remaining useful life prediction based on multi-sensor information integrations by using evidence theory

Document Type : Research Paper


School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran


Estimation of remaining useful life (RUL) is one of most interesting subjects in prognostic and health management. Performing an analysis of the results of such estimation can increase the reliability and the safety of the system, and reduce the unnecessary costs. In this paper, a similarity-based combination method is proposed to combine several run-to-failure historical datasets in order to directly estimate the RUL. In this method, reference datasets are clustered and the initial RUL is calculated based on the artificial neural networks trained by the reference datasets. By using the extended Dempster-Shafer, the similarity between the initial RUL and the average RUL for each dataset is obtained. The proposed methodology is tested and validated on Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), test-bed developed by NASA. The results of the evaluation show that the proposed method outperforms other methods in the literature.


Main Subjects

Ben Ali, J., Chebel-Morello, B., Saidi, L., Malinowski, S., & Fnaiech, F. (2015). Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network. Mechanical Systems and Signal Processing, 56.
Dempster, A. P. (1967). Upper and lower probabilities induced by a multivalued mapping. The Annals of Mathematical Statistics, 325–339.
El-Koujok, M., Gouriveau, R., & Zerhouni, N. (2011). Reducing arbitrary choices in model building for prognostics: An approach by applying parsimony principle on an evolving neuro-fuzzy system. Microelectronics Reliability, 51(2), 310–320.
Giantomassi, A., Ferracuti, F., Benini, A., Ippoliti, G., Longhi, S., & Petrucci, A. (2011). Hidden Markov model for health estimation and prognosis of turbofan engines. In ASME 2011 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference (pp. 681–689). American Society of Mechanical Engineers.
Heimes, F. O. (2008). Recurrent neural networks for remaining useful life estimation. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1–6). IEEE.
Hu, C., Youn, B. D., Wang, P., & Yoon, J. T. (2012). Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life. Reliability Engineering & System Safety, 103, 120–135.
Inagaki, T. (1991). Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory. IEEE Transactions on Reliability, 40(2), 182–188.
Ishibashi, R., & Júnior, C. L. N. (2013). GFRBS-PHM: A genetic fuzzy rule-based system for phm with improved interpretability. PHM 2013 - 2013 IEEE International Conference on Prognostics and Health Management, Conference Proceedings.
Javed, K., Gouriveau, R., & Zerhouni, N. (2015). A new multivariate approach for prognostics based on extreme learning machine and fuzzy clustering. IEEE Transactions on Cybernetics, 45(12), 2626–2639.
Jianzhong, S., Hongfu, Z., Haibin, Y., & Pecht, M. (2010). Study of ensemble learning-based fusion prognostics. In Prognostics and Health Management Conference, 2010. PHM’10. (pp. 1–7). IEEE.
Khelif, R., Chebel-Morello, B., Malinowski, S., Laajili, E., Fnaiech, F., & Zerhouni, N. (2017). Direct Remaining Useful Life Estimation Based on Support Vector Regression. IEEE Trans. Industrial Electronics, 64(3), 2276–2285.
Khelif, R., Malinowski, S., Chebel-Morello, B., & Zerhouni, N. (2014). RUL prediction based on a new similarity-instance based approach. In Industrial Electronics (ISIE), 2014 IEEE 23rd International Symposium on (pp. 2463–2468). IEEE.
Kunche, S., Chen, C., & Pecht, M. (2012). A review of PHM system’s architectural frameworks. In The 54th Meeting of the Society for Machinery Failure Prevention Technology, Dayton, OH.
Lee, J., Wu, F., Zhao, W., Ghaffari, M., Liao, L., & Siegel, D. (2014). Prognostics and health management design for rotary machinery systems—Reviews, methodology and applications. Mechanical Systems and Signal Processing, 42(1), 314–334.
Lei, Y., Li, N., Guo, L., Li, N., Yan, T., & Lin, J. (2018). Machinery health prognostics: A systematic review from data acquisition to RUL prediction. Mechanical Systems and Signal Processing, 104, 799–834.
Lin, Y., Chen, M., & Zhou, D. (2013). Online probabilistic operational safety assessment of multi-mode engineering systems using Bayesian methods. Reliability Engineering & System Safety, 119, 150–157.
Liu, K., Gebraeel, N. Z., & Shi, J. (2013). A Data-Level Fusion Model for Developing Composite Health Indices for Degradation Modeling and Prognostic Analysis, 10(3), 652–664.
Moghaddass, R., & Zuo, M. J. (2014). An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process. Reliability Engineering and System Safety, 124, 92–104.
Mosallam, A., Medjaher, K., & Zerhouni, N. (2016). Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, 27(5), 1037–1048.
Nie, Y., & Wan, J. (2015). Estimation of remaining useful life of bearings using sparse representation method. In Prognostics and System Health Management Conference (PHM), 2015 (pp. 1–6). IEEE.
Peng, Y., & Dong, M. (2011). A hybrid approach of HMM and grey model for age-dependent health prediction of engineering assets. Expert Systems with Applications, 38, 12946–12953.
Peng, Y., Wang, H., Wang, J., Liu, D., & Peng, X. (2012a). A modified echo state network based remaining useful life estimation approach. In Prognostics and Health Management (PHM), 2012 IEEE Conference on (pp. 1–7). IEEE.
Peng, Y., Wang, H., Wang, J., Liu, D., & Peng, X. (2012b). A modified echo state network based remaining useful life estimation approach. PHM 2012 - 2012 IEEE Int. Conf.on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHM Technology and Application, Conference Program.
Ramasso, E. (2014). Investigating computational geometry for failure prognostics. International Journal of Prognostics and Health Management, 5(1), 5.
Ramasso, E., & Denoeux, T. (2014). Making use of partial knowledge about hidden states in HMMs: an approach based on belief functions. IEEE Transactions on Fuzzy Systems, 22(2), 395–405.
Ramasso, E., & Gouriveau, R. (2014). Remaining useful life estimation by classification of predictions based on a neuro-fuzzy system and theory of belief functions. IEEE Transactions on Reliability, 63(2), 555–566.
Ramasso, E., Rombaut, M., & Zerhouni, N. (2013). Joint prediction of continuous and discrete states in time-series based on belief functions. IEEE Transactions on Cybernetics, 43(1), 37–50.
Ramasso, E., Saxena, A., Ramasso, E., Saxena, A., Benchmarking, P., & Meth-, A. P. (2016). Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets . To cite this version : HAL Id : hal-01324587 Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets.
Saxena, A., Goebel, K., Simon, D., & Eklund, N. (2008). Damage propagation modeling for aircraft engine run-to-failure simulation. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1–9). IEEE.
Shafer, G. (1976). A mathematical theory of evidence (Vol. 1). Princeton university press Princeton.
Sun, J., Zuo, H., Wang, W., & Pecht, M. G. (2012). Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance. Mechanical Systems and Signal Processing, 28, 585–596.
Tamilselvan, P., Wang, Y., & Wang, P. (2012). Deep belief network based state classification for structural health diagnosis. IEEE Aerospace Conference Proceedings.
Tobon-meja, D., Medjaher, K., Zerhouni, N., & Iso, T. (2010). The ISO 13381-1 Standard ’ s failure prognostics process through an example.
Van Tung, T., & Yang, B.-S. (2009). Machine fault diagnosis and prognosis: The state of the art. International Journal of Fluid Machinery and Systems, 2(1), 61–71.
Wang, P., Youn, B. D., & Hu, C. (2012). A generic probabilistic framework for structural health prognostics and uncertainty management. Mechanical Systems and Signal Processing, 28, 622–637.
Wang, T., Yu, J., Siegel, D., & Lee, J. (2008). A similarity-based prognostics approach for remaining useful life estimation of engineered systems. In Prognostics and Health Management, 2008. PHM 2008. International Conference on (pp. 1–6). IEEE.
Xi, Z., Jing, R., Wang, P., & Hu, C. (2014). A copula-based sampling method for data-driven prognostics. Reliability Engineering and System Safety, 132, 72–82.
Xu, J., Wang, Y., & Xu, L. (2014). PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data. IEEE Sensors Journal, 14(4), 1124–1132.
Yager, R. R. (1987). On the Dempster-Shafer framework and new combination rules. Information Sciences, 41(2), 93–137.
Yan, J., Koc, M., & Lee, J. (2004). A prognostic algorithm for machine performance assessment and its application. Production Planning & Control, 15(8), 796–801.
Zemouri, R., & Gouriveau, R. (2010). Towards accurate and reproducible predictions for prognostic: an approach combining a RRBF Network and an AutoRegressive Model. In 1st IFAC Workshop on Advanced Maintenance Engineering, Services and Technology, IFAC A-MEST’10. (pp. 163–168).
Zhang, L. (1994). Representation, independence, and combination of evidence in the Dempster-Shafer theory. In Advances in the Dempster-Shafer theory of evidence (pp. 51–69). John Wiley & Sons, Inc.