A Data-Driven method for estimation Remaining Useful Life (RUL) of turbofan based on multi-sensor information integrations

Document Type : Research Paper

Authors

1 Iran University of Science & Technology

2 Department of industrial engineering, Iran university of science and technology

3 department of industrial engineering, Iran university of science and technology

Abstract

Determination of degradation status and estimating remaining useful life are two main activities in prognostics and health management. These two main activities can be perceived as the problem of multi-sensor data fusion. These sensors contain information including speed, pressure, and temperature. In terms of evidence theory, the information obtained from each of these sensors can be regarded as a part of the evidence and determination of degradation status and estimating remaining useful life based upon this information can be considered as the problem of multi-sensor data fusion. In this article, the Dempster-Shafer theory has been employed as a tool for modeling and multi-sensor data fusion as an indicator of the health status of the turbofan. In this regard, initially, the theory of evidence has been reviewed and then how to model the remaining useful life estimation problem by multi-sensor data fusion within the framework of the concepts of this theory including mass function, focal elements and rules of evidence composition is explained. This paper has introduced a new approach for combining the results of different estimates of remaining useful life through determining the weighs. Furthermore, it has offered two different scenarios to specify the similarity between the system under study and the available evidence. Finally, to appraise the proposed method, the turbofan engines data set (C-MAPSS) has been used as it has been extensively addressed in literature by researchers. According to the results, the proposed method revealed better score and performance compared with other available methods in the literature.

Keywords

References

 
[1] Lasheras, F. S., Nieto, P. J. G., de Cos Juez, F. J., Bayon, R. M. and Suarez, V. M. G. (2015). “A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines,” Sensors (Switzerland), 15(3), 7062-7083.
[2] 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, vol. 56,
[3] 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, 2012.
[4] Abiodun, O. I. et al. (2019). “Comprehensive Review of Artificial Neural Network Applications to Pattern Recognition,” IEEE Access, vol. PP, no. February 2017, p. 1
[5] 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, vol. 104, 799-834.
[6] Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N. (2010). “The ISO 13381-1 standard’s failure prognostics process through an example,” Progn. Syst. Heal. Manag. Conf. PHM ’10.
[7] 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.
[8] Baur, M., Albertelli, P., Monno, M. (2020). “A review of prognostics and health management of machine tools,” The International Journal of Advanced Manufacturing Technology, vol. 107,  2843–2863
[9] Javed, K., Gouriveau, R., Zerhouni, N. (2017).“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, 94, 214-36.
[10] Xia, T., Dong, Y., Xiao, L., Du, S., Pan, E., Xi, L. (2018). “Recent advances in prognostics and health management for advanced manufacturing paradigms Recent advances in prognostics and health management for advanced manufacturing paradigms,” Reliability Engineering and System Safety, vol. 178, no. July, 255-268.
[11] Javed, K. (2014). “A robust & reliable Data-driven prognostics approach based on extreme learning machine and fuzzy clustering,” Université de Franche-Comté.
[12] Yager, R. R. (1987). “On the Dempster-Shafer framework and new combination rules,” Information Sciences, 41(2), 93–137.
[13] Abichou, B., Flórez, D., Sayed-Mouchaweh, M., Toubakh, H., François, B., Girard, N.(2014). “Fault diagnosis methods for wind turbines health monitoring: a review,” in European Conference of the Prognostics and Health Management Society, pp. 8-10.
[14] Kadry, S. (2012). Diagnostics and Prognostics of Engineering Systems: Methods and Techniques: Methods and Techniques. IGI Global.
[15] El-Thalji, I., Jantunen, E. (2015). “A summary of fault modelling and predictive health monitoring of rolling element bearings,” Mechanical systems and signal processing, 60, 252–272.
[16] Si, X.-S., Wang, W., Hu, C.-H., Zhou, D.-H. (2011).“Remaining useful life estimation–a review on the statistical data driven approaches,” Eur. J. Oper. Res., 213(1), 1-14.
[17] Moghaddass, R., Zuo, M.J. (2014). “An integrated framework for online diagnostic and prognostic health monitoring using a multistate deterioration process,” Reliab. Eng. Syst. Saf., 124, 92–104.
[18] Yan, J., Koc, M., Lee, J. (2004). “A prognostic algorithm for machine performance assessment and its application,”Production Planning & Control, 15(8), 796-801.
[19] 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.
[20] 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.
[21] 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.
[22] Xue, F., Bonissone, P., Varma, A., Yan, W., Eklund, N., Goebel, K. (2008). “An instance-based method for remaining useful life estimation for aircraft engines,”ournal of failure analysis and prevention, 8(2), 199-206.
[23] 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.
[24] E. Ramasso, M. Rombaut, and N. Zerhouni, “Joint prediction of continuous and discrete states in time-series based on belief functions,” IEEE Trans. Cybern., vol. 43, no. 1, 37–50.
[25] 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.
[26] Hu, C.,  Youn, B. D.,  Wang, P., Taek, J. (2012).  “Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life,” Reliability Engineering and System Safety, 103, 120-135.
[27] 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.
[28] Ishibashi, R., Júnior, C. L. N. (2013). “GFRBS-PHM: A genetic fuzzy rule-based system for phm with improved interpretability,” PHM 2013 - 2013 IEEE Int. Conf. Progn. Heal. Manag. Conf. Proc.
[29] 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.
[30] Heimes, F. O., Systems, B. A. E.  (2016).“Recurrent Neural Networks for Remaining Useful Life Estimation,” no. November 2008.
[31] Zemouri, R., Gouriveau, R., Zemouri, R., Gouriveau, R., Zemouri, R., Gouriveau, R. (2010).“Towards accurate and reproducible predictions for prognostic : an approach combining a RRBF Network and an AutoRegressive Model . To cite this version : HAL Id : hal-00503906 Towards Accurate and Reproducible Predictions for Prognostic : an Approach Combi,”.
[32] 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.
[33] 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,”.
[34] Giantomassi, A., Ferracuti, F., Benini,  A., Ippoliti, G., Longhi, S., Petrucci, A. (2011). “Hidden Markov Model for Health Estimation and Prognosis of Turbofan Engines,” Vol. 3 2011 ASME/IEEE Int. Conf. Mechatron. Embed. Syst. Appl. Parts A B, no. 681-689.
[35] Lin, Y.,  Chen, M., Zhou, D. (2013). “Online probabilistic operational safety assessment of multi-mode engineering systems using Bayesian methods,” Reliability Engineering and System Safety, vol. 119, 150-157.
[36] 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.
[37] 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.
[38] Tamilselvan, P., Wang, Y., Wang, P. (2012). “Deep belief network based state classification for structural health diagnosis,” in Aerospace Conference, 2012 IEEE, pp. 1–11.
[39] 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.
[40] 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.
[41] Peng, Y., Wang, H., Wang, J., Liu, D., Peng, X. (2012). “A modified echo state network based remaining useful life estimation approach,” in Prognostics and Health Management (PHM), 2012 IEEE Conference on, pp. 1-7.
[42] Jianzhong, S., Hongfu, Z., Haibin, Y., Pecht, M. (2010). “Study of ensemble learning-based fusion prognostics,” in Prognostics and Health Management Conference, PHM’10., pp. 1-7.
[43] Li, W., Jiao, Z., Du, L., Fan, W., Zhu, Y. (2019).“ScienceDirect An indirect RUL prognosis for lithium-ion battery under vibration stress using Elman neural network,”International Journal of Hydrogen Energy, 4, 4-10.
[44] Yu, W.,  Yong, I. I., Mechefske, C. (2019). “An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme,”Reliability Engineering and System Safety, vol. 199, no. p. 106926, 2020.
[45] 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.
[46] Wang, X., Jiang, B., Lu, N., V. (2018). Cocquempot, “Accurate Prediction of RUL under Uncertainty Conditions: Application to the Traction System of a High-speed Train,” IFAC-PapersOnLine, 51(24), 401-406.
[47] Ramasso, E. (2014). “Investigating computational geometry for failure prognostics,”International Journal of prognostics and health management, 5(1), 5.
[48] 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.
[49] Muhammad, N., Fang, Z., Shoaib, M. (2019). “Microelectronics Reliability Remaining useful life ( RUL ) estimation of electronic solder joints in rugged environment under random vibration,” Microelectron. Reliab., vol. 107, no. p. 113614, 2020.
[50] Chen, C., Xu, T., Wang, G., Li, B. (2019). “Railway Turnout System RUL Prediction Based on Feature Fusion and Genetic Programming,”.
[51] Jain, A. K. (2010). “Data clustering: 50 years beyond K-means,” Pattern recognition letters, 31(8), 651-666.
[52] Lapira, E., Brisset, D., Ardakani, H. D.,  Siegel, D. Lee, J. (2012). “Wind turbine performance assessment using multi-regime modeling approach,”Renewable Energy, 45, 86-95.
[53] Dempster, A. P. (1961). “Upper and Lower Probabilities Induced by a Multivalued Mapping,” in Classic Works of the Dempster-Shafer Theory of Belief Functions, vol. 32, no. 1, Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 57–72.
[54] Shafer, G. (1976). A mathematical theory of evidence, vol. 1. Princeton university press Princeton.
[55] 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, vol. 64(3), 2276-2285.
[56] Inagaki, T. (1991). “Interdependence between safety-control policy and multiple-sensor schemes via Dempster-Shafer theory,” IEEE Transactions on Reliability, vol. 40(2), 182-188.
[57] 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.
Journal of Industrial Engineering Research in Production Systems
Volume 7, Issue 15
March 2020
Pages 395-411

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