Remaining Useful Life Estimation of Bearings Meta-analysis of Experimental Procedure
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Published
Mar 24, 2021
Hugo M. Ferreira
Alexandre C. de Sousa
Abstract
In the domain of predictive maintenance, when trying to repli- cate and compare research in remaining useful life estimation (RUL), several inconsistencies and errors were identified in the experimental methodology used by various researchers. This makes the replication and the comparison of results diffi- cult, thus severely hindering both progress in this research do- main and its practical application to industry. We survey the literature to evaluate the experimental procedures that were used, and identify the most common errors and omission in both experimental procedures and reporting.
A total of 70 papers on RUL were audited. From this meta- analysis we estimate that approximately 11% of the papers present work that will allow for replication and comparison. Surprisingly, only about 24.3% (17 of the 70 articles) com- pared their results with previous work. Of the remaining work, 41.4% generated and compared several models of their own and, somewhat unsettling, 31.4% of the researchers made no comparison whatsoever. The remaining 2.9% did not use the same data set for comparisons. The results of this study were also aggregated into 3 categories: problem class selec- tion, model fitting best practices and evaluation best practices. We conclude that model evaluation is the most problematic one.
The main contribution of the article is a proposal of an ex- perimental protocol and several recommendations that specif- ically target model evaluation. Adherence to this protocol should substantially facilitate the research and application of RUL prediction models. The goals are to promote the collab- oration between scholars and practitioners alike and advance the research in this domain.
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Keywords
data mining and machine learning, methodology, meta-analysis, prognostics, bearings, remaining useful life
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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 Transactions on Industrial Electronics. doi: 10.1109/TIE.2016.2623260
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. Retrieved from https://doi.org/10.1016/j.ymssp.2017.11.016 doi: 10.1016/j.ymssp.2017.11.016
Li, X., Zhang, W., & Ding, Q. (2019). Deep learning-based remaining useful life estimation of bearings using multi-scale feature extraction. Reliability Engineering and System Safety, 182, 208–218. doi: 10.1016/j.ress.2018.11.011
Li, X., Zhang, W., Ma, H., Luo, Z., & Li, X. (2020, jun). Data alignments in machinery remaining useful life prediction using deep adversarial neural networks. Knowledge-Based Systems, 197, 105843. retrieved from https://www.sciencedirect.com/science/article/abs/pii/S0950705120302124?via{\%}3Dihub doi: 10.1016/J.KNOSYS.2020.105843
Liu, C., Zhang, L., & Wu, C. (2019, dec). Direct remaining Useful Life Prediction for Rolling Bearing Using Temporal Convolutional Networks. In 2019 ieee symposium series on computational intelligence, ssci 2019 (pp. 2965–2971). Institute of Electrical and Electronics Engineers Inc. doi: 10.1109/SSCI44817.2019.9003163
Liu, Z., Zuo, M. J., & Qin, Y. (2015, jun). Remaining useful life prediction of rolling element bearings based on health state assessment. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 230(2), 314–330. Retrieved from https://doi.org/ 10.1177/0954406215590167 doi: 10.1177/0954406215590167
Machacek, V., & Srholec, M. (2019). Predatory publications in scopus: Evidence on cross-country differences (Working Papers IES No. 2019/20). Charles University Prague, Faculty of Social Sciences, Institute of Economic Studies. Retrieved from https://EconPapers.repec.org/RePEc: fau:wpaper:wp2019 20
Mao, W., He, J., Tang, J., & Li, Y. (2018, dec). Predicting remaining useful life of rolling bearings based on deep feature representation and long short-term memory neural network. Advances in Mechanical Engineering, 10(12), 168781401881718. retrieved from http://journals.sagepub.com/ doi/10.1177/1687814018817184 doi: 10.1177/1687814018817184
Measurement Computing Corporation. (2004). Data Acquisition Handbook. A Reference for DAQ And Analog & Digital Signal conditioning (3rd ed.). MA: Author. retrieved from https://www.mccdaq.com/pdfs/ anpdf/Data-Acquisition-Handbook.pdf
Moreno-Torres, J. G., Saez, J. A., & Herrera, F. (2012). Study on the impact of partition-induced dataset shift on k-fold cross-validation. IEEE Transactions on Neural Networks and Learning Systems, 23(8), 1304-1312.
Munafo`, M., Nosek, B., Bishop, D., Button, K., Chambers, C., Percie Du Sert, N., . . . Ioannidis, J. (2017, 1 10). A manifesto for reproducible science. Nature Human Behaviour, 1(1). doi: 10.1038/s41562-016-0021
Murphy, K. (2017, 12). Harking: How badly can cherry-picking and question trolling produce bias in published results? Journal of Business and Psychology. doi: 10.1007/s10869-017-9524-7
Nectoux, P., Gouriveau, R., Medjaher, K., Ramasso, E., Chebel-morello, B., Zerhouni, N., . . . Varnier, C. (2012). PRONOSTIA : An experimental platform for bearings accelerated degradation tests. IEEE International Conference on Prognostics and Health Management, 1–8.
P. Murphy, K. (2012). Machine Learning: A Probabilistic Perspective. Cambridge, MA: The MIT Press.
Peng, Y., Cheng, J., Liu, Y., Li, X., & Peng, Z. (2018, jun). An adaptive data-driven method for accurate prediction of remaining useful life of rolling bearings. Frontiers of Mechanical Engineering, 13(2), 301–310. doi: 10.1007/s11465-017-0449-7
Qiu, H., Lee, J., Lin, J., & Yu, G. (2006). Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, 289(4), 1066 - 1090. Retrieved from http:// www.sciencedirect.com/science/ article/pii/S0022460X0500221X doi: https://doi.org/10.1016/j.jsv.2005.03.007
Russell, S., & Norvig, P. (2010). Artificial Intelligence A Modern Approach (3rd ed.). Pearson. Retrieved from http://aima.cs.berkeley.edu/
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2009). On applying the prognostic performance metrics. Annual Conference of the Prognostics and Health Management Society, PHM 2009, 1–16.
Saxena, A., Celaya, J., Saha, B., Saha, S., & Goebel, K. (2010). Metrics for offline evaluation of prognostic performance. International Journal of Prognostics and Health Management, 1(1), 1–20.
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