Identifying and selecting optimal prognostic health indicators in the context of predictive maintenance is essential to obtain a good model and make accurate predictions. Several metrics have been proposed in the past decade to quantify the relevance of those prognostic parameters. Other works have used the well-known minimum redundancy maximum relevance (mRMR) algorithm to select features that are both relevant and non-redundant. However, the relevance criterion is based on labelled machine malfunctions which are not always available in real life scenarios. In this paper, we develop a prognostic mRMR feature selection, an adaptation of the conventional mRMR algorithm, to a situation where class labels are a priori unknown, which we call unsupervised feature selection. In addition, this paper proposes new metrics for computing the relevance and compares different methods to estimate redundancy between features. We show that using unsupervised feature selection as well as adapting relevance metrics with the dynamic time warping algorithm help increase the effectiveness of the selection of health indicators for a rotating machine case study.
Predictive maintenance, feature selection, mRMR, prognostic
Carino, J. A., Zurita, D., Delgado, M., Ortega, J., & Romero-Troncoso, R. (2015). Remaining useful life estimation of ball bearings by means of monotonic score calibration. In 2015 ieee international conference on industrial technology (icit) (pp. 1752–1758).
Chandrashekar, G., & Sahin, F. (2014). A survey on feature selection methods. Computers & Electrical Engineering, 40(1), 16–28.
Coble, J., & Hines, J. W. (2009). Identifying optimal prognostic parameters from data: a genetic algorithms approach. In Annual conference of the prognostics and health management society (Vol. 27).
Coble, J. B. (2010). Merging data sources to predict remaining useful life–an automated method to identify prognostic parameters. (PhD thesis)
Ding, C., & Peng, H. (2005). Minimum redundancy feature selection from microarray gene expression data. Journal of bioinformatics and computational biology, 3(02), 185–205.
Fernandes, M., Canito, A., Bolón-Canedo, V., Conceic¸ ˜ao, L., Prac¸a, I., & Marreiros, G. (2019). Data analysis and feature selection for predictive maintenance: A case-study in the metallurgic industry. International journal of information management, 46, 252–262.
Gel’Fand, I.,&Yaglom, A. (1959). Calculation of the amount of information abouta random function contained in another such function. Eleven Papers on Analysis, Probability and Topology, 12, 199.
Giorgino, T., et al. (2009). Computing and visualizing dynamic time warping alignments in r: the dtw package. Journal of statistical Software, 31(7), 1–24.
Hearst, M. A., Dumais, S. T., Osuna, E., Platt, J., & Scholkopf, B. (1998). Support vector machines. IEEE Intelligent Systems and their applications, 13(4), 18–28.
Hu, Q., Si, X.-S., Qin, A.-S., Lv, Y.-R., & Zhang, Q.-H. (2020). Machinery fault diagnosis scheme using redefined dimensionless indicators and mrmr feature selection. IEEE Access, 8, 40313–40326.
Javed, K., Gouriveau, R., Zerhouni, N., & Nectoux, P. (2013). A feature extraction procedure based on trigonometric functions and cumulative descriptors to enhance prognostics modeling. In 2013 ieee conference on prognostics and health management (phm) (pp. 1–7).
Javed, K., Gouriveau, R., Zerhouni, N., & Nectoux, P. (2014). Enabling health monitoring approach based on vibration data for accurate prognostics. IEEE Transactions on Industrial Electronics, 62(1), 647–656.
Kleeven, W., Abs, M., Forton, E., Henrotin, S., Jongen, Y., Nuttens, V., . . . others (2013). The IBA superconducting synchrocyclotron project S2C2. In Proc. cyclotrons (pp. 115–119).
Kraskov, A., St¨ogbauer, H., & Grassberger, P. (2004). Estimating mutual information. Physical review E, 69(6), 066138.
Lei, Y., Li, N., Gontarz, S., Lin, J., Radkowski, S., & Dybala, J. (2016). A model-based method for remaining useful life prediction of machinery. IEEE Transactions on Reliability, 65(3), 1314–1326.
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.
Li, N., Lei, Y., Liu, Z., & Lin, J. (2014). A particle filtering-based approach for remaining useful life predication of rolling element bearings. In 2014 international conference on prognostics and health management (pp. 1–8).
Li, Y., Yang, Y., Li, G., Xu, M., & Huang, W. (2017). A fault diagnosis scheme for planetary gearboxes using modified multi-scale symbolic dynamic entropy and mrmr feature selection. Mechanical Systems and Signal Processing, 91, 295–312.
Liu, Z., Zuo, M. J., & Qin, Y. (2016). 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.
Liu, Z., Zuo, M. J., & Xu, H. (2013). Fault diagnosis for planetary gearboxes using multi-criterion fusion feature selection framework. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 227(9), 2064–2076.
MathWorks. (2021). Signal features - matlab & simulink. Retrieved 2021-03-22, from https://mathworks.com/help/predmaint/ug/signal-features.html
Peng, H., Long, F., & Ding, C. (2005). Feature selection based on mutual information: Criteria of Max-Dependency, Max-Relevance, and Min-Redundancy. IEEE Transactions on Pattern Analysis and Machine Intelligence, 27(8), 1226–1238. doi: 10.1109/TPAMI.2005.159
Radovic, M., Ghalwash, M., Filipovic, N., & Obradovic, Z. (2017). Minimum redundancy maximum relevance feature selection approach for temporal gene expression data. BMC bioinformatics, 18(1), 1–14.
Ross, B. C. (2014). Mutual information between discrete and continuous data sets. PloS one, 9(2), e87357.
Schoen, R. R., Habetler, T. G., Kamran, F., & Bartfield, R. (1995). Motor bearing damage detection using stator current monitoring. IEEE transactions on industry applications, 31(6), 1274–1279.
Shahidi, P., Maraini, D., & Hopkins, B. (2016). Railcar diagnostics using minimal-redundancy maximum-relevance feature selection and support vector machine classification. International Journal of Prognostics and Health Management, 7, 2153–2648.
Solorio-Fernández, S., Carrasco-Ochoa, J. A., & Martínez-Trinidad, J. F. (2020). A review of unsupervised feature selection methods. Artificial Intelligence Review, 53(2), 907–948.
Tang, X., He, Q., Gu, X., Li, C., Zhang, H., & Lu, J. (2020). A novel bearing fault diagnosis method based on gl-mrmrsvm. Processes, 8(7), 784.
Wang, D., Tsui, K.-L., & Miao, Q. (2017). Prognostics and health management: A review of vibration based bearing and gear health indicators. Ieee Access, 6, 665–676.
Yan, X., & Jia, M. (2019). Intelligent fault diagnosis of rotating machinery using improved multiscale dispersion entropy and mrmr feature selection. Knowledge-Based Systems, 163, 450–471.
Zhang, B., Zhang, L., & Xu, J. (2016). Degradation feature selection for remaining useful life prediction of rolling element bearings. Quality and Reliability Engineering International, 32(2), 547–554.
Zhang, X., Song, Z., Li, D., Zhang, W., Zhao, Z., & Chen, Y. (2018). Fault diagnosis for reducer via improved lmd and svm-rfe-mrmr. Shock and Vibration, 2018.
Zhao, X., Zuo, M. J., Liu, Z., & Hoseini, M. R. (2013). Diagnosis of artificially created surface damage levels of planet gear teeth using ordinal ranking. Measurement, 46(1), 132–144.