A Semi-Supervised Feature Selection Approach for Fault Diagnostics in Evolving Environments



Yang Hu Piero Baraldi Francesco Di Maio Enrico Zio


This paper introduces a Semi-Supervised Feature Selection (SSFS) approach for selecting the most suitable features for fault diagnostics in evolving environments. The effectiveness of the proposed SSFS approach is verified with respect to an application concerning the classification of the defect type of bearings in Fully Electric Vehicles operating at different loads. The results show that SSFS allows adapting the diagnostic model to the varying load by updating the set of features used for the classification and achieves more satisfactory diagnostic accuracy than the traditional diagnostic models. The proposed diagnostic approach can contribute significantly to the maintenance practice of components such as gearboxes, alternators, shafts and pumps, whose working conditions are usually characterized by evolving environment.

How to Cite

Hu, Y., Baraldi, P., Maio, F. D., & Zio, E. (2016). A Semi-Supervised Feature Selection Approach for Fault Diagnostics in Evolving Environments. PHM Society European Conference, 3(1). https://doi.org/10.36001/phme.2016.v3i1.1627
Abstract 30 | PDF Downloads 28



feature selection, fault diagnostics, evolving environment

Brzezinski, D., & Stefanowski, J. (2014). Reacting to Different Types of Concept Drift: The Accuracy Updated Ensemble Algorithm. Neural Networks and Learning Systems, IEEE Transactions on.
Chang, C., & Lin, C. (2001). LIBSVM: a library for support vector machines. Computer, 1–30. Retrieved from http://citeseerx.ist.psu.edu/viewdoc/download?doi=
Dries, A., & Rückert, U. (2009). Adaptive concept drift detection. Statistical Analysis and Data Mining, 2(5- 6), 311–327. doi:10.1002/sam.10054
Dy, J. G., & Brodley, C. E. (2004). Feature Selection for Unsupervised Learning. Journal of Machine Learning Research, 5, 845–889. Retrieved from http://portal.acm.org/citation.cfm?id=1016787
Emmanouilidis, C., Hunter, A., MacIntyre, J., & Cox, C. (1999). Selecting features in neurofuzzy modelling by multiobjective genetic algorithms. Artificial Neural Networks, 1999. ICANN 99. Ninth International Conference on (Conf. Publ. No. 470). doi:10.1049/cp:19991201
Forman, E., & Peniwati, K. (1998). Aggregating individual judgments and priorities with the analytic hierarchy process. European Journal of Operational Research. doi:10.1016/S0377-2217(97)00244-0
Guyon, I., Guyon, I., Elisseeff, A., & Elisseeff, A. (2003). An introduction to variable and feature selection. Journal of Machine Learning Research, 3, 1157–1182. doi:10.1162/153244303322753616
Matsatsinis, N. F., Grigoroudis, E., & Samaras, A. (2005). Aggregation and disaggregation of preferences for collective decision-making. Group Decision and Negotiation, 14(3), 217–232. doi:10.1007/s10726-005-7443-x
Morais, D. C., & De Almeida, A. T. (2012). Group decision making on water resources based on analysis of individual rankings. Omega, 40(1), 42–52. doi:10.1016/j.omega.2011.03.005
Nandi, S., Toliyat, H. A., & Li, X. (2005). Condition monitoring and fault diagnosis of electrical motors - A review. IEEE Transactions on Energy Conversion. doi:10.1109/TEC.2005.847955
Peng, Z. K., & Chu, F. L. (2004). Application of the wavelet transform in machine condition monitoring and fault diagnostics: a review with bibliography. Mechanical Systems and Signal Processing. doi:10.1016/S0888-3270(03)00075-X
Richard, M. D., & Lippmann, R. P. (1991). Neural Network Classifiers Estimate Bayesian a posteriori Probabilities. Neural Computation, 3(4), 461–483. doi:10.1162/neco.1991.3.4.461
Saari, D. G. (1999). Explaining All Three-Alternative Voting Outcomes. Journal of Economic Theory, 87, 313–355. doi:10.1006/jeth.1999.2541
Saeys, Y., Inza, I., & Larrañaga, P. (2007). A review of feature selection techniques in bioinformatics. Bioinformatics. doi:10.1093/bioinformatics/btm344
Smith, J. H. (1973). Aggregation Preferences with Variable Electorate. Econometrica, 41(6), 1027–1041.
Wan, E. A. (1990). Neural network classification: A Bayesian interpretation. IEEE Transactions on Neural Networks, 1(4), 303–305. doi:10.1109/72.80269
Wu, T.-F., Lin, C.-J., & Weng, R. C. (2004). Probability Estimates for Multi-class Classification by Pairwise Coupling. J. Mach. Learn. Res., 5, 975–1005. doi:10.1016/j.visres.2004.04.006
Zhang, Z., Chen, H., Xu, Y., Zhong, J., Lv, N., & Chen, S. (2015). Multisensor-based real-time quality monitoring by means of feature extraction, selection and modeling for Al alloy in arc welding. Mechanical Systems and Signal Processing, 60-61, 151–165. doi:10.1016/j.ymssp.2014.12.021
Zhao, Z., & Liu, H. (2007). Spectral feature selection for supervised and unsupervised learning. Proceedings of the 24th International Conference on Machine Learning - ICML ’07, 1151–1157. doi:10.1145/1273496.1273641
Zio, E. (2016). Some Challenges and Opportunities in Reliability Engineering. IEEE Transactions on Reliability, -(-), -.
Technical Papers