Pattern classification of bearing faults in PMSM based on time domain feature ensembles

Abstract

This paper aims to identify an effective pattern classification method that can be employed using vibration and current data to identify bearing conditions. The authors attempted non-conventional time-domain features to detect the bearing conditions in permanent magnet synchronous motors (PMSM). This study uses two case studies with eight datasets from Paderborn University to identify the bearing conditions of 3 and 12 classes. Support vector machine, k-nearest neighbor, random forest, decision tree, and naive Bayes classifiers are attempted with 10% holdout validation for 4 data sets with 31 feature ensembles. Also, this paper investigates the Henry Gas Solubility Optimization (HGSO) feature selection approach for identifying the most discriminant features. The effectiveness of these discriminant features is verified with three bearing conditions diagnosis. Results have shown, that four feature ensembles with 2 to 10 features outperformed support vector machine, k-nearest neighbor, and random forest classifiers. In contrast to previous relevant studies, the proposed features are useful in identifying PMSM-bearing conditions with excellent accuracy in vibration and combined current signals under a wide range of operating conditions.