Data-driven vibration-based bearing fault diagnosis using non-steady-state training data

Abstract

This paper presents the extension of an empirical study in which a universally applicable fault diagnosis method is used to analyse vibration data of bearings measured with accelerometers. The motivation for extending the previously published results was to provide a profound analysis of the proposed approach with regard to a more feasible training scenario for real applications. For a detailed assessment of the method, data were acquired on two different test beds: a gearbox test bed equipped with various bearings at different health states and an accelerated lifetime (ALT) test bed to degrade a bearing and introduce an operational fault. Features were extracted from the raw data of two different accelerometers and used to monitor the actual health state of the bearings. For that purpose, feature selection and classifier training are performed in a supervised-learning approach. The accuracy is estimated using an independent test dataset. The results of the gearbox test bed data show that the training of the method can be performed with non-steady-state data and that the same feature set can be used for different revolution speeds if a small decrease in accuracy is acceptable. The results of the ALT test bed show that the same features that were identified in the gearbox test start to change significantly when the bearing starts to degrade. Thus, it is possible to observe the identified features for applying predictive maintenance.