Analysis and comparison of multiple features for fault detection and prognostic in ball bearings

Abstract

Feature extraction is one of the most important elements in Prognostics and Health Management (PHM) systems. Numerous techniques have been proposed for fault detection, diagnostics and prognostics in ball bearings which are key components of rotating machineries, widely used in the industry. Considering the strengths and weaknesses of these techniques, this paper aims at evaluating and analyzing different features in all three signal processing domains: time, frequency and time-frequency. The crucial indicators related to normal and abnormal cases are extracted from both vibration signals and stator current signals. Then, a new metric is proposed to measure the evolution of these indicators with respect to degradation levels of bearings. The performance of every indicator is analyzed to study which feature(s) is(are) better than other(s) and which feature(s) is(are) the best appropriate for vibration and current signals. These results could be effectively used in future for fault detection, diagnostics and prognostics applications.