Vibration-based bearing fault diagnosis using relevant multi-domain features and hierarchical stochastics classification

Abstract

Bearings installed in industrial electric motors, currently are constituted as the main mode of a failure affecting the global energy consumption. Since energy demand from light industry only grows, demand for effective maintenance in electric motors is critical. Proper life management of such assets focuses on the study of the useful life, delivering efficient information about location and severity of different health status, and using vibration signals from bearings with analysis approaches based on characteristics in time, frequency and time-frequency domains. These domains are characterized by its own benefits as well as its shortcomings, and thus most current works focus only on one of these analyses’ domain. This paper studies a possible sub-relevant set of features that favor separability between classes of severity levels to perform training on a concatenation of hierarchical HMM in order to analyze multiple health conditions in bearings, including faults and severities in the following rolling elements: ball, inner race, and outer race. As a result, a substantial improvement is observed in the diagnosis of fault type and severity level present in the bearings and being in concordance with previous studies where just overall process is reported.