Bearing Incipient Fault Detection, Diagnosis, and Unsupervised Prognosis with Failure Thresholding

Abstract

Bearings are core components in rotating machines. Thus, early detection of faults and accurate prediction of a machines health state is highly desirable throughout the total lifecycle of a bearing. Rolling element bearing failure is one of the critical causes of breakdowns in rotating machinery

these types of failures are common in mechanical systems as well. Such failures can be catastrophic and can result in costly downtime. Particularly in industrial fields, minimization of downtime is critical. Thus, health monitoring of rotating machinery during operation is the focus of significant research interest. Accurate bearing health prediction is needed for these settings. There remains a need for health state prediction that can be accomplished in real-time, without future data. Therefore, a data-driven and real-time algorithm for bearing health monitoring is suggested in this thesis. The research objectives pursued to improve the bearing PHM framework include 1) full-time health monitoring, 2) definition of a failure threshold for rolling elements in general bearings, and 3) life prediction in real-time and in unsupervised situations. To classify the health state of bearings for detection of incipient faults and fault points, the Mahalanobis Distance is applied. For life prediction, previous researchers have experienced severe problems, particularly when the life prediction required analytic assumptions as a prerequisite, for example, those emerged at Particle Filters. To solve this problem, the research outlined in this paper suggests a new model and a threshold decision method that enables prediction of the Remaining Useful Life in real time (i.e., in unsupervised situations). This thesis is organized as follows. Section 1 provides an introduction, including the research motivation and an overview of the research objectives. Next, in Section 2, methodologies for detection of incipient anomalies, fault diagnosis, and failure prognosis are explained, along with a suggested definition and a trend projection model. Then, Sections 3 and 4 validate the suggested threshold and model using data acquired from Schaeffler Korea and Seoul National University, respectively. Finally, Chapter 5 concludes this thesis with a summary of the research contributions and suggestions for future work.