A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

• Published in: IEEE transactions on industrial electronics (1982) Vol. 58; no. 5; pp. 2011 - 2018
• Main Authors: Bin Zhang, Sconyers, C, Byington, C, Patrick, R, Orchard, M E, Vachtsevanos, G
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.05.2011; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Aircraft; Algorithms; Bayesian methods; Bearing; Confidence intervals; Degradation; Fatigue; Fault detection; fault progression modeling; Faults; feature extraction; Filtering; Filtering algorithms; Mathematical models; particle filtering; rolling element bearing; Sensor phenomena and characterization; Sensor systems; signal enhancement; State estimation; Studies; Systems engineering and theory
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2010.2058072

This paper introduces a method to detect a fault associated with critical components/subsystems of an engineered system. It is required, in this case, to detect the fault condition as early as possible, with specified degree of confidence and a prescribed false alarm rate. Innovative features of the enabling technologies include a Bayesian estimation algorithm called particle filtering, which employs features or condition indicators derived from sensor data in combination with simple models of the system's degrading state to detect a deviation or discrepancy between a baseline (no-fault) distribution and its current counterpart. The scheme requires a fault progression model describing the degrading state of the system in the operation. A generic model based on fatigue analysis is provided and its parameters adaptation is discussed in detail. The scheme provides the probability of abnormal condition and the presence of a fault is confirmed for a given confidence level. The efficacy of the proposed approach is illustrated with data acquired from bearings typically found on aircraft and monitored via a properly instrumented test rig.