A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection

作者：Zhang Bin^*; Sconyers Chris; Byington Carl; Patrick Romano; Orchard Marcos E; Vachtsevanos George

来源：IEEE Transactions on Industrial Electronics, 2011, 58(5): 2011-2018.

DOI：10.1109/TIE.2010.2058072

• Fault detection
• fault progression modeling
• feature extraction
• particle filtering
• rolling element bearing
• signal enhancement

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.

• 出版日期2011-5