A Probabilistic Fault Detection Approach: Application to Bearing Fault Detection
Institution: | Universidad de Chile |
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Department: | |
Year: | 2011 |
Keywords: | Fault detection; fault progression modeling; feature extraction; particle filtering; rolling element bearing; signal enhancement |
Record ID: | 1092264 |
Full text PDF: | http://www.captura.uchile.cl/handle/2250/14006 |
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.