Abstract

This paper introduces a methodology to detect as early as possible with specified degree of confidence and prescribed false alarm rate an anomaly or novelty (incipient failure) associated with critical components/subsystems of an engineered system that is configured to monitor continuously its health status. Innovative features of the enabling technologies include a Bayesian estimation framework, called particle filtering, that 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 provides the probability of abnormal condition and the probability of false alarm. The presence of an anomaly is confirmed for a given confidence level. The efficacy of the proposed anomaly detection architecture is illustrated with test data acquired from components typically found on aircraft and monitored via a test rig appropriately instrumented. © 2008 IEEE.