INCIPIENT FAULT DETECTION AND REMAINING USEFUL LIFE PREDICTION FOR AVIONICS SYSTEM POWER SUPPLIES

摘要

This paper presents an integrated approach to switching mode power supply health management that implements techniques from engineering disciplines including statistical reliability modeling, damage accumulation models, physics of failure modeling, and sensor-based condition monitoring using automated reasoning algorithms. Novel features extracted from sensed parameters such as temperature, power quality, and efficiency was analyzed using advanced fault detection and damage accumulation algorithms. Using model-based assessments in the absence of fault indications, and updating the model-based assessments with sensed information when it becomes available provides health state awareness at any point in time. Intelligent fusion of this diagnostic information with historical component reliability statistics provides a robust health state awareness as the basis for accurate prognostic predictions. Complementary prognostic techniques including analysis of projected operating conditions by physics-based component aging models, empirical (trending) models, and system level failure progression models will be used to develop verifiable prognostic models. The diagnostic techniques and prognostic models have been demonstrated through accelerated failure testing of switching mode power supplies.