A Model Based Fault Detection and Diagnosis Strategy for Automotive Alternators

Abstract

Faulty manufactured alternators lead to commercial and safety concerns when installed in vehicles. Alternators have a major role in the Electrical Power Generation System (EPGS) of vehicles, and a defective alternator will lead to damaging of the battery and other important electric accessories. Therefore, fault detection and diagnosis of alternators can be implemented to quickly and accurately determine the health of an alternator during end of line testing, and not let faulty components leave the manufacturer. The focus of this research is to develop a Model Based Fault Detection and Diagnosis (FDD) strategy for detecting alternator faults during end of line testing. The proposed solution uses Extended Kalman Smooth Variable Structure Filter (EK-SVSF) to detect common alternator faults. A solution using the Dual Extended Kalman Filter (DEKF) is also discussed. The alternator faults were programmatically simulated on alternator measurements. The experimental results prove that both the EK-SVSF and DEKF strategies were very effective in alternator modeling and detecting open diode faults, shorted diode faults, and stator imbalance faults.