Self-tuning fault diagnosis of MEMS

Multiple-model adaptive estimation techniques have been previously successfully applied to fault diagnosis of microsystems. Their diagnosis performance highly depends on the accuracy of modeling techniques used in representing faults. This paper presents the application of a self-tuning forgetting factor technique in the modeling of faults in MEMS and its effects on diagnosis performance compared with the application of Kalman filters and fixed gain estimation techniques. The self-tuning-based modeling used in the diagnosis algorithm was experimentally implemented. It demonstrated superior results compared to Kalman filter and fixed gain estimation techniques by accelerating the diagnosis process.