Title :
Fault Diagnosis of MEMS Lateral Comb Resonators Using Multiple-Model Adaptive Estimators
Author :
Izadian, Afshin ; Famouri, Parviz
Abstract :
In this brief a fault diagnostic unit is developed for microelectromechanical systems (MEMS) by means of multiple model adaptive estimation technique. Fault modeling tools such as contamination and reliability analysis of microelectromechanical layout enabled interpretation of microsystems behavior by evaluating their structural variations and modeling them in form of electric circuits. This technique cannot directly diagnose the faults during operation of microsystems. However, these fault-representing models can be used in multiple model adaptive estimation technique to form fault diagnosis units. Here, fault-representing systems are modeled by Kalman filters in real-time applications and are used to evaluate the fault in microsystems. MEMS lateral comb resonators are fabricated to experimentally demonstrate the fault diagnosis performance in multiple model adaptive estimation technique.
Keywords :
Kalman filters; adaptive estimation; fault diagnosis; micromechanical resonators; semiconductor device reliability; Kalman filter; MEMS lateral comb resonator; contamination; electric circuit; fault diagnosis; fault modeling tool; fault-representing system; microelectromechanical layout; microelectromechanical system; microsystems behavior; multiple-model adaptive estimator; reliability analysis; structural variation; Adaptive estimation; Circuit faults; Contamination; Fault diagnosis; Fingers; Microelectromechanical systems; Micromechanical devices; Real time systems; Satellites; Springs; Adaptive estimators; Kalman filtering; fault diagnosis; lateral comb resonators (LCRs);
Journal_Title :
Control Systems Technology, IEEE Transactions on
DOI :
10.1109/TCST.2009.2036717