AI-based actuator/sensor fault detection with low computational cost for industrial applications
Journal
IEEE Transactions on Control Systems Technology
Date Issued
January 2016
DOI
10.1109/TCST.2015.2422794
Abstract
A low computational cost method is proposed for detecting actuator/sensor faults. Typical model-based fault detection (FD) units for multiple sensor faults require a bank of estimators [i.e., conventional Kalman estimators or artificial intelligence (AI)-based ones]. The proposed FD scheme uses an AI approach for developing of a low computational power FD unit abbreviated as iFD. In contrast to the bank-of-estimators approach, the proposed iFD unit employs a single estimator
for multiple actuator/sensor FD. The efficacy of the proposed FD scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.
for multiple actuator/sensor FD. The efficacy of the proposed FD scheme is illustrated through a rigorous analysis of the results for a number of sensor fault scenarios on an electromagnetic suspension system.