Sensor fault isolation and detection of smart structures

Abstract

This paper proposes a novel principal component analysis (PCA)-based sensor faultisolation and detection method for smart structures: detectability and isolability of eachsensor fault are analyzed using a PCA-based numeric residual generator and theprobability of errors in each sensor is also determined using the Bayesian probabilisticanalysis of these residuals. To demonstrate the performance of the proposed PCA-basedsensor fault isolation and detection methodology, a seismically excited three-story buildingstructure equipped with a magnetorheological damper that is operated by a semi-activenonlinear fuzzy control system is investigated. It is shown that the proposed PCA-basedsensor fault diagnosis approach is effective in identifying sensor faults of smartstructures for hazard mitigation of large structures as a model-free methodology.