Sensor fault isolation and detection of smart structures

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