Applied sensor fault detection and validation using transposed input data PCA and ANNs

The paper presents an efficient approach for applied sensor fault detection based on an integration of principal component analysis (PCA) and artificial neural networks (ANNs). Specifically, PCA-based y-indices are introduced to measure the differences between groups of sensor readings in a time rolling window, and the relative merits of three types of ANNs are compared for operation classification. Unlike previously reported PCA techniques (commonly based on squared prediction error (SPE)) which can readily detect a sensor fault wrongly when the system data is subject bias or drifting as a result of power or loading changes, here, it is shown that the proposed methodologies are capable of detecting and identifying the emergence of sensor faults during transient conditions. The efficacy and capability of the proposed approach is demonstrated through their application on measurement data taken from an industrial generator.