Differential feature based hierarchical PCA fault detection method for dynamic fault

Abstract

By sensor accuracy degradation or unwanted alternating current signals, sensor fault with zero cross point (ZCP) may occur in real systems and conventional data-driven fault detection methods could be invalid. In this regard, this paper proposes a hierarchical principal component analysis (PCA) fault detection method based on the differential features of dynamic faults to detect the fault with ZCPs. The main contribution of this work are as follows: (1) A new differential based feature extraction method is first proposed to well character the dynamic trend of the observation; (2) then, a hierarchical detection criterion is proposed according to the detection ability of each round of PCA anomaly detection; (3) it is convenient to extend the proposed method to other statistical based fault detection techniques whose detection criteria are also a distance defined by fault amplitude.