An Online Diagnosis Method for Sensor Intermittent Fault Based on Data-Driven Model

The intermittent fault (IF) is usually overlooked in power electronic applications. In this letter, an intelligent diagnosis method based on a data-driven model is proposed for sensor IFs. First, the manifestation of IF in the time domain is discussed to explore its distinctive characteristics. Then, a signal predictor is constructed in a data-driven way by utilizing the nonlinear autoregressive exogenous structure with the extreme learning machine algorithm. In addition, the residual is generated online by comparing the output of the devised data-driven predictor and that of the real sensor. The fault diagnosis decision-making scheme is finally designed based on the residual evaluation to identify the sensor IF and permanent fault simultaneously. The feasibility and effectiveness of the proposed method are demonstrated by offline tests and real-time experimental tests.