A Sensor Failure Detection Method Based on Artificial Neural Network and Signal Processing

摘要

This paper proposes a sensor failure detection method based on artificial neural network and signal processing, in comparison with other methods, which does not need any redundancy information among sensor outputs and divides the output of a sensor into " Signal dominant component" and " Noise dominant component" because the pattern of sensor failure often appears in the " Noise dominant component". With an ARMA model built for " Noise dominant component" using artificial neural network, such sensor failures as bias failure, hard failure, drift failure, spike failure and cyclic failure may be detected through residual analysis, and the type of sensor failure can be indicated by an appropriate indicator. The failure detection procedure for a temperature sensor in a hovercraft engine is simulated to prove the applicability of the method proposed in this paper.