Abstract:

For the problem of sensor fault diagnosis, a sensor fault diagnosis model based on principal component analysis (PCA) and artificial neural network is proposed. Firstly, the forecasting values of sensors are available from historical data measured from sensors in faultfree condition based on PCA model. Secondly, the squared prediction error of the system is calculated, the fault occurred when the squared prediction error (SPE) is suddenly increased. Sensor values are reconstructed respectively to newly calculate the SPE to locate the faulty sensor. Finally, the method proposed is proved feasible and effective by a simulation of multiple sensor fault diagnosis.