并通过与误差反传BP(Back Propagation)神经网络和常规小脑神经网络CMAC(Cerebellar Model Articulation Controller)进行比较
说明了基于CA-CMAC的主元分析模型的优越性.
Abstract
For the problem of fault diagnosis in dynamic system
a principal component analysis model based on credit assigned cerebellar model articulation controller is proposed to carry out on-line fault detection and isolation for multiple sensor system.Firstly
the forecasting values of sensors are available from historical data measured in fault-free condition based on principal component analysis model.Secondly
the Squared Prediction Error of the system is calculated
the fault occurred when the SPE is suddenly increased.Sensor values are reconstructed respectively to newly calculate the SPE to locate the faulty sensor.Finally
Compared to BP and CMAC
the method proposed is proved feasible and effective by a simulation of multiple sensor fault diagnosis.