An Integrated Fault Detection and Diagnosis Approach to Sensor Faults Based on RBF Neural Network

An integrated fault detection and diagnosis approach to sensor faults based on radial basis function (RBF) neural networks is presented in this paper. An RBF neural network is used to estimate the state and fault parameters of the constructed model for sensor faults. The estimated fault parameters are processed by the improved Bayes algorithm to realize online sensor fault detection, isolation, and estimation. The simulation for continuous stirred tank reactor (CSTR) shows the presented approach can isolate and estimate the multiple sensor faults quickly and accurately and the integrated system has tolerant ability to sensor faults.