Fault detection and diagnosis based on KPCA-CDBNs model

In order to improve the accuracy and stability of industrial fault detection and diagnosis, this paper introduces the deep learning theory and proposes an improved Deep Belief Networks (DBNs). In the first, this paper introduces the “centering trick” in the pre-training process of network. This method is done by subtracting offset values from visible and hidden variables. Then, in the process of network fine-tuning, the weights of network are added the White Gaussian Noise. This improved Deep Belief Networks combines with Kernel Principal Component Analysis (KPCA) into KPCA-CDBNs model. This paper applies this model into fault detection research of Tennessee Eastman (TE) and shows that the method of this paper has higher prediction and stability of fault detection and diagnosis.