An Improved Convolutional Neural Network for Recognition of Incipient Faults

In order to overcome the shortcomings that regular recognition methods do not extract the pivotal difference information of incipient faults with overlapped features, two optimized methods are used to enhance the learning ability of extractor and promote the recognition accuracy of classifier. Firstly, based on one-dimensional neural network (1D-CNN) and its variant temporal convolutional network (TCN), an improved network structure called temporal convolutional block-convolutional neural network is proposed. It learns historical information by causal convolution and general trend information by non-causal convolution. Secondly, a new loss item named direct discriminant is proposed to optimize the loss function based on the softmax classifier. It makes classifier inputs have better separability. Compared with existing recognition methods such as variational modal decomposition-stochastic configuration network (VMD-SCN), manifold regularized stochastic configuration network (MRSCN), kernel principal component analysis-deep belief network (KPCA-DBN) and TCN, the superior recognition of the proposed method is illustrated through a numerical example and TE chemical industrial process simulation.