A Frequency Feature Extraction Method Based on Convolutional Neural Network for Recognition of Incipient Fault

Incipient faults often occur in the early stages of mature faults. Because of its small amplitude and energy, detection and diagnosis are more difficult. From the point of view of extracting frequency characteristics, to study the accurate recognition method of incipient faults can effectively improve the reliability and safety of system, which is of great significance to actual industrial process. Since the fluctuation of incipient faults and normal signals is not obvious from the amplitude features of time domain, it is difficult to recognize faults from amplitude by conventional method. But everything vibrates. So, to recognize faults more easily from the perspective of frequency features, a 1-D convolution neural network with extracting frequency features (1D-CNN-EFFs) is proposed. Based on cross entropy loss function, this method adds an optimization index to guide model to extract frequency features. By constraining the minimum frequency bandwidth of node output value, the central frequency of feature extracted is closer to the central frequency of fault. Incipient faults are identified through more frequency features instead of time domain features. This method improves recognition accuracy and reduces the amount of neural network parameters. Compared with existing recognition methods such as 1-D convolutional neural network (1D-CNN), temporal convolutional network, and so on, the superior recognition of the proposed method is illustrated through Case Western Reserve University (CWRU) and Paderborn University bearing dataset. Experiments show that this method can accurately identify incipient faults by non-overlapping frequency features, and has fewer parameters and simpler structure under the same recognition accuracy.