Bearing Fault Diagnosis of End-to-End Model Design Based on 1DCNN-GRU Network

Abstract

At present, the complex and varying operating conditions of bearings make the feature extraction become difficult and lack adaptability. An end-to-end fault diagnosis is proposed. A convolutional neural network (CNN) is good at mining spatial features of samples and has the advantage of “end-to-end.” Gates recurrent neural (GRU) network has good performance in processing time-dependent characteristics of signals. We design an end-to-end adaptive 1DCNN-GRU model (i.e., one-dimensional neural network and gated recurrent unit) which combines the advantages of CNN’s spatial processing capability and GRU’s time-sequence processing capability. CNN is applied instead of manual feature extraction to extract effective features adaptively. Moreover, GRU can learn further the features processed through the CNN and achieve the fault diagnosis. It was shown that the proposed model could adaptively extract spatial and time-dependent features from the raw vibration signal to achieve an “end-to-end” fault diagnosis. The performance of the proposed method is validated using the bearing data collected by Case Western Reserve University (CWRU), and the results showed that the proposed model had recognition accuracy higher than 99%.