Gearbox fault diagnosis based on frequency-domain Gramian angular difference field and deep convolutional neural network
Jianqun Zhang, Qing Zhang, Wenzong Feng, Xianrong Qin, Yuantao Sun
Abstract
Gearbox fault diagnosis is of great significance to avoid accidents and reduce operation and maintenance costs for machinery such as the quayside container crane. In recent years, mechanical fault diagnosis methods based on deep learning have developed rapidly. How to perform diagnosis simply based on deep learning in the case of limited labeled samples is still a challenging problem. To address this issue, this paper proposes a new fault diagnosis method combining the frequency-domain Gramian angular difference field (FDGADF) and deep convolutional neural network (DCNN), namely, FDGADF-DCNN. To explore the availability of the FDGADF-DCNN method, two experiment datasets are studied. When the proportion of labeled samples is 70%, the recognition accuracy of both datasets exceeds 98%. When only 20% of the labeled samples are available (the average number of training samples in each category is only dozens), FDGADF-DCNN can achieve a recognition accuracy of no less than 95% in two datasets, in which a benchmark dataset is 100% and the dataset collected from a scaled-down test bench is 95%. Compared with other 11 methods, the FDGADF-DCNN method achieves the highest recognition accuracy. These results indicate that the FDGADF-DCNN method is expected to be used for gearbox fault diagnosis under limited labeled samples.