A Compressed Unsupervised Deep Domain Adaptation Model for Efficient Cross-Domain Fault Diagnosis

As one of the most important artificial intelligence-enabled industrial applications, fault diagnosis is vital in the safe, stable, and reliable operation of the equipment. Many existing deep learning-based fault diagnosis methods assume that the distribution of training data is the same as that of testing data, which is almost impossible in practical industrial applications. In addition, most of these fault diagnosis methods are generally memory-intensive and computationally expensive. A compressed unsupervised deep domain adaption model-based fault diagnosis method is proposed to overcome the abovementioned two issues. First, a standard unsupervised domain adaption model is designed to extract the features of training data and testing data, respectively. Then, the maximum mean discrepancy term is introduced to minimize the discrepancy between the extracted features of them. Next, the standard model is compressed through iteratively pruning the redundant convolutional channels. Finally, the obtained compressed model is applied to diagnose faults. The performance of the proposed method is verified on the Case Western Reserve University bearing dataset. Experimental results show that the compressed model can significantly reduce the memory occupation, computational cost, and inference time compared with the standard model, but still achieve comparable or even better accuracy on ten transfer diagnostic tasks.