A Fault Diagnosis Method Based on Transfer Convolutional Neural Networks

Early fault detection and diagnosis can increase the stability, reliability and safety of manufacturing equipment. It can be used for protection against unforeseen emergencies in manufacturing system. Recently, fault diagnosis (FD) methods based on deep learning (DL) have become a research hotspot for their excellent performance. However, the training process of deep learning (DL) models is time-consuming because of their high computation complexity. Moreover, most of DL-based FD methods have an assumption the distribution of training datasets in the source domain is the same as that of test datasets in the target domain. However, it is impossible in typical real-world manufacturing applications. In order to cope with these two problems, this paper proposes a FD method based on convolutional neural network (CNN) and transfer learning (TL). Firstly, a CNN model based on LeNet-5 is designed to extract fault features from images which is converted from raw signal data by continuous wavelet transform (CWT), then the performance of the CNN model are improved by fine-tuning which is an effective way of TL. The proposed method is conducted on two well-known datasets and the experimental results show that the proposed method can significantly improve the accuracy and efficiency performance a lot compared with the standard CNN model.

Abstract licence: CC BY-NC-ND

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