Intelligent maintenance of disassembly production line

Abstract: The rapid development of the industrial Internet has boosted the research of using sensor data to realize intelligent fault diagnosis of industrial equipment. Especially, the deep learning methods, such as auto-encoder (AE) and convolutional neural network(CNN), have achieved promising results in the intelligent diagnosis of rolling bearing faults. However, the existing deep learning methods still have the shortcomings of inconspicuous fault features in sensor data, large information loss in the process of feature reconstruction, and poor classification ability of AE-based methods, which limit the further improvement of fault diagnosis algorithms. To overcome the weaknesses, this paper proposes a two-stage fault diagnosis method, namely CNNECAE, to implement efficient feature reconstruction and fault classification. First, a feature reconstruction method based on convolutional auto-encoder (CAE) is proposed to solve the problem that the fault features of vibration data are difficult to capture, and a multi-channel feature coding with prominent fault features and less information loss can be obtained. Then, according to the special input format requirements of multi-channel feature coding and the shortage of poor classification ability of AE-based methods, CNN with strong classification ability is used to classify the faults of multi-channel feature encoding. In this way, better fault diagnosis performance is achieved through the collaboration of the CAE and CNN. Extensive experiments on the CWRU dataset demonstrate the effectiveness of the CNNECAE method.

Keywords: intelligent maintenance, convolutional neural network, convolutional auto-encoder