Convolutional autoencoder neural network for fault diagnosis with multi-sensor data

With machines in manufacturing industry being automated, complex and intelligent, its monitoring systems are equipped with more and more smart sensors. How to extract useful features from great volume of multi-sensor data become a great challenge to the field of fault diagnosis. To overcome such challenge, an improved convolutional autoencoder neural network (CANN) is proposed to fuse and extract effective features of the color images formed by multi-sensor data in this paper. Firstly, the vibration signals of different channels are jointly transformed into color images. Secondly, an improved CANN is constructed by introducing special convolution kernels and residual connection for multi-sensor data fusion and feature extraction. Finally, the encoder part of CANN is connected with the softmax classifier for fault diagnosis. Two datasets collected from Wind Power Test-Bed and Industrial Blower Fan System are used to fully validate the effectiveness of proposed CANN. The results show that it can effectively fuse multi-sensor data and mine the discriminative features. Furthermore, compared with some related state-of-art methods, the CANN obtains higher diagnostic accuracy, especially for less labeled data.