Machine fault diagnosis using a modified transferable CNN

Abstract

Detecting prior bearing faults is an essential task of machine health monitoring because bearings are the crucial parts of rotating machines. The performance of traditional intelligent fault diagnosis methods depends on feature extraction of fault signals, which requires signal processing techniques, expert knowledge, and human labor. Deep learning algorithms have recently been applied for industrial machine health monitoring with their advanced features. With the capacity to automatically learn complex features of input data, deep learning architectures have great potential to overcome the drawbacks of traditional intelligent fault diagnosis. This paper proposes a rolling bearing fault diagnosis method based on Convolutional Neural Network and Leaky ReLU to solve the above problems. Firstly, the Continuous Wavelet Transform converts one-dimensional original vibration signals into two-dimensional time-frequency images. Secondly, the obtained time-frequency images are used to train the constructed model. Finally, the diagnosis of the fault location and severity is completed. The method is verified on the MFPT, MIMII data set, and vehicle engine. The results demonstrate that the suggested approach achieves higher diagnostic accuracy which is 95.49% on average and 2% greater than other advanced techniques. We have also incorporated XAI in the input images to make the network more transparent.