A Transformer Model-Based Approach to Bearing Fault Diagnosis

Bearings are an important component in rotating machinery and their failure can lead to serious injuries and economic losses, therefore the diagnosis of bearing faults and the guarantee of their smooth operation are essential steps in maintaining the safe and stable operation of modern machinery and equipment. Traditional bearing fault diagnosis methods focus on manually designing complex noise reduction, filtering, and feature extraction processes, however, these processes are too cumbersome and lack intelligence, making it increasingly difficult to rely on manual diagnosis with large amounts of data. With the development of information technology, convolutional neural networks have been proposed for bearing fault detection and identification. However, these convolutional models have the disadvantage of having difficulty handling fault-time information, leading to a lack of classification accuracy. So this paper proposes a transformer-based fault diagnosis method, using the short-time Fourier transform to convert the one-dimensional fault signal into a two-dimensional image, and then input the two-dimensional image into the transformer model for classification. Experimental results show that the fault classification can reach an accuracy of 98.45%.