Bearing fault detection system based on a deep diffusion model

Bearings are crucial components of modern high-precision machinery and rotating machines. Excellent bearing failure detection systems are vital for ensuring that machines operate precisely. Advances in artificial neural networks (ANNs) and increases in computer processing speed have led to the application of many ANN models in various fields, including bearing failure detection, with excellent outcomes being achieved. However, to construct an ANN model that can precisely detect bearing failures, large quantities of data must be collected on various types of bearing failures. Thus, considerable time must be spent in data collection before rotating machines are operated on the production line, which increases costs for manufacturers. To overcome this problem, the present study used a diffusion model for data augmentation to improve the accuracy of an ANN model trained on a small quantity of bearing sound data. This study performed time-delay mapping to preprocess the data and convert them into a two-dimensional time-delay mapping diagram to reduce the dimensionality of the data features, a novel approach in the field of bearing failure detection. Finally, this study used a convolutional neural network model, which exhibited the optimal classification performance for time-delay mapping diagrams, for bearing failure detection. By comparing the results obtained from augmented and raw data, this study confirmed that using a diffusion model to augment data can improve the generalization ability of bearing failure detection models trained on a small quantity of data.