DTM-Bearing: A Novel Framework for Speed-Invariant Bearing Fault Diagnosis Based on Diffusion Transformation Model (DTM)

Fault diagnosis holds important significance in mitigating financial losses and ensuring equipment safety. As a crucial aspect of industrial machinery, bearing fault diagnosis becomes imperative. Nevertheless, in reality, identifying faults becomes challenging due to the presence of diverse variations in abnormal data, such as different vibration rotational speeds. In this paper, a novel framework for bearing fault diagnosis is proposed, called DTM-bearing, built upon the diffusion transformation model (DTM). This approach can transfer signals across different vibration speeds into a standardized signal aligned with a speed template. The primary purpose of DTM-bearing is to eliminate speed variations and extract speed-invariant features. Consequently, bolster the robustness of bearing fault diagnosis across diverse vibration speed scenarios. To the best of our knowledge, the proposed method is the first to combine the concepts of diffusion model and transformation in the domain of bearing fault diagnosis. Various experiments are performed on some datasets with multiple different speeds, which shows proposal can effectively improve the performance of bearing diagnosis. The framework based on the diffusion transformation model is expected to eliminate additional variations and improve the effectiveness of bearing diagnosis in practical applications.