Application of the DIMSST in Fault Diagnosis of Rolling Bearing under Variable Speed Conditions

Rolling bearings are indispensable parts of high-end mechanical equipment. To ensure safe production, it is essential to promptly detect and diagnose issues in high-end equipment, particularly when it comes to critical component diagnosis. However, there are a lot of uncertainties in the operation of the machine, such as variable speed and noise interference, which pose a challenge to the fault diagnosis of rolling bearings. A new post-processing method for time-frequency analysis, DIMSST, is introduced in this paper. Based on preserving the high time-frequency aggregation of IMSST, the idea of average threshold denoising is introduced, which makes DIMSST obtain the high energy concentration time-frequency representation and enhance the anti-noise performance. Finally, the proposed method successfully diagnosed the rolling bearing with outer ring fault and inner ring fault, and verified the potential of its application in engineering.