Fault Diagnosis of Rotating Machinery Based on Wavelet Domain Denoising and Metric Distance

Abstract

In the monitoring process of petrochemical equipment rotating machinery, the collected large data easily lead to valuable data loss in the pre-processing process and affecting the accuracy of the fault diagnosis. This paper proposes a method for the fault diagnosis of the rotating machinery based on the wavelet-domain denoising and metric distance. The wavelet-domain denoising uses wavelet coefficients of signal and noise that have different properties on different scales and process noisy signal wavelet coefficients. Metric distance is to compare two independent statistical samples with each other after denoising to determine whether they belong to the same sample. First, the denoising of the vibration time-domain signal is based on the wavelet-domain denoising method. Then, the tested fault samples are compared with the known fault samples by metric distance. Finally, the fault types are identified according to the metric distance. Verification of the algorithm performance and the simulation experiment of petrochemical large units show that the method is not only simple and effective but also has better faults recognition. It can guide the faults diagnosis of large petrochemical units and other large units rotating machinery.