Research on Real-Time Monitoring System of Motor Bearing State based on Improved Wavelet Threshold Denoising Method

Currently, motor bearing monitoring typically operates in an offline mode, meaning faults are detected only after they occur, leading to delays and low sensitivity in motor bearing condition monitoring. To address this, a real-time motor bearing condition monitoring system based on an improved wavelet threshold denoising method is proposed. A second-order passive low-pass filter hardware circuit is designed to perform initial filtering on the collected current signals, and the improved wavelet threshold algorithm is used to denoise the filtered signals, eliminating and reducing noise and frequencies unrelated to the characteristic signals. The FFT algorithm is employed to perform spectral analysis on the denoised current signal and calculate harmonic component diagnostic values, achieving real-time signal processing for the motor bearing condition monitoring system. Experimental results show that the proposed improved wavelet threshold denoising method makes the characteristic frequencies of the motor bearing more prominent, improving monitoring sensitivity and significantly reducing misjudgments in bearing degradation assessments.