Rotating machinery fault diagnosis based on Micro-Electro-Mechanical Systems (MEMS) technologies has recently received considerable attention. The significant advancements in MEMS make it easier and more conceivable to mount a low-cost MEMS sensor directly on the rotating shaft, allowing more accurate dynamic characteristics of the rotor to be obtained for mechanical condition monitoring and fault diagnosis. However, the measured signals contain strong background noise and modulation effect, which results in low signal-to-noise ratio (SNR), and consequently attenuate the accuracy of the diagnosis results. To improve the SNR of the measured signals, a denoising method based on empirical mode decomposition (EMD) is developed in this paper to suppress the background noise and enhance the modulation components for accurate fault feature extraction. Firstly, EMD is applied to decompose the original signal into a list of intrinsic mode functions (IMFs), and then the IMF with the highest correlation coefficient value is selected for further analysis. Finally, the envelop analysis (EA) is employed to demodulate the denoised signal by EMD for fault feature extraction and diagnosis. The experimental results show that the proposed EMD based denoising approach gives a promising result in diagnosing common PG faults.
|Conference||25th IEEE International Conference on Automation and Computing|
|Abbreviated title||ICAC 2019|
|Period||5/09/19 → 7/09/19|