A New Feature Extraction Technique for Early Degeneration Detection of Rolling Bearings

Abstract

Feature extraction technology is an important part of bearing diagnosis, especially for early degradation detection. However, the traditional feature extraction technology can not effectively remove noise or is not sensitive to periodic weak faults, which leads to be inclined to raise false alarms and prediction delay for early degradation detection. In order to solve these two issues, a new feature extraction technique is presented based on Envelope Harmonic-to-noise Ratio (EHNR) and Adaptive Variational Mode Decomposition (AVMD). First of all, the minimum average envelope entropy is used as the objective function to search the optimal parameters of the Variational Modal Decomposition (VMD) adaptively by the Grey Wolf Optimization (GWO) algorithm. The problem of under-decomposition or overdecomposition caused by improper parameter setting is avoided. Then, a new index called Effective Weighted Sparseness Kurtosis (EWSK) is proposed. This index can separate the effective modal components and noise modal components only by the positive and negative results, so as to achieve the purpose of removing noise interference and retaining a large amount of fault information. Finally, the EHNR of the reconstructed signal is calculated, and its sensitivity to periodic fault shock is utilized to detect the early degradation starting point of the rolling bearing. Experimental results show that the proposed method outperforms several state-of-the-art detection methods in terms of early degradation point detection, false alarm rate and computational complexity. The superior performances of the presented AVMD-EHNR method can provide the basis for early fault diagnosis and remaining useful life prediction of rolling bearings.indicator TERMS early degradation detection, rolling bearings, envelope harmonic-to-noise ratio (EHNR), adaptive variational mode decomposition (AVMD), effective weighted sparseness kurtosis (EWSK) index.

Fault Diagnosis for Rolling Bearing of Combine Harvester Based on Composite-Scale-Variable Dispersion Entropy and Self-Optimization Variational Mode Decomposition Algorithm

“…In essence, the fault diagnosis results provided by the proposed method are equivalent to the classification results of the softmax classifier. In addition, based on the diagnosis results, two commonly used evaluation metrics are calculated to analyze the performance of diagnosis method, including diagnosis accuracy and false alarm rate (FPR) [ 42 , 43 ]. Based on the machine learning theory related to the classification problem, the definitions of these two metrics are presented as follows.…”

Fault Diagnosis Method for Rolling Bearings Based on Grey Relation Degree

“…For this reason, Wang et al [ 10 ] used the genetic mutation particle swarm optimization algorithm to optimize VMD and created a fitness function with cyclic information entropy to find the best parameters. Lv et al [ 11 ] took the minimum average envelope entropy as the objective function and used the grey wolf optimization (GWO) algorithm to adaptively search for the optimal parameters of VMD, avoiding the under-decomposition or over-decomposition problems caused by improper parameter settings.…”