Rolling Bearing Failure Feature Extraction Based on Transform and Stochastic Resonance

Zengqing Ma,Jianhua Liang,Yingna Yang

doi:10.19026/rjaset.6.3790

Abstract

Based on the generate mechanism of rolling bearing fault signal and its modulation model in the process of spreading, an improved method that combining Hilbert transformation and Stochastic Resonance (SR) is proposed for rolling bearing fault features extraction. Subsequently, the method is used to extract fault signal features from three kinds of typical faults, the surface damage of the inner ring, outer ring stripping injury and roller electrical erosion. First, low frequency envelope components are acquired from rolling bearing vibration signals through Hilbert transformation. Then, depending on the advantage of SR that SR is immune to noise and sensitive to periodic signal, cyclical faults signal of the low frequency envelope is highlighted by using the variable step size solution that can overcome adiabatic condition limitation of SR system. The experimental results show that the algorithm can extract the fault feature and identify the fault type effectively.

Highlights

Rolling bearings as one of the most commonly used general spare parts in all kinds of rotating machinery, on-line monitoring and fault diagnosis always drew much attention in the field of engineering technology at home and abroad
When some local damage appear in rolling bearing and other components in contact surface occur periodic collision, which is called through vibration
The data source: The fault data is about the 352226×22RZ rolling bearing

Summary

Introduction

Rolling bearings as one of the most commonly used general spare parts in all kinds of rotating machinery, on-line monitoring and fault diagnosis always drew much attention in the field of engineering technology at home and abroad. Wenhu et al (1999) summarized that failure feature information extraction was one of the key problems in the rolling bearing fault diagnosis. Traditional diagnosis technology always based on the time domain or frequency domain characteristics of vibration signals to extract feature vector and identify fault type. Commonly used methods include wavelet packet technique, Empirical Mode Decomposition (EMD) and Support Vector Machine (SVM), etc. Lei et al (2009) considered that SVM was a new machine learning method based on statistical learning theory, which showed unique advantages and good applicative prospect in the small sample problem solving and has good generalization ability. Methods above were all based on the data results to analyze the fault features and achieve fault diagnosis results

Methods

Results

Conclusion