Periodic impulse signal separation based on resonance-based sparse signal decomposition and its application to the fault detection of rolling bearing

Du Juan,Lu Yan,Tao Xian,Chen Guo Chu,Zheng Yu

doi:10.1177/0020294019866858

Abstract

The main purpose of the paper is to propose a new method to achieve separating periodic impulse signal among multi-component mixture signal and its application to the fault detection of rolling bearing. In general, as local defects occur in a rotating machinery, the vibration signal always consists of periodic impulse components along with other components such as harmonic component and noise; impulse component reflects the condition of rolling bearing. However, different components of multi-component mixture signal may approximately have same center frequency and bandwidth coincides with each other that is difficult to disentangle by linear frequency-based filtering. In order to solve this problem, the author introduces a proposed method based on resonance-based sparse signal decomposition integrated with empirical mode decomposition and demodulation that can separate the impulse component from the signal, according to the different Q-factors of impulse component and harmonic component. Simulation and application examples have proved the effectiveness of the method to achieve fault detection of rolling bearing and signal preprocessing.

Highlights

When local defects occur in a rotating machinery, the corresponding vibration signal will comprise periodic impulse component along with other components such as rotating harmonic component and noise
It has been noted that resonance-based sparse signal decomposition (RSSD) acts as a new nonlinear signal analysis approach concentrating on signal resonance attribute instead of scale or frequency, as provided by the wavelet transform and Fourier transform.[1,2,3,4]
Considering the particularity of rotating machinery, especially the research object of this paper is rolling bearing, the highresonance component corresponds to rotating harmonic component and noise, and low-resonance component corresponds to rolling bearing fault component

Summary

Introduction

When local defects occur in a rotating machinery, the corresponding vibration signal will comprise periodic impulse component along with other components such as rotating harmonic component and noise. In order to theoretically verify the effectiveness and superiority of proposed method, the author sets the following synthetic signal x(t) as shown in equation (6) In this synthetic signal, x1(t) is a periodic impulse component that represents rolling bearing fault, x2(t) is an AM component that represents harmonic interference and n(t) is a stochastic noise component whose amplitude is 0.2. The envelope spectrum of lowresonance component is shown in Figure 9; it is obvious that the envelope spectrum can significantly detect the fault characteristic frequency fc and its second harmonic generation (SHG) This phenomenon shows that the proposed method can effectively extract modulation information of periodic impact component. As for outer raceway fault, the fault diameter is 4.3 mm and fault depth is 3.6 mm, the experimental rotating frequency is 28.83 Hz, the sample frequency is 12,000 Hz; according to equation (10), the fault characteristic frequency of bearing for outer raceway fault is calculated to be 103.18 Hz

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Conclusion