Fusion Fault Diagnosis Approach to Rolling Bearing with Vibrational and Acoustic Emission Signals

This paper first of all gives an introduction to the structure of rolling bearing, development stage of fault and the main fault types; then, it makes an analysis of the common detection methods and the technologies involved in rolling bearing fault; at last, based on the emphasis on the rolling bearing on-line detection and fault diagnosis system of acoustic emission technology, it elaborates the basic principles of acoustic emission, rolling bearing fault detection and diagnosis system experiment setting. Meanwhile, it introduces modern signal processing technology into acoustic emission information feature extraction and state recognition, such as wavelet analysis and wavelet packet analysis.

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A Fault Diagnosis Approach for Rolling Bearing Integrated SGMD, IMSDE and Multiclass Relevance Vector Machine

Sensors ◽

10.3390/s20154352 ◽

2020 ◽

Vol 20 (15) ◽

pp. 4352 ◽

Cited By ~ 4

Author(s):

Xiaoan Yan ◽

Ying Liu ◽

Minping Jia

Keyword(s):

Fault Diagnosis ◽

Symplectic Geometry ◽

Vibration Signal ◽

Rolling Bearing ◽

Relevance Vector Machine ◽

Symbolic Dynamic ◽

Bearing Fault ◽

Fault Features ◽

Bearing Vibration ◽

Diagnosis Approach

The vibration signal induced by bearing local fault has strong nonstationary and nonlinear property, which indicates that the conventional methods are difficult to recognize bearing fault patterns effectively. Hence, to obtain an efficient diagnosis result, the paper proposes an intelligent fault diagnosis approach for rolling bearing integrated symplectic geometry mode decomposition (SGMD), improved multiscale symbolic dynamic entropy (IMSDE) and multiclass relevance vector machine (MRVM). Firstly, SGMD is employed to decompose the original bearing vibration signal into several symplectic geometry components (SGC), which is aimed at reconstructing the original bearing vibration signal and achieving the purpose of noise reduction. Secondly, the bat algorithm (BA)-based optimized IMSDE is presented to evaluate the complexity of reconstruction signal and extract bearing fault features, which can solve the problems of missing of partial fault information existing in the original multiscale symbolic dynamic entropy (MSDE). Finally, IMSDE-based bearing fault features are fed to MRVM for achieving the identification of bearing fault categories. The validity of the proposed method is verified by the experimental and contrastive analysis. The results show that our approach can precisely identify different fault patterns of rolling bearings. Moreover, our approach can achieve higher recognition accuracy than several existing methods involved in this paper. This study provides a new research idea for improvement of bearing fault identification.

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Multi-feature entropy distance approach with vibration and acoustic emission signals for process feature recognition of rolling element bearing faults

Structural Health Monitoring ◽

10.1177/1475921716687167 ◽

2017 ◽

Vol 17 (2) ◽

pp. 156-168 ◽

Cited By ~ 14

Author(s):

Cheng-Wei Fei ◽

Yat-Sze Choy ◽

Guang-Chen Bai ◽

Wen-Zhong Tang

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Frequency Domain ◽

Rolling Bearing ◽

Distance Method ◽

Bearing Faults ◽

Bearing Fault ◽

Bearing Fault Diagnosis ◽

Rolling Element ◽

Entropy Distance

To accurately reveal rolling bearing operating status, multi-feature entropy distance method was proposed for the process character analysis and diagnosis of rolling bearing faults by the integration of four information entropies in time domain, frequency domain and time–frequency domain and two kinds of signals including vibration signals and acoustic emission signals. The multi-feature entropy distance method was investigated and the basic thought of rolling bearing fault diagnosis with multi-feature entropy distance method was given. Through rotor simulation test rig, the vibration and acoustic emission signals of six rolling bearing faults (ball fault, inner race fault, outer race fault, inner ball faults, inner–outer faults and normal) are gained under different rotational speeds. In the view of the multi-feature entropy distance method, the process diagnosis of rolling bearing faults was implemented. The analytical results show that multi-feature entropy distance fully reflects the process feature of rolling bearing faults with the change of rotating speed; the multi-feature entropy distance with vibration and acoustic emission signals better reports signal features than single type of signal (vibration or acoustic emission signal) in rolling bearing fault diagnosis; the proposed multi-feature entropy distance method holds high diagnostic precision and strong robustness (anti-noise capacity). This study provides a novel and useful methodology for the process feature extraction and fault diagnosis of rolling element bearings and other rotating machinery.

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The Comparison of Acoustic Emission with Vibration for Fault Diagnosis of the Bearing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.141.539 ◽

2011 ◽

Vol 141 ◽

pp. 539-543 ◽

Cited By ~ 1

Author(s):

Xiao Liang Feng ◽

Guo Feng Wang ◽

Xu Da Qin ◽

Chang Liu

Keyword(s):

Acoustic Emission ◽

Wavelet Transform ◽

Fault Diagnosis ◽

Significant Role ◽

Vibration Signal ◽

Rolling Bearing ◽

Rotating Machinery ◽

Outer Race ◽

Hilbert Envelope

The fault diagnosis of rolling bearing plays a significant role in rotating machinery. This paper makes a comparison between the acoustic emission and vibration signal in the fault diagnosis of the bearing of outer race pitting. The acoustic emission and vibration signal are processed by the wavelet transform, Hilbert envelope transform and FFT transform. Finally, the spectrum charts of the signals of acoustic emission and vibration are drew out. Based on the analysis results, the conclusion can be drawn that acoustic emission is superior to vibration in the fault diagnosis of the bearing.

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AE Fault Diagnosis of Rolling Bearing Based on Base

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.143-144.664 ◽

2011 ◽

Vol 143-144 ◽

pp. 664-668

Author(s):

D.L. Yang ◽

X.J. Li

Keyword(s):

Acoustic Emission ◽

Fault Diagnosis ◽

Dimensionality Reduction ◽

Rolling Bearing ◽

Acoustic Emission Sensors ◽

Ae Signal ◽

Acoustic Emission Test ◽

Fault Source ◽

Original Feature

In the acoustic emission fault diagnosis, the acoustic emission sensors was installed on the bearing pedestal where near from the fault source so that can collected stronger fault AE signal, however ,sometimes, it is inconvenience for AE sensor installation. This paper proposed that install the AE sensor on the base for collect the fault AE signal, but the signal was weak, so carried on EMD first, and selected the former 8 IMF to construct the original feature, than carried on KPCA for dimensionality reduction and get the optimized feature. In this paper, taking bearing acoustic emission test for example, by compared the base fault feature with the bearing pedestal fault feature, verified that the method that the AE sensor install on the base is feasible.

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