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

2011 ◽  
Vol 141 ◽  
pp. 539-543 ◽  
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.

scholarly journals A fault diagnosis method based on Auxiliary Classifier Generative Adversarial Network for rolling bearing

PLoS ONE ◽  
2021 ◽  
Vol 16 (3) ◽  
pp. e0246905
Author(s):  
Chunming Wu ◽  
Zhou Zeng
Keyword(s):  
Fault Diagnosis ◽  
Noise Pollution ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Generative Adversarial Network ◽  
High Background ◽  
Bearing Fault Diagnosis ◽  
Adversarial Network ◽  
Practical Engineering

Rolling bearing fault diagnosis is one of the challenging tasks and hot research topics in the condition monitoring and fault diagnosis of rotating machinery. However, in practical engineering applications, the working conditions of rotating machinery are various, and it is difficult to extract the effective features of early fault due to the vibration signal accompanied by high background noise pollution, and there are only a small number of fault samples for fault diagnosis, which leads to the significant decline of diagnostic performance. In order to solve above problems, by combining Auxiliary Classifier Generative Adversarial Network (ACGAN) and Stacked Denoising Auto Encoder (SDAE), a novel method is proposed for fault diagnosis. Among them, during the process of training the ACGAN-SDAE, the generator and discriminator are alternately optimized through the adversarial learning mechanism, which makes the model have significant diagnostic accuracy and generalization ability. The experimental results show that our proposed ACGAN-SDAE can maintain a high diagnosis accuracy under small fault samples, and have the best adaptation performance across different load domains and better anti-noise performance.

Vibration Signal Analysis of Bearing Based on EMD and Resonance Demodulation

2014 ◽  
Vol 556-562 ◽  
pp. 1286-1289 ◽  
Author(s):  
Jie Shi ◽  
Xing Wu ◽  
Nan Pan ◽  
Sen Wang ◽  
Jun Zhou
Keyword(s):  
Fault Diagnosis ◽  
Correlation Coefficient ◽  
Signal Analysis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Experimental Station ◽  
Fault Type ◽  
Mode Function ◽  
Vibration Signal Analysis

In order to monitor the operation state and implement fault diagnosis of rolling bearing in rotating machinery, this paper presents a method of fault diagnosis of rolling bearing, which is based on EMD and resonance demodulation. Using EMD to decompose the signal, which comes from QPZZ-II experimental station, the components of intrinsic mode function (IMF) will be obtained. Then, calculating the correlation coefficient of each IMF component, the highest correlation coefficient of IMF component will be analyzed by resonance demodulation. Finally, the experimental results show that the method can accurately identify and diagnose the running state and bearing fault type.

Multi-Scale Hermitian Wavelet Envelope Spectrum Based Bearing Fault Detection

2012 ◽  
Vol 459 ◽  
pp. 132-136 ◽  
Author(s):  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Three Dimensions ◽  
Noise Condition ◽  
Outer Race ◽  
Bearing Fault ◽  
Multi Scale ◽  
Integrated Technique ◽  
Envelope Spectrum

Hermitian wavelet is a low-oscillation, complex valued wavelet, which can detect the singularity characteristic of a signal precisely under strong background noise condition. A new method of bearing fault diagnosis based on multi-scale Hermitian wavelet envelope spectrum is proposed. The multi scale Hermitian wavelet envelope spectrum technique combines the advantages of Hermitian wavelet transform, envelope spectrum and three dimensions color map into one integrated technique. The bearing fault vibration signal is firstly decomposed using Hermitian continuous wavelet transform. Then the real and imaginary parts are obtained. In the end, the multi scale Hermitian wavelet envelope spectrum is obtained and the characteristics of the bearing fault can be recognized according to the multi-scale Hermitian wavelet envelope spectrum. The proposed method has been proved by vibration signals obtained from rolling bearing with inner or outer race fault. The experimental results verified the effectiveness of the proposed method.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Tunable Q-Factor Wavelet Transform and Convolutional Neural Network

2020 ◽  
Vol 16 (02) ◽  
pp. 47
Author(s):  
Liqun Hou ◽  
Zijing Li
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Convolutional Neural Network ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Operating Conditions ◽  
Fault Classification ◽  
Rolling Ball ◽  
Diagnosis Method

Rolling bearing plays an important role in rotary machines and industrial processes. Effective fault diagnosis technology for rolling bearing directly affects the life and operator safety of the devices. In this paper, a fault diagnosis method based on tunable-Q wavelet transform (TQWT) and convolutional neural network (CNN) is proposed to reduce the influence of noise on bearing vibration signal and the dependence on the experience of traditional diagnosis methods. TQWT is used to decompose and denoise the vibration signal, while the CNN is adopted to extract fault features and carry out fault classification. Seven motor operating conditions—normal, drive end rolling ball failure (DE-B), drive end inner raceway failure (DE-IR), drive end outer raceway failure (DE-OR), fan end rolling ball failure (FE-B), fan end inner raceway fault (FE-IR) and fan end outer raceway fault (FE-OR)—are used to evaluate the proposed approach. The experimental results indicate that the fault diagnosis accuracy of the proposed method reaches 99.8%.

Bearing Fault Diagnosis Based on Dual-Tree Complex Wavelet Transform

2012 ◽  
Vol 490-495 ◽  
pp. 128-132
Author(s):  
Hui Li
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Complex Wavelet Transform ◽  
Noise Condition ◽  
Outer Race ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Complex Wavelet ◽  
Envelope Spectrum

A novel method of bearing fault diagnosis based on demodulation technique of dual-tree complex wavelet transform (DTCWT) is proposed. It is demonstrated that the proposed dual-tree complex wavelet transform has better shift invariance, reduced frequency aliasing effect and de-noising ability. The bearing fault vibration signal is firstly decomposed and reconstructed using dual-tree complex wavelet transform. Then the real and imaginary parts are obtained and the vibration signal is amplitude demodulated. In the end, the amplitude envelope and wavelet envelope spectrum are computed. Therefore, the character of the bearing fault can be recognized according to the wavelet envelope spectrum. The experimental results show that dual-tree complex wavelet transform can effectively reduce spectral aliasing and fault diagnosis based on dual-tree complex wavelet transform can effectively diagnose bearing inner and outer race fault under strong background noise condition.

scholarly journals Fault Diagnosis of Rolling Bearing Based on Shift Invariant Sparse Feature and Optimized Support Vector Machine

Machines ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 98
Author(s):  
Haodong Yuan ◽  
Nailong Wu ◽  
Xinyuan Chen ◽  
Yueying Wang
Keyword(s):  
Support Vector Machine ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Support Vector ◽  
Bearing Fault Diagnosis ◽  
Training Samples ◽  
Diagnosis Method ◽  
Fault Feature Extraction

The vibration signal of rotating machinery fault is a periodic impact signal and the fault characteristics appear periodically. The shift invariant K-SVD algorithm can solve this problem effectively and is thus suitable for fault feature extraction of rotating machinery. With the over-complete dictionary learned by the training samples, including thedifferent classes, shift invariant sparse feature for the training as well as test samples can be formed through sparse codes and employed as the input of classifier. A support vector machine (SVM) with optimized parameters has been extensively used in intelligent diagnosis of machinery fault. Hence, in this study, a novel fault diagnosis method of rolling bearings using shift invariant sparse feature and optimized SVM is proposed. Firstly, dictionary learning by shift invariant K-SVD algorithm is conducted. Then, shift invariant sparse feature is constructed with the learned over-complete dictionary. Finally, optimized SVM is employed for classification of the shift invariant sparse feature corresponding to different classes, hence, bearing fault diagnosis is achieved. With regard to the optimized SVM, three methods including grid search, generic algorithm (GA), and particle swarm optimization (PSO) are respectively carried out. The experiment results show that the shift invariant sparse feature using shift invariant K-SVD can effectively distinguish the bearing vibration signals corresponding to different running states. Moreover, optimized SVM can significantly improve the diagnosis precision.

Harmonic Response Analysis of Rotor-Rolling Bearing-Base System for Fault Diagnosis of Rotating Machinery Based on Base

2011 ◽  
Vol 143-144 ◽  
pp. 669-674
Author(s):  
B. Qin ◽  
J.G. Wu ◽  
X.J. Li ◽  
B.H. Yao
Keyword(s):  
Fault Diagnosis ◽  
Condition Monitoring ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rotating Machinery ◽  
Response Analysis ◽  
Base System ◽  
Test Bed ◽  
Harmonic Response ◽  
Harmonic Response Analysis

In the fault diagnosis of rotating machinery through vibration analysis of the base, the signal may be weak and impure since the vibration signal which collected at the base is far away from the fault source. In order to provide useful evidences for the condition monitoring and fault diagnosis of rolling bearing based on the base vibration signal analysis, the rotor-bearing-base system model is built by taking the Spectra Quest comprehensive fault simulation test-bed as the object, the harmonic response analysis of the entire system is done with finite element analysis software ANSYS, and the ideal locations where sensors are installed on the base are obtained. These will form the foundation for the condition monitoring and fault diagnosis of rolling bearing based on the base vibration signal.

scholarly journals Optimal SES Selection Based on SVD and Its Application to Incipient Bearing Fault Diagnosis

2018 ◽  
Vol 2018 ◽  
pp. 1-13 ◽  
Author(s):  
Longlong Li ◽  
Yahui Cui ◽  
Runlin Chen ◽  
Xiaolin Liu
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Degradation Process ◽  
Rolling Bearing ◽  
Industrial Applications ◽  
Rotating Machinery ◽  
Accelerated Life Test ◽  
Rolling Element Bearing ◽  
Main Task ◽  
Detection Techniques

Rotating machinery has extensive industrial applications, and rolling element bearing (REB) is one of the core parts. To distinguish the incipient fault of bearing before it steps into serious failure is the main task of condition monitoring and fault diagnosis technology which could guarantee the reliability and security of rotating machinery. The early defect occurring in the REB is too weak and manifests itself in heavy surrounding noise, thus leading to the inefficiency of the fault detection techniques. Aiming at the vibration signal purification and promoting the potential of defects detection, a new method is proposed in this paper based on the combination of singular value decomposition (SVD) technique and squared envelope spectrum (SES). The kurtosis of SES (KSES) is employed to select the optimal singular component (SC) obtained by applying SVD to vibration signal, which provides the information of the REB for fault diagnosis. Moreover, the rolling bearing accelerated life test with the bearing running from normal state to failure is adopted to evaluate the performance of the SVD-KSES, and results demonstrate the proposed approach can detect the incipient faults from vibration signal in the natural degradation process.

Fault Diagnosis of Rolling Bearing Based on Dual-Tree Complex Wavelet Transform and AR Power Spectrum

2013 ◽  
Vol 819 ◽  
pp. 271-276 ◽  
Author(s):  
Zhi Peng Meng ◽  
Yong Gang Xu ◽  
Guo Liang Zhao ◽  
Sheng Fu
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Power Spectrum ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Complex Wavelet Transform ◽  
Noise Interference ◽  
Diagnosis Method ◽  
Complex Wavelet ◽  
Effective Signal

Aiming at the strong background noise involved in the signals of rolling bearing and the difficulty to extract fault feature in practice, a new fault diagnosis method is proposed based on Dual-tree Complex Wavelet Transform (DT-CWT) and AR power spectrum. Firstly, the non-stationary and complex vibration signal is decomposed into several different frequency band components through dual-tree complex wavelet decomposition; Secondly, Hilbert envelope is formed from the components which contains the fault information. Finally, the auto-power spectrum can be obtained by auto-regressive (AR) spectrum. The noise interference was eliminated effectively, and the effective signal information was retained at the same time. Thus, the fault feature information was extracted. In this paper, the fault test and the engineering practical fault data of rolling bearing were analyzed by dual-tree complex wavelet transform and AR power spectrum. The results show that the noise of the vibration signal was eliminated effectively, and the fault feature were extracted. The feasibility and effectiveness of the method were verified.

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