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.

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.

Intelligent fault diagnosis method of rolling bearing based on stacked denoising autoencoder and convolutional neural network

2020 ◽  
Vol 72 (7) ◽  
pp. 947-953 ◽  
Author(s):  
Changchang Che ◽  
Huawei Wang ◽  
Xiaomei Ni ◽  
Qiang Fu
Keyword(s):  
Time Series ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Intelligent Fault Diagnosis ◽  
Content Type ◽  
Noise Interference ◽  
Proposed Model ◽  
Long Time ◽  
Diagnosis Method

Purpose The purpose of this study is to analyze the intelligent fault diagnosis method of rolling bearing. Design/methodology/approach The vibration signal data of rolling bearing has long time series and strong noise interference, which brings great difficulties for the accurate diagnosis of bearing faults. To solve those problems, an intelligent fault diagnosis model based on stacked denoising autoencoder (SDAE) and convolutional neural network (CNN) is proposed in this paper. The SDAE is used to process the time series data with multiple dimensions and noise interference. Then the dimension-reduced samples can be put into CNN model, and the fault classification results can be obtained by convolution and pooling operations of hidden layers in CNN. Findings The effectiveness of the proposed method is validated through experimental verification and comparative experimental analysis. The results demonstrate that the proposed model can achieve an average classification accuracy of 96.5% under three noise levels, which is 3-13% higher than the traditional models and single deep-learning models. Originality/value The combined SDAE–CNN model proposed in this paper can denoise and reduce dimensions of raw vibration signal data, and extract the in-depth features in image samples of rolling bearing. Consequently, the proposed model has more accurate fault diagnosis results for the rolling bearing vibration signal data with long time series and noise interference. Peer review The peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-11-2019-0496/

Compound Fault Feature Separation of Rolling Bearing Based on Complex Wavelet and Energy Operator Demodulation

2012 ◽  
Vol 226-228 ◽  
pp. 765-771
Author(s):  
Yang Yang ◽  
Jian Yu Zhang ◽  
Sui Zheng Zhang
Keyword(s):  
Wavelet Transform ◽  
Wavelet Coefficient ◽  
Energy Operator ◽  
Rolling Bearing ◽  
Difficult Problem ◽  
Transform Domain ◽  
Complex Wavelet Transform ◽  
Transform Coefficients ◽  
Complex Wavelet ◽  
Fault Information

Compound fault feature separation is a difficult problem in diagnosis field of mechanical system. For the rolling bearing with compound fault on outer and inner race, feature separation technology based on complex wavelet transform and energy operator demodulation is introduced. Through continuous wavelet transform, coefficients of mixed fault signal can be achieved in different wavelet transform domain (i.e. real, imaginary, modulus and phase domain). Furthermore, wavelet power spectrum contours and time average wavelet energy spectrum are applied to extract the scales which hold rich fault information, and the wavelet coefficient slice of specific scale is also drawn. For wavelet coefficients in different domain, spectrum analysis and energy operator demodulation can be used successfully to separate mixed fault. The comparison of feature extraction effect between complex wavelet and real wavelet transform shows that complex wavelet transform is obviously better than the latter.

A rolling bearing fault diagnosis method based on fastDTW and an AGBDBN

2020 ◽  
Vol 62 (8) ◽  
pp. 457-463 ◽  
Author(s):  
Shang Zhiwu ◽  
Liu Xia ◽  
Li Wanxiang ◽  
Gao Maosheng ◽  
Yu Yan
Keyword(s):  
Fault Diagnosis ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Deep Belief Network ◽  
Residual Vector ◽  
Belief Network ◽  
Network Training ◽  
Diagnosis Method ◽  
Stationary Vibration ◽  
Dynamic Time

In order to improve fault feature extraction and diagnosis for rolling bearings, a fault diagnosis method based on fast dynamic time warping (fastDTW) and an adaptive Gaussian-Bernoulli deep belief network (AGBDBN) is proposed in this paper. Firstly, for the non-stationary vibration signal characteristics of the bearing, the fastDTW algorithm is used to calculate the residual vector of the fault signal, thereby enhancing the fault characteristic information. Then, according to the continuous vibration value of the bearing vibration signal, a standard deep belief network (DBN) is improved to deal with the problem that the optimal setting for the learning rate is difficult to achieve in the deep neural network training process and the AGBDBN model is used for fault diagnosis. Finally, the proposed method is compared with a variety of model diagnosis methods. The experimental results show that the proposed method achieved good diagnostic results.

scholarly journals Gearbox Composite Fault Diagnosis Method Based on Minimum Entropy Deconvolution and Improved Dual-Tree Complex Wavelet Transform

Entropy ◽  
2018 ◽  
Vol 21 (1) ◽  
pp. 18 ◽  
Author(s):  
Ziying Zhang ◽  
Xi Zhang ◽  
Panpan Zhang ◽  
Fengbiao Wu ◽  
Xuehui Li
Keyword(s):  
Wavelet Transform ◽  
Empirical Mode Decomposition ◽  
Notch Filter ◽  
Vibration Signal ◽  
Coefficient Matrix ◽  
Ensemble Empirical Mode Decomposition ◽  
Minimum Entropy ◽  
Complex Wavelet Transform ◽  
Mode Decomposition ◽  
Complex Wavelet

Dual-tree complex wavelet transform has been successfully applied to the composite diagnosis of a gearbox and has achieved good results. However, it has some fatal weaknesses, so this paper proposes an improved dual-tree complex wavelet transform (IDTCWT), and combines minimum entropy deconvolution (MED) to diagnose the composite fault of a gearbox. Firstly, the number of decomposition levels and the effective sub-bands of the DTCWT are adaptively determined according to the correlation coefficient matrix. Secondly, frequency mixing is removed by notch filter. Thirdly, each of the obtained sub-bands further reduces the noise by minimum entropy deconvolution. Then, the proposed method and the existing adaptive noise reduction methods, such as empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and variational mode decomposition (VMD), are used to decompose the two sets of simulation signals in comparison, and the feasibility of the proposed method has been verified. Finally, the proposed method is applied to the compound fault vibration signal of a gearbox. The results show the proposed method successfully extracts the outer ring fault at a frequency of 160 Hz, the gearbox fault with a characteristic frequency of 360 Hz and its double frequency of 720 Hz, and that there is no mode mixing. The method proposed in this paper provides a new idea for the feature extraction of a gearbox compound fault.

Fault diagnosis method based on integration of RSSD and wavelet transform to rolling bearing

Measurement ◽  
2019 ◽  
Vol 131 ◽  
pp. 400-411 ◽  
Author(s):  
Baojia Chen ◽  
Baoming Shen ◽  
Fafa Chen ◽  
Hongliang Tian ◽  
Wenrong Xiao ◽  
...  
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Diagnosis Method

Information Fusion of the Vibration and Acoustic Signals Based Rolling Bearing Incipient Fault Diagnosis Method

2013 ◽  
Vol 774-776 ◽  
pp. 1499-1502
Author(s):  
Ting Feng Ming ◽  
Yong Xiang Zhang ◽  
Jing Li
Keyword(s):  
Fault Diagnosis ◽  
Correlation Analysis ◽  
Information Fusion ◽  
Vibration Signal ◽  
Acoustic Resonance ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Diagnosis Method ◽  
Incipient Fault ◽  
Fusion Analysis

The feature of correlation analysis were described and applied to analyzing the vibration signal of the gearbox. Aiming to that the diagnosis effect of the rolling bearings incipient fault was not good through the vibration spectrum and the resonance demodulation spectrum directly, the information fusion technology based on the correlation analysis is proposed to processing the vibration and acoustic resonance demodulation signal. The experimental results show that the presented correlation fusion analysis technology can be as the basis of the effective fault diagnosis method for the rolling bearings incipient defect.

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