An Improved Envelope Spectrum via Candidate Fault Frequency Optimization- gram for Bearing Fault Diagnosis

2022 ◽  
pp. 116746
Author(s):  
Yao Cheng ◽  
Shengbo Wang ◽  
Bingyan Chen ◽  
Guiming Mei ◽  
Weihua Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Frequency Optimization ◽  
Envelope Spectrum

Rolling Element Bearing Fault Diagnosis Based on EEMD and Index of Envelope Spectrum Sparse Ratio

2021 ◽  
Author(s):  
Zhe Li ◽  
Menghao Zhang ◽  
Longlong Li ◽  
Runlin Chen ◽  
Yanchao Zhang ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Rolling Element Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Rolling Element ◽  
Element Bearing ◽  
Envelope Spectrum

scholarly journals Autonomous Bearing Fault Diagnosis Method based on Envelope Spectrum

2017 ◽  
Vol 50 (1) ◽  
pp. 13378-13383 ◽  
Author(s):  
Andreas Klausen ◽  
Kjell G. Robbersmyr ◽  
Hamid R. Karimi
Keyword(s):  
Fault Diagnosis ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Diagnosis Method ◽  
Envelope Spectrum

scholarly journals Fault Diagnosis of Train Axle Box Bearing Based on Multifeature Parameters

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Xiaofeng Li ◽  
Limin Jia ◽  
Xin Yang
Keyword(s):  
Fault Diagnosis ◽  
Amplitude Ratio ◽  
Great Loss ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Vibration Sensors ◽  
Mode Decomposition ◽  
Fault Recognition ◽  
Weak Fault ◽  
Envelope Spectrum

Failure of the train axle box bearing will cause great loss. Now, condition-based maintenance of train axle box bearing has been a research hotspot around the world. Vibration signals generated by train axle box bearing have nonlinear and nonstationary characteristics. The methods used in traditional bearing fault diagnosis do not work well with the train axle box. To solve this problem, an effective method of axle box bearing fault diagnosis based on multifeature parameters is presented in this paper. This method can be divided into three parts, namely, weak fault signal extraction, feature extraction, and fault recognition. In the first part, a db4 wavelet is employed for denoising the original signals from the vibration sensors. In the second part, five time-domain parameters, five IMF energy-torque features, and two amplitude-ratio features are extracted. The latter seven frequency domain features are calculated based on the empirical mode decomposition and envelope spectrum analysis. In the third part, a fault classifier based on BP neural network is designed for automatic fault pattern recognition. A series of tests are carried out to verify the proposed method, which show that the accuracy is above 90%.

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 Rolling Bearing Fault Diagnosis based on Residual Neural Network

2020 ◽  
Vol 2 (4) ◽  
pp. 89
Author(s):  
Haopeng Liang
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Bearing Fault ◽  
Bearing Fault Diagnosis ◽  
Multi Scale ◽  
Diagnosis Method ◽  
Hilbert Envelope ◽  
Envelope Spectrum ◽  
Residual Block

<div class="Section0"><div>Because rolling bearings have been working in an environment with complex and variable working conditions and large noise interference for a long time, the bearing fault diagnosis method has a poor diagnostic effect under variable working conditions. To solve this problem, we propose a residual neural network based on the diagnosis method of rolling bearing fault. The proposed method takes rolling bearing time-domain signal data as input. Because bearing signals have strong time-varying properties, we construct a multi-scale residual block that can not only learn features at different levels, but also expand the width and depth of the residual neural network. We use the advantages of the dilated convolution to expand the receptive field, replace part of the ordinary convolution in the multi-scale residual block with the dilated convolution, and design a multi-scale hollow residual block. The advantage is that the method is made by expanding the receptive field. It has a strong feature learning ability and can learn better features under limited data. Finally, we add a Dropout layer to discard a certain proportion of neurons after the fully connected layer, which can effectively avoid the negative impact of overfitting, and use Case Western Reserve University bearing dataset, the simulation experiment, and the SVM + EMD + Hilbert envelope spectrum, BPNN + EMD + Hilbert envelope spectrum and Resnet three ways of comparative analysis, the results show that the method under the variable condition of the fault diagnosis of rolling bearing has higher diagnosis accuracy, stronger noise resistance, and generalization ability.</div><p> </p></div><p> </p>

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