scholarly journals Early Weak Fault Diagnosis of Rolling Bearing Based on Multilayer Reconstruction Filter

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Quanfu Li ◽  
Yuxuan Zhou ◽  
Gang Tang ◽  
Chunlin Xin ◽  
Tao Zhang
Keyword(s):  
Wavelet Decomposition ◽  
Filter Bank ◽  
Adaptive Estimation ◽  
Wiener Filter ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Noise Interference ◽  
Empirical Wavelet Transform ◽  
Reconstruction Filter ◽  
Weak Fault

The early weak fault characteristics of rolling bearings are extremely weak and are easily overwhelmed by other noises. In order to effectively extract the characteristics of the early weak faults of the rolling bearings and draw on the multilayer wavelet decomposition idea, a method for diagnosing the early weak faults of the rolling bearing based on the multilayer reconstruction filter is proposed. As we all know, empirical wavelet transform (EWT) makes full use of wavelet filter bank, and variational mode decomposition (VMD) uses Wiener filter bank. This paper fully combines the advantages of the above two methods, adaptively determines the number of modes through empirical wavelet decomposition and divides the original signal, extracts the frequency band that contains the fault characteristic information, and effectively eliminates noise interference. These steps are repeated until the optimal component of the condition is obtained. In the output layer, the weak fault impact components are further separated by the strong filtering and signal decomposition capability of VMD. The advantages of the proposed method are proved by the experiment of weak fault of rolling bearing and the accelerated failure experiment of full life. The proposed method has the advantages of reducing noise influence and adaptive estimation of decomposed modes, which can be applied more efficiently in practice.

Feature Extraction of Nonstationarity Vibration Signal Based on Wavelet Decomposition

2012 ◽  
Vol 220-223 ◽  
pp. 2228-2234 ◽  
Author(s):  
Yong Hui Chen ◽  
Xue Liang Zhang ◽  
Hai Hong Li
Keyword(s):  
Frequency Band ◽  
Hilbert Transform ◽  
Extraction Method ◽  
Wavelet Decomposition ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Feature Representation ◽  
Rolling Bearings ◽  
Localized Defects ◽  
Fault Mode

Abstract. Nonstationarity feature representation and extraction method based on the wavelet decomposition and demodulation techniques are studied. Some component in special frequency band included faulty information is selected to reconstruct by wavelet analysis. The mono-components with fault feature in different frequency band would be captured and separated out. The demodulated and spectrally signals are analyzed by Hilbert transform, and it presents an approach to get the characteristic frequency of fault signals. So what kind of the fault mode is can be estimated. For the nonstationarity and modulation feature of rolling bearing fault signals, wavelet decomposition combined with Hilbert transform is effective in identifying the localized defects of rolling bearings.

The Wavelet De-Noising of Vibration Signals for Aircraft Rolling Bearings

2014 ◽  
Vol 912-914 ◽  
pp. 873-877 ◽  
Author(s):  
Feng Kui Cui ◽  
Fei Fei Lv ◽  
Xiao Qiang Wang ◽  
Dong Ying Zhang
Keyword(s):  
Fourier Transform ◽  
Fast Fourier Transform ◽  
Noise Reduction ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Noise Interference ◽  
Low Snr ◽  
Contrast Analysis

Aiming at air rolling bearing vibration signals low SNR and nonstationary characteristics, taking wavelet theory and principles of the wavelet noise reduction for air vibration signals of rolling bearings to conduct wavelet noise reduction processing.By means of the simulation signal wavelet noise reduction processing and fast Fourier transform, the contrast analysis of the vibration signals after wavelet noise reduction and FFT transform and the original signal directly to the result of the fast Fourier transform, and thus prove the validity of the vibration signal wavelet noise reduction. Through the actual vibration signals of bearing conductnoise reduction processing, the result is a further indication of the superiority of wavelet noise reduction in eliminate noise interference.

scholarly journals Research on Rolling Bearing Fault Diagnosis Using Improved Majorization-Minimization-Based Total Variation and Empirical Wavelet Transform

2020 ◽  
Vol 2020 ◽  
pp. 1-11 ◽  
Author(s):  
Yangli Ou ◽  
Shuilong He ◽  
Chaofan Hu ◽  
Jiading Bao ◽  
Wenjie Li
Keyword(s):  
Genetic Algorithms ◽  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Total Variation ◽  
Adaptive Signal Processing ◽  
Economic Losses ◽  
Particle Swarm Algorithm ◽  
Rolling Bearings ◽  
Noise Interference ◽  
Empirical Wavelet Transform

Bearings are among the most widely used core components in mechanical equipment. Their failure creates the potential for serious accidents and economic losses. Vibration signature analyses are the most common approach to assess the viability of bearings due to its ease of measurement and high correlation with structural dynamics. However, the collected vibration signals of rolling bearings are usually nonstationary and are inevitably accompanied by noise interference. This makes it difficult to extract the feature frequency for the failed bearing and affects the diagnosis accuracy. The majorization-minimization-based total variation (TV-MM) denoising algorithm effectively removes the noise interference from the signal and highlights the related feature information. The value of its main parameter λ determines the quality of the denoising effect. However, manually selecting parameters requires professional experience in a process that it is time-consuming and laborious, while the use of genetic algorithms is cumbersome. Therefore, an improved particle swarm algorithm (IPSO) is used to find the optimal solution of λ. The IPSO utilises the mutation concept in genetic algorithms to reinitialise the particles with a certain probability after each update. In addition, the empirical wavelet transform (EWT) is an adaptive signal processing method suitable for processing nonlinear and nonstationary signals. Therefore, this paper presents an ensemble analysis method that combines the IPSO, TV-MM, and EWT. First, IPSO is used to optimise the denoising parameter λ. The TV-MM under this parameter effectively removes the background noise interference and improves the accuracy of the subsequent modal decomposition. Then, the EWT is used for the adaptive division to produce a set of sequences. Finally, Hilbert envelope demodulation is performed on each component to realise fault diagnosis. The results from simulations and signals received from defective bearings with outer race fault, inner race fault, and rolling element fault demonstrate the effectiveness of the proposed method for fault diagnosis of rolling bearings.

scholarly journals Rolling Bearing Fault Diagnosis Based on EWT Sub-Modal Hypothesis Test and Ambiguity Correlation Classification

Symmetry ◽  
2018 ◽  
Vol 10 (12) ◽  
pp. 730 ◽  
Author(s):  
Mingtao Ge ◽  
Jie Wang ◽  
Yicun Xu ◽  
Fangfang Zhang ◽  
Ke Bai ◽  
...  
Keyword(s):  
Gaussian Distribution ◽  
Gaussian Noise ◽  
Hypothesis Test ◽  
Signal Reconstruction ◽  
Rolling Bearing ◽  
Rolling Bearings ◽  
Vibration Signals ◽  
Outer Race ◽  
Empirical Wavelet Transform ◽  
Non Stationary Signal

Because of the cyclic symmetric structure of rolling bearings, its vibration signals are regular when the rolling bearing is working in a normal state. But when the rolling bearing fails, whether the outer race fault or the inner race fault, the symmetry of the rolling bearing is broken and the fault destroys the rolling bearing’s stable working state. Whenever the bearing passes through the fault point, it will send out vibration signals representing the fault characteristics. These signals are often non-linear, non-stationary, and full of Gaussian noise which are quite different from normal signals. According to this, the sub-modal obtained by empirical wavelet transform (EWT), secondary decomposition is tested by the Gaussian distribution hypothesis test. It is regarded that sub-modal following Gaussian distribution is Gaussian noise which is filtered during signal reconstruction. Then by taking advantage of the ambiguity function superiority in non-stationary signal processing and combining correlation coefficient, an ambiguity correlation classifier is constructed. After training, the classifier can recognize vibration signals of rolling bearings under different working conditions, so that the purpose of identifying rolling bearing faults can be achieved. Finally, the method effect was verified by experiments.

scholarly journals An Improved Empirical Wavelet Transform and Its Applications in Rolling Bearing Fault Diagnosis

2018 ◽  
Vol 8 (12) ◽  
pp. 2352 ◽  
Author(s):  
Yonggang Xu ◽  
Kun Zhang ◽  
Chaoyong Ma ◽  
Xiaoqing Li ◽  
Jianyu Zhang
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Vibration Acceleration ◽  
Rolling Bearings ◽  
Bearing Fault ◽  
Acceleration Signal ◽  
Trend Component ◽  
Empirical Wavelet Transform ◽  
The Fourier Transform

As essential but easily damaged parts of rotating machinery, rolling bearings have been deeply researched and widely used in mechanical processes. The real-time detection of bearing state and simple, rapid, and accurate diagnosis of bearing fault are indispensable to the industrial system. The bearing’s inner ring and outer ring vibration acceleration can be measured by high-precision sensors, and the running state of the bearing can be effectively extracted. The empirical wavelet transform (EWT) can adaptively decompose the vibration acceleration signal into a series of empirical modes. However, this method not only runs slowly, but also causes inexplicable empirical modes due to the unreasonable boundaries of the frequency domain division. In this paper, a new method is proposed to improve the empirical wavelet transform by dividing the boundaries from the spectrum, named the fast empirical wavelet transform (FEWT). The proposed method chooses different points in the Fourier transform of the spectrum (key function) to reconstruct the trend component of the spectrum. The minimum points in the trend component divide the spectrum into a series of bands. A more reasonable set of boundaries can be found by choosing appropriate trend components to obtain effective empirical modes. The simulation results show that the proposed method is effective and that the acquired empirical mode is more reasonable than the EWT method. Combining kurtosis with fault feature extraction of inner and outer rings of bearings, the method is successfully applied to the fault diagnosis of rolling bearings.

scholarly journals Negentropy Spectrum Decomposition and Its Application in Compound Fault Diagnosis of Rolling Bearing

Entropy ◽  
2019 ◽  
Vol 21 (5) ◽  
pp. 490 ◽  
Author(s):  
Yonggang Xu ◽  
Junran Chen ◽  
Chaoyong Ma ◽  
Kun Zhang ◽  
Jinxin Cao
Keyword(s):  
Wavelet Transform ◽  
Fault Diagnosis ◽  
Rolling Bearing ◽  
Practical Significance ◽  
Measured Signal ◽  
Rolling Bearings ◽  
Fault Features ◽  
Empirical Wavelet Transform ◽  
Simulation Signal ◽  
Spectrum Decomposition

The rolling bearings often suffer from compound fault in practice. Compared with single fault, compound fault contains multiple fault features that are coupled together and make it difficult to detect and extract all fault features by traditional methods such as Hilbert envelope demodulation, wavelet transform and empirical node decomposition (EMD). In order to realize the compound fault diagnosis of rolling bearings and improve the diagnostic accuracy, we developed negentropy spectrum decomposition (NSD), which is based on fast empirical wavelet transform (FEWT) and spectral negentropy, with cyclic extraction as the extraction method. The infogram is constructed by FEWT combined with spectral negentropy to select the best band center and bandwidth for band-pass filtering. The filtered signal is used as a new measured signal, and the fast empirical wavelet transform combined with spectral negentropy is used to filter the new measured signal again. This operation is repeated to achieve cyclic extraction, until the signal no longer contains obvious fault features. After obtaining the envelope of all extracted components, compound fault diagnosis of rolling bearings can be realized. The analysis of the simulation signal and the experimental signal shows that the method can realize the compound fault diagnosis of rolling bearings, which verifies the feasibility and effectiveness of the method. The method proposed in this paper can detect and extract all the fault features of compound fault completely, and it is more reliable for the diagnosis of compound fault. Therefore, the method has practical significance in rolling bearing compound fault diagnosis.

scholarly journals Research on the Initial Fault Prediction Method of Rolling Bearings Based on DCAE-TCN Transfer Learning

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Huaitao Shi ◽  
Yajun Shang ◽  
Xiaochen Zhang ◽  
Yinghan Tang
Keyword(s):  
Transfer Learning ◽  
Prediction Method ◽  
Vibration Signal ◽  
Rolling Bearing ◽  
Fault Prediction ◽  
Target Domain ◽  
Rolling Bearings ◽  
Noise Interference ◽  
Fault Features ◽  
Early Failures

In actual working conditions, the initial faults of rolling bearings are difficult to effectively predict due to the lack of evolution knowledge, weak fault information, and strong noise interference. In this paper, a rolling bearing initial fault prediction model that is based on transfer learning and the DCAE-TCN is presented. Firstly, a deep autoencoder (DAE as the first two hidden layers and CAE as the last hidden layer) is used to extract fault features from the rolling bearing vibration signal data. Then, the balanced distributed adaptation (BDA) is used to minimise the distribution difference and class spacing between extracted fault features, and a common feature set is constructed. The temporal features of the original vibration signal in the target domain are extracted using the advantages of the TCN. The experiments are conducted on the publicly available XJTU-SY dataset. The experimental results show that the proposed method can effectively learn the transferable features and compensate the differences between the source and target domains and has a promising application with higher accuracy and robustness for the prediction of early failures of rolling bearings.

Efficiency of application of the phase-chronometric method and neurodiagnostics for monitoring the degradation of rolling bearings during operation

2020 ◽  
pp. 43-50
Author(s):  
A.S. Komshin ◽  
K.G. Potapov ◽  
V.I. Pronyakin ◽  
A.B. Syritskii
Keyword(s):  
Life Cycle ◽  
Wavelet Analysis ◽  
Metrological Support ◽  
Rolling Bearing ◽  
Technical Condition ◽  
Measurement Information ◽  
Rolling Bearings ◽  
Vibration Diagnostics ◽  
Bearing Life ◽  
Alternative Approach

The paper presents an alternative approach to metrological support and assessment of the technical condition of rolling bearings in operation. The analysis of existing approaches, including methods of vibration diagnostics, envelope analysis, wavelet analysis, etc. Considers the possibility of applying a phase-chronometric method for support on the basis of neurodiagnostics bearing life cycle on the basis of the unified format of measurement information. The possibility of diagnosing a rolling bearing when analyzing measurement information from the shaft and separator was evaluated.

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