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

Fei, Chengwei; Choy, Yat Sze; Bai, Guang-Chen; Tang, Wen

doi:10.1177/1475921716687167

Cited by 46 publications

(37 citation statements)

References 41 publications

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“…W � [|W f (a, b)| 2 /C ψ a 2 ] is the energy distribution matrix of the signal in the two-dimensional wavelet space. Similar to the singular spectrum entropy in the time domain, the singular value decomposition for W determines the time-frequency characteristic spectrum of wavelet space spectrum entropy H ws [26,27] which is expressed as…”

Section: Time-frequency Domain Information Exergy Of Ae Signalmentioning

confidence: 99%

Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

Jun

Fei

et al. 2018

Shock and Vibration

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For the fault diagnosis of intershaft bearings, the fusion information exergy distance method (FIEDM) is proposed by fusing four information exergies, such as singular spectrum exergy, power spectrum exergy, wavelet energy spectrum exergy, and wavelet space spectrum exergy, which are extracted from acoustic emission (AE) signals under multiple rotational speeds and multimeasuring points. The theory of FIEDM is investigated based on four information exergy distances under multirotational speeds. As for rolling bearings, four faults and one normal condition are simulated on a birotor test rig to collect the AE signals, in which the four faults are inner ring fault, outer ring fault, rolling element fault, and inner race-rolling element coupling fault. The faults of the intershaft bearings are analyzed and diagnosed by using the FIEDM. From the investigation, it is demonstrated that the faults of the intershaft bearings are accurately diagnosed and identified, and the FIEDM is effective for the analysis and diagnosis of intershaft bearing faults. Furthermore, the fault diagnosis precision of intershaft bearings becomes higher with increasing rotational speed.

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Section: Time-frequency Domain Information Exergy Of Ae Signalmentioning

confidence: 99%

Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

Jun

Fei

et al. 2018

Shock and Vibration

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“…A typical rolling bearing fault diagnosis mainly contains three main steps: data acquisition, feature extraction and fault diagnosis. In the data acquisition step, vibration signals, motor current signals, temperature signals and acoustic emission signals are frequently used for analysis [ 5 , 6 ]. In the feature extraction step, statistical time domain features such as root mean square, skewness as well as kurtosis [ 7 ] and frequency domain features exposed by Fourier transform [ 8 ] are the common choices to feed to the diagnosis models.…”

Section: Introductionmentioning

confidence: 99%

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

Xiong

Zhou

et al. 2020

Sensors

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Accidental failures of rotating machinery components such as rolling bearings may trigger the sudden breakdown of the whole manufacturing system, thus, fault diagnosis is vital in industry to avoid these massive economical costs and casualties. Since convolutional neural networks (CNN) are poor in extracting reliable features from original signal data, the time-frequency analysis method is usually called for to transform 1D signal into a 2D time-frequency coefficient matrix in which richer information could be exposed more easily. However, realistic fault diagnosis applications face a dilemma in that signal time-frequency analysis and fault classification cannot be implemented together, which means manual signal conversion work is also needed, which reduces the integrity and robustness of the fault diagnosis method. In this paper, a novel network named WPT-CNN is proposed for end-to-end intelligent fault diagnosis of rolling bearings. WPT-CNN creatively uses the standard deep neural network structure to realize the wavelet packet transform (WPT) time-frequency analysis function, which seamlessly integrates fault diagnosis domain knowledge into deep learning algorithms. The overall network architecture can be trained with gradient descent backpropagation algorithms, indicating that the time-frequency analysis module of WPT-CNN is also able to learn the dataset characteristics, adaptively representing signal information in the most suitable way. Two experimental rolling bearing fault datasets were used to validate the proposed method. Testing results showed that WPT-CNN obtained the testing accuracies of 99.73% and 99.89%, respectively, in two datasets, which exhibited a better and more reliable diagnosis performance than any other existing deep learning and machine learning methods.

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“…In this paper, Variational Mode Decomposition (VMD) is used to decompose the acceleration of rolling bearings because it has the advantages of high decomposition accuracy and strong noise robustness [11,12,13,14]. Then, time-domain features are adopted to construct feature sets from signal components [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

2020

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In the real environment of industrial equipment, the vibration signals of essential components show deviations due to the fault and noise. Notably, the noise in the signal will interfere with the diagnosis process of the signal and reduce the accuracy of fault diagnosis. Based on the above problem, adaptive filtering (AF) is used as an excellent method to attenuate noise without specifying the noise type. However, how to define the most appropriate length and type of morphological filter element is the most inherent problem which needs to be solved first. This paper proposed a cooperative diagnosis method of rolling bearings vibration signal based on improved adaptive filtering and joint distribution adaptation (JDA). First, the kurtosis under different element types and lengths is calculated as an index. The structural element corresponding to maximum kurtosis is selected as the most suitable morphological filter element because the different morphological filter elements reflect the effect of feature extraction. Then, JDA aims to improve both the marginal distribution and the conditional distribution to solve the chaotic distribution of time-domain features under variable working conditions. Finally, the improved least squares support vector machine (LSSVM) verified the effectiveness and improvement of the proposed method under bearing acceleration signal. At the same time, the comparative experiment proved that the proposed method not only directly corrects the most appropriate elements greatly optimizes the feature structure, but also enhances the accuracy of fault diagnosis. INDEX TERMS adaptive filters, morphological operation, time domain features, transfer learning, joint distribution adaptation, LSSVM, fault diagnosis.

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

Cited by 46 publications

References 41 publications

Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

Fault Diagnosis of Intershaft Bearings Using Fusion Information Exergy Distance Method

A Novel End-To-End Fault Diagnosis Approach for Rolling Bearings by Integrating Wavelet Packet Transform into Convolutional Neural Network Structures

A Fault Diagnosis Method Based on Improved Adaptive Filtering and Joint Distribution Adaptation

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