Intelligent Fault Diagnosis of Rolling Bearing Using FCM Clustering of EMD-PWVD Vibration Images

Fan, Hongwei; Shao, Sijie; Zhang, Xuhui; Wan, Xiang; Cao, Xiangang; Ma, Hongwei

doi:10.1109/access.2020.3012559

Cited by 37 publications

(24 citation statements)

References 22 publications

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“…Yang_2017 [161] Isham_2019 [162] Amarnath_2013 [163] Mao_2018 [164] Chen_2015 [165] Rafiq_2021 [166] Isham_2018 [167] Jegadeeshwaran_2014 [168] Cyclostationary and cyclo-non-stationary analysis [173] Sun_2020 [174] Jeon_2020 [175] Fan_2020 [176] Youcef_2020 [177] Yang_2019 [178] Xin_2018 [179] Hamadache_2018 [180] Song_2018 [181] Golbaghi_2017 [182] Li_2016c [137] Raj_2015 [183] Ocak_2001 [184] Oh_2018 [185] Tarek_2020 [186] Li_2018 [187] Hong_2017 [188] Cerrada_2015 [189] Fan_2015 [190] Yang_2018 [191] Qiang_2014 [192] Moghadam_2021 [193] He_2016 [194] Gierlak_2017 [195] Zhao_2019b [196] Unique Jablon_2021 [197] Gu_2021 [198] Mohamad_2020 [2] Yan_2019 [199] Barbini_2018 [200] Khan_2016 [201] Biswas_2013 [202] Bai_2021a [203] Mohamad_2020 [2] Hizarci_2019 [204] Medina_2019 [205] Chen_2002 [206] Chen_2002…”

Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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Monitoring vibrations in rotating machinery allows effective diagnostics, as abnormal functioning states are related to specific patterns that can be extracted from vibration signals. Extensively studied issues concern the different methodologies used for carrying out the main phases (signal measurements, pre-processing and processing, feature selection, and fault diagnosis) of a malfunction automatic diagnosis. In addition, vibration-based condition monitoring has been applied to a number of different mechanical systems or components. In this review, a systematic study of the works related to the topic was carried out. A preliminary phase involved the analysis of the publication distribution, to understand what was the interest in studying the application of the method to the various rotating machineries, to identify the interest in the investigation of the main phases of the diagnostic process, and to identify the techniques mainly used for each single phase of the process. Subsequently, the different techniques of signal processing, feature selection, and diagnosis are analyzed in detail, highlighting their effectiveness as a function of the investigated aspects and of the results obtained in the various studies. The most significant research trends, as well as the main innovations related to the various phases of vibration-based condition monitoring, emerge from the review, and the conclusions provide hints for future ideas.

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Section: Stft Wavelet Wigner-ville (Wv) Distribution Hilbert-huang Transform Cohen Class Functionsmentioning

confidence: 99%

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

et al. 2022

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“…The algorithm calculates (I) correlation between features in ( 9), (II) correlation between features and classification in ( 8), (III) Merit value in (10). Then, (IV) fitness value W fi is calculated for Merit_new value in (11).…”

Section: Cffsmentioning

confidence: 99%

“…For example, FFT and GT are sensitive to noise [7]. The cross-term interference of nonstationary signals limits the performance of WVD [11]. The predefined wavelet-based parameters cause the WT may not be able to adaptively process nonstationary signals [12].…”

Section: Introductionmentioning

confidence: 99%

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

et al. 2022

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This paper proposes a fault-detection system for faulty induction motors (bearing faults, interturn shorts, and broken rotor bars) based on multiresolution analysis (MRA), correlation and fitness values-based feature selection (CFFS), and artificial neural network (ANN). First, this study compares two feature-extraction methods: the MRA and the Hilbert Huang transform (HHT) for induction-motor-current signature analysis. Furthermore, feature-selection methods are compared to reduce the number of features and maintain the best accuracy of the detection system to lower operating costs. Finally, the proposed detection system is tested with additive white Gaussian noise, and the signal-processing method and feature-selection method with good performance are selected to establish the best detection system. According to the results, features extracted from MRA can achieve better performance than HHT using CFFS and ANN. In the proposed detection system, CFFS significantly reduces the operation cost (95% of the number of features) and maintains 93% accuracy using ANN.

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“…e authors in [7][8][9][10] adopted the comprehensive analysis method of EMD and pseudo-Wigner-Ville distribution (PWVD), which not only has the time-frequency focusing but also avoids the problem of cross-interference terms in multicomponent signal processing. e frequency band energy was divided according to time-frequency image, which was used as the characteristic index for the unsupervised clustering calculation and achieved a good classification effect [11].…”

Section: Introductionmentioning

confidence: 99%

Vibration Images‐Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise

Fan

Xue

Zhang

et al. 2021

Shock and Vibration

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Deep learning-based fault diagnosis of rolling bearings is a hot research topic, and a rapid and accurate diagnosis is important. In this paper, aiming at the vibration image samples of rolling bearing affected by strong noise, the convolutional neural network- (CNN-) and transfer learning- (TL-) based fault diagnosis method is proposed. Firstly, four kinds of vibration image generation method with different characteristics are put forward, and the corresponding pure vibration image samples are obtained according to the original data. Secondly, using CNN as the adaptive feature extraction and recognition model, the influences of main sensitive parameters of CNN on the network recognition effect are studied, such as learning rate, optimizer, and L1 regularization, and the best model is determined. In order to obtain the pretraining parameters, the training and fault classification test for different image samples are carried out, respectively. Thirdly, the Gaussian white noise with different levels is added to the original signals, and four kinds of noised vibration image samples are obtained. The previous pretrained model parameters are shared for the TL. Each kind of sample research compares the impact of thirteen data sharing schemes on the TL accuracy and efficiency, and finally, the test accuracy and time index are introduced to evaluate the model. The results show that, among the four kinds of image generation method, the classification performance of data obtained by empirical mode decomposition-pseudo-Wigner–Ville distribution (EP) is the best; when the signal to noise ratio (SNR) is 10 dB, the model test accuracy obtained by TL is 96.67% and the training time is 170.46 s.

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Intelligent Fault Diagnosis of Rolling Bearing Using FCM Clustering of EMD-PWVD Vibration Images

Cited by 37 publications

References 22 publications

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

A Review on Vibration-Based Condition Monitoring of Rotating Machinery

Application of ANN in Induction-Motor Fault-Detection System Established with MRA and CFFS

Vibration Images‐Driven Fault Diagnosis Based on CNN and Transfer Learning of Rolling Bearing under Strong Noise

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