Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform

Liang, Ping; Deng, Chao; Wu, Jun; Yang, Zhixin; Zhu, Jinxuan; Zhang, Zihan

doi:10.1016/j.compind.2019.103132

Cited by 144 publications

(61 citation statements)

References 36 publications

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“…However, varying designs of CNN could concentrate on the different problems and aim at the promotion of the classification performance of the approaches. In the light of the raw vibration signals and the spectral images in intelligent machinery fault diagnosis, the commonly-used structure involves one dimensional (1D) and two dimensional (2D) CNN [71], [72], [80]. Therefore, each kind of rotating machinery will be analyzed and discussed in accordance with the research on 1D and 2D CNN respectively.…”

Section: Cnn-based Fault Diagnosis For Rotating Machinerymentioning

confidence: 99%

“…Aiming at the exploration of complex compound-faults for gearbox, Liang and his colleagues combined wavelet transform and multilabel classification, and designed a new CNN model to achieve satisfying diagnosis performance [80]. It should be noted that this approach could be applied for single fault and composite fault.…”

Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

confidence: 99%

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Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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Rotating machinery is of vital importance in the field of engineering, including aviation and navigation. Its failure will lead to severe loss to personnel safety and the stability of the equipment system. It is a long way to investigate the relevant fault diagnosis method, especially the intelligent fault diagnosis method on the basis of deep learning. In consideration of the limitations of traditional fault diagnosis approaches based on shallow layer network structure, the methods based on deep neural network (DNN) are worthy of thorough exploration. As a common DNN with special structure, deep convolutional neural network is of great concern in intelligent fault diagnosis due to its advantages in processing nonlinear problems. This review will play an emphasis on convolutional neural network (CNN). The basic structure and principle are introduced. The applications of CNN-based fault diagnosis method in rotating machinery are summarized and analyzed. Furthermore, the diagnosis performance and potential mechanism from different CNN methods are discussed. In the end, this review is highlighted on the challenges and the potential key points in research on novel intelligent fault diagnosis strategies. The corresponding analysis and discussion will provide some references and lay the foundation for the investigation in related fields. INDEX TERMS Deep learning, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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Section: Cnn-based Fault Diagnosis For Rotating Machinerymentioning

confidence: 99%

Section: B Cnn-based Fault Diagnosis For Gear and Gearboxmentioning

confidence: 99%

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

2020

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“…Different wavelet analysis methods have been applied to the intelligent fault diagnosis of rotating machinery. Inspired by the learning difficulty of complicated CNN model, Liang et al employed WT to acquire the preliminary feature extraction of raw vibration data, satisfying the requirements for 2D images of CNN input [76,77]. In comparison to raw data, the transformed images laid the foundation for the following automatic feature learning and enhancement of expected classification performance.…”

Section: A Wavelet Transformmentioning

confidence: 99%

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

2020

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Rotating machinery plays a critical role in many significant fields. However, the unpredictable machinery faults may lead to the severe damage and losses. Hence, it is of great value to explore the precise approaches for fault diagnosis. With the development of the intelligent fault diagnosis methods based on deep learning, convolutional neural network (CNN) has aroused the attention of researchers in machinery fault diagnosis. In the light of the reduction of difficulty in feature learning and the improvement of final diagnosis accuracy, data preprocessing is necessary and crucial in CNN-based fault diagnosis methods. This review focuses on CNN-based fault diagnosis approaches in rotating machinery. Firstly, data preprocessing methods are overviewed. Then, we emphatically analyze and discuss several main techniques applied in CNN-based intelligent diagnosis, principally including the fast Fourier transform, wavelet transform, data augmentation, S-transform, and cyclic spectral analysis. Finally, the potential challenges and research objects are prospected on data preprocessing in intelligent fault diagnosis of rotary machinery. INDEX TERMS Data preprocessing, convolutional neural network, intelligent fault diagnosis, rotating machinery.

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“…Nevertheless, the efficiency of the FK is limited by the complex background noise in the original signal [24]. To tackle the complex background, several signal processing methods such as Wavelet Transform (WT) [25][26], Empirical Mode Decomposition (EMD) [27][28][29][30][31][32], Ensemble Empirical Mode Decomposition (EEMD) [33][34], and Local Mean Decomposition (LMD) [35][36] have been applied. However, the treatment effect of these methods is often not satisfactory because of the inherent limitations.…”

Section: •3•mentioning

confidence: 99%

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

Shi

Wen

Pan

et al. 2020

Preprint

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Gear fault diagnosis has been a vital technology to enhance the reliability and reduce the maintenance cost of gear systems. Tooth spalling is one of the most destructive surface failure models of the gear faults. Revealing the dynamic characteristics of a gear system with spalling fault and extracting the fault feature are the premise and basis for effective fault diagnosis. Previous studies have mainly concentrated on the spalling damage on a single gear tooth, but the spalling distributed over double teeth which usually occurs in practical engineering problems is rarely reported. To remedy this deficiency, this paper constructs a new dynamical model of a gear system with double-teeth spalling fault and validates this model with various experimental tests. The dynamic characteristics of gear systems are obtained by considering the excitations induced by the number of spalling teeth, the relative position of two faulty teeth, and the rotational speed. The method based on the Variational Mode Decomposition (VMD) and the Fast Kurtogram (FK) is proposed to extract the features of the double-teeth spalling fault. First, the raw signal is decomposed into a set of Intrinsic Mode Functions (IMFs) by applying the VMD, and the IMFs with strong correlation are summed as a reconstructed signal. The reconstructed signal is then filtered by an optimal band-pass filter based on the FK. Combined with envelope spectrum analysis, the feature extraction ability of the proposed method is compared with that of the original FK method and the method based on the Empirical Mode Decomposition and the FK, respectively. The results indicate that the proposed dynamic model and fault feature extraction method can provide a theoretical basis for spalling defect diagnosis of gear systems.

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Compound Fault Diagnosis of Gearboxes via Multi-label Convolutional Neural Network and Wavelet Transform

Cited by 144 publications

References 36 publications

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Convolutional Neural Network in Intelligent Fault Diagnosis Toward Rotatory Machinery

Data Preprocessing Techniques in Convolutional Neural Network Based on Fault Diagnosis Towards Rotating Machinery

Dynamic characteristics of a gear system with double-teeth spalling fault and its fault feature analysis

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