Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Xu, Zifei; Li, Chun; Yang, Yang

doi:10.1016/j.asoc.2020.106515

Cited by 129 publications

(50 citation statements)

References 23 publications

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“…21. Figure 21 shows that the maximum value of MAED curve is 14.6402, and the parameter combination is (4,2). This shows that for the WTGF data set, the RGCMSJE algorithm can extract more abundant fault information efficiently when the parameter combination is c = 4 and m = 2.…”

Section: Parameter Selection and Feature Sets Determinationmentioning

confidence: 91%

“…It is worth noting that gearbox and bearing are one of the most critical components of wind turbine, and also the main place of failure. Therefore, it is crucial to find a reliable fault diagnosis methodology to timely and accurately monitor the operation status of gearbox and bearing in order to reduce the cost of operation and maintenance (O&M) [4].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

Dong¹,

zhang²,

Hu³

et al. 2021

Preprint

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The fault diagnosis of gearbox and bearing in wind turbine is crucial to improve service life and reduce maintenance cost. This paper proposes a novel fault diagnosis method based on refined generalized composite multi-scale state joint entropy (RGCMSJE), robust spectral learning framework for unsupervised feature selection (RSFS) and extreme learning machine (ELM) to identify the different health conditions of gearboxes, including feature extraction, feature reduction and pattern recognition. In this method, MAED is firstly adopted to assist RGCMSJE in parameter selection. Second, RGCMSJE is utilized to extract the multi-scale features of gearbox vibration signal and construct high-dimension feature set. Thirdly, RSFS method is used to reduce the dimension of high-dimensional RGCMSJE feature set. In the end, the obtained low-dimensional features are input to the ELM classifier to realize fault pattern recognition. Through two gearbox fault diagnosis experiments, the effectiveness of the fault diagnosis method is verified. The analysis results show that this method can effectively and accurately identify different fault types of wind turbine gearbox.

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Section: Parameter Selection and Feature Sets Determinationmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

Dong¹,

zhang²,

Hu³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…In terms of artificial intelligence, machine learning is the solution of choice to effectively address major issues faced by data-driven failure detection and diagnosis approaches [14]. In this machine-learning context, convolutional neural networks (CNNs) are well-adapted for feature extraction [2], while recurrent neural networks (RNNs), with their new long short-term memory (LSTM) variant, are better suited for learning and classifying time series [15,16].…”

Section: Introductionmentioning

confidence: 99%

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Habbouche

Benkedjouh

Benbouzid

2021

Entropy

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Failure detection and diagnosis are of crucial importance for the reliable and safe operation of industrial equipment and systems, while gearbox failures are one of the main factors leading to long-term downtime. Condition-based maintenance addresses this issue using several expert systems for early failure diagnosis to avoid unplanned shutdowns. In this context, this paper provides a comparative study of two machine-learning-based approaches for gearbox failure diagnosis. The first uses linear predictive coefficients for signal processing and long short-term memory for learning, while the second is based on mel-frequency cepstral coefficients for signal processing, a convolutional neural network for feature extraction, and long short-term memory for classification. This comparative study proposes an improved predictive method using the early fusion technique of multisource sensing data. Using an experimental dataset, the proposals were tested, and their effectiveness was evaluated considering predictions based on statistical metrics.

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“…The equipment vibration signals under negative conditions and the signals under normal operating conditions often have different characteristics. Therefore, vibration signals analysis for power equipment can distinguish different operating conditions of equipment, thereby helping people to diagnose faults and find the cause [1][2][3][4][5][6][7][8][9][10][11][12][13][14]. Different types of sound signals are closely related to the characteristics of different space or objects.…”

Section: Introductionmentioning

confidence: 99%

“…6, it can be found that if the datasets are arranged according to the classification accuracy of IAoptimal CNN from largest to smallest, the order is:arrhythmia signals dataset, English spoken digit signals dataset, bearing failure signals dataset, gearbox signals dataset, P300 EEG signals dataset. The accuracy rankings of other A-optimal-based classification algorithms are roughly the same.…”

mentioning

confidence: 99%

Signals classification based on IA-optimal CNN

Zhang

et al. 2021

Neural Comput & Applic

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The versatility of the existing A-optimal-based CNN for solving multiple types of signals classification problems has not been verified by different signals datasets. Moreover, the existing A-optimal-based CNN uses a simplified approximate function as the optimization objective function instead of precise analytical function, which affects the signals classification accuracy to a certain extent. In this paper, a classification method called IA-optimal CNN is proposed. To improve the stability of the classifier, the trace of the covariance matrix of the weights of the fully connected layer is used as the optimization objective function, and the parameter optimization model is established without any simplification of the optimization objective function. In addition, to avoid the difficulty of not being able to obtain the analytical expression formula of the partial derivative of the inverse matrix with regard to the networks parameters, a novel dual function is introduced to transform the optimization problem into an equivalent binary function optimization problem. Furthermore, based on the above analytical solution results, the parameters are updated using the alternate iterative optimization method and the accurate weight update formula is deduced in detail. Five signals datasets are used to test the universality of the IAoptimal CNN in signals classification fields. The performance of IA-optimal CNN is showed, and the experimental results are compared with the existing A-optimal-based classification algorithm. Lastly, the following conclusion is proved theoretically: For the A-optimal-based CNN, the trace of the covariance matrix will continue to decrease and approach a convergence value in the iterative process, but it is impossible for the networks to strictly reach the A-optimal state.

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Fault diagnosis of rolling bearing of wind turbines based on the Variational Mode Decomposition and Deep Convolutional Neural Networks

Cited by 129 publications

References 23 publications

Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

Intelligent Fault Diagnosis of Wind Turbine Gearbox based on Refined Generalized Multi-scale State Joint Entropy and RSFS Feature Selection

Gearbox Failure Diagnosis Using a Multisensor Data-Fusion Machine-Learning-Based Approach

Signals classification based on IA-optimal CNN

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