Fault diagnosis method of rolling bearing based on deep belief network

Shang, Zhiwu; Liao, Xiangxiang; Geng, Rui; Gao, Maosheng; Liu, Xia

doi:10.1007/s12206-018-1012-0

Cited by 22 publications

(12 citation statements)

References 10 publications

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“…Experiment 1 (the test bearing data set of Western Reserve University). In order to verify the superiority of the proposed method, the XCN model proposed in the previous section is compared with other deep learning algorithms for nearly three years: DWAE+ELM [23] which is based on deep wavelet autoencoder with extreme learning machine, CapsNet which is based on standard capsule neural network, MPE+ISVM+BT [22] which is based on multiscale permutation entropy and improved support vector machine based binary tree, AE+ES+CNN [26] which is based on an acoustic emission analysis-based bearing fault diagnosis invariant under fluctuations of rotational speeds using envelope spectrums and a convolutional neural network, and DBN [25] which is based on the standard deep belief network. Finally, the test accuracy of each algorithm is shown in Table 2 and Figure 15.…”

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

“…Secondly, the timefrequency graphs were imported into the XCN model after adjusting the pixel size. Finally, the same input was imported into other algorithms such as DWAE+ELM [23], CapsNet, MPE+ISVM+BT [22], AE+ES+CNN [26], and DBN [25] and tested the failure diagnosis performance of these methods. In this experiment, sixty percent of the adjusted timefrequency graphs were used for algorithm training and forty percent were used for testing.…”

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

“…In addition, there are other ways, such as improved ensemble local mean decomposition (IELMD) [16,17], kernel regression residual [18], and modified variable modal decomposition (MVMD) [19,20]. Methods based on big data and machine learning or 2 Complexity deep learning include support vector machine (SVM) [21,22], extreme learning machine (ELM) [23], kernel extreme learning machine (KELM) [24], deep belief network (DBN) [25], and convolutional neural network (CNN) [26,27]. In general, these methods can solve most classification problems well.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.

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Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

Section: Reliably Of the Xcn Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

Wang

Zheng

et al. 2019

Complexity

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“…Shao et al [14] proposed the use of a wavelet function for the activation function of an autoencoder to construct a deep wavelet autoencoder and enhance the feature extraction of the original signal, and then combined it with an extreme learning machine to realize an intelligent fault diagnosis of the rolling bearings. Shang et al [15] extracted the features of the time domain, frequency domain, and time-frequency domain from vibration signals as the input of the DBN model. They then recognized the fault severity of the rolling bearings.…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

Shang

Gao

et al. 2021

Chin. J. Mech. Eng.

Self Cite

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For a single-structure deep learning fault diagnosis model, its disadvantages are an insufficient feature extraction and weak fault classification capability. This paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information entropy. First, a normal autoencoder, denoising autoencoder, sparse autoencoder, and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure. A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep features. Finally, the advantage of the deep belief network probability model is used as the fault classifier to identify the faults. The effectiveness of the proposed method was verified by a gearbox test-bed. Experimental results show that, compared with traditional and existing intelligent fault diagnosis methods, the proposed method can obtain representative information and features from the raw data with higher classification accuracy.

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“…DBN was used to discriminate health states on the learning features extracted from SAE. Shang et al [ 25 ] published a DBN-based fault diagnosis model for rolling bearings, which could evade the complex structure of deep neural nets to some extent. The proposed model has the merits of easily training and good fault diagnosis capability.…”

Section: Introductionmentioning

confidence: 99%

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

Yang

Yin

Chang

et al. 2020

Sensors

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Aiming at the fault diagnosis issue of rotating machinery, a novel method based on the deep learning theory is presented in this paper. By combining one-dimensional convolutional neural networks (1D-CNN) with self-normalizing neural networks (SNN), the proposed method can achieve high fault identification accuracy in a simple and compact architecture configuration. By taking advantage of the self-normalizing properties of the activation function SeLU, the stability and convergence of the fault diagnosis model are maintained. By introducing α -dropout mechanism twice to regularize the training process, the overfitting problem is resolved and the generalization capability of the model is further improved. The experimental results on the benchmark dataset show that the proposed method possesses high fault identification accuracy and excellent cross-load fault diagnosis capability.

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Fault diagnosis method of rolling bearing based on deep belief network

Cited by 22 publications

References 10 publications

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network

An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

A Fault Diagnosis Method of Rotating Machinery Based on One-Dimensional, Self-Normalizing Convolutional Neural Networks

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