A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network
Condition monitoring and accurate fault diagnosis are always concerned for stable operating of mechanical equipment. The fault diagnosis based on supervised deep learning has been proved to be effective by their powerful capacities in feature extracting, but usually requiring large number of labeled...
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| Vydáno v: | Nonlinear dynamics Ročník 113; číslo 1; s. 459 - 478 |
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01.01.2025
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| Abstract | Condition monitoring and accurate fault diagnosis are always concerned for stable operating of mechanical equipment. The fault diagnosis based on supervised deep learning has been proved to be effective by their powerful capacities in feature extracting, but usually requiring large number of labeled data. Faced with the actual situation that labeled samples are often in short, data are imbalanced in category etc., accurate fault diagnosis based on deep learning is still challenging, so does to explore and explain the evolution of complex faults. A mechanical fault diagnosis model with Semi-Supervised Variational Autoencoder based on Long Short-Term Memory network (LSTM-SSVAE) is proposed in this paper. Through semi-supervised learning, LSTM-SSVAE uses unlabeled data to enhance the extraction of discriminant features of data, which make the model less dependent on only labeled data while giving improved fault diagnosis accuracy. The LSTM networks are applied as the encoder and decoder innovatively, and regularization constraints are added in loss function, to improve the clustering effect of the intermediate hidden variables, so that to achieve effective feature extraction and state detection. Based on open datasets, experimental results show that with the same number of labeled samples, the fault diagnosis accuracy obtained by using LSTM-SSVAE is higher than other typical semi-supervised learning models. Based on actual vibration data of working equipment in coal mining, the feasibility of clustering analysis of intermediate hidden variables also proves that the LSTM-SSVAE model is recommendable for fault evolution analysis and is potential for operating conditions prediction of mechanical equipment. |
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| AbstractList | Condition monitoring and accurate fault diagnosis are always concerned for stable operating of mechanical equipment. The fault diagnosis based on supervised deep learning has been proved to be effective by their powerful capacities in feature extracting, but usually requiring large number of labeled data. Faced with the actual situation that labeled samples are often in short, data are imbalanced in category etc., accurate fault diagnosis based on deep learning is still challenging, so does to explore and explain the evolution of complex faults. A mechanical fault diagnosis model with Semi-Supervised Variational Autoencoder based on Long Short-Term Memory network (LSTM-SSVAE) is proposed in this paper. Through semi-supervised learning, LSTM-SSVAE uses unlabeled data to enhance the extraction of discriminant features of data, which make the model less dependent on only labeled data while giving improved fault diagnosis accuracy. The LSTM networks are applied as the encoder and decoder innovatively, and regularization constraints are added in loss function, to improve the clustering effect of the intermediate hidden variables, so that to achieve effective feature extraction and state detection. Based on open datasets, experimental results show that with the same number of labeled samples, the fault diagnosis accuracy obtained by using LSTM-SSVAE is higher than other typical semi-supervised learning models. Based on actual vibration data of working equipment in coal mining, the feasibility of clustering analysis of intermediate hidden variables also proves that the LSTM-SSVAE model is recommendable for fault evolution analysis and is potential for operating conditions prediction of mechanical equipment. |
| Author | Zhang, Yupeng Fu, Shichen Wang, Zhisheng Li, Tao Chen, Jian Qu, Yuanyuan |
| Author_xml | – sequence: 1 givenname: Yuanyuan surname: Qu fullname: Qu, Yuanyuan organization: School of Mechanical and Electronic Engineering, China University of Mining and Technology-Beijing – sequence: 2 givenname: Tao orcidid: 0009-0003-1081-6679 surname: Li fullname: Li, Tao email: litao04206819@163.com organization: School of Mechanical and Electronic Engineering, China University of Mining and Technology-Beijing – sequence: 3 givenname: Shichen surname: Fu fullname: Fu, Shichen organization: School of Mechanical and Electronic Engineering, China University of Mining and Technology-Beijing – sequence: 4 givenname: Zhisheng surname: Wang fullname: Wang, Zhisheng organization: School of Mechanical and Electronic Engineering, China University of Mining and Technology-Beijing – sequence: 5 givenname: Jian surname: Chen fullname: Chen, Jian organization: School of Mechanical and Electronic Engineering, China University of Mining and Technology-Beijing – sequence: 6 givenname: Yupeng surname: Zhang fullname: Zhang, Yupeng organization: School of Mechanical and Electronic Engineering, China University of Mining and Technology-Beijing |
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| Keywords | Fault diagnosis Variational autoencoder Long short-term memory network Clustering analysis Semi-supervised learning |
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Rev.2015152962345478510.1016/j.cosrev.2015.03.001 – reference: DingXHeQTime-frequency manifold sparse reconstruction: a novel method for bearing fault feature extractionMech. Syst. Signal Process.2016803924132016MSSP...80..392D10.1016/j.ymssp.2016.04.024 – reference: WangHXuJYanRGaoRXA new intelligent bearing fault diagnosis method using sdp representation and se-cnnIEEE Trans. Instrum. Meas.2019695237723892020ITIM...69.2377W10.1109/TIM.2019.2956332 – reference: Liang, M., Zhou, K.: Joint loss learning-enabled semi-supervised autoencoder for bearing fault diagnosis under limited labeled vibration signals. 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| SubjectTerms | Accuracy Applications of Nonlinear Dynamics and Chaos Theory Classical Mechanics Cluster analysis Clustering Coal mining Condition monitoring Control Deep learning Dynamical Systems Fault diagnosis Feature extraction Machine learning Original Paper Physics Physics and Astronomy Regularization Semi-supervised learning Statistical Physics and Dynamical Systems Vibration Vibration analysis |
| Title | A mechanical fault diagnosis model with semi-supervised variational autoencoder based on long short-term memory network |
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