Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions
Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep meta-learning and variational autoencoder (DML-VAE)...
Published in: | Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science |
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crsagepubl:10.1177/09544062221101834 2024-05-12T08:02:56+00:00 Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions Che, Changchang Wang, Huawei Lin, Ruiguan Ni, Xiaomei 2022 http://dx.doi.org/10.1177/09544062221101834 http://journals.sagepub.com/doi/pdf/10.1177/09544062221101834 http://journals.sagepub.com/doi/full-xml/10.1177/09544062221101834 en eng SAGE Publications http://journals.sagepub.com/page/policies/text-and-data-mining-license Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science volume 236, issue 17, page 9900-9913 ISSN 0954-4062 2041-2983 Mechanical Engineering journal-article 2022 crsagepubl https://doi.org/10.1177/09544062221101834 2024-04-18T08:32:22Z Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep meta-learning and variational autoencoder (DML-VAE) is applied for coupling fault diagnosis of rolling bearing under variable working conditions. The collected vibration signals of rolling bearing are divided into long time series samples, including normal samples, single fault samples, and coupling fault samples. Then, variational autoencoder (VAE) is utilized for data augmentation of time series samples, and the generated samples are brought into one-dimensional deep convolutional neural network (1-DCNN) for further classification of multiple coupling faults. Subsequently, the trained 1-DCNN is regarded as embedding model. Training samples and other working condition samples are defined as support set and query set. Based on metric-based meta-learning method, sample pairs composed of support set and query set are constructed and brought into the embedding model to get the category with shortest metric distance as the classification result. In addition, the embedding model can be optimized by minimizing the contrastive loss among these sample pairs. The case study shows that the DML-VAE can achieve accurate classification results under the coupling of two faults and three faults, and maintain high diagnostic accuracy under variable working conditions. Compared with other models, the proposed model can also get the most accurate fault diagnosis results for all categories under unbalanced samples. Article in Journal/Newspaper DML SAGE Publications Vae ENVELOPE(27.945,27.945,70.829,70.829) Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 095440622211018 |
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SAGE Publications |
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English |
topic |
Mechanical Engineering |
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Mechanical Engineering Che, Changchang Wang, Huawei Lin, Ruiguan Ni, Xiaomei Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
topic_facet |
Mechanical Engineering |
description |
Considering the characteristics of rolling bearing such as variable working conditions, unbalanced fault sample size, and multiple coupling fault types, it is a great challenge to achieve general accurate fault diagnosis model. In this paper, deep meta-learning and variational autoencoder (DML-VAE) is applied for coupling fault diagnosis of rolling bearing under variable working conditions. The collected vibration signals of rolling bearing are divided into long time series samples, including normal samples, single fault samples, and coupling fault samples. Then, variational autoencoder (VAE) is utilized for data augmentation of time series samples, and the generated samples are brought into one-dimensional deep convolutional neural network (1-DCNN) for further classification of multiple coupling faults. Subsequently, the trained 1-DCNN is regarded as embedding model. Training samples and other working condition samples are defined as support set and query set. Based on metric-based meta-learning method, sample pairs composed of support set and query set are constructed and brought into the embedding model to get the category with shortest metric distance as the classification result. In addition, the embedding model can be optimized by minimizing the contrastive loss among these sample pairs. The case study shows that the DML-VAE can achieve accurate classification results under the coupling of two faults and three faults, and maintain high diagnostic accuracy under variable working conditions. Compared with other models, the proposed model can also get the most accurate fault diagnosis results for all categories under unbalanced samples. |
format |
Article in Journal/Newspaper |
author |
Che, Changchang Wang, Huawei Lin, Ruiguan Ni, Xiaomei |
author_facet |
Che, Changchang Wang, Huawei Lin, Ruiguan Ni, Xiaomei |
author_sort |
Che, Changchang |
title |
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
title_short |
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
title_full |
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
title_fullStr |
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
title_full_unstemmed |
Deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
title_sort |
deep meta-learning and variational autoencoder for coupling fault diagnosis of rolling bearing under variable working conditions |
publisher |
SAGE Publications |
publishDate |
2022 |
url |
http://dx.doi.org/10.1177/09544062221101834 http://journals.sagepub.com/doi/pdf/10.1177/09544062221101834 http://journals.sagepub.com/doi/full-xml/10.1177/09544062221101834 |
long_lat |
ENVELOPE(27.945,27.945,70.829,70.829) |
geographic |
Vae |
geographic_facet |
Vae |
genre |
DML |
genre_facet |
DML |
op_source |
Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science volume 236, issue 17, page 9900-9913 ISSN 0954-4062 2041-2983 |
op_rights |
http://journals.sagepub.com/page/policies/text-and-data-mining-license |
op_doi |
https://doi.org/10.1177/09544062221101834 |
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Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science |
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095440622211018 |
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