A Novel Multimode Fault Classification Method Based on Deep Learning
Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification...
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| Format: | Article |
| Language: | English |
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Wiley
2017-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2017/3583610 |
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| _version_ | 1832561261225705472 |
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| author | Funa Zhou Yulin Gao Chenglin Wen |
| author_facet | Funa Zhou Yulin Gao Chenglin Wen |
| author_sort | Funa Zhou |
| collection | DOAJ |
| description | Due to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition. In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result. For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity. The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method. |
| format | Article |
| id | doaj-art-ff5490fb78c74b068b5c2578cc9e2da0 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2017-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-ff5490fb78c74b068b5c2578cc9e2da02025-02-03T01:25:32ZengWileyJournal of Control Science and Engineering1687-52491687-52572017-01-01201710.1155/2017/35836103583610A Novel Multimode Fault Classification Method Based on Deep LearningFuna Zhou0Yulin Gao1Chenglin Wen2School of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Computer and Information Engineering, Henan University, Kaifeng, ChinaSchool of Automation, Hangzhou Dianzi University, Hangzhou, ChinaDue to the problem of load varying or environment changing, machinery equipment often operates in multimode. The data feature involved in the observation often varies with mode changing. Mode partition is a fundamental step before fault classification. This paper proposes a multimode classification method based on deep learning by constructing a hierarchical DNN model with the first hierarchy specially devised for the purpose of mode partition. In the second hierarchy , different DNN classification models are constructed for each mode to get more accurate fault classification result. For the purpose of providing helpful information for predictive maintenance, an additional DNN is constructed in the third hierarchy to further classify a certain fault in a given mode into several classes with different fault severity. The application to multimode fault classification of rolling bearing fault shows the effectiveness of the proposed method.http://dx.doi.org/10.1155/2017/3583610 |
| spellingShingle | Funa Zhou Yulin Gao Chenglin Wen A Novel Multimode Fault Classification Method Based on Deep Learning Journal of Control Science and Engineering |
| title | A Novel Multimode Fault Classification Method Based on Deep Learning |
| title_full | A Novel Multimode Fault Classification Method Based on Deep Learning |
| title_fullStr | A Novel Multimode Fault Classification Method Based on Deep Learning |
| title_full_unstemmed | A Novel Multimode Fault Classification Method Based on Deep Learning |
| title_short | A Novel Multimode Fault Classification Method Based on Deep Learning |
| title_sort | novel multimode fault classification method based on deep learning |
| url | http://dx.doi.org/10.1155/2017/3583610 |
| work_keys_str_mv | AT funazhou anovelmultimodefaultclassificationmethodbasedondeeplearning AT yulingao anovelmultimodefaultclassificationmethodbasedondeeplearning AT chenglinwen anovelmultimodefaultclassificationmethodbasedondeeplearning AT funazhou novelmultimodefaultclassificationmethodbasedondeeplearning AT yulingao novelmultimodefaultclassificationmethodbasedondeeplearning AT chenglinwen novelmultimodefaultclassificationmethodbasedondeeplearning |