Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM
Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and...
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| Format: | Article |
| Language: | English |
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Tsinghua University Press
2019-03-01
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| Series: | Big Data Mining and Analytics |
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| Online Access: | https://www.sciopen.com/article/10.26599/BDMA.2018.9020027 |
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| author | Qi Rao Yan Yang Yongquan Jiang |
| author_facet | Qi Rao Yan Yang Yongquan Jiang |
| author_sort | Qi Rao |
| collection | DOAJ |
| description | Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms. |
| format | Article |
| id | doaj-art-7b5c29adde3e41299d58df58aabcf43f |
| institution | Kabale University |
| issn | 2096-0654 |
| language | English |
| publishDate | 2019-03-01 |
| publisher | Tsinghua University Press |
| record_format | Article |
| series | Big Data Mining and Analytics |
| spelling | doaj-art-7b5c29adde3e41299d58df58aabcf43f2025-02-02T06:50:33ZengTsinghua University PressBig Data Mining and Analytics2096-06542019-03-012111110.26599/BDMA.2018.9020027Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCMQi Rao0Yan Yang1Yongquan Jiang2<institution content-type="dept">School of Information Science and Technology</institution>, <institution>Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country>.<institution content-type="dept">School of Information Science and Technology</institution>, <institution>Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country>.<institution content-type="dept">State Key Laboratory of Traction Power</institution>, <institution>Southwest Jiaotong University</institution>, <city>Chengdu</city> <postal-code>611756</postal-code>, <country>China</country>.Monitoring the operating status of a High-Speed Train (HST) at any moment is necessary to ensure its security. Multi-channel vibration signals are collected by sensors installed on bogies and beneficial information are extracted to determine the running condition. Based on multi-view clustering and considering different views of complementary information, this study proposes a Multi-view Kernel Fuzzy C-Means (MvKFCM) model for condition recognition of the HST bogie. First, fast Fourier transform coefficients of HST vibration signals of all channels are extracted. Then, the fuzzy classification coefficient of every channel is calculated after clustering to select the appropriate channels. Finally, the selected channels are used to cluster by MvKFCM and the conditions of HST are determined. Experimental results show that the selection is effective to maintain rich feature information and remove redundancy. Furthermore, the condition recognition rate of MvKFCM is higher than that of single-view and four other multiple-view clustering algorithms.https://www.sciopen.com/article/10.26599/BDMA.2018.9020027high-speed train (hst)condition recognitionmulti-view clusteringfuzzy clustering |
| spellingShingle | Qi Rao Yan Yang Yongquan Jiang Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM Big Data Mining and Analytics high-speed train (hst) condition recognition multi-view clustering fuzzy clustering |
| title | Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM |
| title_full | Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM |
| title_fullStr | Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM |
| title_full_unstemmed | Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM |
| title_short | Condition Recognition of High-Speed Train Bogie Based on Multi-View Kernel FCM |
| title_sort | condition recognition of high speed train bogie based on multi view kernel fcm |
| topic | high-speed train (hst) condition recognition multi-view clustering fuzzy clustering |
| url | https://www.sciopen.com/article/10.26599/BDMA.2018.9020027 |
| work_keys_str_mv | AT qirao conditionrecognitionofhighspeedtrainbogiebasedonmultiviewkernelfcm AT yanyang conditionrecognitionofhighspeedtrainbogiebasedonmultiviewkernelfcm AT yongquanjiang conditionrecognitionofhighspeedtrainbogiebasedonmultiviewkernelfcm |