Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.
To overcome the false alarm problem that arises for mine wind-velocity sensors due to air-door and mine-car operation, a wind-velocity disturbance identification method based on the wavelet packet transform and gradient lifting decision tree is proposed. In this method, a multi-scale sliding window...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2023-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0284316 |
| _version_ | 1797839117316259840 |
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| author | Wentian Shang Lijun Deng Jian Liu Yukai Zhou |
| author_facet | Wentian Shang Lijun Deng Jian Liu Yukai Zhou |
| author_sort | Wentian Shang |
| collection | DOAJ |
| description | To overcome the false alarm problem that arises for mine wind-velocity sensors due to air-door and mine-car operation, a wind-velocity disturbance identification method based on the wavelet packet transform and gradient lifting decision tree is proposed. In this method, a multi-scale sliding window discretizes continuous wind-velocity monitoring data, the wavelet packet transform extracts the hidden features of discrete data, and a gradient lifting decision tree multi-disturbance classification model is established. Based on the overlap degree rule, the disturbance identification results are merged, modified, combined, and optimized. In accordance with a least absolute shrinkage and selection operator regression, the air-door operation information is further extracted. A similarity experiment is performed to verify the method performance. For the disturbance identification task, the recognition accuracy, accuracy, and recall of the proposed method are 94.58%, 95.70% and 92.99%, respectively, and for the task involving further extraction of disturbance information related to air-door operation, those values are 72.36%, 73.08%, and 71.02%, respectively. This algorithm gives a new recognition method for abnormal time series data. |
| first_indexed | 2024-04-09T15:53:02Z |
| format | Article |
| id | doaj.art-6a7909d281f349f182cda0aa3baa7d6d |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-09T15:53:02Z |
| publishDate | 2023-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6a7909d281f349f182cda0aa3baa7d6d2023-04-26T05:31:43ZengPublic Library of Science (PLoS)PLoS ONE1932-62032023-01-01184e028431610.1371/journal.pone.0284316Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT.Wentian ShangLijun DengJian LiuYukai ZhouTo overcome the false alarm problem that arises for mine wind-velocity sensors due to air-door and mine-car operation, a wind-velocity disturbance identification method based on the wavelet packet transform and gradient lifting decision tree is proposed. In this method, a multi-scale sliding window discretizes continuous wind-velocity monitoring data, the wavelet packet transform extracts the hidden features of discrete data, and a gradient lifting decision tree multi-disturbance classification model is established. Based on the overlap degree rule, the disturbance identification results are merged, modified, combined, and optimized. In accordance with a least absolute shrinkage and selection operator regression, the air-door operation information is further extracted. A similarity experiment is performed to verify the method performance. For the disturbance identification task, the recognition accuracy, accuracy, and recall of the proposed method are 94.58%, 95.70% and 92.99%, respectively, and for the task involving further extraction of disturbance information related to air-door operation, those values are 72.36%, 73.08%, and 71.02%, respectively. This algorithm gives a new recognition method for abnormal time series data.https://doi.org/10.1371/journal.pone.0284316 |
| spellingShingle | Wentian Shang Lijun Deng Jian Liu Yukai Zhou Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT. PLoS ONE |
| title | Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT. |
| title_full | Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT. |
| title_fullStr | Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT. |
| title_full_unstemmed | Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT. |
| title_short | Multi-disturbance identification from mine wind-velocity data based on MSSW and WPT-GBDT. |
| title_sort | multi disturbance identification from mine wind velocity data based on mssw and wpt gbdt |
| url | https://doi.org/10.1371/journal.pone.0284316 |
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