Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD
Abstract Background The gas explosion risk caused by gas overrun is a serious threat to safety production in coalmines in China, quantitative determination of the production impact of the gas concentration is very important for reliable safety prewarning in coalmines. In view of the mine gas concent...
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Springer
2025-08-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-07608-8 |
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| author | Dingwen Dong Yuehao Wang Wenpei Zhao |
| author_facet | Dingwen Dong Yuehao Wang Wenpei Zhao |
| author_sort | Dingwen Dong |
| collection | DOAJ |
| description | Abstract Background The gas explosion risk caused by gas overrun is a serious threat to safety production in coalmines in China, quantitative determination of the production impact of the gas concentration is very important for reliable safety prewarning in coalmines. In view of the mine gas concentration varies with the production process. Methods The data sequence of real-time gas monitoring were decomposed into the Intrinsic Mode Function (IMF) with different time scales by using Empirical Mode Decomposition (EMD), then the noise signals in it is eliminated by using Hilbert spectrum analysis, furthermore, combined EMD with Singular Value Decomposition (SVD), the singular value sequence is extracted to reflect the intrinsic feature of the gas concentration. According to the singular value extracted by work shifts, the maximum feature value and the mean increment of the gas concentration affected by production process are determined; according to the singular value extracted by hour, the time of the gas concentration decreasing to a stable level after production stop is determined, and then the prewarning level is determined. Results The case study shows that the mean variation trend of the gas concentration obtained by feature extraction conforms to the theoretical characteristics of mine gas flow in ventilation roadway. The prewarning parameters obtained by feature extraction are quantitative, and their values can change dynamically with the production conditions, and the determination of prewarning threshold is more refined. Conclusion the mean reduction of the gas concentration between production shift and non-production shift, and the time of the gas concentration decreasing to a stable level after production stop are important basis for safety prewarning decision under the normal mine ventilation conditions. |
| format | Article |
| id | doaj-art-0cc00b436a404a48a3a521dcc11e23e4 |
| institution | Kabale University |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-0cc00b436a404a48a3a521dcc11e23e42025-08-20T03:46:13ZengSpringerDiscover Applied Sciences3004-92612025-08-017811610.1007/s42452-025-07608-8Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVDDingwen Dong0Yuehao Wang1Wenpei Zhao2School of Safety Science and Engineering, Xi’an University of Science and TechnologySchool of Safety Science and Engineering, Xi’an University of Science and TechnologySchool of Safety Science and Engineering, Xi’an University of Science and TechnologyAbstract Background The gas explosion risk caused by gas overrun is a serious threat to safety production in coalmines in China, quantitative determination of the production impact of the gas concentration is very important for reliable safety prewarning in coalmines. In view of the mine gas concentration varies with the production process. Methods The data sequence of real-time gas monitoring were decomposed into the Intrinsic Mode Function (IMF) with different time scales by using Empirical Mode Decomposition (EMD), then the noise signals in it is eliminated by using Hilbert spectrum analysis, furthermore, combined EMD with Singular Value Decomposition (SVD), the singular value sequence is extracted to reflect the intrinsic feature of the gas concentration. According to the singular value extracted by work shifts, the maximum feature value and the mean increment of the gas concentration affected by production process are determined; according to the singular value extracted by hour, the time of the gas concentration decreasing to a stable level after production stop is determined, and then the prewarning level is determined. Results The case study shows that the mean variation trend of the gas concentration obtained by feature extraction conforms to the theoretical characteristics of mine gas flow in ventilation roadway. The prewarning parameters obtained by feature extraction are quantitative, and their values can change dynamically with the production conditions, and the determination of prewarning threshold is more refined. Conclusion the mean reduction of the gas concentration between production shift and non-production shift, and the time of the gas concentration decreasing to a stable level after production stop are important basis for safety prewarning decision under the normal mine ventilation conditions.https://doi.org/10.1007/s42452-025-07608-8Mine gasMonitoring dataFeature extractionSafety prewarningHHT-SVD |
| spellingShingle | Dingwen Dong Yuehao Wang Wenpei Zhao Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD Discover Applied Sciences Mine gas Monitoring data Feature extraction Safety prewarning HHT-SVD |
| title | Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD |
| title_full | Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD |
| title_fullStr | Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD |
| title_full_unstemmed | Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD |
| title_short | Feature extraction for safety coalmine gas concentration prewarning based on HHT and SVD |
| title_sort | feature extraction for safety coalmine gas concentration prewarning based on hht and svd |
| topic | Mine gas Monitoring data Feature extraction Safety prewarning HHT-SVD |
| url | https://doi.org/10.1007/s42452-025-07608-8 |
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