Mine-Microseismic-Signal Recognition Based on LMD–PNN Method
The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neura...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/12/11/5509 |
| _version_ | 1797494205340188672 |
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| author | Qiang Li Yingchun Li Qingyuan He |
| author_facet | Qiang Li Yingchun Li Qingyuan He |
| author_sort | Qiang Li |
| collection | DOAJ |
| description | The effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance. |
| first_indexed | 2024-03-10T01:31:00Z |
| format | Article |
| id | doaj.art-ca2debdf5c7d42f8b949a88fe9c6a644 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T01:31:00Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-ca2debdf5c7d42f8b949a88fe9c6a6442023-11-23T13:42:58ZengMDPI AGApplied Sciences2076-34172022-05-011211550910.3390/app12115509Mine-Microseismic-Signal Recognition Based on LMD–PNN MethodQiang Li0Yingchun Li1Qingyuan He2Shaanxi Yanchang Petroleum (Group) Co., Ltd., Xi’an 710065, ChinaState Key Laboratory of Coastal and Offshore Engineering, Dalian University of Technology, Dalian 116024, ChinaState Key Laboratory of Coal Resources and Safe Mining, China University of Mining and Technology, Xuzhou 221116, ChinaThe effective recognition of microseismic signal is related to the accuracy of mine-dynamic-disaster precursor-information processing, which is a difficult method of microseismic-data processing. A mine-microseismic-signal-identification method based on LMD energy entropy and the probabilistic neural network (PNN) is proposed. First, the Local-Mean-Decomposition (LMD) method is used to decompose the mine microseismic signal. Considering the problem of vector redundancy, combined with the correlation-coefficient method, the energy entropy of the effective product-function component (PF) is extracted as the feature vector of mine-microseismic-signal classification. Furthermore, the probabilistic neural network (PNN) is used for learning and training, and the blasting-vibration signal and the coal–rock-mass-rupture signal are effectively identified. The test results show that the recognition accuracy of the PNN is up to 90%, the calculation time and classification effect of the PNN are better, and the recognition accuracy is increased by 15% and 7.5%, respectively, compared with the traditional PBNN and GRNN. This method can accurately and effectively identify the microseismic signals of mines and has good generalization performance.https://www.mdpi.com/2076-3417/12/11/5509microseismic-signal recognitionlocal mean decomposition (LMD)energy entropyprobabilistic neural network (PNN)correlation analysis |
| spellingShingle | Qiang Li Yingchun Li Qingyuan He Mine-Microseismic-Signal Recognition Based on LMD–PNN Method Applied Sciences microseismic-signal recognition local mean decomposition (LMD) energy entropy probabilistic neural network (PNN) correlation analysis |
| title | Mine-Microseismic-Signal Recognition Based on LMD–PNN Method |
| title_full | Mine-Microseismic-Signal Recognition Based on LMD–PNN Method |
| title_fullStr | Mine-Microseismic-Signal Recognition Based on LMD–PNN Method |
| title_full_unstemmed | Mine-Microseismic-Signal Recognition Based on LMD–PNN Method |
| title_short | Mine-Microseismic-Signal Recognition Based on LMD–PNN Method |
| title_sort | mine microseismic signal recognition based on lmd pnn method |
| topic | microseismic-signal recognition local mean decomposition (LMD) energy entropy probabilistic neural network (PNN) correlation analysis |
| url | https://www.mdpi.com/2076-3417/12/11/5509 |
| work_keys_str_mv | AT qiangli minemicroseismicsignalrecognitionbasedonlmdpnnmethod AT yingchunli minemicroseismicsignalrecognitionbasedonlmdpnnmethod AT qingyuanhe minemicroseismicsignalrecognitionbasedonlmdpnnmethod |