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| Application of improved EWT_MPE model in feature extraction of mine micro-seismic signals |
| CHENG Tiedong, YI Qiwen, WU Yiwen, DAI Congcong, CAI Gaipin, YANG Lirong, YIN Baoyong |
| Jiangxi University of Science and Technology, Ganzhou 341000, China |
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Abstract Here, aiming at the problem of mine micro-seismic and blast vibration signals being difficult to automatically identify, a signal feature extraction method based on improved EWT_MPE (experience wavelet transform _multi-scale permutation entropy) was proposed, and applied in mine micro-seismic signal feature extraction.Firstly, a new improved method was proposed for the over-segmentation problem of EWT in previous processing complex signal spectra, and the feasibility and correctness of the improved algorithm were verified using simulated signals.Secondly, the actual collected micro-seismic and blast signals were decomposed using the improved EWT, and by means of correlation analysis, the optimal components IMF1-IMF5 were selected from the decomposed IMF (intrinsic mode function) components.The selected IMF components were used to reconstruct a signal, and calculate its MPE value.Finally, the GK fuzzy clustering algorithm was used to classify and identify micro-seismic and blast vibration signals of rock mass.The results showed that micro-seismic signal’s MPE value is smaller than blast signal’s; when the embedding dimension m=5, the scale factor s=12, and the time delay =1, the difference of the two signals’ MPE values is the largest; the classification recognition correctness rate based on the improved EWT_MPE_GK fuzzy clustering algorithm reaches 93.5%, and the average fuzzy entropy (E) is closer to 0, and the classification coefficient (C) is closer to 1; compared with the traditional EWT_MPE_GK fuzzy clustering and EMD_MPE_GK fuzzy clustering, the improved EWT_MPE_GK fuzzy clustering algorithm’s effect is better and its recognition correctness rate rises by 3% and 5.5%, respectively.
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Received: 29 October 2019
Published: 15 May 2021
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