Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis
Abstract Acoustic emission information can describe the damage degree of rock samples in the process of failure. However, as a discrete non-stationary signal, acoustic emission information is difficult to be effectively processed by conventional methods, while wavelet analysis is an effective method...
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| Language: | English |
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Nature Portfolio
2024-12-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-83153-5 |
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| author | Shaojie Ma Yan Zhou Depeng Ma |
| author_facet | Shaojie Ma Yan Zhou Depeng Ma |
| author_sort | Shaojie Ma |
| collection | DOAJ |
| description | Abstract Acoustic emission information can describe the damage degree of rock samples in the process of failure. However, as a discrete non-stationary signal, acoustic emission information is difficult to be effectively processed by conventional methods, while wavelet analysis is an effective method for non-stationary signal processing. Therefore, acoustic emission signal is deeply studied by using wavelet analysis method. In this paper, on the basis of noise reduction of acoustic emission signal, Matlab calculation program is used to decompose the acoustic emission signal of coal sample under the confining pressure test of triaxial unloading, and the singularity detection is carried out. The results show that the time when the Lipschitz index value first appears α negative can be used as the prediction time. However, the corresponding time when the Lipschitz index value is -0.15~-0.31 should be excluded; The absolute range of the difference between the final forecast time and the actual rupture time of coal samples is [5.2s, 17.1s], and the coal samples with the absolute value of time difference within [5.2s, 10.0s] account for 63.6% of the total. |
| format | Article |
| id | doaj-art-18959acba72f4bee8bda7e5ecd188e52 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-18959acba72f4bee8bda7e5ecd188e522025-01-05T12:27:06ZengNature PortfolioScientific Reports2045-23222024-12-0114111110.1038/s41598-024-83153-5Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysisShaojie Ma0Yan Zhou1Depeng Ma2Chongqing Vocational Institute of EngineeringShandong Agricultural UniversityShandong Agricultural UniversityAbstract Acoustic emission information can describe the damage degree of rock samples in the process of failure. However, as a discrete non-stationary signal, acoustic emission information is difficult to be effectively processed by conventional methods, while wavelet analysis is an effective method for non-stationary signal processing. Therefore, acoustic emission signal is deeply studied by using wavelet analysis method. In this paper, on the basis of noise reduction of acoustic emission signal, Matlab calculation program is used to decompose the acoustic emission signal of coal sample under the confining pressure test of triaxial unloading, and the singularity detection is carried out. The results show that the time when the Lipschitz index value first appears α negative can be used as the prediction time. However, the corresponding time when the Lipschitz index value is -0.15~-0.31 should be excluded; The absolute range of the difference between the final forecast time and the actual rupture time of coal samples is [5.2s, 17.1s], and the coal samples with the absolute value of time difference within [5.2s, 10.0s] account for 63.6% of the total.https://doi.org/10.1038/s41598-024-83153-5Acoustic emissionUnloading confining pressureDiscrete waveletLipschitzCoal sample |
| spellingShingle | Shaojie Ma Yan Zhou Depeng Ma Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis Scientific Reports Acoustic emission Unloading confining pressure Discrete wavelet Lipschitz Coal sample |
| title | Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis |
| title_full | Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis |
| title_fullStr | Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis |
| title_full_unstemmed | Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis |
| title_short | Research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis |
| title_sort | research on acoustic emission precursor characteristics of coal sample unloading failure based on discrete wavelet analysis |
| topic | Acoustic emission Unloading confining pressure Discrete wavelet Lipschitz Coal sample |
| url | https://doi.org/10.1038/s41598-024-83153-5 |
| work_keys_str_mv | AT shaojiema researchonacousticemissionprecursorcharacteristicsofcoalsampleunloadingfailurebasedondiscretewaveletanalysis AT yanzhou researchonacousticemissionprecursorcharacteristicsofcoalsampleunloadingfailurebasedondiscretewaveletanalysis AT depengma researchonacousticemissionprecursorcharacteristicsofcoalsampleunloadingfailurebasedondiscretewaveletanalysis |