{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T11:02:25Z","timestamp":1765969345758,"version":"3.48.0"},"reference-count":50,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2025,12,13]],"date-time":"2025-12-13T00:00:00Z","timestamp":1765584000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Research Development Program of Shanxi Province","award":["202304290000002"],"award-info":[{"award-number":["202304290000002"]}]},{"name":"Basic Research Program of Datong City","award":["2023055"],"award-info":[{"award-number":["2023055"]}]},{"name":"Science and Technology Innovation Program of Shanxi Datong University","award":["2022CXY18"],"award-info":[{"award-number":["2022CXY18"]}]},{"name":"Graduate Research Innovation Program of Shanxi Datong University","award":["25SJCX22"],"award-info":[{"award-number":["25SJCX22"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The timely identification of rock fractures is crucial in deep subterranean engineering. However, it remains necessary to identify reliable warning indicators and establish effective warning levels. This study introduces the Acoustic Emission Transformer for FRActure Prediction (AET-FRAP) multi-input time series forecasting framework, which employs acoustic emission feature parameters. First, Empirical Mode Decomposition (EMD) combined with Fast Fourier Transform (FFT) is employed to identify and filter periodicities among diverse indicators and select input channels with enhanced informative value, with the aim of predicting cumulative energy. Thereafter, the one-dimensional sequence is transformed into a two-dimensional tensor based on its predominant period via spectral analysis. This is coupled with InceptionNeXt\u2014an efficient multiscale convolution and amplitude spectrum-weighted aggregate\u2014to enhance pattern identification across various timeframes. A secondary criterion is created based on the prediction sequence, employing cosine similarity and kurtosis to collaboratively identify abrupt changes. This transforms single-point threshold detection into robust sequence behavior pattern identification, indicating clearly quantifiable trigger criteria. AET-FRAP exhibits improvements in accuracy relative to long short-term memory (LSTM) on uniaxial compression test data, with R2 approaching 1 and reductions in Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). It accurately delineates energy accumulation spikes in the pre-fracture period and provides advanced warning. The collaborative thresholds effectively reduce noise-induced false alarms, demonstrating significant stability and engineering significance.<\/jats:p>","DOI":"10.3390\/s25247580","type":"journal-article","created":{"date-parts":[[2025,12,16]],"date-time":"2025-12-16T08:46:52Z","timestamp":1765874812000},"page":"7580","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["AET-FRAP\u2014A Periodic Reshape Transformer Framework for Rock Fracture Early Warning Using Acoustic Emission Multi-Parameter Time Series"],"prefix":"10.3390","volume":"25","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2446-1050","authenticated-orcid":false,"given":"Donghui","family":"Yang","sequence":"first","affiliation":[{"name":"School of Coal Engineering, Shanxi Datong University, Datong 037003, China"},{"name":"The Cultivation Base of Shanxi Key Laboratory of Coal Mine Water Jet Technology and Equipment, Shanxi Datong University, Datong 037003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zechao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Coal Engineering, Shanxi Datong University, Datong 037003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zichu","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Coal Engineering, Shanxi Datong University, Datong 037003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqi","family":"Li","sequence":"additional","affiliation":[{"name":"School of Coal Engineering, Shanxi Datong University, Datong 037003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Linhuan","family":"Jin","sequence":"additional","affiliation":[{"name":"School of Coal Engineering, Shanxi Datong University, Datong 037003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"ref_1","unstructured":"Esterhuizen, G.S., Dolinar, D.R., Ellenberger, J.L., and Prosser, L.J. (2011). Pillar and Roof Span Design Guidelines for Underground Stone Mines, National Institute for Occupational Safety and Health."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"7591","DOI":"10.1007\/s00603-025-04497-6","article-title":"Disaster-Causing Mechanism of Spalling Rock Burst Based on Folding Catastrophe Model in Coal Mine","volume":"58","author":"Cao","year":"2025","journal-title":"Rock. Mech. Rock. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"275","DOI":"10.1016\/j.jrmge.2020.06.011","article-title":"Integration of three-dimensional continuum model and two-dimensional bonded block model for studying the damage process in a granite pillar at the Creighton Mine, Sudbury, Canada","volume":"13","author":"Sinha","year":"2021","journal-title":"J. Rock. Mech. Geotech. Eng."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1016\/j.ijmst.2018.09.002","article-title":"Rockburst mechanism research and its control","volume":"28","author":"He","year":"2018","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_5","first-page":"649","article-title":"Study on the development process of rockbursts","volume":"38","author":"Feng","year":"2019","journal-title":"Chin. J. Rock. Mech. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"29440","DOI":"10.1038\/s41598-025-15115-4","article-title":"Determination method of rational position for working face entries in coordinated mining of section coal pillars and lower sub-layer","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1016\/j.undsp.2021.11.008","article-title":"Rockburst in underground excavations: A review of mechanism, classification, and prediction methods","volume":"7","author":"Askaripour","year":"2022","journal-title":"Undergr. Space"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103069","DOI":"10.1016\/j.tust.2019.103069","article-title":"Rock burst prediction probability model based on case analysis","volume":"93","author":"Wu","year":"2019","journal-title":"Tunn. Undergr. Space Technol."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"565","DOI":"10.1016\/j.ijmst.2019.06.009","article-title":"Machine learning methods for rockburst predictionstate-of-the-art review","volume":"29","author":"Pu","year":"2019","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"70","DOI":"10.1016\/j.undsp.2023.05.009","article-title":"Rockburst Prediction and Prevention in Underground Space Excavation","volume":"14","author":"Zhou","year":"2023","journal-title":"Undergr. Space"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1737","DOI":"10.1016\/j.jrmge.2021.12.015","article-title":"Theoretical verification of the rationality of strain energy storage index as rockburst criterion based on linear energy storage law","volume":"14","author":"Gong","year":"2022","journal-title":"J. Rock. Mech. Geotech."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1016\/j.jrmge.2020.06.008","article-title":"Rock brittleness indices and their applications to different fields of rock engineering: A review","volume":"13","author":"Meng","year":"2021","journal-title":"J. Rock. Mech. Geotech."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ijrmms.2018.02.018","article-title":"Dynamic stress concentration and energy evolution of deep-buried tunnels under blasting loads","volume":"104","author":"Li","year":"2018","journal-title":"Int. J. Rock. Mech. Min."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1016\/j.ijmst.2020.06.006","article-title":"Research on the energy evolution characteristics and the failure intensity of rocks","volume":"30","author":"Gao","year":"2020","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"5207","DOI":"10.1007\/s00603-024-03811-y","article-title":"Rockburst intensity grade prediction based on data preprocessing techniques and multi-model ensemble learning algorithms","volume":"57","author":"Jia","year":"2024","journal-title":"Rock. Mech. Rock. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"104420","DOI":"10.1016\/j.ijrmms.2020.104420","article-title":"Experimental study on the effect of strain rate on rock acoustic emission characteristics","volume":"133","author":"Liu","year":"2020","journal-title":"Int. J. Rock. Mech. Min."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"7791","DOI":"10.1007\/s10064-021-02446-5","article-title":"An early-warning method for rock failure based on Hurst exponent in acoustic emission\/microseismic activity monitoring","volume":"80","author":"Li","year":"2021","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"6245","DOI":"10.1029\/JD090iD04p06245","article-title":"Electromagnetic radiations from rocks","volume":"90","author":"Ogawa","year":"1985","journal-title":"J. Geophys. Res. Atmos."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"104320","DOI":"10.1016\/j.infrared.2022.104320","article-title":"Experimental investigation of key infrared radiation signals on rock during damaging process","volume":"126","author":"Chen","year":"2022","journal-title":"Infrared Phys. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"883","DOI":"10.1016\/0148-9062(93)90041-B","article-title":"The role of acoustic emission in the study of rock fracture","volume":"30","author":"Lockner","year":"1993","journal-title":"Int. J. Rock. Mech. Min. Sci. Geomech. Abstr."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Grosse, C., and Ohtsu, M. (2008). Acoustic Emission Testing\u2013Basics for Research\u2013Applications in Civil Engineering, Springer. [1st ed.].","DOI":"10.1007\/978-3-540-69972-9"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1785\/BSSA0340040185","article-title":"Frequency of earthquakes in California","volume":"34","author":"Gutenberg","year":"1944","journal-title":"Bull. Seism. Soc. Am."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/S0191-8141(03)00095-6","article-title":"Detailed analysis of acoustic emission activity during catastrophic fracture of faults in rock","volume":"26","author":"Lei","year":"2004","journal-title":"J. Struct. Geol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"2339","DOI":"10.1016\/j.conbuildmat.2010.05.004","article-title":"Crack classification in concrete based on acoustic emission","volume":"24","author":"Ohno","year":"2010","journal-title":"Constr. Build. Mater."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"153","DOI":"10.1016\/j.mechrescom.2011.03.007","article-title":"Classification of cracking mode in concrete by acoustic emission parameters","volume":"38","author":"Aggelis","year":"2011","journal-title":"Mech. Res. Commun."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"104323","DOI":"10.1016\/j.ijrmms.2020.104323","article-title":"A new method for determining the crack classification criterion in acoustic emission parameter analysis","volume":"130","author":"Zhang","year":"2020","journal-title":"Int. J. Rock. Mech. Min. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"104411","DOI":"10.1016\/j.ijrmms.2020.104411","article-title":"Experimental study on acoustic emission (AE) characteristics and crack classification during rock fracture in several basic lab tests","volume":"133","author":"Du","year":"2020","journal-title":"Int. J. Rock. Mech. Min. Sci."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"54","DOI":"10.1016\/j.engfracmech.2018.06.027","article-title":"Application of acoustic emission monitoring and signal analysis to the qualitative and quantitative characterization of the fracturing process in rocks","volume":"210","author":"Celestino","year":"2019","journal-title":"Eng. Fract. Mech."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"103666","DOI":"10.1016\/j.tafmec.2022.103666","article-title":"Acoustic emission characteristics and failure mode analysis of rock failure under complex stress state","volume":"122","author":"Li","year":"2022","journal-title":"Theor. Appl. Fract. Mech."},{"key":"ref_30","first-page":"2977","article-title":"Rockburst criterion based on artificial neural networks and nonlinear regression","volume":"44","author":"Zhang","year":"2013","journal-title":"J. Cent. South. Univ. Sci. Technol."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3453614","DOI":"10.1155\/2019\/3453614","article-title":"Rockburst prediction model based on entropy weight integrated with grey relational, B.P. neural network","volume":"2019","author":"Zheng","year":"2019","journal-title":"Adv. Civ. Eng."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2105","DOI":"10.1007\/s11771-017-3619-1","article-title":"Rock burst prediction based on genetic algorithms and extreme learning machine","volume":"24","author":"Li","year":"2017","journal-title":"J. Cent. South Univ."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"213","DOI":"10.1007\/s00366-018-00695-9","article-title":"Evaluation of rockburst occurrence and intensity in underground structures using decision tree approach","volume":"36","author":"Ghasemi","year":"2020","journal-title":"Eng. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1449","DOI":"10.1007\/s00603-020-02314-w","article-title":"Rock burst precursor electromagnetic radiation signal recognition method and early warning application based on recurrent neural networks","volume":"54","author":"Di","year":"2021","journal-title":"Rock. Mech. Rock. Eng."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1016\/j.ijmst.2018.08.007","article-title":"Comprehensive early warning of rock burst utilizing microseismic multi-parameter indices","volume":"28","author":"Dou","year":"2018","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1269537","DOI":"10.1155\/2019\/1269537","article-title":"Combined early warning method for rock burst and its engineering application","volume":"2019","author":"Qin","year":"2019","journal-title":"Adv. Civ. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"104767","DOI":"10.1016\/j.ijrmms.2021.104767","article-title":"Integrated rockburst early warning model based on fuzzy comprehensive evaluation method","volume":"142","author":"He","year":"2021","journal-title":"Int. J. Rock. Mech. Min."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"903","DOI":"10.1098\/rspa.1998.0193","article-title":"The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis","volume":"454","author":"Huang","year":"1998","journal-title":"Proc. R. Soc. Lond. Ser. A Math. Phys. Eng. Sci."},{"key":"ref_39","unstructured":"Chatfield, C. (1981). The Analysis of Time Series: An introduction, Chapman and Hall\/CRC."},{"key":"ref_40","first-page":"1907","article-title":"The Analysis of Time Series: An Introduction with R","volume":"91","author":"Zhou","year":"2021","journal-title":"J. Stat. Comput. Simul."},{"key":"ref_41","unstructured":"Wu, H., Hu, T., Liu, Y., Zhou, H., Wang, J., and Long, M. (2025). Timesnet: Temporal 2d-variation modeling for general time series analysis. arXiv."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Yu, W., Zhou, P., Yan, S., and Wang, X. (2024, January 16\u201322). Inceptionnext: When inception meets convnext. Proceedings of the IEEE\/Cvf Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA.","DOI":"10.1109\/CVPR52733.2024.00542"},{"key":"ref_45","first-page":"22419","article-title":"Autoformer: Decomposition transformers with auto-correlation for long-term series forecasting","volume":"34","author":"Wu","year":"2021","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"106097","DOI":"10.1016\/j.ijrmms.2025.106097","article-title":"Rockburst probability early warning method based on integrated infrared temperature and acoustic emission parameters","volume":"189","author":"Ren","year":"2025","journal-title":"Int. J. Rock. Mech. Min. Sci."},{"key":"ref_47","unstructured":"(2009). Methods for Determining the Physical and Mechanical Properties of Coal and Rock (Standard No. GB\/T 23561.1\u20132009)."},{"key":"ref_48","unstructured":"Roman, F.N. (2011). Elements of Information Theory. Perspectives in Electronic Structure Theory, Springer."},{"key":"ref_49","first-page":"659","article-title":"Time-series prediction method for risk level of rockburst disaster based on CNN-LSTM","volume":"52","author":"Liu","year":"2021","journal-title":"J. Cent. South Univ. (Sci. Technol.)"},{"key":"ref_50","first-page":"191","article-title":"Prediction of intensity classification of rockburst based on deep neural network","volume":"45","author":"Tian","year":"2020","journal-title":"J. China Coal Soc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7580\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,12,17]],"date-time":"2025-12-17T10:45:40Z","timestamp":1765968340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/25\/24\/7580"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12,13]]},"references-count":50,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["s25247580"],"URL":"https:\/\/doi.org\/10.3390\/s25247580","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2025,12,13]]}}}