乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,30]],"date-time":"2025-04-30T08:46:05Z","timestamp":1746002765800,"version":"3.37.3"},"reference-count":49,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T00:00:00Z","timestamp":1697500800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the Natural Science Foundation of Hebei","award":["No. E2021209148"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"crossref","award":["51804122, 52074123"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"The characteristics of acoustic emission signals generated in the process of rock deformation and fission contain rich information on internal rock damage. The use of acoustic emissions monitoring technology can analyze and identify the precursor information of rock failure. At present, in the field of acoustic emissions monitoring and the early warning of rock fracture disasters, there is no real-time identification method for a disaster precursor characteristic signal. It is easy to lose information by analyzing the characteristic parameters of traditional acoustic emissions to find signals that serve as precursors to disasters, and analysis has mostly been based on post-analysis, which leads to poor real-time recognition of disaster precursor characteristics and low application levels in the engineering field. Based on this, this paper regards the acoustic emissions signal of rock fracture as a kind of speech signal generated by rock fracture uses this idea of speech recognition for reference alongside spectral analysis (STFT) and Mel frequency analysis to realize the feature extraction of acoustic emissions from rock fracture. In deep learning, based on the VGG16 convolutional neural network and AlexNet convolutional neural network, six intelligent real-time recognition models of rock fracture and key acoustic emission signals were constructed, and the network structure and loss function of traditional VGG16 were optimized. The experimental results show that these six deep-learning models can achieve the real-time intelligent recognition of key signals, and Mel, combined with the improved VGG16, achieved the best performance with 87.68% accuracy and 81.05% recall. Then, by comparing multiple groups of signal recognition models, Mel+VGG-FL proposed in this paper was verified as having a high recognition accuracy and certain recognition efficiency, performing the intelligent real-time recognition of key acoustic emission signals in the process of rock fracture more accurately, which can provide new ideas and methods for related research and the real-time intelligent recognition of rock fracture precursor characteristics.<\/jats:p>","DOI":"10.3390\/s23208513","type":"journal-article","created":{"date-parts":[[2023,10,17]],"date-time":"2023-10-17T12:25:09Z","timestamp":1697545509000},"page":"8513","source":"Crossref","is-referenced-by-count":3,"title":["Real-Time Recognition Method for Key Signals of Rock Fracture Acoustic Emissions Based on Deep Learning"],"prefix":"10.3390","volume":"23","author":[{"given":"Lin","family":"Sun","sequence":"first","affiliation":[{"name":"Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China"},{"name":"College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-2488-4768","authenticated-orcid":false,"given":"Lisen","family":"Lin","sequence":"additional","affiliation":[{"name":"Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China"},{"name":"College of Artificial Intelligence, North China University of Science and Technology, Tangshan 063210, China"}]},{"given":"Xulong","family":"Yao","sequence":"additional","affiliation":[{"name":"Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China"},{"name":"College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China"}]},{"given":"Yanbo","family":"Zhang","sequence":"additional","affiliation":[{"name":"Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China"},{"name":"College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China"}]},{"given":"Zhigang","family":"Tao","sequence":"additional","affiliation":[{"name":"School of Mechanical and Architectural Engineering, China University of Mining and Technology-Beijing, Beijing 100083, China"},{"name":"State Key Laboratory for Geomechanics and Deep Underground Engineering, Beijing 100083, China"}]},{"given":"Peng","family":"Ling","sequence":"additional","affiliation":[{"name":"Hebei Green Intelligent Mining Technology Innovation Center, Tangshan 063210, China"},{"name":"College of Mining Engineering, North China University of Science and Technology, Tangshan 063210, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,10,17]]},"reference":[{"key":"ref_1","first-page":"5387459","article-title":"Acoustic emission characteristics and failure mechanism of fractured rock under different loading rates","volume":"2017","author":"Zhang","year":"2017","journal-title":"Shock Vib."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1585","DOI":"10.1007\/s00603-021-02727-1","article-title":"An energy-based method to determine rock brittleness by considering rock damage","volume":"55","author":"Wang","year":"2022","journal-title":"Rock Mech."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"818452","DOI":"10.3389\/feart.2022.818452","article-title":"Research on coal acoustic emission characteristics and damage evolution during cyclic loading","volume":"10","author":"Su","year":"2022","journal-title":"Front. 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