{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T16:01:50Z","timestamp":1784476910702,"version":"3.55.0"},"reference-count":23,"publisher":"Fuji Technology Press Ltd.","issue":"4","funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62503441"],"award-info":[{"award-number":["62503441"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012166","name":"National Key Research and Development Program of China","doi-asserted-by":"publisher","award":["2022YFB4703600"],"award-info":[{"award-number":["2022YFB4703600"]}],"id":[{"id":"10.13039\/501100012166","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013314","name":"Higher Education Discipline Innovation Project","doi-asserted-by":"publisher","award":["B17040"],"award-info":[{"award-number":["B17040"]}],"id":[{"id":"10.13039\/501100013314","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015314","name":"China University of Geosciences","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100015314","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JACIII","J. Adv. Comput. Intell. Intell. Inform."],"published-print":{"date-parts":[[2026,7,20]]},"abstract":"<jats:p>Tunnel drilling rig is the key equipment used for exploration in underground coal mine. Because it operates for long periods in environments characterized by high humidity, intense vibration, pressure fluctuations, and unstable geological formations, various faults tend to appear frequently. If these faults are not identified in time, they may gradually worsen and ultimately result in severe accidents. Traditional fault detection methods mainly rely on manual inspection, which makes it difficult to obtain reliable information and respond effectively under complex and changing working conditions. To overcome these shortcomings, this study proposes a fault detection approach based on a sparse autoencoder. The raw signals, including pressure, speed, and feed rate, are first preprocessed. After that, a normal operating model of the drilling rig is learned through the sparse autoencoder. Faults are then detected by comparing the real-time reconstruction errors with a preset threshold. Finally, experiments based on actual drilling data are performed, and the results demonstrate the effectiveness of the proposed method.<\/jats:p>","DOI":"10.20965\/jaciii.2026.p1004","type":"journal-article","created":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T15:02:07Z","timestamp":1784473327000},"page":"1004-1014","source":"Crossref","is-referenced-by-count":0,"title":["Fault Detection and Diagnosis Based on Sparse Autoencoder for Tunnel Drilling Rig in Underground Coal Mine"],"prefix":"10.20965","volume":"30","author":[{"given":"Haitao","family":"Song","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence and Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"CCTEG Xi\u2019an Research Institute (Group) Co., Ltd., No.82 Jinye 1st Road, Gaoxin District, Xi\u2019an, Shaanxi 710077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shatie","family":"Zuo","sequence":"additional","affiliation":[{"name":"School of Future Technology, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aoxue","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yafeng","family":"Yao","sequence":"additional","affiliation":[{"name":"CCTEG Xi\u2019an Research Institute (Group) Co., Ltd., No.82 Jinye 1st Road, Gaoxin District, Xi\u2019an, Shaanxi 710077, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xuzhi","family":"Lai","sequence":"additional","affiliation":[{"name":"School of Artificial Intelligence and Automation, China University of Geosciences, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"},{"name":"Engineering Research Center of Intelligent Technology for Geo-Exploration, Ministry of Education, No.388 Lumo Road, Hongshan District, Wuhan, Hubei 430074, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"8550","published-online":{"date-parts":[[2026,7,20]]},"reference":[{"key":"key-10.20965\/jaciii.2026.p1004-1","doi-asserted-by":"crossref","unstructured":"H. Wang, J. Zhou, H. Chen, B. Xu, and Z. Shen, \u201cHydraulic system fault diagnosis decoupling method based on 2D time-series modeling and self-attention fusion,\u201d Scientific Reports, Vol.14, Article No.15620, 2024. https:\/\/doi.org\/10.1038\/s41598-024-66541-9","DOI":"10.1038\/s41598-024-66541-9"},{"key":"key-10.20965\/jaciii.2026.p1004-2","doi-asserted-by":"crossref","unstructured":"D.-H. Yoon and J. Yoon, \u201cDevelopment of a real-time fault detection method for electric power system via transformer-based deep learning model,\u201d Int. J. of Electrical Power & Energy Systems, Vol.159, Article No.110069, 2024. https:\/\/doi.org\/10.1016\/j.ijepes.2024.110069","DOI":"10.1016\/j.ijepes.2024.110069"},{"key":"key-10.20965\/jaciii.2026.p1004-3","doi-asserted-by":"crossref","unstructured":"T. Li et al., \u201cWaveletKernelNet: An interpretable deep neural network for industrial intelligent diagnosis,\u201d IEEE Trans. on Systems, Man, and Cybernetics: Systems, Vol.52, No.4, pp. 2302-2312, 2022. https:\/\/doi.org\/10.1109\/TSMC.2020.3048950","DOI":"10.1109\/TSMC.2020.3048950"},{"key":"key-10.20965\/jaciii.2026.p1004-4","doi-asserted-by":"crossref","unstructured":"W. Zhang, X. Li, H. Ma, Z. Luo, and X. Li, \u201cUniversal domain adaptation in fault diagnostics with hybrid weighted deep adversarial learning,\u201d IEEE Trans. on Industrial Informatics, Vol.17, No.12, pp. 7957-7967, 2021. https:\/\/doi.org\/10.1109\/TII.2021.3064377","DOI":"10.1109\/TII.2021.3064377"},{"key":"key-10.20965\/jaciii.2026.p1004-5","doi-asserted-by":"crossref","unstructured":"W. Su, Z. Han, X. Liu, and Y. Yin, \u201cGeneralized universal domain adaptation,\u201d Knowledge-Based Systems, Vol.302, Article No.112344, 2024. https:\/\/doi.org\/10.1016\/j.knosys.2024.112344","DOI":"10.1016\/j.knosys.2024.112344"},{"key":"key-10.20965\/jaciii.2026.p1004-6","doi-asserted-by":"crossref","unstructured":"R. Zhao et al., \u201cMachine health monitoring using local feature-based gated recurrent unit networks,\u201d IEEE Trans. on Industrial Electronics, Vol.65, No.2, pp. 1539-1548, 2018. https:\/\/doi.org\/10.1109\/TIE.2017.2733438","DOI":"10.1109\/TIE.2017.2733438"},{"key":"key-10.20965\/jaciii.2026.p1004-7","doi-asserted-by":"crossref","unstructured":"O. Janssens et al., \u201cConvolutional neural network based fault detection for rotating machinery,\u201d J. of Sound and Vibration, Vol.377, pp. 331-345, 2016. https:\/\/doi.org\/10.1016\/j.jsv.2016.05.027","DOI":"10.1016\/j.jsv.2016.05.027"},{"key":"key-10.20965\/jaciii.2026.p1004-8","doi-asserted-by":"crossref","unstructured":"Y. Lei et al., \u201cApplications of machine learning to machine fault diagnosis: A review and roadmap,\u201d Mechanical Systems and Signal Processing, Vol.138, Article No.106587, 2020. https:\/\/doi.org\/10.1016\/j.ymssp.2019.106587","DOI":"10.1016\/j.ymssp.2019.106587"},{"key":"key-10.20965\/jaciii.2026.p1004-9","doi-asserted-by":"crossref","unstructured":"M. Xia et al., \u201cIntelligent fault diagnosis of machinery using digital twin-assisted deep transfer learning,\u201d Reliability Engineering & System Safety, Vol.215, Article No.107938, 2021. https:\/\/doi.org\/10.1016\/j.ress.2021.107938","DOI":"10.1016\/j.ress.2021.107938"},{"key":"key-10.20965\/jaciii.2026.p1004-10","doi-asserted-by":"crossref","unstructured":"L. Zhang et al., \u201cEnd-to-end unsupervised fault detection using a flow-based model,\u201d Reliability Engineering & System Safety, Vol.215, Article No.107805, 2021. https:\/\/doi.org\/10.1016\/j.ress.2021.107805","DOI":"10.1016\/j.ress.2021.107805"},{"key":"key-10.20965\/jaciii.2026.p1004-11","doi-asserted-by":"crossref","unstructured":"N. N. Kulkarni, N. A. Valente, and A. Sabato, \u201cTime-inferred autoencoder: A noise adaptive condition monitoring tool,\u201d Mechanical Systems and Signal Processing, Vol.204, Article No.110789, 2023. https:\/\/doi.org\/10.1016\/j.ymssp.2023.110789","DOI":"10.1016\/j.ymssp.2023.110789"},{"key":"key-10.20965\/jaciii.2026.p1004-12","doi-asserted-by":"crossref","unstructured":"X. Chen, H. Liu, and N. Nikitas, \u201cInternal pump leakage detection of the hydraulic systems with highly incomplete flow data,\u201d Advanced Engineering Informatics, Vol.56, Article No.101974, 2023. https:\/\/doi.org\/10.1016\/j.aei.2023.101974","DOI":"10.1016\/j.aei.2023.101974"},{"key":"key-10.20965\/jaciii.2026.p1004-13","doi-asserted-by":"crossref","unstructured":"K. Huang, S. Wu, F. Li, C. Yang, and W. Gui, \u201cFault diagnosis of hydraulic systems based on deep learning model with multirate data samples,\u201d IEEE Trans. on Neural Networks and Learning Systems, Vol.33, No.11, pp. 6789-6801, 2022. https:\/\/doi.org\/10.1109\/TNNLS.2021.3083401","DOI":"10.1109\/TNNLS.2021.3083401"},{"key":"key-10.20965\/jaciii.2026.p1004-14","doi-asserted-by":"crossref","unstructured":"B. Wang, Z. Li, Z. Dai, N. Lawrence, and X. Yan, \u201cData-driven mode identification and unsupervised fault detection for nonlinear multimode processes,\u201d IEEE Trans. on Industrial Informatics, Vol.16, No.6, pp. 3651-3661, 2020. https:\/\/doi.org\/10.1109\/TII.2019.2942650","DOI":"10.1109\/TII.2019.2942650"},{"key":"key-10.20965\/jaciii.2026.p1004-15","doi-asserted-by":"crossref","unstructured":"S. Zheng, C. Wang, E. Zio, and J. Liu, \u201cFault detection in complex mechatronic systems by a hierarchical graph convolution attention network based on causal paths,\u201d Reliability Engineering & System Safety, Vol.243, Article No.109872, 2024. https:\/\/doi.org\/10.1016\/j.ress.2023.109872","DOI":"10.1016\/j.ress.2023.109872"},{"key":"key-10.20965\/jaciii.2026.p1004-16","doi-asserted-by":"crossref","unstructured":"A. Dallabona, M. Blanke, H. C. Pedersen, and D. Papageorgiou, \u201cFault diagnosis and prognosis capabilities for wind turbine hydraulic pitch systems,\u201d Mechanical Systems and Signal Processing, Vol.224, Article No.111941, 2025. https:\/\/doi.org\/10.1016\/j.ymssp.2024.111941","DOI":"10.1016\/j.ymssp.2024.111941"},{"key":"key-10.20965\/jaciii.2026.p1004-17","doi-asserted-by":"crossref","unstructured":"C. Lai, P. Baraldi, and E. Zio, \u201cPhysics-informed deep autoencoder for fault detection in new-design systems,\u201d Mechanical Systems and Signal Processing, Vol.215, Article No.111420, 2024. https:\/\/doi.org\/10.1016\/j.ymssp.2024.111420","DOI":"10.1016\/j.ymssp.2024.111420"},{"key":"key-10.20965\/jaciii.2026.p1004-18","doi-asserted-by":"crossref","unstructured":"M. Rao, M. J. Zuo, and Z. Tian, \u201cA speed normalized autoencoder for rotating machinery fault detection under varying speed conditions,\u201d Mechanical Systems and Signal Processing, Vol.189, Article No.110109, 2023. https:\/\/doi.org\/10.1016\/j.ymssp.2023.110109","DOI":"10.1016\/j.ymssp.2023.110109"},{"key":"key-10.20965\/jaciii.2026.p1004-19","doi-asserted-by":"crossref","unstructured":"B. Song et al., \u201cA fault-targeted gated recurrent unit-canonical correlation analysis method for incipient fault detection,\u201d IEEE Trans. on Industrial Informatics, Vol.20, No.6, pp. 8739-8748, 2024. https:\/\/doi.org\/10.1109\/TII.2024.3372023","DOI":"10.1109\/TII.2024.3372023"},{"key":"key-10.20965\/jaciii.2026.p1004-20","doi-asserted-by":"crossref","unstructured":"X. Kong, X. Li, Q. Zhou, Z. Hu, and C. Shi, \u201cAttention recurrent autoencoder hybrid model for early fault diagnosis of rotating machinery,\u201d IEEE Trans. on Instrumentation and Measurement, Vol.70, Article No.2505110, 2021. https:\/\/doi.org\/10.1109\/TIM.2021.3051948","DOI":"10.1109\/TIM.2021.3051948"},{"key":"key-10.20965\/jaciii.2026.p1004-21","doi-asserted-by":"crossref","unstructured":"Z.-H. Liu et al., \u201cA stacked auto-encoder based partial adversarial domain adaptation model for intelligent fault diagnosis of rotating machines,\u201d IEEE Trans. on Industrial Informatics, Vol.17, No.10, pp. 6798-6809, 2021. https:\/\/doi.org\/10.1109\/TII.2020.3045002","DOI":"10.1109\/TII.2020.3045002"},{"key":"key-10.20965\/jaciii.2026.p1004-22","doi-asserted-by":"crossref","unstructured":"W. Sun et al., \u201cA sparse auto-encoder-based deep neural network approach for induction motor faults classification,\u201d Measurement, Vol.89, pp. 171-178, 2016. https:\/\/doi.org\/10.1016\/j.measurement.2016.04.007","DOI":"10.1016\/j.measurement.2016.04.007"},{"key":"key-10.20965\/jaciii.2026.p1004-23","doi-asserted-by":"crossref","unstructured":"M. Niu, H. Jiang, Z. Wu, and H. Shao, \u201cAn enhanced sparse autoencoder for machinery interpretable fault diagnosis,\u201d Measurement Science and Technology, Vol.35, No.5, Article No.055108, 2024. https:\/\/doi.org\/10.1088\/1361-6501\/ad24ba","DOI":"10.1088\/1361-6501\/ad24ba"}],"container-title":["Journal of Advanced Computational Intelligence and Intelligent Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.fujipress.jp\/main\/wp-content\/themes\/Fujipress\/hyosetsu.php?ppno=jacii003000040005","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,19]],"date-time":"2026-07-19T15:02:26Z","timestamp":1784473346000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.fujipress.jp\/jaciii\/jc\/jacii003000041004"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7,20]]},"references-count":23,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,7,20]]},"published-print":{"date-parts":[[2026,7,20]]}},"URL":"https:\/\/doi.org\/10.20965\/jaciii.2026.p1004","relation":{},"ISSN":["1883-8014","1343-0130"],"issn-type":[{"value":"1883-8014","type":"electronic"},{"value":"1343-0130","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7,20]]}}}