{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T02:40:07Z","timestamp":1773369607252,"version":"3.50.1"},"reference-count":25,"publisher":"Springer Science and Business Media LLC","issue":"2","license":[{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T00:00:00Z","timestamp":1735344000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100017550","name":"Shaanxi Science and Technology Association","doi-asserted-by":"publisher","award":["2021JM-169"],"award-info":[{"award-number":["2021JM-169"]}],"id":[{"id":"10.13039\/501100017550","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100017550","name":"Shaanxi Science and Technology Association","doi-asserted-by":"publisher","award":["2021JM-169"],"award-info":[{"award-number":["2021JM-169"]}],"id":[{"id":"10.13039\/501100017550","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016074","name":"Foundation of Equipment Pre-research Area","doi-asserted-by":"publisher","award":["6141A02033111"],"award-info":[{"award-number":["6141A02033111"]}],"id":[{"id":"10.13039\/100016074","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100016074","name":"Foundation of Equipment Pre-research Area","doi-asserted-by":"publisher","award":["6141A02033111"],"award-info":[{"award-number":["6141A02033111"]}],"id":[{"id":"10.13039\/100016074","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"crossref","award":["2016JQ5030"],"award-info":[{"award-number":["2016JQ5030"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"crossref","award":["2016JQ5030"],"award-info":[{"award-number":["2016JQ5030"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,2]]},"DOI":"10.1007\/s11760-024-03752-7","type":"journal-article","created":{"date-parts":[[2024,12,28]],"date-time":"2024-12-28T13:29:33Z","timestamp":1735392573000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Intelligent identification of bolt looseness with one-dimensional deep convolutional neural networks"],"prefix":"10.1007","volume":"19","author":[{"given":"XiaoLi","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yong","family":"Xia","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junfeng","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,12,28]]},"reference":[{"key":"3752_CR1","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.jsv.2016.10.043","volume":"388","author":"O Abdeljaber","year":"2017","unstructured":"Abdeljaber, O., Avci, O., Kiranyaz, S.: Real-time vibration-based structural damage detection using one-dimensional convolutional neural networks. J. Sound. Vibration 388, 154\u2013170 (2017)","journal-title":"J. Sound. Vibration"},{"key":"3752_CR2","doi-asserted-by":"crossref","unstructured":"Chen, X., Lin, Q., Luo, C.: Neural feature search: A neural architecture for automated feature engineering. In: Wang, J., Shim, K., Wu, X. (eds.) 19th IEEE International conference on data mining (ICDM), pp. 71\u201380. IEEE; IEEE Comp Soc, Beijing (2019)","DOI":"10.1109\/ICDM.2019.00017"},{"key":"3752_CR3","first-page":"1","volume":"2015","author":"Z Chen","year":"2015","unstructured":"Chen, Z., Li, C., Sanchez, R.-V.: Gearbox fault identification and classification with convolutional neural networks. Shock Vibration 2015, 1\u201310 (2015)","journal-title":"Shock Vibration"},{"key":"3752_CR4","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1016\/j.isatra.2021.04.042","volume":"122","author":"S Chen","year":"2022","unstructured":"Chen, S., Yu, J., Wang, S.: One-dimensional convolutional neural network-based active feature extraction for fault detection and diagnosis of industrial processes and its understanding via visualization. ISA Trans. 122, 424\u2013443 (2022)","journal-title":"ISA Trans."},{"issue":"8","key":"3752_CR5","doi-asserted-by":"publisher","first-page":"1926","DOI":"10.1109\/TIM.2017.2674738","volume":"66","author":"X Ding","year":"2017","unstructured":"Ding, X., He, Q.: Energy-fluctuated multiscale feature learning with deep convnet for intelligent spindle bearing fault diagnosis. IEEE Trans. Insrum. Measure. 66(8), 1926\u20131935 (2017)","journal-title":"IEEE Trans. Insrum. Measure."},{"issue":"10","key":"3752_CR6","doi-asserted-by":"publisher","first-page":"849","DOI":"10.1111\/mice.12376","volume":"33","author":"H Hashemi","year":"2018","unstructured":"Hashemi, H., Abdelghany, K.: End-to-end deep learning methodology for real-time traffic network management. Computer-aided Civil Infrastruct. Eng. 33(10), 849\u2013863 (2018)","journal-title":"Computer-aided Civil Infrastruct. Eng."},{"issue":"11","key":"3752_CR7","doi-asserted-by":"publisher","first-page":"7067","DOI":"10.1109\/TIE.2016.2582729","volume":"63","author":"T Ince","year":"2016","unstructured":"Ince, T., Kiranyaz, S., Eren, L.: Real-time motor fault detection by 1-d convolutional neural networks. IEEE Trans. Indus. Electron. 63(11), 7067\u20137075 (2016)","journal-title":"IEEE Trans. Indus. Electron."},{"issue":"6245, SI","key":"3752_CR8","doi-asserted-by":"publisher","first-page":"255","DOI":"10.1126\/science.aaa8415","volume":"349","author":"MI Jordan","year":"2015","unstructured":"Jordan, M.I., Mitchell, T.M.: Machine learning: trends, perspectives, and prospects. Science 349(6245, SI), 255\u2013260 (2015)","journal-title":"Science"},{"key":"3752_CR9","first-page":"351","volume":"38","author":"Z Junfeng","year":"2019","unstructured":"Junfeng, Z., Xiaoli, Z., Qiang, Y.: Dynamic feature learning and assembly tightness intelligent monitoring of bolted joint structure. Mech. Sci. Technol. Aerosp. Eng. 38, 351 (2019)","journal-title":"Mech. Sci. Technol. Aerosp. Eng."},{"key":"3752_CR10","unstructured":"Le\u00a0Cun, Y., Boser, B., Denker, J.S.: Handwritten digit recognition with a back-propagation network. In: Touretzky., D. (ed.) Proceedings of the 2nd international conference on neural information processing systems, vol. 2, pp. 396\u2013404. MIT Press, Cambridge (1989)"},{"key":"3752_CR11","doi-asserted-by":"crossref","unstructured":"Li, X., Wang, S., Zhou, W.: Research on fault diagnosis algorithm based on convolutional neural network. In: 11th International conference on intelligent human-machine systems and cybernetics (IHMSC), vol. 1, pp. 8\u201312. IEEE Comp Soc, Zhejiang Univ, Hangzhou (2019)","DOI":"10.1109\/IHMSC.2019.00010"},{"key":"3752_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.113703","volume":"223","author":"Z Liu","year":"2023","unstructured":"Liu, Z., Xinbo, H., Zhao, L.: Research on online monitoring technology for transmission tower bolt looseness. Measurement 223, 113703 (2023)","journal-title":"Measurement"},{"key":"3752_CR13","doi-asserted-by":"publisher","DOI":"10.1016\/j.dibe.2023.100122","volume":"14","author":"T-T Nguyen","year":"2023","unstructured":"Nguyen, T.-T., Ta, Q.-B., Ho, D.-D.: A method for automated bolt-loosening monitoring and assessment using impedance technique and deep learning. Develop. Built Environ. 14, 100122 (2023)","journal-title":"Develop. Built Environ."},{"issue":"6","key":"3752_CR14","doi-asserted-by":"publisher","first-page":"1137","DOI":"10.1109\/TPAMI.2016.2577031","volume":"39","author":"S Ren","year":"2017","unstructured":"Ren, S., He, K., Girshick, R., Sun, J.: Faster R-CNN: Towards real-time object detection with region proposal networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137\u20131149 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"3752_CR15","doi-asserted-by":"publisher","first-page":"170","DOI":"10.1016\/j.engstruct.2018.05.084","volume":"171","author":"H Salehi","year":"2018","unstructured":"Salehi, H., Burgue\u00f1o, R.: Emerging artificial intelligence methods in structural engineering. Eng. Struct. 171, 170\u2013189 (2018)","journal-title":"Eng. Struct."},{"issue":"7","key":"3752_CR16","doi-asserted-by":"publisher","first-page":"790","DOI":"10.3390\/ma10070790","volume":"10","author":"W Sun","year":"2017","unstructured":"Sun, W., Yao, B., Zeng, N.: An intelligent gear fault diagnosis methodology using a complex wavelet enhanced convolutional neural network. Materials 10(7), 790 (2017)","journal-title":"Materials"},{"issue":"3","key":"3752_CR17","first-page":"547","volume":"37","author":"B Wang","year":"2017","unstructured":"Wang, B., Zhang, X., Fuyang, A.: Optimization of support vector machine and its application in intelligent fault diagnosis. J. Vibration, Measure Diagn. 37(3), 547\u2013552 (2017)","journal-title":"J. Vibration, Measure Diagn."},{"issue":"8","key":"3752_CR18","doi-asserted-by":"publisher","first-page":"1906","DOI":"10.3390\/s19081906","volume":"19","author":"J Xu","year":"2019","unstructured":"Xu, J., Dong, J., Li, H., Zhang, C., Ho, S.C.: Looseness monitoring of bolted spherical joint connection using electro-mechanical impedance technique and BP neural networks. Sensors 19(8), 1906 (2019)","journal-title":"Sensors"},{"issue":"3","key":"3752_CR19","doi-asserted-by":"publisher","first-page":"36850420951394","DOI":"10.1177\/0036850420951394","volume":"103","author":"S Xie","year":"2020","unstructured":"Xie, S., Ren, G., Zhu, J.: Application of a new one-dimensional deep convolutional neural network for intelligent fault diagnosis of rolling bearings. Sci. Progr. 103(3), 36850420951394 (2020)","journal-title":"Sci. Progr."},{"issue":"11","key":"3752_CR20","first-page":"29","volume":"26","author":"S-F Yuan","year":"2007","unstructured":"Yuan, S.-F., Chu, F.-L.: Support vector machines and its applications in machine fault diagnosis. J. Vibration Shock. 26(11), 29\u20133558 (2007)","journal-title":"J. Vibration Shock."},{"issue":"12","key":"3752_CR21","doi-asserted-by":"publisher","DOI":"10.1088\/1361-665X\/ab3b39","volume":"28","author":"R Yuan","year":"2019","unstructured":"Yuan, R., Lv, Y., Kong, Q., Song, G.: Percussion-based bolt looseness monitoring using intrinsic multiscale entropy analysis and bp neural network. Smart Mater. Struct. 28(12), 125001 (2019)","journal-title":"Smart Mater. Struct."},{"key":"3752_CR22","doi-asserted-by":"publisher","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","volume":"100","author":"W Zhang","year":"2018","unstructured":"Zhang, W., Li, C., Peng, G.: A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech. Syst. Signal Process. 100, 439\u2013453 (2018)","journal-title":"Mech. Syst. Signal Process."},{"issue":"2","key":"3752_CR23","doi-asserted-by":"publisher","first-page":"90","DOI":"10.3390\/s17020425","volume":"17","author":"W Zhang","year":"2017","unstructured":"Zhang, W., Peng, G., Li, C.: A new deep learning model for fault diagnosis with good anti-noise and domain adaptation ability on raw vibration signals. Sensors 17(2), 90 (2017)","journal-title":"Sensors"},{"issue":"1","key":"3752_CR24","doi-asserted-by":"publisher","first-page":"105","DOI":"10.1177\/1475921719837509","volume":"19","author":"Y Zhang","year":"2020","unstructured":"Zhang, Y., Sun, X., Loh, K.J.: Autonomous bolt loosening detection using deep learning. Struct. Health Monitor.- Int. J. 19(1), 105\u2013122 (2020)","journal-title":"Struct. Health Monitor.- Int. J."},{"key":"3752_CR25","doi-asserted-by":"publisher","first-page":"213","DOI":"10.1016\/j.ymssp.2018.05.050","volume":"115","author":"R Zhao","year":"2019","unstructured":"Zhao, R., Yan, R., Chen, Z.: Deep learning and its applications to machine health monitoring. Mech. Syst. Signal Process. 115, 213\u2013237 (2019)","journal-title":"Mech. Syst. Signal Process."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03752-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-024-03752-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-024-03752-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T15:01:19Z","timestamp":1738335679000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-024-03752-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,28]]},"references-count":25,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2025,2]]}},"alternative-id":["3752"],"URL":"https:\/\/doi.org\/10.1007\/s11760-024-03752-7","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,28]]},"assertion":[{"value":"5 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 November 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 November 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 December 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This chapter does not contain any studies with human participants or animals performed by any of the authors.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"158"}}