{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T23:00:02Z","timestamp":1773615602255,"version":"3.50.1"},"reference-count":22,"publisher":"Allerton Press","issue":"2","license":[{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,1]],"date-time":"2024-04-01T00:00:00Z","timestamp":1711929600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Aut. Control Comp. Sci."],"published-print":{"date-parts":[[2024,4]]},"DOI":"10.3103\/s0146411624700081","type":"journal-article","created":{"date-parts":[[2024,5,6]],"date-time":"2024-05-06T10:02:20Z","timestamp":1714989740000},"page":"185-194","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Diesel Engine Fault Diagnosis Based on Convolutional Autoencoder Using Vibration Signals"],"prefix":"10.3103","volume":"58","author":[{"family":"Feng Xu","sequence":"first","affiliation":[]},{"given":"Shuli","family":"Jia","sequence":"additional","affiliation":[]},{"given":"Chong","family":"Qu","sequence":"additional","affiliation":[]},{"given":"Duo","family":"Chen","sequence":"additional","affiliation":[]},{"given":"Liyong","family":"Ma","sequence":"additional","affiliation":[]}],"member":"1627","published-online":{"date-parts":[[2024,5,6]]},"reference":[{"key":"7694_CR1","doi-asserted-by":"publisher","first-page":"479","DOI":"10.1016\/j.ymssp.2015.07.015","volume":"68\u201369","author":"G.M. Szyma\u0144ski","year":"2016","unstructured":"Szyma\u0144ski, G.M. and Tomaszewski, F., Diagnostics of automatic compensators of valve clearance in combustion engine with the use of vibration signal, Mech. Syst. Signal Process., 2016, vols. 68\u201369, pp. 479\u2013490. https:\/\/doi.org\/10.1016\/j.ymssp.2015.07.015","journal-title":"Mech. Syst. Signal Process."},{"key":"7694_CR2","doi-asserted-by":"publisher","first-page":"1691","DOI":"10.1007\/s10586-017-0748-0","volume":"20","author":"C. Cai","year":"2017","unstructured":"Cai, C., Weng, X., and Zhang, C., A novel approach for marine diesel engine fault diagnosis, Cluster Comput., 2017, vol. 20, no. 2, pp. 1691\u20131702. https:\/\/doi.org\/10.1007\/s10586-017-0748-0","journal-title":"Cluster Comput."},{"key":"7694_CR3","doi-asserted-by":"publisher","first-page":"384","DOI":"10.1007\/s11801-016-6162-7","volume":"12","author":"H. Zhang","year":"2016","unstructured":"Zhang, H., Jiang, Q., Wang, B., and Wang, J., Monitoring diesel engine parameters based on FBG probe, Optoelectron. Lett., 2016, vol. 12, no. 5, pp. 384\u2013388. https:\/\/doi.org\/10.1007\/s11801-016-6162-7","journal-title":"Optoelectron. Lett."},{"key":"7694_CR4","doi-asserted-by":"publisher","unstructured":"Jia, S., Ma, L., and Zhang, S., Big data prototype practice for unmanned surface vehicle, Proc. 4th Int. Conf. on Communication and Information Processing, Qingdao, China, 2018, New York: Association for Computing Machinery, 2018, pp. 43\u201347. https:\/\/doi.org\/10.1145\/3290420.3290466","DOI":"10.1145\/3290420.3290466"},{"key":"7694_CR5","doi-asserted-by":"publisher","DOI":"10.3233\/faia200812","volume-title":"A survey on ship intelligent cabin, Machine Learning and Artificial Intelligence, Frontiers in Artificial Intelligence and Applications","author":"Sh. Jia","year":"2020","unstructured":"Jia, Sh., Wang, F., Dong, M., and Ma, L., A survey on ship intelligent cabin, Machine Learning and Artificial Intelligence, Frontiers in Artificial Intelligence and Applications, vol. 332, IOS Press, 2020, pp. 453\u2013458. https:\/\/doi.org\/10.3233\/faia200812"},{"key":"7694_CR6","first-page":"19","volume":"30","author":"Zh. Shen","year":"2010","unstructured":"Shen, Zh., Huang, X., and Ma, X., Fault diagnosis of diesel engine based on empirical mode decomposition and support vector machine, J. Vib. Meas. Diagnosis, 2010, vol. 30, no. 1, pp. 19\u201322.","journal-title":"J. Vib. Meas. Diagnosis"},{"key":"7694_CR7","doi-asserted-by":"publisher","unstructured":"Paulraj, M.P., Yaacob, S., and Zin, M.Z.Md., Entropy based feature extraction for motorbike engine faults diagnosing using neural network and wavelet transform, 2009 5th Int. Colloq. on Signal Processing & Its Applications, Kuala Lumpur, Malaysia, 2009, IEEE, 2009, pp. 47\u201351. https:\/\/doi.org\/10.1109\/cspa.2009.5069186","DOI":"10.1109\/cspa.2009.5069186"},{"key":"7694_CR8","first-page":"68","volume":"33","author":"H. Qiang","year":"2005","unstructured":"Qiang, H., Gao, S., Hongzan, B., and Yongchang, L., The method of vibration diagnosis for diesel engine based on the fractal theory and neural network, J. Huazhong Univ. Sci. Tech., 2005, vol. 33, no. 9, pp. 68\u201370.","journal-title":"J. Huazhong Univ. Sci. Tech."},{"key":"7694_CR9","doi-asserted-by":"publisher","first-page":"1677","DOI":"10.1177\/14759217221113323","volume":"22","author":"Ya. Liu","year":"2022","unstructured":"Liu, Ya., Kang, J., Bai, Yu., and Guo, C., A novel adaptive fault diagnosis algorithm for multi-machine equipment: Application in bearing and diesel engine, Struct. Health Monitoring, 2022, vol. 22, no. 3, pp. 1677\u20131707. https:\/\/doi.org\/10.1177\/14759217221113323","journal-title":"Struct. Health Monitoring"},{"key":"7694_CR10","doi-asserted-by":"publisher","first-page":"21","DOI":"10.1109\/tvt.2014.2317736","volume":"64","author":"R. Ahmed","year":"2015","unstructured":"Ahmed, R., El Sayed, M., Gadsden, S.A., Tjong, J., and Habibi, S., Automotive internal-combustion-engine fault detection and classification using artificial neural network techniques, IEEE Trans. Veh. Technol., 2015, vol. 64, no. 1, pp. 21\u201333. https:\/\/doi.org\/10.1109\/tvt.2014.2317736","journal-title":"IEEE Trans. Veh. Technol."},{"key":"7694_CR11","first-page":"234","volume":"42","author":"C. Wen","year":"2020","unstructured":"Wen, C. and Lu, F., Review on deep learning based fault diagnosis, J. Electron. Inf. Tech., 2020, vol. 42, no. 1, pp. 234\u2013248.","journal-title":"J. Electron. Inf. Tech."},{"key":"7694_CR12","doi-asserted-by":"publisher","first-page":"880","DOI":"10.1016\/j.egyr.2021.09.185","volume":"7","author":"C. Qu","year":"2021","unstructured":"Qu, C., Zhou, Z., Liu, Z., Jia, S., Wang, L., and Ma, L., State prediction for marine diesel engine based on variational modal decomposition and long short-term memory, Energy Rep., 2021, vol. 7, pp. 880\u2013886. https:\/\/doi.org\/10.1016\/j.egyr.2021.09.185","journal-title":"Energy Rep."},{"key":"7694_CR13","doi-asserted-by":"publisher","first-page":"6212759","DOI":"10.1155\/2019\/6212759","volume":"2019","author":"L. Ma","year":"2019","unstructured":"Ma, L., Ma, C., Liu, Yu., and Wang, X., Thyroid diagnosis from SPECT images using convolutional neural network with optimization, Comput. Intell. Neurosci., 2019, vol. 2019, p. 6212759. https:\/\/doi.org\/10.1155\/2019\/6212759","journal-title":"Comput. Intell. Neurosci."},{"key":"7694_CR14","doi-asserted-by":"publisher","first-page":"110460","DOI":"10.1016\/j.measurement.2021.110460","volume":"189","author":"Zh. Yang","year":"2022","unstructured":"Yang, Zh., Xu, B., Luo, W., and Chen, F., Autoencoder-based representation learning and its application in intelligent fault diagnosis: A review, Measurement, 2022, vol. 189, p. 110460. https:\/\/doi.org\/10.1016\/j.measurement.2021.110460","journal-title":"Measurement"},{"key":"7694_CR15","doi-asserted-by":"publisher","first-page":"7103","DOI":"10.1002\/int.22582","volume":"36","author":"Z. Cheng","year":"2021","unstructured":"Cheng, Z., Wang, S., Zhang, P., Wang, S., Liu, X., and Zhu, E., Improved autoencoder for unsupervised anomaly detection, Int. J. Intell. Syst., 2021, vol. 36, no. 12, pp. 7103\u20137125. https:\/\/doi.org\/10.1002\/int.22582","journal-title":"Int. J. Intell. Syst."},{"key":"7694_CR16","doi-asserted-by":"publisher","first-page":"938","DOI":"10.1016\/j.egyr.2021.09.179","volume":"7","author":"H. Wang","year":"2021","unstructured":"Wang, H., Liu, X., Ma, L., and Zhang, Yo., Anomaly detection for hydropower turbine unit based on variational modal decomposition and deep autoencoder, Energy Rep., 2021, vol. 7, pp. 938\u2013946. https:\/\/doi.org\/10.1016\/j.egyr.2021.09.179","journal-title":"Energy Rep."},{"key":"7694_CR17","doi-asserted-by":"publisher","first-page":"110960","DOI":"10.1016\/j.measurement.2022.110960","volume":"193","author":"S. Ou","year":"2022","unstructured":"Ou, S., Yu, Yo., and Yang, J., Identification and reconstruction of anomalous sensing data for combustion analysis of marine diesel engines, Measurement, 2022, vol. 193, p. 110960. https:\/\/doi.org\/10.1016\/j.measurement.2022.110960","journal-title":"Measurement"},{"key":"7694_CR18","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1016\/j.ymssp.2015.10.025","volume":"72\u201373","author":"F. Jia","year":"2016","unstructured":"Jia, F., Lei, Ya., Lin, J., Zhou, X., and Lu, N., Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data, Mech. Syst. Signal Process., 2016, vols.\u00a072\u201373, pp. 303\u2013315. https:\/\/doi.org\/10.1016\/j.ymssp.2015.10.025","journal-title":"Mech. Syst. Signal Process."},{"key":"7694_CR19","doi-asserted-by":"publisher","first-page":"930","DOI":"10.1016\/j.apenergy.2018.10.113","volume":"233\u2013234","author":"Z. Zhang","year":"2019","unstructured":"Zhang, Z., Li, Sh., Xiao, Ya., and Yang, Yu., Intelligent simultaneous fault diagnosis for solid oxide fuel cell system based on deep learning, Appl. Energy, 2019, vols. 233\u2013234, pp. 930\u2013942. https:\/\/doi.org\/10.1016\/j.apenergy.2018.10.113","journal-title":"Appl. Energy"},{"key":"7694_CR20","doi-asserted-by":"publisher","first-page":"111","DOI":"10.1016\/j.knosys.2019.04.022","volume":"178","author":"J.-B. Yu","year":"2019","unstructured":"Yu, J.-B., Evolutionary manifold regularized stacked denoising autoencoders for gearbox fault diagnosis, Knowl.-Based Syst., 2019, vol. 178, pp. 111\u2013122. https:\/\/doi.org\/10.1016\/j.knosys.2019.04.022","journal-title":"Knowl.-Based Syst."},{"key":"7694_CR21","doi-asserted-by":"publisher","first-page":"31043","DOI":"10.1109\/access.2021.3057399","volume":"9","author":"G. Xiong","year":"2021","unstructured":"Xiong, G., Ma, W., Zhao, N., Zhang, J., Jiang, Zh., and Mao, Zh., Multi-type diesel engines operating condition recognition method based on stacked auto-encoder and feature transfer learning, IEEE Access, 2021, vol.\u00a09, pp. 31043\u201331052. https:\/\/doi.org\/10.1109\/access.2021.3057399","journal-title":"IEEE Access"},{"key":"7694_CR22","doi-asserted-by":"publisher","first-page":"2249","DOI":"10.3390\/electronics11142249","volume":"11","author":"H. Bai","year":"2022","unstructured":"Bai, H., Zhan, X., Yan, H., Wen, L., Yan, Yu., and Jia, X., Research on diesel engine fault diagnosis method based on stacked sparse autoencoder and support vector machine, Electronics, 2022, vol. 11, no. 14, p. 2249. https:\/\/doi.org\/10.3390\/electronics11142249","journal-title":"Electronics"}],"container-title":["Automatic Control and Computer Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3103\/S0146411624700081.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.3103\/S0146411624700081","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.3103\/S0146411624700081.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,15]],"date-time":"2026-03-15T22:02:06Z","timestamp":1773612126000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.3103\/S0146411624700081"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4]]},"references-count":22,"journal-issue":{"issue":"2","published-print":{"date-parts":[[2024,4]]}},"alternative-id":["7694"],"URL":"https:\/\/doi.org\/10.3103\/s0146411624700081","relation":{},"ISSN":["0146-4116","1558-108X"],"issn-type":[{"value":"0146-4116","type":"print"},{"value":"1558-108X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4]]},"assertion":[{"value":"12 April 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"19 June 2023","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2023","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"6 May 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors have no relevant financial or nonfinancial interests to disclose.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"CONFLICT OF INTEREST"}}]}}