{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,25]],"date-time":"2026-01-25T08:37:25Z","timestamp":1769330245264,"version":"3.49.0"},"reference-count":29,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T00:00:00Z","timestamp":1692576000000},"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":["J Intell Manuf"],"published-print":{"date-parts":[[2024,10]]},"DOI":"10.1007\/s10845-023-02185-2","type":"journal-article","created":{"date-parts":[[2023,8,21]],"date-time":"2023-08-21T17:02:00Z","timestamp":1692637320000},"page":"3115-3129","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["A novel technique for multiple failure modes classification based on deep forest algorithm"],"prefix":"10.1007","volume":"35","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9775-0590","authenticated-orcid":false,"given":"John","family":"Taco","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8336-5878","authenticated-orcid":false,"given":"Pradeep","family":"Kundu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4022-4274","authenticated-orcid":false,"given":"Jay","family":"Lee","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,8,21]]},"reference":[{"key":"2185_CR1","doi-asserted-by":"publisher","first-page":"2207","DOI":"10.1007\/s10845-022-01916-1","volume":"34","author":"K Akkad","year":"2023","unstructured":"Akkad, K., & He, D. (2023). A dynamic mode decomposition based deep learning technique for prognostics. Journal of Intelligent Manufacturing, 34, 2207\u20132224. https:\/\/doi.org\/10.1007\/s10845-022-01916-1.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2185_CR2","doi-asserted-by":"publisher","DOI":"10.1155\/2020\/5281512","author":"J Ding","year":"2020","unstructured":"Ding, J., Luo, Q., Jia, L., & You, J. (2020). Deep forest-based fault diagnosis method for chemical process. Mathematical Problems in Engineering. https:\/\/doi.org\/10.1155\/2020\/5281512","journal-title":"Mathematical Problems in Engineering"},{"key":"2185_CR3","doi-asserted-by":"publisher","first-page":"1229","DOI":"10.1007\/s10845-021-01844-6","volume":"34","author":"K Guan","year":"2023","unstructured":"Guan, K., Yang, G., Du, L., Li, Z., & Yang, X. (2023). Method for fusion of neighborhood rough set and XGBoost in welding process decision-making. Journal of Intelligent Manufacturing, 34, 1229\u20131240. https:\/\/doi.org\/10.1007\/s10845-021-01844-6.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2185_CR4","doi-asserted-by":"publisher","first-page":"8433","DOI":"10.1007\/s00170-022-09716-w","volume":"121","author":"J Huang","year":"2022","unstructured":"Huang, J., & Ting, C. (2022). Deep learning object detection applied to defect recognition of memory modules. International Journal of Advanced Manufacturing Technology, 121, 8433\u20138445. https:\/\/doi.org\/10.1007\/s00170-022-09716-w.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2185_CR5","doi-asserted-by":"publisher","unstructured":"Jakobsson, E., Frisk, E., Krysander, M., & Pettersson, R. (2022). Time series fault classification for wave propagation systems with sparse fault data. https:\/\/doi.org\/10.48550\/arXiv.2203.16121.","DOI":"10.48550\/arXiv.2203.16121"},{"issue":"10","key":"2185_CR6","doi-asserted-by":"publisher","first-page":"10087","DOI":"10.1109\/TIE.2020.3020252","volume":"68","author":"Z Jia","year":"2021","unstructured":"Jia, Z., Liu, Z., Gan, Y., Vong, C., & Pecht, M. (2021). A deep forest-based fault diagnosis scheme for electronics-rich analog circuit systems. IEEE Transactions on Industrial Electronics, 68(10), 10087\u201310096. https:\/\/doi.org\/10.1109\/TIE.2020.3020252.","journal-title":"IEEE Transactions on Industrial Electronics"},{"key":"2185_CR7","doi-asserted-by":"publisher","first-page":"437","DOI":"10.1007\/BF02769608","volume":"36","author":"R Khrunyk","year":"2000","unstructured":"Khrunyk, R. (2000). Development of new low-alloy steels for rock roller drill bits. Materials Science, 36, 437\u2013444. https:\/\/doi.org\/10.1007\/BF02769608.","journal-title":"Materials Science"},{"key":"2185_CR8","doi-asserted-by":"publisher","DOI":"10.1088\/1742-6596\/1314\/1\/012148","author":"Y Lai","year":"2019","unstructured":"Lai, Y. (2019). A comparison of traditional machine learning and deep learning in image recognition. Journal of Physics: Conference Series. https:\/\/doi.org\/10.1088\/1742-6596\/1314\/1\/012148","journal-title":"Journal of Physics: Conference Series"},{"key":"2185_CR9","doi-asserted-by":"publisher","first-page":"436","DOI":"10.1038\/nature14539","volume":"521","author":"Y Lecun","year":"2015","unstructured":"Lecun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521, 436\u2013444. https:\/\/doi.org\/10.1038\/nature14539.","journal-title":"Nature"},{"issue":"2","key":"2185_CR10","doi-asserted-by":"publisher","first-page":"741","DOI":"10.1109\/TASE.2022.3164831","volume":"20","author":"W Li","year":"2023","unstructured":"Li, W., Jia, X., Hsu, Y., Liao, C., Wang, Y., Lin, M., & Lee, J. (2023). A novel methodology for lens matching in compact lens module assembly. IEEE Transactions on Automation Science and Engineering, 20(2), 741\u2013750. https:\/\/doi.org\/10.1109\/TASE.2022.3164831.","journal-title":"IEEE Transactions on Automation Science and Engineering"},{"key":"2185_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","volume":"172","author":"X Li","year":"2018","unstructured":"Li, X., Ding, Q., & Sun, J. Q. (2018). Remaining useful life estimation in prognostics using deep convolution neural networks. Reliability Engineering and System Safety, 172, 1\u201311. https:\/\/doi.org\/10.1016\/j.ress.2017.11.021.","journal-title":"Reliability Engineering and System Safety"},{"issue":"2","key":"2185_CR12","doi-asserted-by":"publisher","first-page":"10324","DOI":"10.1016\/j.ifacol.2020.12.2768","volume":"53","author":"C Liu","year":"2020","unstructured":"Liu, C., Mauricio, A., Chen, Z., Declercq, K., Meerten, Y., Vonderscher, Y., & Gryllias, K. (2020). Gear grinding monitoring based on deep convolutional neural networks. IFAC-PapersOnLine, 53(2), 10324\u201310329. https:\/\/doi.org\/10.1016\/j.ifacol.2020.12.2768.","journal-title":"IFAC-PapersOnLine"},{"key":"2185_CR13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-020-71450-8","author":"A Lopez-del Rio","year":"2020","unstructured":"Lopez-del Rio, A., Martin, M., Perera-Lluna, A., & Saidi, R. (2020). Effect of sequence padding on the performance of deep learning models in archaeal protein functional prediction. Scientific Reports. https:\/\/doi.org\/10.1038\/s41598-020-71450-8","journal-title":"Scientific Reports"},{"key":"2185_CR14","doi-asserted-by":"publisher","DOI":"10.1177\/1687814019841486","author":"W Ma","year":"2019","unstructured":"Ma, W., Geng, X., Jia, C., Gao, L., Liu, Y., & Tian, X. (2019). Percussion characteristic analysis for hydraulic rock drill with no constant-pressurized chamber through numerical simulation and experiment. Advances in Mechanical Engineering. https:\/\/doi.org\/10.1177\/1687814019841486","journal-title":"Advances in Mechanical Engineering"},{"key":"2185_CR15","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1007\/s12541-016-0107-8","volume":"17","author":"J Oh","year":"2016","unstructured":"Oh, J., Song, C., Kim, D., Kim, J., Park, J., & Cho, J. (2016). Numerical investigation of performance of hydraulic percussion drifter. International Journal of Precision Engineering and Manufacturing, 17, 879\u2013885. https:\/\/doi.org\/10.1007\/s12541-016-0107-8.","journal-title":"International Journal of Precision Engineering and Manufacturing"},{"key":"2185_CR16","doi-asserted-by":"publisher","unstructured":"Pang, M., Ting, K. M., Zhao, P., & Zhou, Z. (2018). Improving deep forest by confidence screening. Proceedings-IEEE international conference on data mining 2018, 1194\u20131199. https:\/\/doi.org\/10.1109\/ICDM.2018.00158.","DOI":"10.1109\/ICDM.2018.00158"},{"key":"2185_CR17","unstructured":"PHM Society (2022). 2022 PHM conference data challenge. PHM society data https:\/\/data.phmsociety.org\/2022-phm-conference-data-challenge\/."},{"key":"2185_CR18","doi-asserted-by":"publisher","DOI":"10.1007\/978-981-16-9865-1","volume-title":"Fundamentals of Partial Differential Equations","author":"A Razdan","year":"2022","unstructured":"Razdan, A., & Ravichandran, V. (2022). Fundamentals of Partial Differential Equations. Springer. https:\/\/doi.org\/10.1007\/978-981-16-9865-1"},{"key":"2185_CR19","doi-asserted-by":"publisher","first-page":"1099","DOI":"10.1007\/s10845-021-01906-9","volume":"33","author":"T Schlosser","year":"2022","unstructured":"Schlosser, T., Friedrich, M., Beuth, F., & Kowerko, D. (2022). Improving automated visual fault inspection for semiconductor manufacturing using a hybrid multistage system of deep neural networks. Journal of Intelligent Manufacturing, 33, 1099\u20131123. https:\/\/doi.org\/10.1007\/s10845-021-01906-9.","journal-title":"Journal of Intelligent Manufacturing"},{"key":"2185_CR20","doi-asserted-by":"publisher","first-page":"1054","DOI":"10.1007\/s40435-020-00669-0","volume":"8","author":"S Siahpour","year":"2020","unstructured":"Siahpour, S., Li, X., & Lee, J. (2020). Deep learning-based cross-sensor domain adaptation for fault diagnosis of electro-mechanical actuators. International Journal of Dynamics and Control, 8, 1054\u20131062. https:\/\/doi.org\/10.1007\/s40435-020-00669-0.","journal-title":"International Journal of Dynamics and Control"},{"issue":"1","key":"2185_CR21","doi-asserted-by":"publisher","first-page":"556","DOI":"10.36001\/phme.2022.v7i1.3373","volume":"7","author":"J Taco","year":"2022","unstructured":"Taco, J., Gore, P., Minami, T., Kundu, P., Suer, A., & Lee, J. (2022). A novel methodology for health assessment in printed circuit boards. PHM Society European Conference, 7(1), 556\u2013562. https:\/\/doi.org\/10.36001\/phme.2022.v7i1.3373.","journal-title":"PHM Society European Conference"},{"issue":"9","key":"2185_CR22","doi-asserted-by":"publisher","first-page":"11311","DOI":"10.1016\/j.eswa.2011.02.181","volume":"38","author":"Y Yang","year":"2011","unstructured":"Yang, Y., Liao, Y., Meng, G., & Lee, J. (2011). A hybrid feature selection scheme for unsupervised learning and its application in bearing fault diagnosis. Expert Systems with Applications, 38(9), 11311\u201311320. https:\/\/doi.org\/10.1016\/j.eswa.2011.02.181.","journal-title":"Expert Systems with Applications"},{"key":"2185_CR23","doi-asserted-by":"publisher","first-page":"1077","DOI":"10.1007\/s00170-021-07950-2","volume":"118","author":"J Yao","year":"2022","unstructured":"Yao, J., Lu, B., & Zhang, J. (2022). Tool remaining useful life prediction using deep transfer reinforcement learning based on long short-term memory networks. International Journal of Advanced Manufacturing Technology, 118, 1077\u20131086. https:\/\/doi.org\/10.1007\/s00170-021-07950-2.","journal-title":"International Journal of Advanced Manufacturing Technology"},{"key":"2185_CR24","doi-asserted-by":"publisher","DOI":"10.1145\/3342241","author":"Y Zhang","year":"2019","unstructured":"Zhang, Y., Zhou, J., Zheng, W., Feng, J., Li, L., Liu, Z., Li, M., Zhang, Z., Chen, C., Li, X., Qi, Y., & Zhou, Z. (2019). Distributed deep forest and its application to automatic detection of cash-out fraud. ACM Transactions on Intelligent Systems and Technology. https:\/\/doi.org\/10.1145\/3342241","journal-title":"ACM Transactions on Intelligent Systems and Technology"},{"key":"2185_CR26","doi-asserted-by":"publisher","DOI":"10.1201\/b12207","volume-title":"Ensemble Methods: Foundations and Algorithms","author":"Z Zhou","year":"2012","unstructured":"Zhou, Z. (2012). Ensemble Methods: Foundations and Algorithms. CRC Press."},{"issue":"1","key":"2185_CR27","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1093\/nsr\/nwy108","volume":"6","author":"Z Zhou","year":"2019","unstructured":"Zhou, Z., & Feng, J. (2019). Deep forest. National Science Review, 6(1), 74\u201386. https:\/\/doi.org\/10.1093\/nsr\/nwy108.","journal-title":"National Science Review"},{"key":"2185_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2019.07.008","author":"F Zhou","year":"2020","unstructured":"Zhou, F., Yang, S., Fujita, H., Chen, D., & Wen, C. (2020). Deep learning fault diagnosis method based on global optimization GAN for unbalanced data. Knowledge-Based Systems. https:\/\/doi.org\/10.1016\/j.knosys.2019.07.008","journal-title":"Knowledge-Based Systems"},{"key":"2185_CR28","doi-asserted-by":"publisher","DOI":"10.32474\/mams.2020.02.000138","author":"B Zohuri","year":"2020","unstructured":"Zohuri, B., & Moghaddam, M. (2020). Deep learning limitations and flaws. Modern Approaches on Material Science. https:\/\/doi.org\/10.32474\/mams.2020.02.000138","journal-title":"Modern Approaches on Material Science"},{"key":"2185_CR29","doi-asserted-by":"publisher","first-page":"1085","DOI":"10.1007\/s11771-014-2040-2","volume":"21","author":"H Zuo","year":"2014","unstructured":"Zuo, H., Luo, Z., Guan, J., & Wang, Y. (2014). Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine. Journal of Central South University, 21, 1085\u20131090. https:\/\/doi.org\/10.1007\/s11771-014-2040-2.","journal-title":"Journal of Central South University"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02185-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-023-02185-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-023-02185-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,9,5]],"date-time":"2024-09-05T12:28:53Z","timestamp":1725539333000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-023-02185-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,21]]},"references-count":29,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2024,10]]}},"alternative-id":["2185"],"URL":"https:\/\/doi.org\/10.1007\/s10845-023-02185-2","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"value":"0956-5515","type":"print"},{"value":"1572-8145","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,21]]},"assertion":[{"value":"21 November 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no relevant financial or non-financial interests to disclose.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participant"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}]}}