{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,7]],"date-time":"2025-11-07T13:38:54Z","timestamp":1762522734161,"version":"3.37.3"},"reference-count":32,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T00:00:00Z","timestamp":1650844800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000084","name":"Directorate for Engineering","doi-asserted-by":"publisher","award":["1727846"],"award-info":[{"award-number":["1727846"]}],"id":[{"id":"10.13039\/100000084","id-type":"DOI","asserted-by":"publisher"}]},{"name":"National Oilwell Varco, Inc."}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Intell Manuf"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s10845-022-01942-z","type":"journal-article","created":{"date-parts":[[2022,4,25]],"date-time":"2022-04-25T09:16:36Z","timestamp":1650878196000},"page":"2611-2624","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Industrial system working condition identification using operation-adjusted hidden Markov model"],"prefix":"10.1007","volume":"34","author":[{"given":"Jinwen","family":"Sun","sequence":"first","affiliation":[]},{"given":"Akash","family":"Deep","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5902-8812","authenticated-orcid":false,"given":"Shiyu","family":"Zhou","sequence":"additional","affiliation":[]},{"given":"Dharmaraj","family":"Veeramani","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,4,25]]},"reference":[{"key":"1942_CR1","doi-asserted-by":"publisher","DOI":"10.5120\/8469-2395","author":"JC Badajena","year":"2012","unstructured":"Badajena, J. C., & Rout, C. (2012). Incorporating hidden Markov model into anomaly detection technique for network intrusion detection. International Journal of Computer Applications. https:\/\/doi.org\/10.5120\/8469-2395","journal-title":"International Journal of Computer Applications"},{"issue":"1","key":"1942_CR2","first-page":"012031","volume":"364","author":"F Cartella","year":"2012","unstructured":"Cartella, F., Liu, T., Meganck, S., Lemeire, J., & Sahli, H. (2012). Online adaptive learning of left-right continuous HMM for bearings condition assessment. Journal of Physics: Conference Series, 364(1), 012031.","journal-title":"Journal of Physics: Conference Series"},{"key":"1942_CR3","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.sna.2015.05.015","volume":"232","author":"F Casta\u00f1o","year":"2015","unstructured":"Casta\u00f1o, F., del Toro, R. M., Haber, R. E., & Beruvides, G. (2015). Conductance sensing for monitoring micromechanical machining of conductive materials. Sensors and Actuators a: Physical, 232, 163\u2013171.","journal-title":"Sensors and Actuators a: Physical"},{"key":"1942_CR4","doi-asserted-by":"publisher","first-page":"489","DOI":"10.1016\/j.ymssp.2016.06.027","volume":"83","author":"F Casta\u00f1o","year":"2017","unstructured":"Casta\u00f1o, F., Haber, R. E., & del Toro, R. M. (2017). Characterization of tool-workpiece contact during the micromachining of conductive materials. Mechanical Systems and Signal Processing, 83, 489\u2013505.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"1942_CR5","unstructured":"Chen, J., & Patton, R. J. (2012). Robust model-based fault diagnosis for dynamic systems (Vol. 3). Springer."},{"key":"1942_CR6","volume-title":"Fault detection and diagnosis in industrial systems","author":"LH Chiang","year":"2012","unstructured":"Chiang, L. H., Russell, E. L., & Braatz, R. D. (2012). Fault detection and diagnosis in industrial systems. Springer."},{"issue":"4","key":"1942_CR7","doi-asserted-by":"publisher","first-page":"454","DOI":"10.1109\/TSM.2013.2268861","volume":"26","author":"ME Cholette","year":"2013","unstructured":"Cholette, M. E., Celen, M., Djurdjanovic, D., & Rasberry, J. D. (2013). Condition monitoring and operational decision making in semiconductor manufacturing. IEEE Transactions on Semiconductor Manufacturing, 26(4), 454\u2013464.","journal-title":"IEEE Transactions on Semiconductor Manufacturing"},{"issue":"6","key":"1942_CR8","doi-asserted-by":"publisher","first-page":"16","DOI":"10.1109\/MSP.2018.2867638","volume":"35","author":"J Ding","year":"2018","unstructured":"Ding, J., Tarokh, V., & Yang, Y. (2018). Model selection techniques: An overview. IEEE Signal Processing Magazine, 35(6), 16\u201334.","journal-title":"IEEE Signal Processing Magazine"},{"issue":"4","key":"1942_CR9","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1109\/TII.2012.2205583","volume":"8","author":"O Geramifard","year":"2012","unstructured":"Geramifard, O., Xu, J.-X., Zhou, J.-H., & Li, X. (2012). A physically segmented hidden Markov model approach for continuous tool condition monitoring: Diagnostics and prognostics. IEEE Transactions on Industrial Informatics, 8(4), 964\u2013973.","journal-title":"IEEE Transactions on Industrial Informatics"},{"key":"1942_CR10","doi-asserted-by":"crossref","unstructured":"Guo, G., Wang, H., Bell, D., Bi, Y., & Greer, K. (2003). KNN model-based approach in classification. In OTM confederated international conferences \u201cOn the Move to Meaningful Internet Systems\u201d (pp. 986\u2013996).","DOI":"10.1007\/978-3-540-39964-3_62"},{"key":"1942_CR11","doi-asserted-by":"crossref","unstructured":"Hosmer, Jr., D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). Wiley.","DOI":"10.1002\/9781118548387"},{"issue":"7","key":"1942_CR12","doi-asserted-by":"publisher","first-page":"1667","DOI":"10.1162\/089976603321891855","volume":"15","author":"SS Keerthi","year":"2003","unstructured":"Keerthi, S. S., & Lin, C.-J. (2003). Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Computation, 15(7), 1667\u20131689.","journal-title":"Neural Computation"},{"issue":"2","key":"1942_CR13","doi-asserted-by":"publisher","first-page":"91","DOI":"10.1016\/j.patrec.2009.09.023","volume":"31","author":"W Khreich","year":"2010","unstructured":"Khreich, W., Granger, E., Miri, A., & Sabourin, R. (2010). On the memory complexity of the forward\u2013backward algorithm. Pattern Recognition Letters, 31(2), 91\u201399.","journal-title":"Pattern Recognition Letters"},{"issue":"1","key":"1942_CR14","doi-asserted-by":"publisher","first-page":"108","DOI":"10.3390\/s21010108","volume":"21","author":"M Kunto\u011flu","year":"2021","unstructured":"Kunto\u011flu, M., Aslan, A., Pimenov, D. Y., Usca, \u00dc. A., Salur, E., Gupta, M. K., Mikolajczyk, T., Giasin, K., Kap\u0142onek, W., & Sharma, S. (2021). A review of indirect tool condition monitoring systems and decision-making methods in turning: Critical analysis and trends. Sensors, 21(1), 108.","journal-title":"Sensors"},{"key":"1942_CR15","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.asoc.2017.06.035","volume":"60","author":"J Li","year":"2017","unstructured":"Li, J., Pedrycz, W., & Jamal, I. (2017). Multivariate time series anomaly detection: A framework of Hidden Markov Models. Applied Soft Computing, 60, 229\u2013240.","journal-title":"Applied Soft Computing"},{"key":"1942_CR16","doi-asserted-by":"publisher","first-page":"1076","DOI":"10.1016\/j.renene.2018.08.048","volume":"132","author":"J Li","year":"2019","unstructured":"Li, J., Zhang, X., Zhou, X., & Lu, L. (2019). Reliability assessment of wind turbine bearing based on the degradation-Hidden-Markov model. Renewable Energy, 132, 1076\u20131087.","journal-title":"Renewable Energy"},{"key":"1942_CR17","doi-asserted-by":"publisher","first-page":"689","DOI":"10.1016\/j.ymssp.2019.06.021","volume":"131","author":"W Li","year":"2019","unstructured":"Li, W., & Liu, T. (2019). Time varying and condition adaptive hidden Markov model for tool wear state estimation and remaining useful life prediction in micro-milling. Mechanical Systems and Signal Processing, 131, 689\u2013702.","journal-title":"Mechanical Systems and Signal Processing"},{"key":"1942_CR18","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1016\/j.mechmachtheory.2018.11.005","volume":"133","author":"X Li","year":"2019","unstructured":"Li, X., Jiang, H., Xiong, X., & Shao, H. (2019). Rolling bearing health prognosis using a modified health index based hierarchical gated recurrent unit network. Mechanism and Machine Theory, 133, 229\u2013249.","journal-title":"Mechanism and Machine Theory"},{"issue":"8","key":"1942_CR19","doi-asserted-by":"publisher","first-page":"1845","DOI":"10.1007\/s10845-016-1222-1","volume":"29","author":"W Liao","year":"2018","unstructured":"Liao, W., Li, D., & Cui, S. (2018). A heuristic optimization algorithm for HMM based on SA and EM in machinery diagnosis. Journal of Intelligent Manufacturing, 29(8), 1845\u20131857.","journal-title":"Journal of Intelligent Manufacturing"},{"issue":"2","key":"1942_CR20","doi-asserted-by":"publisher","first-page":"199","DOI":"10.1002\/wics.199","volume":"4","author":"AA Neath","year":"2012","unstructured":"Neath, A. A., & Cavanaugh, J. E. (2012). The Bayesian information criterion: Background, derivation, and applications. Wiley Interdisciplinary Reviews: Computational Statistics, 4(2), 199\u2013203.","journal-title":"Wiley Interdisciplinary Reviews: Computational Statistics"},{"issue":"2","key":"1942_CR21","first-page":"101","volume":"1","author":"I Patel","year":"2010","unstructured":"Patel, I., & Rao, Y. S. (2010). Speech recognition using HMM with MFCC\u2014an analysis using frequency specral decomposion technique. Signal & Image Processing: An International Journal (SIPIJ), 1(2), 101\u2013110.","journal-title":"Signal & Image Processing: An International Journal (SIPIJ)"},{"issue":"2","key":"1942_CR22","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1109\/TIM.2008.2005965","volume":"58","author":"B Saha","year":"2008","unstructured":"Saha, B., Goebel, K., Poll, S., & Christophersen, J. (2008). Prognostics methods for battery health monitoring using a Bayesian framework. IEEE Transactions on Instrumentation and Measurement, 58(2), 291\u2013296.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1942_CR23","doi-asserted-by":"crossref","unstructured":"Saha, B., Koshimoto, E., Quach, C. C., Hogge, E. F., Strom, T. H., Hill, B. L., Vazquez, S. L., & Goebel, K. (2011). Battery health management system for electric UAVs. In 2011 aerospace conference (pp. 1\u20139).","DOI":"10.1109\/AERO.2011.5747587"},{"issue":"4","key":"1942_CR24","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1007\/s00521-005-0469-9","volume":"14","author":"C Scheffer","year":"2005","unstructured":"Scheffer, C., Engelbrecht, H., & Heyns, P. S. (2005). A comparative evaluation of neural networks and hidden Markov models for monitoring turning tool wear. Neural Computing & Applications, 14(4), 325\u2013336.","journal-title":"Neural Computing & Applications"},{"key":"1942_CR25","doi-asserted-by":"crossref","unstructured":"Stefanidis, K., & Voyiatzis, A. G. (2016). An HMM-based anomaly detection approach for SCADA systems. In IFIP international conference on information security theory and practice (pp. 85\u201399).","DOI":"10.1007\/978-3-319-45931-8_6"},{"issue":"2","key":"1942_CR26","doi-asserted-by":"publisher","first-page":"491","DOI":"10.1109\/TR.2012.2194177","volume":"61","author":"DA Tobon-Mejia","year":"2012","unstructured":"Tobon-Mejia, D. A., Medjaher, K., Zerhouni, N., & Tripot, G. (2012). A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models. IEEE Transactions on Reliability, 61(2), 491\u2013503.","journal-title":"IEEE Transactions on Reliability"},{"issue":"6","key":"1942_CR27","doi-asserted-by":"publisher","first-page":"2320","DOI":"10.1109\/TIM.2006.887042","volume":"55","author":"R Yan","year":"2006","unstructured":"Yan, R., & Gao, R. X. (2006). Hilbert-Huang transform-based vibration signal analysis for machine health monitoring. IEEE Transactions on Instrumentation and Measurement, 55(6), 2320\u20132329.","journal-title":"IEEE Transactions on Instrumentation and Measurement"},{"key":"1942_CR28","doi-asserted-by":"publisher","first-page":"492","DOI":"10.4028\/www.scientific.net\/AMM.378.492","volume":"378","author":"G Yu","year":"2013","unstructured":"Yu, G., Sheng, L. Y., & Guo, M. M. (2013). Degradation model prediction for battery of electric vehicle based on hidden Markov model. Applied Mechanics and Materials, 378, 492\u2013495.","journal-title":"Applied Mechanics and Materials"},{"issue":"1","key":"1942_CR29","doi-asserted-by":"publisher","first-page":"201","DOI":"10.1007\/s00170-016-9711-0","volume":"91","author":"J Yu","year":"2017","unstructured":"Yu, J., Liang, S., Tang, D., & Liu, H. (2017). A weighted hidden Markov model approach for continuous-state tool wear monitoring and tool life prediction. The International Journal of Advanced Manufacturing Technology, 91(1), 201\u2013211.","journal-title":"The International Journal of Advanced Manufacturing Technology"},{"issue":"3","key":"1942_CR30","doi-asserted-by":"publisher","first-page":"1471","DOI":"10.1109\/TR.2016.2570561","volume":"65","author":"D Zhang","year":"2016","unstructured":"Zhang, D., Bailey, A. D., & Djurdjanovic, D. (2016). Bayesian identification of hidden Markov models and their use for condition-based monitoring. IEEE Transactions on Reliability, 65(3), 1471\u20131482.","journal-title":"IEEE Transactions on Reliability"},{"key":"1942_CR31","volume-title":"Industrial data analytics for diagnosis and prognosis\u2014A random effects modelling approach","author":"S Zhou","year":"2021","unstructured":"Zhou, S., & Chen, Y. (2021). Industrial data analytics for diagnosis and prognosis\u2014A random effects modelling approach. Wiley."},{"issue":"1","key":"1942_CR32","doi-asserted-by":"publisher","first-page":"247","DOI":"10.1007\/s10845-020-01663-1","volume":"33","author":"Y Zhou","year":"2020","unstructured":"Zhou, Y., Sun, B., Sun, W., & Lei, Z. (2020). Tool wear condition monitoring based on a two-layer angle kernel extreme learning machine using sound sensor for milling process. Journal of Intelligent Manufacturing, 33(1), 247\u2013258.","journal-title":"Journal of Intelligent Manufacturing"}],"container-title":["Journal of Intelligent Manufacturing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-01942-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10845-022-01942-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10845-022-01942-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,6,9]],"date-time":"2023-06-09T12:04:15Z","timestamp":1686312255000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10845-022-01942-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,4,25]]},"references-count":32,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["1942"],"URL":"https:\/\/doi.org\/10.1007\/s10845-022-01942-z","relation":{},"ISSN":["0956-5515","1572-8145"],"issn-type":[{"type":"print","value":"0956-5515"},{"type":"electronic","value":"1572-8145"}],"subject":[],"published":{"date-parts":[[2022,4,25]]},"assertion":[{"value":"1 November 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 March 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 April 2022","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 competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}