{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,21]],"date-time":"2026-07-21T04:09:25Z","timestamp":1784606965049,"version":"3.55.0"},"reference-count":143,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,18]],"date-time":"2021-11-18T00:00:00Z","timestamp":1637193600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1A4A4079904"],"award-info":[{"award-number":["2020R1A4A4079904"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Prognostics and health management (PHM) has become an essential function for safe system operation and scheduling economic maintenance. To date, there has been much research and publications on component-level prognostics. In practice, however, most industrial systems consist of multiple components that are interlinked. This paper aims to provide a review of approaches for system-level prognostics. To achieve this goal, the approaches are grouped into four categories: health index-based, component RUL-based, influenced component-based, and multiple failure mode-based prognostics. Issues of each approach are presented in terms of the target systems and employed algorithms. Two examples of PHM datasets are used to demonstrate how the system-level prognostics should be conducted. Challenges for practical system-level prognostics are also addressed.<\/jats:p>","DOI":"10.3390\/s21227655","type":"journal-article","created":{"date-parts":[[2021,11,19]],"date-time":"2021-11-19T02:43:09Z","timestamp":1637289789000},"page":"7655","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Challenges and Opportunities of System-Level Prognostics"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1205-7736","authenticated-orcid":false,"given":"Seokgoo","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA"},{"name":"School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1709-4491","authenticated-orcid":false,"given":"Joo-Ho","family":"Choi","sequence":"additional","affiliation":[{"name":"School of Aerospace and Mechanical Engineering, Korea Aerospace University, Goyang 10540, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0221-9749","authenticated-orcid":false,"given":"Nam H.","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical and Aerospace Engineering, University of Florida, Gainesville, FL 32611, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1016\/j.ymssp.2013.06.004","article-title":"Prognostics and health management design for rotary machinery systems\u2014Reviews, methodology and applications","volume":"42","author":"Lee","year":"2014","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1079","DOI":"10.1177\/1045389X14541496","article-title":"A probabilistic detectability-based sensor network design method for system health monitoring and prognostics","volume":"26","author":"Wang","year":"2015","journal-title":"J. Intell. Mater. Syst. Struct."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1809","DOI":"10.1088\/0957-0233\/17\/7\/020","article-title":"An approach to model-based fault detection in industrial measurement systems with application to engine test benches","volume":"17","author":"Angelov","year":"2006","journal-title":"Meas. Sci. Technol."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"29857","DOI":"10.1109\/ACCESS.2020.2972859","article-title":"Deep learning algorithms for bearing fault diagnostics\u2014A comprehensive review","volume":"8","author":"Zhang","year":"2020","journal-title":"IEEE Access"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Park, Y.-J., Fan, S.-K.S., and Hsu, C.-Y. (2020). A review on fault detection and process diagnostics in industrial processes. Processes, 8.","DOI":"10.3390\/pr8091123"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s42417-021-00286-x","article-title":"Deep Learning Algorithms for Machinery Health Prognostics Using Time-Series Data: A Review","volume":"9","author":"Thoppil","year":"2021","journal-title":"J. Vib. Eng. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1016\/j.microrel.2007.02.016","article-title":"A maintenance planning and business case development model for the application of prognostics and health management (PHM) to electronic systems","volume":"47","author":"Sandborn","year":"2007","journal-title":"Microelectron. Reliab."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Saxena, A., Celaya, J., Balaban, E., Goebel, K., Saha, B., Saha, S., and Schwabacher, M. (2008, January 6\u20139). Metrics for evaluating performance of prognostic techniques. Proceedings of the 2008 International Conference on Prognostics and Health Management PHM 2008, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711436"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1483","DOI":"10.1016\/j.ymssp.2005.09.012","article-title":"A review on machinery diagnostics and prognostics implementing condition-based maintenance","volume":"20","author":"Jardine","year":"2006","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_10","first-page":"1","article-title":"Prognostics and health management for maintenance practitioners-review, implementation and tools evaluation","volume":"8","author":"Atamuradov","year":"2017","journal-title":"Int. J. Progn. Heal. Manag."},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Coble, J.B., Ramuhalli, P., Bond, L.J., Hines, W., and Upadhyaya, B. (2012). Prognostics and Health Management in Nuclear Power Plants: A Review of Technologies and Applications.","DOI":"10.2172\/1047416"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"125","DOI":"10.1007\/s40747-016-0019-3","article-title":"Prognostics: A literature review","volume":"2","author":"Elattar","year":"2016","journal-title":"Complex. Intell. Syst."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1109\/TR.2012.2194173","article-title":"Benefits and challenges of system prognostics","volume":"61","author":"Sun","year":"2012","journal-title":"IEEE Trans. Reliab."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1016\/j.ymssp.2017.11.016","article-title":"Machinery health prognostics: A systematic review from data acquisition to RUL prediction","volume":"104","author":"Lei","year":"2018","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1007\/s40684-018-0055-0","article-title":"A framework for prognostics and health management applications toward smart manufacturing systems","volume":"5","author":"Shin","year":"2018","journal-title":"Int. J. Precis. Eng. Manuf. Technol."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kang, M., and Tian, J. (2018). Machine Learning: Data Pre-processing. Prognostics and Health Management of Electronics: Fundamentals, Machine Learning, and the Internet of Things, John Wiley and Sons.","DOI":"10.1002\/9781119515326.ch5"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1687814020919207","DOI":"10.1177\/1687814020919207","article-title":"An intelligent approach for data pre-processing and analysis in predictive maintenance with an industrial case study","volume":"12","author":"Bekar","year":"2020","journal-title":"Adv. Mech. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"793161","DOI":"10.1155\/2015\/793161","article-title":"Prognostics and Health Management: A Review on Data Driven Approaches","volume":"2015","author":"Tsui","year":"2015","journal-title":"Math. Probl. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s00170-009-2482-0","article-title":"Current status of machine prognostics in condition-based maintenance: A review","volume":"50","author":"Peng","year":"2010","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ymssp.2015.02.016","article-title":"A review on prognostic techniques for non-stationary and non-linear rotating systems","volume":"62","author":"Kan","year":"2015","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2010.11.018","article-title":"Remaining useful life estimation\u2014A review on the statistical data driven approaches","volume":"213","author":"Si","year":"2011","journal-title":"Eur. J. Oper. Res."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.ress.2014.09.014","article-title":"Practical options for selecting data-driven or physics-based prognostics algorithms with reviews","volume":"133","author":"An","year":"2015","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"846","DOI":"10.1109\/TR.2012.2220697","article-title":"Methodology and framework for predicting helicopter rolling element bearing failure","volume":"61","author":"Siegel","year":"2012","journal-title":"IEEE Trans. Reliab."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1109\/TR.2012.2194175","article-title":"Remaining useful life estimation of critical components with application to bearings","volume":"61","author":"Medjaher","year":"2012","journal-title":"IEEE Trans. Reliab."},{"key":"ref_25","unstructured":"He, D., Bechhoefer, E., Dempsey, P., and Ma, J. (2012, January 1\u20133). An integrated approach for gear health prognostics. Proceedings of the Annual Forum Proceedings\u2014AHS International, Fort Worth, TX, USA."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"221","DOI":"10.1177\/0142331208092026","article-title":"A particle-filtering approach for on-line fault diagnosis and failure prognosis","volume":"31","author":"Orchard","year":"2009","journal-title":"Trans. Inst. Meas. Control."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1109\/TIM.2008.2005965","article-title":"Prognostics methods for battery health monitoring using a Bayesian framework","volume":"58","author":"Saha","year":"2009","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"6007","DOI":"10.1016\/j.jpowsour.2011.03.101","article-title":"A review on prognostics and health monitoring of Li-ion battery","volume":"196","author":"Zhang","year":"2011","journal-title":"J. Power Sources"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"293","DOI":"10.1177\/0142331208092030","article-title":"Comparison of prognostic algorithms for estimating remaining useful life of batteries","volume":"31","author":"Saha","year":"2009","journal-title":"Trans. Inst. Meas. Control."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"999","DOI":"10.1007\/s11831-019-09339-7","article-title":"Comparison of Computational Prognostic Methods for Complex Systems under Dynamic Regimes: A Review of Perspectives","volume":"27","author":"Bektas","year":"2019","journal-title":"Arch. Comput. Methods Eng."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.ress.2016.05.006","article-title":"Methodologies for system-level remaining useful life prediction","volume":"154","author":"Khorasgani","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Li, X., Duan, F., Mba, D., and Bennett, I. (2018). Rotating machine prognostics using system-level models. Engineering Asset Management 2016, Springer.","DOI":"10.1007\/978-3-319-62274-3_11"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Saxena, A., Sankararaman, S., and Goebel, K. (2014, January 8\u201310). Performance Evaluation for Fleet-based and Unit-based Prognostic Methods. Proceedings of the Second European Conference of the Prognostics and Health Management Society, Nantes, France.","DOI":"10.36001\/phme.2014.v2i1.1511"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Sankararaman, S. (2015, January 18\u201324). Remaining useful life prediction through failure probability computation for conditioned-based prognostics. Proceedings of the Annual Conference of the Prognostics and Health Management Society, Coronado, CA, USA.","DOI":"10.36001\/phmconf.2015.v7i1.2566"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1115\/1.3656900","article-title":"A Critical Analysis of Crack Propagation Laws","volume":"85","author":"Paris","year":"1960","journal-title":"J. Basic Eng."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.ijfatigue.2007.03.004","article-title":"An engineering model of fatigue crack growth under variable amplitude loading","volume":"30","author":"Huang","year":"2008","journal-title":"Int. J. Fatigue"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"161","DOI":"10.1016\/j.ress.2013.02.019","article-title":"Prognostics 101: A tutorial for particle filter-based prognostics algorithm using Matlab","volume":"115","author":"An","year":"2013","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_38","unstructured":"Bechhoefer, E. (May, January 29). A method for generalized prognostics of a component using Paris Law. Proceedings of the Annual Forum Proceedings\u2014AHS International, Montreal, CA, USA."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Peel, L. (2008, January 6\u20139). Data driven prognostics using a Kalman filter ensemble of neural network models. Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711423"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Heimes, F.O. (2008, January 6\u20139). Recurrent Neural Networks for Remaining Useful Life Estimation. Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711422"},{"key":"ref_41","unstructured":"Babu, G.S., Zhao, P., and Li, X.-L. (2016, January 16\u201319). Deep convolutional neural network based regression approach for estimation of remaining useful life. Proceedings of the International Conference on Database Systems for Advanced Applications, Dallas, TX, USA."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zheng, S., Ristovski, K., Farahat, A., and Gupta, C. (2017, January 19\u201321). Long short-term memory network for remaining useful life estimation. Proceedings of the Prognostics and Health Management (ICPHM), Piscataway, NJ, USA.","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Vachtsevanos, G.J., and Vachtsevanos, G.J. (2006). Intelligent Fault Diagnosis and Prognosis for Engineering Systems, Wiley Online Library.","DOI":"10.1002\/9780470117842"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2412","DOI":"10.1016\/j.eswa.2009.07.014","article-title":"Machine condition prognosis based on sequential Monte Carlo method","volume":"37","author":"Caesarendra","year":"2010","journal-title":"Expert Syst. Appl."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"3485","DOI":"10.1109\/TIE.2020.2978688","article-title":"A novel prognostics approach using shifting kernel particle filter of Li-ion batteries under state changes","volume":"68","author":"Kim","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Hsu, C.S., and Jiang, J.R. (2018, January 13\u201317). Remaining useful life estimation using long short-term memory deep learning. Proceedings of the 4th IEEE International Conference on Applied System Innovation 2018, ICASI 2018, Tokyo, Japan.","DOI":"10.1109\/ICASI.2018.8394326"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1007\/s00170-018-2874-0","article-title":"A neural network filtering approach for similarity-based remaining useful life estimation","volume":"101","author":"Bektas","year":"2019","journal-title":"Int. J. Adv. Manuf. Technol."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"8430","DOI":"10.1016\/j.eswa.2011.01.038","article-title":"Machine health prognostics using survival probability and support vector machine","volume":"38","author":"Widodo","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"2592","DOI":"10.1016\/j.eswa.2010.08.049","article-title":"Application of relevance vector machine and survival probability to machine degradation assessment","volume":"38","author":"Widodo","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1016\/j.probengmech.2010.09.008","article-title":"Combination of probability approach and support vector machine towards machine health prognostics","volume":"26","author":"Caesarendra","year":"2011","journal-title":"Probabilistic Eng. Mech."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"320","DOI":"10.1016\/j.ymssp.2012.02.015","article-title":"Machine performance degradation assessment and remaining useful life prediction using proportional hazard model and support vector machine","volume":"32","author":"Pham","year":"2012","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"585","DOI":"10.1016\/j.ymssp.2011.09.029","article-title":"Application of a state space modeling technique to system prognostics based on a health index for condition-based maintenance","volume":"28","author":"Sun","year":"2012","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1016\/j.pnucene.2014.10.013","article-title":"On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs","volume":"79","author":"Messai","year":"2015","journal-title":"Prog. Nucl. Energy"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Zhang, A., Wang, H., Li, S., Cui, Y., Liu, Z., Yang, G., and Hu, J. (2018). Transfer learning with deep recurrent neural networks for remaining useful life estimation. Appl. Sci., 8.","DOI":"10.3390\/app8122416"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","article-title":"Remaining useful life estimation in prognostics using deep convolution neural networks","volume":"172","author":"Li","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1016\/j.neucom.2017.05.063","article-title":"Remaining useful life estimation of engineered systems using vanilla LSTM neural networks","volume":"275","author":"Wu","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1016\/j.compind.2006.12.004","article-title":"Similarity based method for manufacturing process performance prediction and diagnosis","volume":"58","author":"Liu","year":"2007","journal-title":"Comput. Ind."},{"key":"ref_58","unstructured":"Wang, T. (2010). Trajectory Similarity Based Prediction for Remaining Useful Life Estimation. [Ph.D. Thesis, University of Cincinnati]."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Eker, O.F., Camci, F., and Jennions, I.K. (2014, January 8\u201310). A Similarity-based Prognostics Approach for Remaining Useful Life Prediction. Proceedings of the Second European Conference of the Prognostics and Health Management Society, Nantes, France.","DOI":"10.36001\/phme.2014.v2i1.1479"},{"key":"ref_60","doi-asserted-by":"crossref","unstructured":"Khelif, R., Malinowski, S., Chebel-Morello, B., and Zerhouni, N. (2014, January 1\u20134). RUL prediction based on a new similarity-instance based approach. Proceedings of the IEEE International Symposium on Industrial Electronics, Istanbul, Turkey.","DOI":"10.1109\/ISIE.2014.6865006"},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Lam, J., Sankararaman, S., and Stewart, B. (October, January 29). Enhanced trajectory based similarity prediction with uncertainty quantification. Proceedings of the PHM 2014\u2014Proceedings of the Annual Conference of the Prognostics and Health Management Society 2014, Fort Worth, TX, USA.","DOI":"10.36001\/phmconf.2014.v6i1.2513"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1111\/j.2517-6161.1972.tb00899.x","article-title":"Regression Models and Life-Tables","volume":"34","author":"Cox","year":"1972","journal-title":"J. R. Stat. Soc. Ser. B"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"1189","DOI":"10.1016\/j.ymssp.2004.10.009","article-title":"Mechanical systems hazard estimation using condition monitoring","volume":"20","author":"Sun","year":"2006","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/TMECH.2017.2713722","article-title":"Remaining useful life prediction for multiple-component systems based on a system-level performance indicator","volume":"23","author":"Rodrigues","year":"2017","journal-title":"IEEE ASME Trans. Mechatron."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Wang, J.B., Wang, X.H., and Wang, L.Z. (2017). Modeling of BN lifetime prediction of a system based on integrated multi-level information. Sensors, 17.","DOI":"10.3390\/s17092123"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"1287","DOI":"10.1109\/TR.2015.2418294","article-title":"Dynamic Reliability Assessment for Multi-State Systems Utilizing System-Level Inspection Data","volume":"64","author":"Liu","year":"2015","journal-title":"IEEE Trans. Reliab."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"796","DOI":"10.1080\/09537280412331309208","article-title":"A prognostic algorithm for machine performance assessment and its application","volume":"15","author":"Yan","year":"2004","journal-title":"Prod. Plan. Control."},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Wang, T., Yu, J., Siegel, D., and Lee, J. (2008, January 6\u20139). A similarity-based prognostics approach for remaining useful life estimation of engineered systems. Proceedings of the 2008 International Conference on Prognostics and Health Management PHM 2008, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711421"},{"key":"ref_69","doi-asserted-by":"crossref","first-page":"67","DOI":"10.1016\/j.compind.2019.01.003","article-title":"A fault early warning method for auxiliary equipment based on multivariate state estimation technique and sliding window similarity","volume":"107","author":"Zhang","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_70","unstructured":"Garvey, D., Garvey, J., Seibert, R., Hines, J.W., and Arndt, S.A. (2006, January 12\u201316). Application of on-line monitoring techniques to nuclear plant data. Proceedings of the 5th International Topical Meeting on Nuclear Plant Instrumentation Controls, and Human Machine Interface Technology (NPIC and HMIT 2006), Albuquerque, NM, USA."},{"key":"ref_71","first-page":"71","article-title":"Applying the general path model to estimation of remaining useful life","volume":"2","author":"Coble","year":"2011","journal-title":"Int. J. Progn. Heal. Manag."},{"key":"ref_72","first-page":"52","article-title":"Evaluation of neural networks in the subject of prognostics as compared to linear regression model","volume":"10","author":"Riad","year":"2010","journal-title":"Int. J. Eng. Technol."},{"key":"ref_73","unstructured":"Abbas, M. (2010). System Level Health Assessment of Complex Engineered Processes. [Ph.D. Thesis, Georgia Institute of Technology]."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"862","DOI":"10.3934\/mbe.2019040","article-title":"A new ensemble residual convolutional neural network for remaining useful life estimation","volume":"16","author":"Wen","year":"2019","journal-title":"Math. Biosci. Eng."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"106113","DOI":"10.1016\/j.asoc.2020.106113","article-title":"Remaining useful life prediction using multi-scale deep convolutional neural network","volume":"89","author":"Li","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ins.2020.12.032","article-title":"Multiscale deep bidirectional gated recurrent neural networks based prognostic method for complex non-linear degradation systems","volume":"554","author":"Behera","year":"2021","journal-title":"Inf. Sci."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ress.2012.03.008","article-title":"Ensemble of data-driven prognostic algorithms for robust prediction of remaining useful life","volume":"103","author":"Hu","year":"2012","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1016\/j.eswa.2014.10.041","article-title":"Remaining useful life estimation for mechanical systems based on similarity of phase space trajectory","volume":"42","author":"Zhang","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1016\/j.ress.2006.12.004","article-title":"Formalisation of a new prognosis model for supporting proactive maintenance implementation on industrial system","volume":"93","author":"Muller","year":"2008","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.apacoust.2017.12.003","article-title":"Reciprocating compressor prognostics of an instantaneous failure mode utilising temperature only measurements","volume":"147","author":"Loukopoulos","year":"2019","journal-title":"Appl. Acoust."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s12541-021-00513-1","article-title":"Machine Health Assessment Based on an Anomaly Indicator Using a Generative Adversarial Network","volume":"22","author":"Park","year":"2021","journal-title":"Int. J. Precis. Eng. Manuf."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"107028","DOI":"10.1016\/j.ress.2020.107028","article-title":"Fault monitoring and remaining useful life prediction framework for multiple fault modes in prognostics","volume":"203","author":"Jiao","year":"2020","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"1124","DOI":"10.1109\/JSEN.2013.2293517","article-title":"PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data","volume":"14","author":"Xu","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"106612","DOI":"10.1016\/j.ymssp.2019.106612","article-title":"Deep digital twins for detection, diagnostics and prognostics","volume":"140","author":"Booyse","year":"2020","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_85","unstructured":"Gomes, J.P.P., Rodrigues, L.R., Galv\u00e3o, R.K.H., and Yoneyama, T. (2013, January 14\u201317). System level RUL estimation for multiple-component systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society, New Orleans, FL, USA."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Ferri, F.A.S., Rodrigues, L.R., Gomes, J.P.P., De Medeiros, I.P., Galvao, R.K.H., and Nascimento, C.L. (2013, January 15\u201318). Combining PHM information and system architecture to support aircraft maintenance planning. Proceedings of the SysCon 2013\u20147th Annual IEEE International Systems Conference Proceedings, Orlando, FL, USA.","DOI":"10.1109\/SysCon.2013.6549859"},{"key":"ref_87","doi-asserted-by":"crossref","unstructured":"Daigle, M., Bregon, A., and Roychoudhury, I. (2012). A Distributed Approach to System-Level Prognostics.","DOI":"10.36001\/phmconf.2012.v4i1.2112"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"495","DOI":"10.1109\/TR.2014.2313791","article-title":"Distributed prognostics based on structural model decomposition","volume":"63","author":"Daigle","year":"2014","journal-title":"IEEE Trans. Reliab."},{"key":"ref_89","doi-asserted-by":"crossref","unstructured":"Daigle, M., Sankararaman, S., and Roychoudhury, I. (2016, January 3\u20136). System-level Prognostics for the National Airspace. Proceedings of the Annual Conference of the Prognostics and Health Management Society, Denver, CO, USA.","DOI":"10.36001\/phmconf.2016.v8i1.2583"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Daigle, M., and Goebel, K. (2011, January 5\u201312). Multiple damage progression paths in model-based prognostics. Proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2011.5747574"},{"key":"ref_91","unstructured":"Vasan, A.S.S., Chen, C., and Pecht, M. (2013, January 24\u201327). A circuit-centric approach to electronic system-level diagnostics and prognostics. Proceedings of the PHM 2013\u20142013 IEEE International Conference on Prognostics and Health Management, Gaithersburg, MD, USA."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Chiachio, M., Chiachio, J., Sankararaman, S., and Andrews, J. (2017, January 2\u20135). Integration of prognostics at a system level: A Petri net approach. Proceedings of the Annual Conference Of The PHM Society, St. Petersburg, FL, USA.","DOI":"10.36001\/phmconf.2017.v9i1.2475"},{"key":"ref_93","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1109\/JSYST.2017.2667232","article-title":"Predictive maintenance optimization for aircraft redundant systems subjected to multiple wear profiles","volume":"12","author":"Vianna","year":"2017","journal-title":"IEEE Syst. J."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"1197","DOI":"10.1109\/JSYST.2014.2343752","article-title":"Use of PHM Information and System Architecture for Optimized Aircraft Maintenance Planning","volume":"9","author":"Rodrigues","year":"2015","journal-title":"IEEE Syst. J."},{"key":"ref_95","doi-asserted-by":"crossref","first-page":"113676","DOI":"10.1016\/j.microrel.2020.113676","article-title":"A hybrid system-level prognostics approach with online RUL forecasting for electronics-rich systems with unknown degradation behaviors","volume":"111","author":"Hoblos","year":"2020","journal-title":"Microelectron. Reliab."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ejor.2017.02.044","article-title":"Condition-based maintenance policies for systems with multiple dependent components: A review","volume":"261","author":"Flapper","year":"2017","journal-title":"Eur. J. Oper. Res."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"1628","DOI":"10.1109\/JSYST.2020.2983376","article-title":"Degradation Modeling and Uncertainty Quantification for System-Level Prognostics","volume":"15","author":"Tamssaouet","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"4659","DOI":"10.1109\/TSMC.2019.2944834","article-title":"System-Level Prognostics Under Mission Profile Effects Using Inoperability Input-Output Model","volume":"51","author":"Tamssaouet","year":"2019","journal-title":"IEEE Trans. Syst. Man Cybern. Syst."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Tamssaouet, F., Nguyen, T.P.K., Medjaher, K., and Orchard, M.E. (2019, January 23\u201326). Uncertainty Quantification in System-level Prognostics: Application to Tennessee Eastman Process. Proceedings of the 6th International Conference on Control, Decision and Information Technologies (CoDIT), Paris, France.","DOI":"10.1109\/CoDIT.2019.8820618"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Tamssaouet, F., Nguyen, T.P.K., and Medjaher, K. (2018, January 24\u201327). System-level prognostics based on inoperability input-output model. Proceedings of the Annual Conference of the PHM Society, Philadelphia, PA, USA.","DOI":"10.36001\/phmconf.2018.v10i1.487"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"52","DOI":"10.1016\/j.isatra.2020.05.002","article-title":"Online joint estimation and prediction for system-level prognostics under component interactions and mission profile effects","volume":"113","author":"Tamssaouet","year":"2020","journal-title":"ISA Trans."},{"key":"ref_102","unstructured":"Tamssaouet, F., Nguyen, K.T.P., and Medjaher, K. (2019, January 22\u201324). System Remaining Useful Life Maximization through Mission Profile Optimization. Proceedings of the Asia-Pacific Conference of the Prognostics and Health Management Society, Bejing, China."},{"key":"ref_103","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1016\/j.ress.2016.12.003","article-title":"System dynamic reliability assessment and failure prognostics","volume":"160","author":"Liu","year":"2017","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"504","DOI":"10.1016\/j.engfailanal.2017.04.015","article-title":"DBN based failure prognosis method considering the response of protective layers for the complex industrial systems","volume":"79","author":"Hu","year":"2017","journal-title":"Eng. Fail. Anal."},{"key":"ref_105","doi-asserted-by":"crossref","unstructured":"Maitre, J., Gupta, J.S., Medjaher, K., and Zerhouni, N. (2016, January 5\u201312). A PHM system approach: Application to a simplified aircraft bleed system. Proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2016.7500617"},{"key":"ref_106","doi-asserted-by":"crossref","unstructured":"Hafsa, W., Chebel-Morello, B., Varnier, C., Medjaher, K., and Zerhouni, N. (2015, January 21\u201323). Prognostics of health status of multi-component systems with degradation interactions. Proceedings of the Proceedings of International Conference on Industrial Engineering and Systems Management IEEE IESM 2015, Seville, Spain.","DOI":"10.1109\/IESM.2015.7380258"},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"429","DOI":"10.21629\/JSEE.2018.02.22","article-title":"Remaining useful life prediction for a nonlinear multi-degradation system with public noise","volume":"29","author":"Hanwen","year":"2018","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1002\/nav.21583","article-title":"Stochastic framework for partially degradation systems with continuous component degradation-rate-interactions","volume":"61","author":"Bian","year":"2014","journal-title":"Nav. Res. Logist."},{"key":"ref_109","first-page":"470","article-title":"Stochastic modeling and real-time prognostics for multi-component systems with degradation rate interactions","volume":"46","author":"Bian","year":"2014","journal-title":"IIE Trans. Institute Ind. Eng."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"1556","DOI":"10.1109\/TSMC.2015.2500020","article-title":"A reliability assessment framework for systems with degradation dependency by combining binary decision diagrams and Monte Carlo simulation","volume":"46","author":"Lin","year":"2015","journal-title":"IEEE Trans. Syst. Man, Cybern. Syst."},{"key":"ref_111","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1109\/TR.2015.2500684","article-title":"Component importance measures for components with multiple dependent competing degradation processes and subject to maintenance","volume":"65","author":"Lin","year":"2015","journal-title":"IEEE Trans. Reliab."},{"key":"ref_112","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.conengprac.2017.11.003","article-title":"Model-based multi-component adaptive prognosis for hybrid dynamical systems","volume":"72","author":"Prakash","year":"2018","journal-title":"Control. Eng. Pract."},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"659","DOI":"10.1002\/qre.2428","article-title":"Optimal Bayesian maintenance policy for a gearbox subject to two dependent failure modes","volume":"35","author":"Li","year":"2019","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ress.2015.11.010","article-title":"Condition-based maintenance of multi-component systems with degradation state-rate interactions","volume":"148","author":"Rasmekomen","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_115","first-page":"228","article-title":"Condition-based maintenance for multi-component systems using importance measure and predictive information","volume":"1","author":"Nguyen","year":"2014","journal-title":"Int. J. Syst. Sci. Oper. Logist."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Ragab, A., Yacout, S., Ouali, M.S., and Osman, H. (2015, January 26\u201329). Multiple failure modes prognostics using logical analysis of data. Proceedings of the Annual Reliability and Maintainability Symposium, Palm Harbor, FL, USA.","DOI":"10.1109\/RAMS.2015.7105165"},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Sankavaram, C., Kodali, A., Pattipati, K., Wang, B., Azam, M.S., and Singh, S. (2011, January 20\u201323). A prognostic framework for health management of coupled systems. Proceedings of the 2011 IEEE International Conference on Prognostics and Health Management PHM 2011, Denver, CO, USA.","DOI":"10.1109\/ICPHM.2011.6024334"},{"key":"ref_118","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.ymssp.2013.10.013","article-title":"A mixture Weibull proportional hazard model for mechanical system failure prediction utilising lifetime and monitoring data","volume":"43","author":"Zhang","year":"2014","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_119","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1109\/MAES.2013.6495647","article-title":"Multiple model moving horizon estimation approach to prognostics in coupled systems","volume":"28","author":"Pattipati","year":"2013","journal-title":"IEEE Aerosp. Electron. Syst. Mag."},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1016\/j.ress.2018.07.006","article-title":"A holistic multi-failure mode prognosis approach for complex equipment","volume":"180","author":"Blancke","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_121","unstructured":"Zhang, B., Sconyers, C., Patrick, R., and Vachtsevanos, G.J. (October, January 27). A multi-fault modeling approach for fault diagnosis and failure prognosis of engineering systems. Proceedings of the Annual Conference of the Prognostics and Health Management Society, San Diego, CA, USA."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Raghavan, N., and Frey, D.D. (2015, January 22\u201325). Remaining useful life estimation for systems subject to multiple degradation mechanisms. Proceedings of the 2015 IEEE Conference on Prognostics and Health Management: Enhancing Safety, Efficiency, Availability, and Effectiveness of Systems Through PHAf Technology and Application PHM, Austin, TX, USA.","DOI":"10.1109\/ICPHM.2015.7245036"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Daigle, M., and Goebel, K. (2010, January 6\u201313). Model-based prognostics under limited sensing. Proceedings of the IEEE Aerospace Conference Proceedings, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2010.5446822"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"104748","DOI":"10.1016\/j.conengprac.2021.104748","article-title":"Remaining useful life prediction for ion etching machine cooling system using deep recurrent neural network-based approaches","volume":"109","author":"Wu","year":"2021","journal-title":"Control. Eng. Pract."},{"key":"ref_125","unstructured":"Vishnu, T.V., Gupta, P., Malhotra, P., Vig, L., and Shroff, G. (2018, January 22). Recurrent neural networks for online remaining useful life estimation in ion mill etching system. Proceedings of the Annual Conference of the PHM Society, Philadelphia, PA, USA."},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"101008","DOI":"10.1115\/1.4044248","article-title":"Failure Detection and Remaining Life Estimation for Ion Mill Etching Process through Deep-Learning Based Multimodal Data Fusion","volume":"141","author":"He","year":"2019","journal-title":"J. Manuf. Sci. Eng."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"185","DOI":"10.1109\/TSM.2021.3059025","article-title":"Two-Stage Transfer Learning for Fault Prognosis of Ion Mill Etching Process","volume":"34","author":"Liu","year":"2021","journal-title":"IEEE Trans. Semicond. Manuf."},{"key":"ref_128","unstructured":"Saxena, A., and Goebel, K. (2008). Turbofan Engine Degradation Simulation Data Set."},{"key":"ref_129","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., and Eklund, N. (2008, January 6\u20139). Damage propagation modeling for aircraft engine run-to-failure simulation. Proceedings of the 2008 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref_130","unstructured":"Frederick, D.K., DeCastro, J.A., and Litt, J.S. (2007). User\u2019s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS)."},{"key":"ref_131","doi-asserted-by":"crossref","unstructured":"Khumprom, P., Grewell, D., and Yodo, N. (2020). Deep Neural Network Feature Selection Approaches for Data-Driven Prognostic Model of Aircraft Engines. Aerospace, 7.","DOI":"10.3390\/aerospace7090132"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1269","DOI":"10.1109\/TR.2018.2829738","article-title":"New methodology for improving the inspection policies for degradation model selection according to prognostic measures","volume":"67","author":"Nguyen","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"5872","DOI":"10.1109\/TIE.2017.2777383","article-title":"Assessment of Data Suitability for Machine Prognosis Using Maximum Mean Discrepancy","volume":"65","author":"Jia","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_134","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.ymssp.2017.06.025","article-title":"Simulation-driven machine learning: Bearing fault classification","volume":"99","author":"Sobie","year":"2018","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/j.ymssp.2015.03.004","article-title":"A co-training-based approach for prediction of remaining useful life utilizing both failure and suspension data","volume":"62","author":"Hu","year":"2015","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"2497","DOI":"10.1007\/s12206-018-0507-z","article-title":"Prediction of remaining useful life under different conditions using accelerated life testing data","volume":"32","author":"An","year":"2018","journal-title":"J. Mech. Sci. Technol."},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"106486","DOI":"10.1016\/j.ymssp.2019.106486","article-title":"Prediction of remaining useful life by data augmentation technique based on dynamic time warping","volume":"136","author":"Kim","year":"2020","journal-title":"Mech. Syst. Signal. Process."},{"key":"ref_138","first-page":"1","article-title":"Metrics for offline evaluation of prognostic performance","volume":"1","author":"Saxena","year":"2010","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"718","DOI":"10.1109\/TR.2015.2500681","article-title":"Online Performance Assessment Method for a Model-Based Prognostic Approach","volume":"65","author":"Hu","year":"2016","journal-title":"IEEE Trans. Reliab."},{"key":"ref_140","doi-asserted-by":"crossref","first-page":"86","DOI":"10.1016\/j.ress.2017.09.021","article-title":"Noise-dependent ranking of prognostics algorithms based on discrepancy without true damage information","volume":"184","author":"Wang","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_141","doi-asserted-by":"crossref","unstructured":"Sankararaman, S., and Goebel, K. (2013, January 14\u201317). Why is the remaining useful life prediction uncertain?. Proceedings of the Annual Conference of the Prognostics and Health Management Society, New Orleans, FL, USA.","DOI":"10.36001\/phmconf.2013.v5i1.2263"},{"key":"ref_142","first-page":"1","article-title":"Uncertainty in prognostics and systems health management","volume":"6","author":"Sankararaman","year":"2015","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Dong, T., and Kim, N.H. (2018). Cost-effectiveness of structural health monitoring in fuselage maintenance of the civil aviation industry. Aerospace, 5.","DOI":"10.3390\/aerospace5030087"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7655\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:32:05Z","timestamp":1760167925000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/22\/7655"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,18]]},"references-count":143,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["s21227655"],"URL":"https:\/\/doi.org\/10.3390\/s21227655","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,18]]}}}