{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:53:22Z","timestamp":1777449202671,"version":"3.51.4"},"reference-count":40,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2020,3,27]],"date-time":"2020-03-27T00:00:00Z","timestamp":1585267200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["U1808214"],"award-info":[{"award-number":["U1808214"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51875082"],"award-info":[{"award-number":["51875082"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Major Special Plan Project of Liaoning Province","award":["2019JH1\/10100019"],"award-info":[{"award-number":["2019JH1\/10100019"]}]},{"name":"Key R &amp; D projects of Ningxia Hui Autonomous Region","award":["2018BDE02045"],"award-info":[{"award-number":["2018BDE02045"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Prognostics and health management technology (PHM), a measure to ensure the reliability and safety of the operation of industrial machinery, has attracted attention and application adequately. However, how to use the monitored information to evaluate the degradation of rolling bearings is a significant issue for its predictive maintenance and autonomic logistics. This work presents a reliable health prognosis approach to estimate the health indicator (HI) and remaining useful life (RUL) of rolling bearings. Firstly, to accurately capture the degradation process, a novel health index (HI) is constructed based on correlation kurtosis for different iteration periods and a Gaussian process latency variable model (GPLVM). Then, a multiple convolutional long short-term memory (MCLSTM) network is proposed to predict HI values and RUL values. Finally, we perform experimental datasets of rolling bearings, demonstrating that the presented method surpasses other state-of-the-art prognosis approaches. The results also confirm the feasibility of the presented method in industrial machinery.<\/jats:p>","DOI":"10.3390\/s20071864","type":"journal-article","created":{"date-parts":[[2020,4,1]],"date-time":"2020-04-01T03:44:13Z","timestamp":1585712653000},"page":"1864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["A Reliable Prognosis Approach for Degradation Evaluation of Rolling Bearing Using MCLSTM"],"prefix":"10.3390","volume":"20","author":[{"given":"Gangjin","family":"Huang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongkun","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiayu","family":"Ou","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yuanliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mingliang","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.jpowsour.2018.01.004","article-title":"Improving optimal control of grid-connected lithium-ion batteries through more accurate battery and degradation modelling","volume":"379","author":"Reniers","year":"2018","journal-title":"J. Power Sources"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Sohaib, M., Kim, C.H., and Kim, J.M. (2017). A hybrid feature model and deep-learning-based bearing fault diagnosis. Sensors, 17.","DOI":"10.3390\/s17122876"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"1163","DOI":"10.1080\/00207721.2019.1597941","article-title":"Robust fault tolerant controller design for Takagi-Sugeno systems under input saturation","volume":"50","author":"Aouaouda","year":"2019","journal-title":"Int. J. Syst. Sci."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"693","DOI":"10.1016\/j.renene.2019.03.136","article-title":"Reliability prediction of an offshore wind turbine gearbox","volume":"141","author":"Bhardwaj","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"191","DOI":"10.1109\/TR.2014.2299152","article-title":"Review of hybrid prognostics approaches for remaining useful life prediction of engineered systems, and an application to battery life prediction","volume":"63","author":"Liao","year":"2014","journal-title":"IEEE Trans. Reliab."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1488","DOI":"10.1111\/ffe.12791","article-title":"A multi-axial low-cycle fatigue life prediction model considering effects of additional hardening","volume":"41","author":"Zhao","year":"2018","journal-title":"Fatigue Fract. Eng. Mater. Struct."},{"key":"ref_7","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 Proc."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1314","DOI":"10.1109\/TR.2016.2570568","article-title":"A model-based method for remaining useful life prediction of machinery","volume":"65","author":"Lei","year":"2016","journal-title":"IEEE Trans. Reliab."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"705","DOI":"10.1007\/s11340-011-9536-6","article-title":"An energy-based torsional-shear fatigue lifing method","volume":"52","author":"Wertz","year":"2012","journal-title":"Exp. Mech."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"66","DOI":"10.1080\/21642583.2013.850754","article-title":"Stochastic and nonlinear-based prognostic model","volume":"1","author":"Jaoude","year":"2013","journal-title":"Syst. Sci. Control Eng."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.ijfatigue.2016.04.007","article-title":"Rolling-sliding contact fatigue of surfaces with sinusoidal roughness","volume":"90","author":"Pu","year":"2016","journal-title":"Int. J. Fatigue"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Wang, J., Gao, R.X., Yuan, Z., Fan, Z., and Zhang, L. (2016). A joint particle filter and expectation maximization approach to machine condition prognosis. J. Intell. Manuf., 1\u20137.","DOI":"10.1007\/s10845-016-1268-0"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1177\/1687814016664660","article-title":"A review of physics-based models in prognostics: Application to gears and bearings of rotating machinery","volume":"8","author":"Cubillo","year":"2016","journal-title":"Adv. Mech. Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1049\/iet-cta.2016.0912","article-title":"Distributed state estimation, fault detection and isolation filter design for heterogeneous multi-agent linear parameter-varying systems","volume":"11","author":"Chadli","year":"2017","journal-title":"IET Control Theory Appl."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"549","DOI":"10.1016\/j.ymssp.2016.06.031","article-title":"A multi-time scale approach to remaining useful life prediction in rolling bearing","volume":"83","author":"Qian","year":"2017","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"61","DOI":"10.1016\/j.neucom.2018.03.014","article-title":"ACDIN: Bridging the gap between artificial and real bearing damages for bearing fault diagnosis","volume":"294","author":"Chen","year":"2018","journal-title":"Neurocomputing"},{"key":"ref_17","first-page":"42","article-title":"Design of robust fuzzy fault detection filter for polynomial fuzzy systems with new finite frequency specifications","volume":"29","author":"Chadli","year":"2018","journal-title":"Automatica"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1016\/j.neucom.2018.09.050","article-title":"A convolutional neural network based on a capsule network with strong generalization for bearing fault diagnosis","volume":"323","author":"Zhu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_19","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_20","doi-asserted-by":"crossref","first-page":"647","DOI":"10.1109\/TIE.2014.2327917","article-title":"Enabling health monitoring approach based on vibration data for accurate prognostics","volume":"62","author":"Javed","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"1549","DOI":"10.1109\/TIE.2017.2733469","article-title":"Prognostics and health management of bearings based on logarithmic linear recursive least-squares and recursive maximum likelihood estimation","volume":"62","author":"Liu","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.jmsy.2018.05.011","article-title":"Long short-term memory for machine remaining life prediction","volume":"48","author":"Zhang","year":"2018","journal-title":"J. Manuf. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1016\/j.measurement.2019.06.004","article-title":"A recurrent neural network approach for remaining useful life prediction utilizing a novel trend features construction method","volume":"146","author":"Zhao","year":"2019","journal-title":"Measurement"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neucom.2019.10.064","article-title":"Recurrent convolutional neural network: A new framework for remaining useful life prediction of machinery","volume":"379","author":"Wang","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"372","DOI":"10.1016\/j.ress.2019.01.006","article-title":"Gate recurrent unit based recurrent neural network for remaining useful life prediction of nonlinear deterioration process","volume":"185","author":"Chen","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"157","DOI":"10.1016\/j.measurement.2016.12.058","article-title":"Enhancement of fault diagnosis of rolling element bearing using maximum kurtosis fast nonlocal means denoising","volume":"100","author":"Laha","year":"2017","journal-title":"Measurement"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1016\/j.ymssp.2015.04.039","article-title":"Spectral kurtosis for fault detection, diagnosis and prognostics of rotating machines: A review with applications","volume":"66","author":"Wang","year":"2016","journal-title":"Mech. Sys. Signal Proc."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1016\/j.procs.2019.12.111","article-title":"Data dimensional reduction and principal components analysis","volume":"163","author":"Nema","year":"2019","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","first-page":"1783","article-title":"Probabilistic Non-linear principal component analysis with Gaussian process latent variable models","volume":"6","author":"Lawrence","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.trit.2016.11.004","article-title":"A review on Gaussian process latent variable models","volume":"1","author":"Li","year":"2016","journal-title":"CAAI Trans. Intell. Technol."},{"key":"ref_31","first-page":"422","article-title":"Multi-kernel Gaussian process latent variable regression model for high-dimensional sequential data modeling","volume":"133","author":"Zhu","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"422","DOI":"10.1016\/j.renene.2018.10.031","article-title":"Fault diagnosis of wind turbine based on Long Short-term memory networks","volume":"133","author":"Lei","year":"2019","journal-title":"Renew. Energy"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.neucom.2019.11.006","article-title":"Bayesian approach and time series dimensionality reduction to LSTM-based model-building for fault diagnosis of a reciprocating compressor","volume":"380","author":"Cabrera","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.jvcir.2018.12.039","article-title":"Video facial emotion recognition based on local enhanced motion history image and CNN-CTSLSTM networks","volume":"59","author":"Hu","year":"2019","journal-title":"J. Vis. Commun. Image Represent."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.neucom.2019.11.049","article-title":"Adaptive embedding gate for attention-based scene text recognition","volume":"381","author":"Chen","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"Lecun","year":"1998","journal-title":"Proc. IEEE"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"249","DOI":"10.1016\/j.procs.2017.08.239","article-title":"Enhancing assessment of personalized multi-agent system through convlstm","volume":"112","author":"Trifa","year":"2017","journal-title":"Procedia Comput. Sci."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"885","DOI":"10.1016\/j.jhydrol.2019.04.015","article-title":"A deep learning approach to anomaly detection in geological carbon sequestration sites using pressure measurements","volume":"573","author":"Zhi","year":"2019","journal-title":"J. Hydrol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.neucom.2018.10.049","article-title":"Batch-normalized deep neural networks for achieving fast intelligent fault diagnosis of machines","volume":"329","author":"Wang","year":"2019","journal-title":"Neurocomputing"},{"key":"ref_40","first-page":"150","article-title":"Accurate bearing remaining useful life prediction based on Weibull distribution and artificial neural network","volume":"56","author":"Jaouher","year":"2015","journal-title":"Mech. Syst. Signal Proc."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/1864\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T09:12:23Z","timestamp":1760173943000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/7\/1864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,3,27]]},"references-count":40,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2020,4]]}},"alternative-id":["s20071864"],"URL":"https:\/\/doi.org\/10.3390\/s20071864","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,3,27]]}}}