{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T13:39:14Z","timestamp":1776346754491,"version":"3.51.2"},"reference-count":40,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,9]],"date-time":"2020-02-09T00:00:00Z","timestamp":1581206400000},"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":["51605065"],"award-info":[{"award-number":["51605065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Science and Technology Research Program of Chongqing Municipal Education Commission","award":["KJ1704109"],"award-info":[{"award-number":["KJ1704109"]}]},{"name":"Open Found of The State Key Laboratory of Mechanical Transmissions","award":["SKLMT-KFKT-201809"],"award-info":[{"award-number":["SKLMT-KFKT-201809"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Engine prognostics are critical to improve safety, reliability, and operational efficiency of an aircraft. With the development in sensor technology, multiple sensors are embedded or deployed to monitor the health condition of the aircraft engine. Thus, the challenge of engine prognostics lies in how to model and predict future health by appropriate utilization of these sensor information. In this paper, a prognostic approach is developed based on informative sensor selection and adaptive degradation modeling with functional data analysis. The presented approach selects sensors based on metrics and constructs health index to characterize engine degradation by fusing the selected informative sensors. Next, the engine degradation is adaptively modeled with the functional principal component analysis (FPCA) method and future health is prognosticated using the Bayesian inference. The prognostic approach is applied to run-to-failure data sets of C-MAPSS test-bed developed by NASA. Results show that the proposed method can effectively select the informative sensors and accurately predict the complex degradation of the aircraft engine.<\/jats:p>","DOI":"10.3390\/s20030920","type":"journal-article","created":{"date-parts":[[2020,2,10]],"date-time":"2020-02-10T11:48:51Z","timestamp":1581335331000},"page":"920","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Aircraft Engine Prognostics Based on Informative Sensor Selection and Adaptive Degradation Modeling with Functional Principal Component Analysis"],"prefix":"10.3390","volume":"20","author":[{"given":"Bin","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"},{"name":"The State Key Laboratory of Mechanical Transmissions, Chongqing University, Chongqing 400044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kai","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8298-5418","authenticated-orcid":false,"given":"Qingqing","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Automation, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9347-5165","authenticated-orcid":false,"given":"Song","family":"Feng","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shangqi","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yi","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Advanced Manufacturing Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"051201","DOI":"10.1115\/1.4026126","article-title":"Gas turbine engine health management: Past, present, and future trends","volume":"136","author":"Volponi","year":"2014","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_2","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_3","doi-asserted-by":"crossref","first-page":"1340","DOI":"10.1109\/TR.2018.2822702","article-title":"Performance-based gas turbine health monitoring, diagnostics, and prognostics: A survey","volume":"67","author":"Hanachi","year":"2018","journal-title":"IEEE Trans. Reliab."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"091201","DOI":"10.1115\/1.4032680","article-title":"Markov nonlinear system estimation for engine performance tracking","volume":"138","author":"Wang","year":"2016","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1016\/j.ress.2016.07.019","article-title":"Remaining useful life estimation in heterogeneous fleets working under variable operating conditions","volume":"156","author":"Baraldi","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.ast.2017.05.030","article-title":"Aircraft engine health prognostics based on logistic regression with penalization regularization and state-space-based degradation framework","volume":"68","author":"Yu","year":"2017","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Skordilis, E., and Moghaddass, R. (2019). A double hybrid state-Space model for real-time sensor-driven monitoring of deteriorating systems. IEEE Trans. Autom. Sci. Eng.","DOI":"10.1109\/TASE.2019.2921285"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1037","DOI":"10.1007\/s10845-014-0933-4","article-title":"Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction","volume":"27","author":"Mosallam","year":"2016","journal-title":"J. Intell. Manuf."},{"key":"ref_9","unstructured":"Wang, C.S., Lu, N.Y., Cheng, Y.H., and Jiang, B. (2019). A data-driven aero-engine degradation prognostic strategy. IEEE Trans. Cybern."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2276","DOI":"10.1109\/TIE.2016.2623260","article-title":"Direct remaining useful life estimation based on support vector regression","volume":"64","author":"Khelif","year":"2016","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"661","DOI":"10.1016\/j.ast.2018.09.044","article-title":"Aircraft engine degradation prognostics based on logistic regression and novel OS-ELM algorithm","volume":"84","author":"Lu","year":"2019","journal-title":"Aerosp. Sci. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8792","DOI":"10.1109\/TIE.2019.2891463","article-title":"A Bi-Directional LSTM prognostics method under multiple operational conditions","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"041008","DOI":"10.1115\/1.4041674","article-title":"Degradation modeling and remaining useful life prediction of aircraft engines using ensemble learning","volume":"141","author":"Li","year":"2019","journal-title":"J. Eng. Gas Turbines Power"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.ejor.2010.11.018","article-title":"Remaining useful life estimation\u2013a review on the statistical data driven approaches","volume":"213","author":"Si","year":"2011","journal-title":"Eur. J. Oper. Res."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"7062","DOI":"10.3390\/s150307062","article-title":"A hybrid PCA-CART-MARS-based prognostic approach of the remaining useful life for aircraft engines","volume":"15","author":"Lasheras","year":"2015","journal-title":"Sensors"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.ymssp.2015.09.014","article-title":"Gas turbine engine prognostics using Bayesian hierarchical models: A variational approach","volume":"70\u201371","author":"Zaidan","year":"2016","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"344","DOI":"10.1109\/TASE.2014.2349733","article-title":"Integration of data fusion methodology and degradation modeling process to improve prognostics","volume":"13","author":"Liu","year":"2016","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"853","DOI":"10.1080\/24725854.2018.1440673","article-title":"Statistical degradation modeling and prognostics of multiple sensor signals via data fusion: A composite health index approach","volume":"50","author":"Song","year":"2018","journal-title":"IISE Trans."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"775","DOI":"10.1016\/j.ejor.2018.02.033","article-title":"Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods","volume":"271","author":"Zhang","year":"2018","journal-title":"Eur. J. Oper. Res."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1016\/j.ress.2015.12.016","article-title":"Remaining useful lifetime estimation and noisy gamma deterioration process","volume":"149","author":"Fouladirad","year":"2016","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1109\/TR.2016.2635149","article-title":"Bayesian degradation analysis with inverse Gaussian process models under time-varying degradation rates","volume":"66","author":"Peng","year":"2017","journal-title":"IEEE Trans. Reliab."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"622","DOI":"10.1016\/j.ymssp.2011.10.019","article-title":"A generic probabilistic framework for structural health prognostics and uncertainty management","volume":"28","author":"Wang","year":"2012","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1426","DOI":"10.1109\/TASE.2018.2890608","article-title":"A generic health index approach for multisensor degradation modeling and sensor selection","volume":"16","author":"Kim","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_24","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. Health Manag."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.microrel.2017.03.008","article-title":"Quantitative selection of sensor data based on improved permutation entropy for system remaining useful life prediction","volume":"75","author":"Liu","year":"2017","journal-title":"Microelectron. Reliab."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1774","DOI":"10.1109\/TASE.2019.2897784","article-title":"Sensor fusion via statistical hypothesis testing for prognosis and degradation analysis","volume":"16","author":"Chehade","year":"2019","journal-title":"IEEE Trans. Autom. Sci. Eng."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.ress.2016.11.008","article-title":"Multistream sensor fusion-based prognostics model for systems with single failure modes","volume":"159","author":"Fang","year":"2017","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1002\/asmb.2063","article-title":"Stochastic modelling and analysis of degradation for highly reliable products","volume":"31","author":"Ye","year":"2015","journal-title":"Appl. Stoch. Models Bus. Ind."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"547","DOI":"10.1002\/qre.1771","article-title":"Degradation feature selection for remaining useful life prediction of rolling element bearings","volume":"32","author":"Zhang","year":"2016","journal-title":"Qual. Reliab. Eng. Int."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1007\/s10182-013-0213-1","article-title":"A survey of functional principal component analysis","volume":"98","author":"Shang","year":"2014","journal-title":"AStA Adv. Stat. Anal."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1146\/annurev-statistics-041715-033624","article-title":"Functional data analysis","volume":"3","author":"Wang","year":"2016","journal-title":"Annu. Rev. Stat. Its Appl."},{"key":"ref_32","unstructured":"Chung, S., and Kontar, R. (2019). Functional principal component analysis for extrapolating multi-stream longitudinal data. arXiv Preprint."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"20110553","DOI":"10.1098\/rsta.2011.0553","article-title":"Bayesian non-parametrics and the probabilistic approach to modelling","volume":"371","author":"Ghahramani","year":"2013","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.ress.2014.08.013","article-title":"An adaptive functional regression-based prognostic model for applications with missing data","volume":"133","author":"Fang","year":"2015","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"577","DOI":"10.1198\/016214504000001745","article-title":"Functional data analysis for sparse longitudinal data","volume":"100","author":"Yao","year":"2005","journal-title":"J. Am. Stat. Assoc."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1586","DOI":"10.1214\/10-AOAS448","article-title":"Degradation applied to residual lifetime prediction using functional data analysis","volume":"5","author":"Zhou","year":"2011","journal-title":"Ann. Appl. Stat."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Saxena, A., Goebel, K., Simon, D., and Eklund, N. (2008, January 6\u20139). Damage Propagation for Aircraft Engine Run-To-Failure Simulation. Proceedings of the International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref_38","unstructured":"Saxena, A., and Goebel, K. (2018, August 15). Turbo Fan Engine Degradation Simulation Dataset, NASA Ames Prognostics Data Repository, Available online: http:\/\/ti.arc.nasa.gov\/tech\/dash\/pcoe\/prognostic-data-repository."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"4065","DOI":"10.1007\/s11042-017-5204-x","article-title":"A novel soft computing method for engine RUL prediction","volume":"78","author":"Singh","year":"2019","journal-title":"Multimed. Tools Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"219","DOI":"10.1016\/j.ress.2015.02.001","article-title":"Hybrid PSO\u2013SVM-based method for forecasting of the remaining useful life for aircraft engines and evaluation of its reliability","volume":"138","author":"Nieto","year":"2015","journal-title":"Reliab. Eng. Syst. Saf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/920\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:56:16Z","timestamp":1760172976000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/920"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,9]]},"references-count":40,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030920"],"URL":"https:\/\/doi.org\/10.3390\/s20030920","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,9]]}}}