{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T01:18:16Z","timestamp":1775265496395,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2018,11,6]],"date-time":"2018-11-06T00:00:00Z","timestamp":1541462400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Data"],"abstract":"<jats:p>In the aerospace industry, every minute of downtime because of equipment failure impacts operations significantly. Therefore, efficient maintenance, repair and overhaul processes to aid maximum equipment availability are essential. However, scheduled maintenance is costly and does not track the degradation of the equipment which could result in unexpected failure of the equipment. Prognostic Health Management (PHM) provides techniques to monitor the precise degradation of the equipment along with cost-effective reliability. This article presents an adaptive data-driven prognostics reasoning approach. An engineering case study of Turbofan Jet Engine has been used to demonstrate the prognostic reasoning approach. The emphasis of this article is on an adaptive data-driven degradation model and how to improve the remaining useful life (RUL) prediction performance in condition monitoring of a Turbofan Jet Engine. The RUL prediction results show low prediction errors regardless of operating conditions, which contrasts with a conventional data-driven model (a non-parameterised Neural Network model) where prediction errors increase as operating conditions deviate from the nominal condition. In this article, the Neural Network has been used to build the Nominal model and Particle Filter has been used to track the present degradation along with degradation parameter.<\/jats:p>","DOI":"10.3390\/data3040049","type":"journal-article","created":{"date-parts":[[2018,11,7]],"date-time":"2018-11-07T03:45:22Z","timestamp":1541562322000},"page":"49","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Adaptive Degradation Prognostic Reasoning by Particle Filter with a Neural Network Degradation Model for Turbofan Jet Engine"],"prefix":"10.3390","volume":"3","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6634-5736","authenticated-orcid":false,"given":"Faisal","family":"Khan","sequence":"first","affiliation":[{"name":"IVHM Centre, Cranfield University, Bedford MK43 0AL, UK"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0140-2322","authenticated-orcid":false,"given":"Omer F.","family":"Eker","sequence":"additional","affiliation":[{"name":"Artesis, 41480 Gebze, Kocaeli, Turkey"}]},{"given":"Atif","family":"Khan","sequence":"additional","affiliation":[{"name":"IVHM Centre, Cranfield University, Bedford MK43 0AL, UK"}]},{"given":"Wasim","family":"Orfali","sequence":"additional","affiliation":[{"name":"College of Engineering, Taibah University, Al-Medina Al-Munawara, Medina 42353, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2018,11,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Peng, Y., Wang, Y., and Zi, Y. (2018). Switching state-space degradation model with recursive filter\/smoother for prognostics of remaining useful life. IEEE Trans. Ind. Inform.","DOI":"10.1109\/TII.2018.2810284"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Eker, \u00d6.F., Camci, F., and Jennions, I.K. (2012, January 3\u20135). Major challenges in prognostics: Study on benchmarking prognostics datasets. Proceedings of the First European Conference of the Prognostics and Health Management Society, Dresden, Germany.","DOI":"10.36001\/phme.2012.v1i1.1409"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"529","DOI":"10.1109\/TIE.2018.2811366","article-title":"Degradation Data-Driven Time-To-Failure Prognostics Approach for Rolling Element Bearings in Electrical Machines","volume":"66","author":"Wu","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"403","DOI":"10.1016\/j.ress.2010.08.009","article-title":"Particle filtering prognostic estimation of the remaining useful life of nonlinear components","volume":"96","author":"Zio","year":"2011","journal-title":"Reliabil. Eng. Syst. Saf."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Heimes, F.O. (2008, January 6\u20139). Recurrent neural networks for remaining useful life estimation. Proceedings of the International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711422"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1016\/j.neucom.2017.02.045","article-title":"A recurrent neural network based health indicator for remaining useful life prediction of bearings","volume":"240","author":"Guo","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"193","DOI":"10.1016\/j.ymssp.2005.11.008","article-title":"Residual life predictions for ball bearing based on self-organizing map and back propagation neural network methods","volume":"21","author":"Huang","year":"2007","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Byington, C.S., Watson, M., and Edwards, D. (2004, January 6\u201313). Data-driven neural network methodology to remaining life predictions for aircraft actuator components. Proceedings of the IEEE Aerospace Conference, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2004.1368175"},{"key":"ref_9","first-page":"50","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_10","doi-asserted-by":"crossref","first-page":"1751","DOI":"10.1016\/j.engappai.2013.02.006","article-title":"Remaining useful life estimation based on nonlinear feature reduction and support vector regression","volume":"26","author":"Benkedjouh","year":"2013","journal-title":"Eng. Appl. Artif. Intell."},{"key":"ref_11","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":"2017","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_12","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 bearing","volume":"61","author":"Medjaher","year":"2012","journal-title":"IEEE Trans. Reliabil."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"491","DOI":"10.1109\/TR.2012.2194177","article-title":"A data-driven failure prognostics method based on mixture of Gaussions Hidden Markov models","volume":"61","author":"Medjaher","year":"2012","journal-title":"IEEE Trans. Reliabil."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"832","DOI":"10.1016\/j.microrel.2013.03.010","article-title":"Prognostics for state of health estimation of lithium-io batteries based on combination Gaussian process functional regression","volume":"53","author":"Liu","year":"2013","journal-title":"Microelectron. Reliabil."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Liu, Y., Mohanty, S., and Chattopadhyay, A. (2009, January 8\u201312). A Gaussian Process based prognostics framework for composite structures. Proceedings of the SPIE, Smart Structures and Materials & Nondestructive, San Diego, CA, USA.","DOI":"10.1117\/12.815889"},{"key":"ref_16","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_17","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_18","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1006\/mssp.1998.0183","article-title":"Adaptive prognostics for rolling element bearing condition","volume":"13","author":"Li","year":"1999","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_19","unstructured":"He, W., Williard, N., Osterman, M., and Pecht, M. (2011, January 17\u201319). Prognostics of lithium-ion batteries using extended Kalman filtering. Proceedings of the IMAPS Advanced Technology Workshop on High Reliability Microelectronics for Military Applications, Linthicum Heights, MD, USA."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"535","DOI":"10.1109\/TSMCA.2012.2207109","article-title":"Model-based prognostics with concurrent damage progression processes","volume":"43","author":"Daigle","year":"2013","journal-title":"IEEE Trans. Syst. Man Cybern."},{"key":"ref_21","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, Big Sky, MT, USA.","DOI":"10.1109\/AERO.2010.5446822"},{"key":"ref_22","unstructured":"Gorijian, N., Ma, L., Mittinty, M., Yarlagadda, P., and San, Y. (2009, January 28\u201330). A review on degradation models in reliability analysis. Proceedings of the 4th World Congress on Engineering Asset Management, Athens, Greece."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1601","DOI":"10.1016\/j.ress.2011.07.007","article-title":"A probabilistic physics-of-failure model for prognostic health management of structures subject to pitting and corrosion-fatige","volume":"96","author":"Chookah","year":"2011","journal-title":"Reliabil. Eng. Syst. Saf."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1061","DOI":"10.1016\/j.ress.2010.04.015","article-title":"Application of physical failure models to enable usage and load based maintenance","volume":"95","author":"Tinga","year":"2010","journal-title":"Reliabil. Eng. Syst. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2152","DOI":"10.1016\/j.apenergy.2009.02.011","article-title":"Gas turbine performance prognostic for condition-based maintenance","volume":"86","author":"Li","year":"2009","journal-title":"Appl. Energy"},{"key":"ref_26","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 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref_27","unstructured":"Sasahara, O. (1985, January 2\u20136). JT9D engine\/module performance deterioration results from back to back testing. Proceedings of the 7th International Symposium on Air Breathing Engines, Beijing, China."},{"key":"ref_28","first-page":"41","article-title":"Factors relating to deterioration based on Rolls-Royce RB211 in service performance","volume":"37","author":"Crosby","year":"1986","journal-title":"Turbomach. Perform. Deterior."},{"key":"ref_29","unstructured":"Saxena, A., and Goebel, K. (2010, August 15). Turbofan Engine Degradation Simulation Data Set, NASA Ames Prognostics Data Repository, Available online: http:\/\/ti.arc.nasa.gov\/tech\/dash\/pcoe\/prognostic-data-repository\/."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"996","DOI":"10.1016\/j.is.2006.10.004","article-title":"Conceptual design of remote monitoring and fault diagnosis systems","volume":"32","author":"Wang","year":"2007","journal-title":"Inf. Syst."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"041602","DOI":"10.1115\/1.2838993","article-title":"Data visualization, data reduction and classifier fusion for intelligent fault diagnosis in gas turbine engines","volume":"130","author":"Donat","year":"2008","journal-title":"J. Eng. Gas Turb. Power"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"835","DOI":"10.1109\/TSP.2008.2009272","article-title":"Fault detection in multivariate signals with applications to gas turbines","volume":"57","author":"Bassily","year":"2009","journal-title":"IEEE Trans. Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"041602","DOI":"10.1115\/1.3204508","article-title":"A fault diagnosis method for industrial gas turbines using Bayesian data analysis","volume":"132","author":"Young","year":"2010","journal-title":"J. Eng. Gas Turb. Power"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"9928","DOI":"10.3390\/s111009928","article-title":"Fault diagnosis for micro-gas turbine engine sensors via wavelet entropy","volume":"11","author":"Yu","year":"2011","journal-title":"Sensors"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1343","DOI":"10.3390\/e14081343","article-title":"Bearing fault diagnosis based on multiscale permutation entropy and support vector machine","volume":"14","author":"Wu","year":"2012","journal-title":"Entropy"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"416","DOI":"10.3390\/e15020416","article-title":"Multi-scale analysis based ball bearing defect diagnostics using Mahalanobis distance and support vector machine","volume":"15","author":"Wu","year":"2013","journal-title":"Entropy"},{"key":"ref_37","unstructured":"Frederick, D., De Castro, J., and Litt, J. (2007). User\u2019s Guide for the Commercial Modular Acro-Propulsion System Simulation (C-MAPSS), NASA."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Rios, M.P., and Lopes, H.F. (2013). The extended Liu and West filter: Parameter learning in Markov switching stochastic volatility models. State-Space Models: Applications in Economics and Finance, Springer.","DOI":"10.1007\/978-1-4614-7789-1_2"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/02331880309257","article-title":"Bayesian filtering: From Kalman filters to particle filters and beyond","volume":"182","author":"Chen","year":"2003","journal-title":"Statistics"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Nemeth, C., Fearnhead, P., Mihaylova, L., and Vorley, D. (2012, January 16\u201317). Particle learning methods for state and parameter estimation. Proceedings of the IET Data Fusion & Target Tracking Conference, London, UK.","DOI":"10.1049\/cp.2012.0412"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Liu, J., and West, M. (2001). Combined parameter and state estimation in simulation-based filtering. Sequential Monte Carlo Methods in Practice, Springer.","DOI":"10.1007\/978-1-4757-3437-9_10"},{"key":"ref_42","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":"Reliabil. Eng. Syst. Saf."},{"key":"ref_43","unstructured":"Saxena, A., Celaya, J., Saha, B., Saha, S., and Goebel, K. (2010, January 10\u201314). Metrics for offline evaluation of prognostic performance. Proceedings of the Annual Conference of the PHM Society, Portland, OR, USA."},{"key":"ref_44","unstructured":"Wang, T. (2010). Trajectory Similarity Based Prediction for Remaining Useful Life Estimation. [Ph.D. Thesis, University of Cincinnati]."},{"key":"ref_45","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 International Conference on Prognostics and Health Management, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711421"},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.ress.2009.08.001","article-title":"A data-driven fuzzy approach for predicting the remaining useful life in dynamic failure scenarios of a nuclear system","volume":"95","author":"Zio","year":"2010","journal-title":"Reliabil. Eng. Syst. Saf."}],"container-title":["Data"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2306-5729\/3\/4\/49\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,4]],"date-time":"2026-04-04T00:12:53Z","timestamp":1775261573000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2306-5729\/3\/4\/49"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2018,11,6]]},"references-count":46,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2018,12]]}},"alternative-id":["data3040049"],"URL":"https:\/\/doi.org\/10.3390\/data3040049","relation":{},"ISSN":["2306-5729"],"issn-type":[{"value":"2306-5729","type":"electronic"}],"subject":[],"published":{"date-parts":[[2018,11,6]]}}}