{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,24]],"date-time":"2026-02-24T14:27:25Z","timestamp":1771943245746,"version":"3.50.1"},"reference-count":35,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T00:00:00Z","timestamp":1639526400000},"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":["61873122, 61973288, 62020106003"],"award-info":[{"award-number":["61873122, 61973288, 62020106003"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research Fund of State Key Laboratory of Mechanics and Control of Mechanical Structures, Nanjing University of Aeronautics and Astronautics","award":["MCMS-I-0521G02"],"award-info":[{"award-number":["MCMS-I-0521G02"]}]},{"DOI":"10.13039\/501100012130","name":"Aeronautical Science Foundation of China","doi-asserted-by":"publisher","award":["20200007018001"],"award-info":[{"award-number":["20200007018001"]}],"id":[{"id":"10.13039\/501100012130","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004543","name":"China Scholarship Council","doi-asserted-by":"publisher","award":["202006830060"],"award-info":[{"award-number":["202006830060"]}],"id":[{"id":"10.13039\/501100004543","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Prognostics and health management (PHM) with failure prognosis and maintenance decision-making as the core is an advanced technology to improve the safety, reliability, and operational economy of engineering systems. However, studies of failure prognosis and maintenance decision-making have been conducted separately over the past years. Key challenges remain open when the joint problem is considered. The aim of this paper is to develop an integrated strategy for dynamic predictive maintenance scheduling (DPMS) based on a deep auto-encoder and deep forest-assisted failure prognosis method. The proposed DPMS method involves a complete process from performing failure prognosis to making maintenance decisions. The first step is to extract representative features reflecting system degradation from raw sensor data by using a deep auto-encoder. Then, the features are fed into the deep forest to compute the failure probabilities in moving time horizons. Finally, an optimal maintenance-related decision is made through quickly evaluating the costs of different decisions with the failure probabilities. Verification was accomplished using NASA\u2019s open datasets of aircraft engines, and the experimental results show that the proposed DPMS method outperforms several state-of-the-art methods, which can benefit precise maintenance decisions and reduce maintenance costs.<\/jats:p>","DOI":"10.3390\/s21248373","type":"journal-article","created":{"date-parts":[[2021,12,15]],"date-time":"2021-12-15T21:47:36Z","timestamp":1639604856000},"page":"8373","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Deep Auto-Encoder and Deep Forest-Assisted Failure Prognosis for Dynamic Predictive Maintenance Scheduling"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7979-7862","authenticated-orcid":false,"given":"Hui","family":"Yu","sequence":"first","affiliation":[{"name":"Integrated System Integration Department, No. 38 Research Institute of CETC, Hefei 230088, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chuang","family":"Chen","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ningyun","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Automation Engineering, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Cunsong","family":"Wang","sequence":"additional","affiliation":[{"name":"Institute of Intelligent Manufacturing, Nanjing Tech University, Nanjing 210009, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Olesen, J.F., and Shaker, H.R. (2020). Predictive Maintenance for Pump Systems and Thermal Power Plants: State-of-the-Art Review, Trends and Challenges. Sensors, 20.","DOI":"10.3390\/s20082425"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Yin, A., Yan, Y., Zhang, Z., Li, C., and S\u00e1nchez, R.-V. (2020). Fault Diagnosis of Wind Turbine Gearbox Based on the Optimized LSTM Neural Network with Cosine Loss. Sensors, 20.","DOI":"10.3390\/s20082339"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Lee, Y., and Lee, Y.S. (2020). A low-cost surge current detection sensor with predictive lifetime display function for maintenance of surge protective devices. Sensors, 20.","DOI":"10.3390\/s20082310"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"3527213","DOI":"10.1109\/TIM.2021.3126006","article-title":"Prediction interval estimation of aero-engine remaining useful life based on bidirectional long short-term memory network","volume":"70","author":"Chen","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"412","DOI":"10.1109\/JAS.2021.1003835","article-title":"A Risk-Averse Remaining Useful Life Estimation for Predictive Maintenance","volume":"8","author":"Chen","year":"2021","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"841","DOI":"10.23919\/JSEE.2020.000057","article-title":"Condition-based maintenance optimization for continuously monitored degrading systems under imperfect maintenance actions","volume":"31","author":"Chen","year":"2020","journal-title":"J. Syst. Eng. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"387","DOI":"10.17531\/ein.2021.2.19","article-title":"A data-driven predictive maintenance strategy based on accurate failure prognostics","volume":"23","author":"Chen","year":"2021","journal-title":"Eksploat. I Niezawodn."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"8767","DOI":"10.1109\/TIE.2019.2947839","article-title":"A Prognostic Model Based on DBN and Diffusion Process for Degrading Bearing","volume":"67","author":"Hu","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"103523","DOI":"10.1016\/j.compind.2021.103523","article-title":"Computational framework for real-time diagnostics and prognostics of aircraft actuation systems","volume":"132","author":"Berri","year":"2021","journal-title":"Comput. Ind."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1531","DOI":"10.1109\/TCYB.2019.2938244","article-title":"A Data-Driven Aero-Engine Degradation Prognostic Strategy","volume":"51","author":"Wang","year":"2019","journal-title":"IEEE Trans. Cybern."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.jmsy.2021.08.012","article-title":"Adoption of machine learning technology for failure prediction in industrial maintenance: A systematic review","volume":"61","author":"Leukel","year":"2021","journal-title":"J. Manuf. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"103298","DOI":"10.1016\/j.compind.2020.103298","article-title":"Machine learning and reasoning for predictive maintenance in Industry 4.0: Current status and challenges","volume":"123","author":"Dalzochio","year":"2020","journal-title":"Comput. Ind."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"104769","DOI":"10.1016\/j.engfailanal.2020.104769","article-title":"Pressure data-driven model for failure prediction of PVC pipelines","volume":"116","author":"Dawood","year":"2020","journal-title":"Eng. Fail. Anal."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"104105","DOI":"10.1016\/j.ijpvp.2020.104105","article-title":"Availability-based reliability-centered maintenance planning for gas transmission pipelines","volume":"183","author":"Zakikhani","year":"2020","journal-title":"Int. J. Press. Vessel. Pip."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Fernandes, S., Antunes, M., Santiago, A.R., Barraca, J.P., Gomes, D., and Aguiar, R.L. (2020). Forecasting Appliances Failures: A Machine-Learning Approach to Predictive Maintenance. Information, 11.","DOI":"10.3390\/info11040208"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1468","DOI":"10.1109\/TMECH.2020.2978136","article-title":"An Integrated Feature-Based Failure Prognosis Method for Wind Turbine Bearings","volume":"25","author":"Rezamand","year":"2020","journal-title":"IEEE\/ASME Trans. Mechatron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1115\/1.4045445","article-title":"Failure Prognosis of Complex Equipment With Multistream Deep Recurrent Neural Network","volume":"20","author":"Su","year":"2020","journal-title":"J. Comput. Inf. Sci. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"585","DOI":"10.2514\/1.G004616","article-title":"Failure Prognosis for Satellite Reaction Wheels Using Kalman Filter and Particle Filter","volume":"43","author":"Rahimi","year":"2020","journal-title":"J. Guid. Control. Dyn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"5407","DOI":"10.1109\/JSYST.2020.2986162","article-title":"A New Hybrid Fault Prognosis Method for MFS Systems Based on Distributed Neural Networks and Recursive Bayesian Algorithm","volume":"14","author":"Kordestani","year":"2020","journal-title":"IEEE Syst. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ruiz-Arenas, S., Rus\u00e1k, Z., Mej\u00eda-Guti\u00e9rrez, R., and Horv\u00e1th, I. (2020). Implementation of System Operation Modes for Health Management and Failure Prognosis in Cyber-Physical Systems. Sensors, 20.","DOI":"10.3390\/s20082429"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"26878","DOI":"10.1109\/JSEN.2021.3119553","article-title":"Dynamic Predictive Maintenance Scheduling Using Deep Learning Ensemble for System Health Prognostics","volume":"21","author":"Chen","year":"2021","journal-title":"IEEE Sensors J."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1093\/nsr\/nwy108","article-title":"Deep forest","volume":"6","author":"Zhou","year":"2019","journal-title":"Natl. Sci. Rev."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1613\/jair.2470","article-title":"Spectrum of Variable-Random Trees","volume":"32","author":"Liu","year":"2008","journal-title":"J. Artif. Intell. Res."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"251","DOI":"10.1016\/j.ress.2019.03.018","article-title":"A new dynamic predictive maintenance framework using deep learning for failure prognostics","volume":"188","author":"Nguyen","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"3616","DOI":"10.1109\/ACCESS.2020.3047928","article-title":"A Data-Driven Maintenance Framework Under Imperfect Inspections for Deteriorating Systems Using Multitask Learning-Based Status Prognostics","volume":"9","author":"Zhang","year":"2021","journal-title":"IEEE Access"},{"key":"ref_26","unstructured":"Saxena, A., and Goebel, K. (2021, July 20). Turbofan Engine Degradation Simulation Data Set, Available online: https:\/\/ti.arc.nasa.gov\/tech\/dash\/groups\/pcoe\/prognostic-data-repository\/."},{"key":"ref_27","unstructured":"Zhou, Z.H., and Feng, J. (2021, July 25). gcForest v1.1.1. Available online: https:\/\/github.com\/kingfengji\/gcForest."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"8792","DOI":"10.1109\/TIE.2019.2891463","article-title":"A Bidirectional LSTM Prognostics Method under Multiple Operational Conditions","volume":"66","author":"Huang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1016\/j.neucom.2021.07.080","article-title":"A data-driven degradation prognostic strategy for aero-engine under various operational conditions","volume":"462","author":"Wang","year":"2021","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"107257","DOI":"10.1016\/j.ress.2020.107257","article-title":"A dual-LSTM framework combining change point detection and remaining useful life prediction","volume":"205","author":"Shi","year":"2021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ress.2018.11.027","article-title":"Remaining useful life predictions for turbofan engine degradation using semi-supervised deep architecture","volume":"183","author":"Ellefsen","year":"2019","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Chui, K.T., Gupta, B.B., and Vasant, P. (2021). A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine. Electronics, 10.","DOI":"10.3390\/electronics10030285"},{"key":"ref_33","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_34","doi-asserted-by":"crossref","unstructured":"Zhang, Y. (2020, January 12\u201314). Aeroengine Fault Prediction Based on Bidirectional LSTM Neural Network. Proceedings of the 2020 International Conference on Big Data, Artificial Intelligence and Internet of Things Engineering (ICBAIE), Fuzhou, China.","DOI":"10.1109\/ICBAIE49996.2020.00073"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2049","DOI":"10.1007\/s12206-019-0408-9","article-title":"Deep forest based intelligent fault diagnosis of hydraulic turbine","volume":"33","author":"Liu","year":"2019","journal-title":"J. Mech. Sci. Technol."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8373\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:48:56Z","timestamp":1760168936000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/24\/8373"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,12,15]]},"references-count":35,"journal-issue":{"issue":"24","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["s21248373"],"URL":"https:\/\/doi.org\/10.3390\/s21248373","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,12,15]]}}}