{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,22]],"date-time":"2026-07-22T16:28:50Z","timestamp":1784737730376,"version":"3.55.0"},"reference-count":46,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,8]],"date-time":"2021-01-08T00:00:00Z","timestamp":1610064000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 61871432, No. 61771492"],"award-info":[{"award-number":["No. 61871432, No. 61771492"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Natural Science Foundation of Hunan Province","award":["No.2020JJ4275, No.2019JJ6008, and No.2019JJ60054"],"award-info":[{"award-number":["No.2020JJ4275, No.2019JJ6008, and No.2019JJ60054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.<\/jats:p>","DOI":"10.3390\/s21020418","type":"journal-article","created":{"date-parts":[[2021,1,10]],"date-time":"2021-01-10T23:03:42Z","timestamp":1610319822000},"page":"418","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":71,"title":["A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1920-7488","authenticated-orcid":false,"given":"Cheng","family":"Peng","sequence":"first","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"},{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yufeng","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Chen","sequence":"additional","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4132-4987","authenticated-orcid":false,"given":"Zhaohui","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lingling","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer, Hunan University of Technology, Zhuzhou 412007, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Weihua","family":"Gui","sequence":"additional","affiliation":[{"name":"School of Automation, Central South University, Changsha 410083, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1115\/1.4039246","article-title":"Study on vibration characteristics of fan shaft of geared turbofan engine with sudden imbalance caused by blade off","volume":"140","author":"Wei","year":"2018","journal-title":"J. Vib. Acoust."},{"key":"ref_2","first-page":"e13547","article-title":"Energy, environment and enviroeconomic analyses and assessments of the turbofan engine used in aviation industry","volume":"3","author":"Tuzcu","year":"2020","journal-title":"Environ. Prog. Sustain. Energy"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"104800","DOI":"10.1016\/j.knosys.2019.06.018","article-title":"A data-driven M2 approach for evidential network structure learning","volume":"187","author":"You","year":"2020","journal-title":"Knowl. Based Syst."},{"key":"ref_4","first-page":"34","article-title":"Attention and long short-term memory network for remaining useful lifetime predictions of turbofan engine degradation","volume":"10","author":"Akcay","year":"2020","journal-title":"Int. J. Progn. Health Manag."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1007\/s11668-020-00832-x","article-title":"Estimating remaining useful life of turbofan engine using data-level fusion and feature-level fusion","volume":"20","author":"Ghorbani","year":"2020","journal-title":"J. Fail. Anal. Prev."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"166541","DOI":"10.1109\/ACCESS.2020.3022771","article-title":"Fault detection for aircraft turbofan engine using a modified moving window KPCA","volume":"8","author":"Sun","year":"2020","journal-title":"IEEE Access"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1007\/s13198-013-0195-0","article-title":"Remaining useful life estimation: Review","volume":"5","author":"Ahmadzadeh","year":"2014","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103957","DOI":"10.1016\/j.compbiomed.2020.103957","article-title":"Automated prediction of sepsis using temporal convolutional network","volume":"127","author":"Kok","year":"2020","journal-title":"Comput. Biol. Med."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"83252","DOI":"10.1109\/ACCESS.2019.2958255","article-title":"Practical automated video analytics for crowd monitoring and counting","volume":"7","author":"Cheong","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"108771","DOI":"10.1016\/j.measurement.2020.108771","article-title":"Hierarchical symbolic analysis and particle swarm optimization based fault diagnosis model for rotating machineries with deep neural networks","volume":"171","author":"Saravanakumar","year":"2021","journal-title":"Measurement"},{"key":"ref_11","first-page":"90","article-title":"Bearing fault diagnosis based on Synchronous Extrusion S transformation and deep learning","volume":"5","author":"Du","year":"2019","journal-title":"Modul. Mach. Tool Autom. Process. Technol."},{"key":"ref_12","first-page":"1","article-title":"A bidirectional weighted boundary distance algorithm for time series similarity computation based on optimized sliding window size","volume":"13","author":"Peng","year":"2019","journal-title":"J. Ind. Manag. Optim."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"151764","DOI":"10.1109\/ACCESS.2020.3017626","article-title":"Review of key technologies and progress in industrial equipment health management","volume":"8","author":"Peng","year":"2020","journal-title":"IEEE Access"},{"key":"ref_14","first-page":"171","article-title":"Driving behavior recognition based on one-dimensional convolutional neural network and noise reduction autoencoder","volume":"37","author":"Yang","year":"2020","journal-title":"Comput. Appl. Softw."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1504\/IJSNET.2020.110467","article-title":"Wind turbine blades icing failure prognosis based on balanced data and improved entropy","volume":"34","author":"Peng","year":"2020","journal-title":"Int. J. Sens. Netw."},{"key":"ref_16","first-page":"489","article-title":"A new method for abnormal behavior propagation in networked software","volume":"19","author":"Peng","year":"2018","journal-title":"J. Internet Technol."},{"key":"ref_17","first-page":"2231","article-title":"Bearing remaining life prediction based on full convolutional layer neural networks","volume":"30","author":"Zhang","year":"2019","journal-title":"China Mech. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"9521","DOI":"10.1109\/TIE.2019.2924605","article-title":"Remaining useful life prediction based on a double-convolutional neural network architecture","volume":"66","author":"Yang","year":"2019","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"91","DOI":"10.1016\/j.comcom.2020.05.035","article-title":"Remaining useful life prediction based on state assessment using edge computing on deep learning","volume":"160","author":"Hsu","year":"2020","journal-title":"Comput. Commun."},{"key":"ref_20","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 convolutional neural networks","volume":"172","author":"Li","year":"2018","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"ref_21","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_22","doi-asserted-by":"crossref","unstructured":"Kong, Z., Cui, Y., Xia, Z., and Lv, H. (2019). Convolution and long short-term memory hybrid deep neural networks for remaining useful life prognostics. Appl. Sci., 9.","DOI":"10.3390\/app9194156"},{"key":"ref_23","first-page":"1611","article-title":"Residual life prediction of turbofan Engines based on Autoencoder-BLSTM","volume":"25","author":"Song","year":"2019","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_24","first-page":"1","article-title":"Development and application of a convolutional neural network model","volume":"18","author":"Yan","year":"2020","journal-title":"Comput. Sci. Explor."},{"key":"ref_25","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press."},{"key":"ref_26","unstructured":"Estrach, J.B., Szlam, A., and LeCun, Y. (2014, January 21\u201326). Signal recovery from pooling representations. Proceedings of the 31st International Conference on Machine Learning (ICML), Beijing, China."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"640","DOI":"10.1109\/TPAMI.2016.2572683","article-title":"Fully convolutional networks for semantic segmentation","volume":"39","author":"Jonathan","year":"2017","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_28","first-page":"312","article-title":"Short-term residential load forecasting based on LSTM recurrent neural network","volume":"12","author":"Zhang","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1016\/j.compind.2019.02.004","article-title":"A multimodal and hybrid deep neural network model for Remaining Useful Life estimation","volume":"108","author":"Zabihi","year":"2019","journal-title":"Comput. Ind."},{"key":"ref_30","unstructured":"Kingma, D., and Ba, J. (2014, January 14\u201316). Adam: A method for stochastic optimization. Proceedings of the International Conference on Learning Representations (ICLR), Banff, AB, Canada."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1551013","DOI":"10.1142\/S0218001415510131","article-title":"Fast linear SVM validation based on early stopping in iterative learning","volume":"29","author":"Famouri","year":"2015","journal-title":"Int. J. Pattern Recognit. Artif. Intell."},{"key":"ref_32","unstructured":"Loffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ramasso, E., and Gouriveau, R. (2010, January 10\u201316). Prognostics in switching systems: Evidential Markovian classification of real-time neuro-fuzzy predictions. Proceedings of the Prognostics and Health Management Conference IEEE PHM, Portland, OR, USA.","DOI":"10.1109\/PHM.2010.5413442"},{"key":"ref_34","unstructured":"Frederick, D., de Castro, J., and Litt, J. (2007). User\u2019s Guide for the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS), NASA\/ARL."},{"key":"ref_35","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 1st International Conference on Prognostics and Health Management (PHM08), Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711414"},{"key":"ref_36","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 IEEE, Denver, CO, USA.","DOI":"10.1109\/PHM.2008.4711423"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Li, N., Lei, Y., Gebraeel, N., Wang, Z., Cai, X., Xu, P., and Wang, B. (2020). Multi-sensor data-driven remaining useful life prediction of semi-observable systems. IEEE Trans. Ind. Electron., 1.","DOI":"10.1109\/TIE.2020.3038069"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"10854","DOI":"10.1109\/TVT.2020.3014932","article-title":"State-of-health estimation and remaining-useful-life prediction for lithium-ion battery using a hybrid data-driven method","volume":"69","author":"Gou","year":"2020","journal-title":"IEEE Trans. Veh. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Hsu, C., and Jiang, J. (2018, January 13\u201317). Remaining useful life estimation using long short-term memory deep learning. Proceedings of the 2018 IEEE International Conference on Applied System Innovation (ICASI), Tokyo, Japan.","DOI":"10.1109\/ICASI.2018.8394326"},{"key":"ref_40","first-page":"1","article-title":"Prediction of remaining service life of turbofan engine based on VAE-D2GAN","volume":"23","author":"Xu","year":"2020","journal-title":"Comput. Integr. Manuf. Syst."},{"key":"ref_41","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_42","doi-asserted-by":"crossref","unstructured":"Liao, Y., Zhang, L., and Liu, C. (2018, January 11\u201313). Uncertainty Prediction of Remaining Useful Life Using Long Short-Term Memory Network Based on Bootstrap Method. Proceedings of the IEEE International Conference on Prognostics and Health Management (ICPHM), Seattle, WA, USA.","DOI":"10.1109\/ICPHM.2018.8448804"},{"key":"ref_43","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 2017 IEEE International Conference on Prognostics and Health Management (ICPHM), Dallas, TX, USA.","DOI":"10.1109\/ICPHM.2017.7998311"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"2306","DOI":"10.1109\/TNNLS.2016.2582798","article-title":"Multiobjective deep belief networks ensemble for remaining useful life estimation in prognostics","volume":"28","author":"Zhang","year":"2017","journal-title":"IEEE Trans. Neural Netw. Learn Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.ymssp.2019.05.005","article-title":"Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme","volume":"129","author":"Yu","year":"2019","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_46","unstructured":"Babu, G.S., Zhao, P., and Li, X. (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."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/418\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:08:55Z","timestamp":1760159335000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/2\/418"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,8]]},"references-count":46,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["s21020418"],"URL":"https:\/\/doi.org\/10.3390\/s21020418","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,8]]}}}