{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,17]],"date-time":"2026-07-17T21:42:36Z","timestamp":1784324556216,"version":"3.55.0"},"reference-count":52,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T00:00:00Z","timestamp":1747008000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52372420"],"award-info":[{"award-number":["52372420"]}],"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":["52372420"],"award-info":[{"award-number":["52372420"]}],"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":["52372420"],"award-info":[{"award-number":["52372420"]}],"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":["52372420"],"award-info":[{"award-number":["52372420"]}],"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":["52372420"],"award-info":[{"award-number":["52372420"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"the Baima Lake Laboratory Joint Fund of the Zheiiang Provincial Natural Science Foundation of China","award":["LBMHZ25F030002"],"award-info":[{"award-number":["LBMHZ25F030002"]}]},{"name":"the Baima Lake Laboratory Joint Fund of the Zheiiang Provincial Natural Science Foundation of China","award":["LBMHZ25F030002"],"award-info":[{"award-number":["LBMHZ25F030002"]}]},{"name":"the Baima Lake Laboratory Joint Fund of the Zheiiang Provincial Natural Science Foundation of China","award":["LBMHZ25F030002"],"award-info":[{"award-number":["LBMHZ25F030002"]}]},{"name":"the Baima Lake Laboratory Joint Fund of the Zheiiang Provincial Natural Science Foundation of China","award":["LBMHZ25F030002"],"award-info":[{"award-number":["LBMHZ25F030002"]}]},{"name":"the Baima Lake Laboratory Joint Fund of the Zheiiang Provincial Natural Science Foundation of China","award":["LBMHZ25F030002"],"award-info":[{"award-number":["LBMHZ25F030002"]}]},{"name":"Scientific Research Foundation of HangZhou City University","award":["X-202404"],"award-info":[{"award-number":["X-202404"]}]},{"name":"Scientific Research Foundation of HangZhou City University","award":["X-202404"],"award-info":[{"award-number":["X-202404"]}]},{"name":"Scientific Research Foundation of HangZhou City University","award":["X-202404"],"award-info":[{"award-number":["X-202404"]}]},{"name":"Scientific Research Foundation of HangZhou City University","award":["X-202404"],"award-info":[{"award-number":["X-202404"]}]},{"name":"Scientific Research Foundation of HangZhou City University","award":["X-202404"],"award-info":[{"award-number":["X-202404"]}]},{"name":"Zhejiang Province Key research project","award":["2024C01039"],"award-info":[{"award-number":["2024C01039"]}]},{"name":"Zhejiang Province Key research project","award":["2024C01039"],"award-info":[{"award-number":["2024C01039"]}]},{"name":"Zhejiang Province Key research project","award":["2024C01039"],"award-info":[{"award-number":["2024C01039"]}]},{"name":"Zhejiang Province Key research project","award":["2024C01039"],"award-info":[{"award-number":["2024C01039"]}]},{"name":"Zhejiang Province Key research project","award":["2024C01039"],"award-info":[{"award-number":["2024C01039"]}]},{"name":"Ningbo's Key Technology Breakthrough Program of KeChuang Yongjiang 2035","award":["2024Z177"],"award-info":[{"award-number":["2024Z177"]}]},{"name":"Ningbo's Key Technology Breakthrough Program of KeChuang Yongjiang 2035","award":["2024Z177"],"award-info":[{"award-number":["2024Z177"]}]},{"name":"Ningbo's Key Technology Breakthrough Program of KeChuang Yongjiang 2035","award":["2024Z177"],"award-info":[{"award-number":["2024Z177"]}]},{"name":"Ningbo's Key Technology Breakthrough Program of KeChuang Yongjiang 2035","award":["2024Z177"],"award-info":[{"award-number":["2024Z177"]}]},{"name":"Ningbo's Key Technology Breakthrough Program of KeChuang Yongjiang 2035","award":["2024Z177"],"award-info":[{"award-number":["2024Z177"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SIViP"],"published-print":{"date-parts":[[2025,7]]},"DOI":"10.1007\/s11760-025-04150-3","type":"journal-article","created":{"date-parts":[[2025,5,12]],"date-time":"2025-05-12T06:10:17Z","timestamp":1747030217000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["A hybrid deep learning model for robust aeroengine remaining useful life prediction"],"prefix":"10.1007","volume":"19","author":[{"given":"Anping","family":"Wan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ting","family":"Chen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Khalil","family":"AL-Bukhaiti","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenhui","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,12]]},"reference":[{"key":"4150_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2023.121859","volume":"238","author":"FF Xiang","year":"2024","unstructured":"Xiang, F.F., Zhang, Y.M., Zhang, S.Y., et al.: Bayesian gated-transformer model for risk-aware prediction of aero-engine remaining useful life. Expert Syst. Appl. 238, 121859 (2024). https:\/\/doi.org\/10.1016\/j.eswa.2023.121859","journal-title":"Expert Syst. Appl."},{"key":"4150_CR2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijfatigue.2023.107510","volume":"170","author":"BW Wang","year":"2023","unstructured":"Wang, B.W., Tang, W.Z., Song, L.K., et al.: Deep neural network-based multiagent synergism method of probabilistic HCF evaluation for aircraft compressor rotor. Int. J. Fatigue 170, 107510 (2023). https:\/\/doi.org\/10.1016\/j.ijfatigue.2023.107510","journal-title":"Int. J. Fatigue"},{"issue":"1","key":"4150_CR3","doi-asserted-by":"publisher","first-page":"343","DOI":"10.1093\/jcde\/qwae018","volume":"11","author":"Q Liu","year":"2023","unstructured":"Liu, Q., Zhang, Z.Y., Guo, P., et al.: Enhancing aircraft engine remaining useful life prediction via multiscale deep transfer learning with limited data. J. Comput. Design Eng. 11(1), 343\u2013355 (2023). https:\/\/doi.org\/10.1093\/jcde\/qwae018","journal-title":"J. Comput. Design Eng."},{"key":"4150_CR4","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.113098","volume":"217","author":"ZQ Xu","year":"2023","unstructured":"Xu, Z.Q., Zhang, Y.J., Miao, J.G., et al.: Global attention mechanism based deep learning for remaining useful life prediction of aero-engine. Measurement 217, 113098 (2023). https:\/\/doi.org\/10.1016\/j.measurement.2023.113098","journal-title":"Measurement"},{"issue":"4","key":"4150_CR5","doi-asserted-by":"publisher","first-page":"1795","DOI":"10.1002\/qre.3494","volume":"40","author":"XH Lu","year":"2024","unstructured":"Lu, X.H., Pan, H.B., Zhang, L.X., et al.: A dual path hybrid neural network framework for remaining useful life prediction of aeroengine. Qual. Reliab. Eng. Int. 40(4), 1795\u20131810 (2024). https:\/\/doi.org\/10.1002\/qre.3494","journal-title":"Qual. Reliab. Eng. Int."},{"issue":"4","key":"4150_CR6","doi-asserted-by":"publisher","first-page":"1223","DOI":"10.1002\/qre.3288","volume":"39","author":"YD Wang","year":"2023","unstructured":"Wang, Y.D., Zhao, Y.F.: Three-stage feature selection approach for deep learning-based RUL prediction methods. Qual. Reliab. Eng. Int. 39(4), 1223\u20131247 (2023). https:\/\/doi.org\/10.1002\/qre.3288","journal-title":"Qual. Reliab. Eng. Int."},{"issue":"8","key":"4150_CR7","doi-asserted-by":"publisher","first-page":"227190","DOI":"10.7527\/S1000-6893.2023.27190","volume":"44","author":"TM Li","year":"2023","unstructured":"Li, T.M., Si, X.S., Zhang, J.X.: Data-model interactive remaining useful life prediction method for multi-sensor monitored linear stochastic degrading devices. Acta Aeronaut. Astronaut. Sin. 44(8), 227190\u2013227190 (2023). https:\/\/doi.org\/10.7527\/S1000-6893.2023.27190","journal-title":"Acta Aeronaut. Astronaut. Sin."},{"key":"4150_CR8","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2023.110836","volume":"147","author":"LK Hu","year":"2023","unstructured":"Hu, L.K., He, X.J., Yin, L.F.: Remaining useful life prediction method combining the life variation laws of aero-turbofan engine and auto-expandable cascaded LSTM model. Appl. Soft Comput. 147, 110836 (2023). https:\/\/doi.org\/10.1016\/j.asoc.2023.110836","journal-title":"Appl. Soft Comput."},{"issue":"2","key":"4150_CR9","doi-asserted-by":"publisher","first-page":"761","DOI":"10.1109\/TR.2020.3002262","volume":"70","author":"H Wang","year":"2021","unstructured":"Wang, H., Liao, H.T., Ma, X.B.: Remaining useful life prediction considering joint dependency of degradation rate and variation on time-varying operating conditions. IEEE Trans. Reliab. 70(2), 761\u2013774 (2021). https:\/\/doi.org\/10.1109\/TR.2020.3002262","journal-title":"IEEE Trans. Reliab."},{"issue":"11","key":"4150_CR10","doi-asserted-by":"publisher","first-page":"5022","DOI":"10.1109\/TNNLS.2020.3026644","volume":"32","author":"Z Li","year":"2021","unstructured":"Li, Z., Wu, J.G., Yue, X.W.: A shape-constrained neural data fusion network for health index construction and residual life prediction. IEEE Trans. Neural Netw. Learn. Syst. 32(11), 5022\u20135033 (2021). https:\/\/doi.org\/10.1109\/TNNLS.2020.3026644","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"3","key":"4150_CR11","doi-asserted-by":"publisher","first-page":"796","DOI":"10.1002\/qre.3256","volume":"39","author":"TF Han","year":"2023","unstructured":"Han, T.F., Li, Y.P.: An ensemble model considering health index-based classification for remaining useful life prediction. Qual. Reliab. Eng. Int. 39(3), 796\u2013819 (2023). https:\/\/doi.org\/10.1002\/qre.3256","journal-title":"Qual. Reliab. Eng. Int."},{"key":"4150_CR12","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.107530","volume":"211","author":"ZY Xu","year":"2021","unstructured":"Xu, Z.Y., Saleh, J.H.: Machine learning for reliability engineering and safety applications: review of current status and future opportunities. Reliab. Eng. Syst. Saf. 211, 107530 (2021). https:\/\/doi.org\/10.1016\/j.ress.2021.107530","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"23","key":"4150_CR13","doi-asserted-by":"publisher","first-page":"20903","DOI":"10.1007\/s11071-024-10157-1","volume":"112","author":"KS You","year":"2021","unstructured":"You, K.S., Lian, Z.W., Gu, Y.K.: A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME. Nonlinear Dyn. 112(23), 20903\u201320940 (2021). https:\/\/doi.org\/10.1007\/s11071-024-10157-1","journal-title":"Nonlinear Dyn."},{"key":"4150_CR14","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109696","volume":"241","author":"S Fu","year":"2023","unstructured":"Fu, S., Lin, L., Wang, Y., et al.: MCA-DTCN: a novel dual-task temporal convolutional network with multi-channel attention for first prediction time detection and remaining useful life prediction. Reliab. Eng. Syst. Saf. 241, 109696 (2023). https:\/\/doi.org\/10.1016\/j.ress.2023.109696","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR15","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110288","volume":"250","author":"ZQ Xu","year":"2024","unstructured":"Xu, Z.Q., Zhang, Y.J., Miao, Q.: An attention-based multi-scale temporal convolutional network for remaining useful life prediction. Reliab. Eng. Syst. Saf. 250, 110288 (2024). https:\/\/doi.org\/10.1016\/j.ress.2024.110288","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR16","doi-asserted-by":"publisher","unstructured":"Kumari, N., Kumar, R., Mohanty, A.R., et al.: Remaining useful life prediction using hybrid neural network and genetic algorithm approaches. In: 2021 International conference on maintenance and intelligent asset management (ICMIAM). Ballarat, Australia, pp 1\u20136 (2021). https:\/\/doi.org\/10.1109\/ICMIAM54662.2021.9715210.","DOI":"10.1109\/ICMIAM54662.2021.9715210"},{"issue":"12","key":"4150_CR17","doi-asserted-by":"publisher","first-page":"4557","DOI":"10.1002\/ese3.1597","volume":"11","author":"R Pandit","year":"2023","unstructured":"Pandit, R., Xie, W.X.: Data-driven models for predicting remaining useful life of high-speed shaft bearings in wind turbines using vibration signal analysis and sparrow search algorithm. Energy Sci. Eng. 11(12), 4557\u20134569 (2023). https:\/\/doi.org\/10.1002\/ese3.1597","journal-title":"Energy Sci. Eng."},{"key":"4150_CR18","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1109\/TIM.2023.3322481","volume":"72","author":"HF Wang","year":"2023","unstructured":"Wang, H.F., Zhang, Z., Li, X., et al.: Comprehensive dynamic structure graph neural network for aero-engine remaining useful life prediction. IEEE Trans. Instrum. Meas. 72, 1\u201316 (2023). https:\/\/doi.org\/10.1109\/TIM.2023.3322481","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"4150_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.ress.2017.11.021","volume":"172","author":"X Li","year":"2018","unstructured":"Li, X., Ding, Q., Sun, J.Q.: Remaining useful life estimation in prognostics using deep convolution neural networks. Reliab. Eng. Syst. Saf. 172, 1\u201311 (2018). https:\/\/doi.org\/10.1016\/j.ress.2017.11.021","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR20","doi-asserted-by":"publisher","DOI":"10.1016\/j.asoc.2020.106113","volume":"89","author":"H Li","year":"2020","unstructured":"Li, H., Zhao, W., Zhang, Y.X., et al.: Remaining useful life prediction using multi-scale deep convolutional neural network. Appl. Soft Comput. 89, 106113 (2020). https:\/\/doi.org\/10.1016\/j.asoc.2020.106113","journal-title":"Appl. Soft Comput."},{"issue":"5","key":"4150_CR21","doi-asserted-by":"publisher","DOI":"10.1016\/j.jksuci.2024.102068","volume":"36","author":"SM Al-Selwi","year":"2024","unstructured":"Al-Selwi, S.M., Hassan, M.F., Abdulkadir, S.J., et al.: RNN-LSTM: from applications to modeling techniques and beyond\u2014systematic review. J. King Saud Univ. Comput. Inf. Sci. 36(5), 102068 (2024). https:\/\/doi.org\/10.1016\/j.jksuci.2024.102068","journal-title":"J. King Saud Univ. Comput. Inf. Sci."},{"issue":"15","key":"4150_CR22","doi-asserted-by":"publisher","first-page":"5680","DOI":"10.3390\/s22155680","volume":"22","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Liu, Z.Z., Zuo, H.F., et al.: A DLSTM-network-based approach for mechanical remaining useful life prediction. Sensors. 22(15), 5680 (2022). https:\/\/doi.org\/10.3390\/s22155680","journal-title":"Sensors."},{"issue":"24","key":"4150_CR23","doi-asserted-by":"publisher","first-page":"7109","DOI":"10.3390\/s20247109","volume":"20","author":"CY Zhao","year":"2019","unstructured":"Zhao, C.Y., Huang, X.Z., Li, Y.X., et al.: A double-channel hybrid deep neural network based on CNN and BiLSTM for remaining useful life prediction. Sensors. 20(24), 7109 (2019). https:\/\/doi.org\/10.3390\/s20247109","journal-title":"Sensors."},{"key":"4150_CR24","doi-asserted-by":"publisher","DOI":"10.1016\/j.est.2023.109498","volume":"74","author":"CR Li","year":"2023","unstructured":"Li, C.R., Han, X.J., Zhang, Q., et al.: State-of-health and remaining-useful-life estimations of lithium-ion battery based on temporal convolutional network-long short-term memory. J. Energy Storage. 74, 109498 (2023). https:\/\/doi.org\/10.1016\/j.est.2023.109498","journal-title":"J. Energy Storage."},{"issue":"23","key":"4150_CR25","doi-asserted-by":"publisher","first-page":"23009","DOI":"10.1109\/JSEN.2022.3214608","volume":"22","author":"SG Sun","year":"2022","unstructured":"Sun, S.G., Wei, S., Wang, J.Q., et al.: Remaining useful life prediction for circuit breaker based on opening-related vibration signal and SA-CNN-GRU. IEEE Sensors J. 22(23), 23009\u201323022 (2022). https:\/\/doi.org\/10.1109\/JSEN.2022.3214608","journal-title":"IEEE Sensors J."},{"key":"4150_CR26","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2023.107241","volume":"127","author":"Q Zhang","year":"2023","unstructured":"Zhang, Q., Liu, Q., Ye, Q.: An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine. Eng. Appl. Artif. Intell. 127, 107241 (2023). https:\/\/doi.org\/10.1016\/j.engappai.2023.107241","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4150_CR27","doi-asserted-by":"publisher","DOI":"10.1016\/j.engappai.2025.110072","volume":"143","author":"M Kim","year":"2025","unstructured":"Kim, M., Yoo, S., Son, S., et al.: Physics-informed deep learning framework for explainable remaining useful life prediction. Eng. Appl. Artif. Intell. 143, 110072 (2025). https:\/\/doi.org\/10.1016\/j.engappai.2025.110072","journal-title":"Eng. Appl. Artif. Intell."},{"key":"4150_CR28","unstructured":"Chan, W., Jaitly, N., Le, Q.V, et al.: Listen, Attend and Spell. arxiv preprint arXiv:1508.01211 (2015)"},{"issue":"3","key":"4150_CR29","doi-asserted-by":"publisher","first-page":"55","DOI":"10.1109\/MCI.2018.2840738","volume":"13","author":"T Young","year":"2018","unstructured":"Young, T., Hazarika, D., Poria, S., et al.: Recent trends in deep learning based natural language processing. IEEE Comput. Intell. Mag. 13(3), 55\u201375 (2018). https:\/\/doi.org\/10.1109\/MCI.2018.2840738","journal-title":"IEEE Comput. Intell. Mag."},{"key":"4150_CR30","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110451","volume":"252","author":"M Herv\u00e9 de Beaulieu","year":"2024","unstructured":"Herv\u00e9 de Beaulieu, M., Jha, M.S., Garnier, H., et al.: Remaining useful life prediction based on physics-informed data augmentation. Reliab. Eng. Syst. Saf. 252, 110451 (2024). https:\/\/doi.org\/10.1016\/j.ress.2024.110451","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR31","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2025.110854","volume":"258","author":"Y Guo","year":"2025","unstructured":"Guo, Y., Li, X.Y., Zhang, J.D., et al.: SDCGAN: a CycleGAN-based single-domain generalization method for mechanical fault diagnosis. Reliab. Eng. Syst. Saf. 258, 110854 (2025). https:\/\/doi.org\/10.1016\/j.ress.2025.110854","journal-title":"Reliab. Eng. Syst. Saf."},{"issue":"1","key":"4150_CR32","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s40537-024-01006-4","volume":"11","author":"Y Guo","year":"2024","unstructured":"Guo, Y., Cheng, Z.Y., Zhang, J.D., et al.: A review on adversarial-based deep transfer learning mechanical fault diagnosis. J. Big Data 11(1), 1\u201326 (2024). https:\/\/doi.org\/10.1186\/s40537-024-01006-4","journal-title":"J. Big Data"},{"key":"4150_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109716","volume":"242","author":"SL Yang","year":"2024","unstructured":"Yang, S.L., Tang, B.P., Wang, W.Y., et al.: Physics-informed multi-state temporal frequency network for RUL prediction of rolling bearings. Reliab. Eng. Syst. Saf. 242, 109716 (2024). https:\/\/doi.org\/10.1016\/j.ress.2023.109716","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR34","doi-asserted-by":"publisher","first-page":"82156","DOI":"10.1109\/ACCESS.2022.3188681","volume":"10","author":"H Al-Khazraji","year":"2022","unstructured":"Al-Khazraji, H., Nasser, A.R., Hasan, A.M., et al.: Aircraft engines remaining useful life prediction based on a hybrid model of autoencoder and deep belief network. IEEE Access 10, 82156\u201382163 (2022). https:\/\/doi.org\/10.1109\/ACCESS.2022.3188681","journal-title":"IEEE Access"},{"issue":"5","key":"4150_CR35","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0217634","volume":"14","author":"H Ghazvinian","year":"2019","unstructured":"Ghazvinian, H., Mousavi, S.F., Karami, H., et al.: Integrated support vector regression and an improved particle swarm optimization-based model for solar radiation prediction. PLoS ONE 14(5), e0217634 (2019). https:\/\/doi.org\/10.1371\/journal.pone.0217634","journal-title":"PLoS ONE"},{"issue":"1","key":"4150_CR36","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acfbef","volume":"35","author":"KS You","year":"2024","unstructured":"You, K.S., Qiu, G.Q., Gu, Y.K.: Remaining useful life prediction of lithium-ion batteries using EM-PF-SSA-SVR with gamma stochastic process. Meas. Sci. Technol. 35(1), 015015 (2024). https:\/\/doi.org\/10.1088\/1361-6501\/acfbef","journal-title":"Meas. Sci. Technol."},{"key":"4150_CR37","doi-asserted-by":"publisher","DOI":"10.1016\/j.sciaf.2023.e01796","volume":"21","author":"EM Saoudi","year":"2023","unstructured":"Saoudi, E.M., Jaafari, J., Andaloussi, S.J.: Advancing human action recognition: a hybrid approach using attention-based LSTM and 3D CNN. Sci. Afr. 21, e01796 (2023). https:\/\/doi.org\/10.1016\/j.sciaf.2023.e01796","journal-title":"Sci. Afr."},{"key":"4150_CR38","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109793","volume":"242","author":"KS You","year":"2024","unstructured":"You, K.S., Qiu, G.Q., Gu, Y.K.: Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning. Reliab. Eng. Syst. Saf. 242, 109793 (2024). https:\/\/doi.org\/10.1016\/j.ress.2023.109793","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR39","doi-asserted-by":"publisher","first-page":"1133","DOI":"10.1016\/j.psep.2024.05.106","volume":"188","author":"Y Guo","year":"2024","unstructured":"Guo, Y., Zhang, J.D.: Chemical fault diagnosis network based on single domain generalization. Process. Saf. Environ. Prot. 188, 1133\u20131144 (2024). https:\/\/doi.org\/10.1016\/j.psep.2024.05.106","journal-title":"Process. Saf. Environ. Prot."},{"key":"4150_CR40","doi-asserted-by":"publisher","DOI":"10.1016\/j.oceaneng.2024.117729","volume":"302","author":"Y Guo","year":"2024","unstructured":"Guo, Y., Zhang, J.D., Sun, B., et al.: A universal fault diagnosis framework for marine machinery based on domain adaptation. Ocean Eng. 302, 117729 (2024). https:\/\/doi.org\/10.1016\/j.oceaneng.2024.117729","journal-title":"Ocean Eng."},{"issue":"9","key":"4150_CR41","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6501\/acd5ef","volume":"34","author":"KS You","year":"2023","unstructured":"You, K.S., Qiu, G.Q., Gu, Y.K.: An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults. Meas. Sci. Technol. 34(9), 094001 (2023). https:\/\/doi.org\/10.1088\/1361-6501\/acd5ef","journal-title":"Meas. Sci. Technol."},{"issue":"4","key":"4150_CR42","doi-asserted-by":"publisher","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","volume":"37","author":"B Lim","year":"2021","unstructured":"Lim, B., Ar\u0131k, S.\u00d6., Loeff, N., et al.: Temporal fusion transformers for interpretable multi-horizon time series forecasting. Int. J. Forecast. 37(4), 1748\u20131764 (2021). https:\/\/doi.org\/10.1016\/j.ijforecast.2021.03.012","journal-title":"Int. J. Forecast."},{"issue":"13","key":"4150_CR43","doi-asserted-by":"publisher","first-page":"23002","DOI":"10.1109\/JIOT.2024.3377731","volume":"11","author":"KS You","year":"2024","unstructured":"You, K.S., Wang, P.Z., Gu, Y.K.: Toward efficient and interpretative rolling bearing fault diagnosis via quadratic neural network with Bi-LSTM. IEEE Internet Things J. 11(13), 23002\u201323019 (2024). https:\/\/doi.org\/10.1109\/JIOT.2024.3377731","journal-title":"IEEE Internet Things J."},{"key":"4150_CR44","doi-asserted-by":"publisher","unstructured":"Saxena, A., Goebel, K., Simon, D.: Damage propagation modeling for aircraft engine run-to-failure simulation. In: 2008 International conference on prognostics and health management, pp. 1\u20139. IEEE (2008). https:\/\/doi.org\/10.1109\/PHM.2008.4711414.","DOI":"10.1109\/PHM.2008.4711414"},{"issue":"14","key":"4150_CR45","doi-asserted-by":"publisher","first-page":"21893","DOI":"10.1109\/JSEN.2023.3296670","volume":"24","author":"KS You","year":"2024","unstructured":"You, K.S., Qiu, G.Q., Gu, Y.K.: A 3-D attention-enhanced hybrid neural network for turbofan engine remaining life prediction using CNN and BiLSTM models. IEEE Sensors J. 24(14), 21893\u201321905 (2024). https:\/\/doi.org\/10.1109\/JSEN.2023.3296670","journal-title":"IEEE Sensors J."},{"issue":"2","key":"4150_CR46","doi-asserted-by":"publisher","first-page":"1931","DOI":"10.1109\/TVT.2023.3319377","volume":"73","author":"TT Xu","year":"2024","unstructured":"Xu, T.T., Han, G.J., Zhu, H.B., et al.: Multi-resolution LSTM-based prediction model for remaining useful life of aero-engine. IEEE Trans. Veh. Technol. 73(2), 1931\u20131941 (2024). https:\/\/doi.org\/10.1109\/TVT.2023.3319377","journal-title":"IEEE Trans. Veh. Technol."},{"key":"4150_CR47","doi-asserted-by":"publisher","unstructured":"Ji, W.Q., Cheng, J., Chen, Y.: Remaining useful life prediction for mechanical equipment based on temporal convolutional network. In: IEEE international conference on electronic measurement & instruments, pp. 1192\u20131199. IEEE (2019). https:\/\/doi.org\/10.1109\/PHMNanjing52125.2021.9613038.","DOI":"10.1109\/PHMNanjing52125.2021.9613038"},{"key":"4150_CR48","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2022.108353","volume":"222","author":"N Costa","year":"2022","unstructured":"Costa, N., Sanchez, L.: Variational encoding approach for interpretable assessment of remaining useful life estimation. Reliab. Eng. Syst. Saf. 222, 108353 (2022). https:\/\/doi.org\/10.1016\/j.ress.2022.108353","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR49","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2021.108297","volume":"221","author":"JS Zhang","year":"2022","unstructured":"Zhang, J.S., Jiang, Y.C., Wu, S.M.: Prediction of remaining useful life based on bidirectional gated recurrent unit with temporal self-attention mechanism. Reliab. Eng. Syst. Saf. 221, 108297 (2022). https:\/\/doi.org\/10.1016\/j.ress.2021.108297","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR50","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2023.109096","volume":"233","author":"JS Zhang","year":"2023","unstructured":"Zhang, J.S., Li, X., Tian, J.L., et al.: An integrated multi-head dual sparse self-attention network for remaining useful life prediction. Reliab. Eng. Syst. Saf. 233, 109096 (2023). https:\/\/doi.org\/10.1016\/j.ress.2023.109096","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR51","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2024.110556","volume":"233","author":"KS You","year":"2025","unstructured":"You, K.S., Wang, P.Z., Huang, P., et al.: A sound-vibration physical-information fusion constraint-guided deep learning method for rolling bearing fault diagnosis. Reliab. Eng. Syst. Saf. 233, 110556 (2025). https:\/\/doi.org\/10.1016\/j.ress.2024.110556","journal-title":"Reliab. Eng. Syst. Saf."},{"key":"4150_CR52","doi-asserted-by":"publisher","DOI":"10.1080\/10589759.2024.2425813","author":"KS You","year":"2024","unstructured":"You, K.S., Lian, Z.W., Chen, R.H., et al.: A novel rolling bearing fault diagnosis method based on time-series fusion transformer with interpretability analysis. Nondestruct. Test. Eval. (2024). https:\/\/doi.org\/10.1080\/10589759.2024.2425813","journal-title":"Nondestruct. Test. Eval."}],"container-title":["Signal, Image and Video Processing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04150-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11760-025-04150-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11760-025-04150-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T06:41:29Z","timestamp":1748587289000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11760-025-04150-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,12]]},"references-count":52,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["4150"],"URL":"https:\/\/doi.org\/10.1007\/s11760-025-04150-3","relation":{},"ISSN":["1863-1703","1863-1711"],"issn-type":[{"value":"1863-1703","type":"print"},{"value":"1863-1711","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,12]]},"assertion":[{"value":"17 December 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 March 2025","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"2 April 2025","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2025","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"550"}}