{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,12]],"date-time":"2025-04-12T10:47:17Z","timestamp":1744454837282},"reference-count":43,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T00:00:00Z","timestamp":1706745600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Reliability Engineering & System Safety"],"published-print":{"date-parts":[[2024,2]]},"DOI":"10.1016\/j.ress.2023.109734","type":"journal-article","created":{"date-parts":[[2023,10,13]],"date-time":"2023-10-13T06:28:28Z","timestamp":1697178508000},"page":"109734","update-policy":"http:\/\/dx.doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":7,"special_numbering":"C","title":["Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines"],"prefix":"10.1016","volume":"242","author":[{"ORCID":"http:\/\/orcid.org\/0000-0001-6187-9803","authenticated-orcid":false,"given":"Jian","family":"Wang","sequence":"first","affiliation":[]},{"given":"Shibin","family":"Gao","sequence":"additional","affiliation":[]},{"given":"Long","family":"Yu","sequence":"additional","affiliation":[]},{"given":"Xingyang","family":"Liu","sequence":"additional","affiliation":[]},{"given":"Ferrante","family":"Neri","sequence":"additional","affiliation":[]},{"given":"Dongkai","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lei","family":"Kou","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2023.109734_b1","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109266","article-title":"A data-driven integrated framework for predictive probabilistic risk analytics of overhead contact lines based on dynamic Bayesian network","volume":"235","author":"Wang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2023.109734_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.108091","article-title":"Modeling and vulnerability analysis of interdependent railway and power networks: Application to British test systems","volume":"217","author":"Bell\u00e8","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"issue":"8","key":"10.1016\/j.ress.2023.109734_b3","doi-asserted-by":"crossref","first-page":"10104","DOI":"10.1109\/TITS.2021.3119023","article-title":"A survey on automatic inspections of overhead contact lines by computer vision","volume":"23","author":"Yu","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10.1016\/j.ress.2023.109734_b4","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109462","article-title":"Predicting railway wheel wear by calibrating existing wear models: Principle and application","volume":"238","author":"Ye","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"issue":"2","key":"10.1016\/j.ress.2023.109734_b5","doi-asserted-by":"crossref","first-page":"1299","DOI":"10.1109\/TITS.2020.3024216","article-title":"Adaptive deep learning for high-speed railway catenary swivel clevis defects detection","volume":"23","author":"Gao","year":"2022","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10.1016\/j.ress.2023.109734_b6","first-page":"1","article-title":"Automatic detection and monitoring system of pantograph\u2013catenary in China\u2019s high-speed railways","volume":"70","author":"Gao","year":"2021","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2023.109734_b7","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108726","article-title":"An integrated probabilistic risk assessment methodology for maritime transportation of spent nuclear fuel based on event tree and hydrodynamic model","volume":"227","author":"Tao","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2023.109734_b8","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108891","article-title":"Risk coupling analysis of road transportation accidents of hazardous materials in complicated maritime environment","volume":"229","author":"Guo","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"issue":"6","key":"10.1016\/j.ress.2023.109734_b9","first-page":"1515","article-title":"Lightning scope division and lightning trip-out rate calculation method for overhead catenary system","volume":"39","author":"Cao","year":"2013","journal-title":"High Volt. Eng."},{"issue":"5","key":"10.1016\/j.ress.2023.109734_b10","first-page":"21","article-title":"Risk assessment and early warning of lightning disaster for traction power supply system of high-speed railway","volume":"35","author":"Cheng","year":"2013","journal-title":"J. China Railw. Soc."},{"issue":"10","key":"10.1016\/j.ress.2023.109734_b11","first-page":"191","article-title":"Lightning protection of traction power supply catenary of high-speed railway","volume":"33","author":"Bian","year":"2013","journal-title":"Proc. CSEE"},{"issue":"5","key":"10.1016\/j.ress.2023.109734_b12","first-page":"1526","article-title":"Method of lightning hazard risk evaluation for traction electric network of high-speed railway","volume":"41","author":"Gu","year":"2015","journal-title":"High Volt. Eng."},{"key":"10.1016\/j.ress.2023.109734_b13","series-title":"2019 CAA symposium on fault detection, supervision and safety for technical processes","first-page":"547","article-title":"A dynamic risk analysis method for high-speed railway catenary based on Bayesian network","author":"Ma","year":"2019"},{"issue":"1","key":"10.1016\/j.ress.2023.109734_b14","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1109\/TTE.2022.3198554","article-title":"A multilayer Bayesian network approach-based predictive probabilistic risk assessment for overhead contact lines under external weather conditions","volume":"9","author":"Gao","year":"2023","journal-title":"IEEE Trans. Transp. Electrif."},{"key":"10.1016\/j.ress.2023.109734_b15","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.109016","article-title":"Data-driven lightning-related failure risk prediction of overhead contact lines based on Bayesian network with spatiotemporal fragility model","volume":"231","author":"Wang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2023.109734_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108603","article-title":"Predicting wind-caused floater intrusion risk for overhead contact lines based on Bayesian neural network with spatiotemporal correlation analysis","volume":"225","author":"Wang","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2023.109734_b17","doi-asserted-by":"crossref","first-page":"176","DOI":"10.1016\/j.ins.2022.01.021","article-title":"An integrated surrogate model constructing method: Annealing combinable Gaussian process","volume":"591","author":"Wang","year":"2022","journal-title":"Inform Sci"},{"key":"10.1016\/j.ress.2023.109734_b18","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1016\/j.ins.2022.05.028","article-title":"Transfer learning based on sparse Gaussian process for regression","volume":"605","author":"Yang","year":"2022","journal-title":"Inform Sci"},{"key":"10.1016\/j.ress.2023.109734_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2021.107898","article-title":"Stock index prediction and uncertainty analysis using multi-scale nonlinear ensemble paradigm of optimal feature extraction, two-stage deep learning and Gaussian process regression","volume":"113","author":"Wang","year":"2021","journal-title":"Appl. Soft Comput."},{"issue":"55","key":"10.1016\/j.ress.2023.109734_b20","doi-asserted-by":"crossref","first-page":"30942","DOI":"10.1016\/j.ijhydene.2020.08.052","article-title":"A novel pem fuel cell remaining useful life prediction method based on singular spectrum analysis and deep Gaussian processes","volume":"45","author":"Xie","year":"2020","journal-title":"Int J Hydrogen Energy"},{"issue":"2","key":"10.1016\/j.ress.2023.109734_b21","doi-asserted-by":"crossref","first-page":"1142","DOI":"10.1109\/TII.2021.3081531","article-title":"Robust deep Gaussian process-based probabilistic electrical load forecasting against anomalous events","volume":"18","author":"Cao","year":"2022","journal-title":"IEEE Trans Ind Inf"},{"key":"10.1016\/j.ress.2023.109734_b22","doi-asserted-by":"crossref","first-page":"322","DOI":"10.1016\/j.ins.2022.07.174","article-title":"Multi-objective multitasking optimization based on positive knowledge transfer mechanism","volume":"612","author":"Dang","year":"2022","journal-title":"Inform Sci"},{"key":"10.1016\/j.ress.2023.109734_b23","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1038\/nature11767","article-title":"Trustworthy predictions","volume":"493","author":"Kent","year":"2013","journal-title":"Nature"},{"key":"10.1016\/j.ress.2023.109734_b24","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2022.108525","article-title":"Towards trustworthy machine fault diagnosis: A probabilistic Bayesian deep learning framework","volume":"224","author":"Zhou","year":"2022","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2023.109734_b25","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1002\/stc.2811","article-title":"A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties","volume":"28","author":"Caceres","year":"2021","journal-title":"Struct. Control Health Monit."},{"issue":"9","key":"10.1016\/j.ress.2023.109734_b26","doi-asserted-by":"crossref","first-page":"8829","DOI":"10.1109\/TIE.2020.3009593","article-title":"A Bayesian deep learning rul framework integrating epistemic and aleatoric uncertainties","volume":"68","author":"Li","year":"2021","journal-title":"IEEE Trans Ind Electron"},{"key":"10.1016\/j.ress.2023.109734_b27","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2021.108498","article-title":"Uncertainty estimation for stereo matching based on evidential deep learning","volume":"124","author":"Wang","year":"2022","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.ress.2023.109734_b28","doi-asserted-by":"crossref","first-page":"199","DOI":"10.1016\/j.neunet.2021.10.020","article-title":"Detecting out-of-distribution samples via variational auto-encoder with reliable uncertainty estimation","volume":"145","author":"Ran","year":"2022","journal-title":"Neural Netw"},{"key":"10.1016\/j.ress.2023.109734_b29","series-title":"Proceedings of the 32nd international conference on neural information processing systems","first-page":"7517","article-title":"Inference in deep Gaussian processes using stochastic gradient hamiltonian monte carlo","author":"Havasi","year":"2018"},{"key":"10.1016\/j.ress.2023.109734_b30","series-title":"Doubly stochastic variational inference for deep Gaussian processes","first-page":"4591","author":"Salimbeni","year":"2017"},{"key":"10.1016\/j.ress.2023.109734_b31","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2022.108804","article-title":"A typhoon trajectory prediction model based on multimodal and multitask learning","volume":"122","author":"Qin","year":"2022","journal-title":"Appl Soft Comput"},{"key":"10.1016\/j.ress.2023.109734_b32","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.ins.2022.05.019","article-title":"Deepfr: A trajectory prediction model based on deep feature representation","volume":"604","author":"Qin","year":"2022","journal-title":"Inform Sci"},{"key":"10.1016\/j.ress.2023.109734_b33","article-title":"MTL-deep-STF: A multitask learning based deep spatiotemporal fusion model for outdoor air temperature prediction in building HVAC systems","volume":"62","author":"Qiao","year":"2022","journal-title":"J Build Eng"},{"issue":"7","key":"10.1016\/j.ress.2023.109734_b34","doi-asserted-by":"crossref","first-page":"4670","DOI":"10.1109\/TITS.2021.3054437","article-title":"Online performance prediction of perception dnns by multi-task learning with depth estimation","volume":"22","author":"Klingner","year":"2021","journal-title":"IEEE Trans Intell Transp Syst"},{"key":"10.1016\/j.ress.2023.109734_b35","doi-asserted-by":"crossref","first-page":"438","DOI":"10.1016\/j.neucom.2018.10.097","article-title":"A deep learning based multitask model for network-wide traffic speed prediction","volume":"396","author":"Zhang","year":"2020","journal-title":"Neurocomputing"},{"issue":"4","key":"10.1016\/j.ress.2023.109734_b36","doi-asserted-by":"crossref","first-page":"2363","DOI":"10.1109\/TPWRD.2021.3066157","article-title":"Resilience-oriented transmission line fragility modeling and real-time risk assessment of thunderstorms","volume":"36","author":"Bao","year":"2021","journal-title":"IEEE Trans Power Deliv"},{"key":"10.1016\/j.ress.2023.109734_b37","series-title":"Ieee guide for improving the lightning performance of electric power overhead distribution lines","first-page":"1","year":"2011"},{"issue":"1","key":"10.1016\/j.ress.2023.109734_b38","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1109\/61.847285","article-title":"Observation of current waveshapes of lightning strokes on transmission towers","volume":"15","author":"Narita","year":"2000","journal-title":"IEEE Trans Power Deliv"},{"key":"10.1016\/j.ress.2023.109734_b39","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2022.104079","article-title":"A multi-task Gaussian process self-attention neural network for real-time prediction of the need for mechanical ventilators in COVID-19 patients","volume":"130","author":"Zhang","year":"2022","journal-title":"J Biomed Inform"},{"key":"10.1016\/j.ress.2023.109734_b40","series-title":"Non-linear multitask learning with deep Gaussian processes","author":"Boustati","year":"2019"},{"key":"10.1016\/j.ress.2023.109734_b41","series-title":"Advances in neural information processing systems, vol. 30","article-title":"What uncertainties do we need in Bayesian deep learning for computer vision?","author":"Kendall","year":"2017"},{"key":"10.1016\/j.ress.2023.109734_b42","doi-asserted-by":"crossref","first-page":"457","DOI":"10.1007\/s10994-021-05946-3","article-title":"Aleatoric and epistemic uncertainty in machine learning: An introduction to concepts and methods","volume":"110","author":"H\u00fcllermeier","year":"2019","journal-title":"Mach Learn"},{"issue":"10","key":"10.1016\/j.ress.2023.109734_b43","doi-asserted-by":"crossref","first-page":"7274","DOI":"10.1109\/TII.2022.3156965","article-title":"A Bayesian deep learning framework for rul prediction incorporating uncertainty quantification and calibration","volume":"18","author":"Lin","year":"2022","journal-title":"IEEE Trans Ind Inf"}],"container-title":["Reliability Engineering & System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832023006488?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832023006488?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2023,12,5]],"date-time":"2023-12-05T21:05:49Z","timestamp":1701810349000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832023006488"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,2]]},"references-count":43,"alternative-id":["S0951832023006488"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2023.109734","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2024,2]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Uncertainty-aware trustworthy weather-driven failure risk predictor for overhead contact lines","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2023.109734","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2023 Elsevier Ltd. All rights reserved.","name":"copyright","label":"Copyright"}],"article-number":"109734"}}