{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:58:13Z","timestamp":1780937893209,"version":"3.54.1"},"reference-count":44,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:00:00Z","timestamp":1793491200000},"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":[[2026,11]]},"DOI":"10.1016\/j.ress.2026.112762","type":"journal-article","created":{"date-parts":[[2026,4,15]],"date-time":"2026-04-15T08:53:56Z","timestamp":1776243236000},"page":"112762","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"P1","title":["Likelihood-driven reinforcement learning synergizing knowledge graph embedding and supervised pre-training for system-level fault localization"],"prefix":"10.1016","volume":"275","author":[{"given":"Bingbing","family":"Wang","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Menghui","family":"Yan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1678-4538","authenticated-orcid":false,"given":"Tao","family":"Cui","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Borui","family":"Hu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Fujun","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"1","key":"10.1016\/j.ress.2026.112762_bib0001","doi-asserted-by":"crossref","first-page":"366","DOI":"10.1016\/j.ijhydene.2022.09.256","article-title":"Hydrogen engine operation strategies: recent progress, industrialization challenges, and perspectives","volume":"48","author":"Sun","year":"2023","journal-title":"Int J Hydrog Energy"},{"key":"10.1016\/j.ress.2026.112762_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.nxener.2025.100399","article-title":"Towards enhanced durability: a review of fuel cell electric vehicle development","volume":"9","author":"Luo","year":"2025","journal-title":"Next Energy"},{"key":"10.1016\/j.ress.2026.112762_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2025.118544","article-title":"Meta-learning driven energy management in integrated systems with electric vehicles as mobile storage units","volume":"137","author":"Jayakumar","year":"2025","journal-title":"J Energy Storage"},{"key":"10.1016\/j.ress.2026.112762_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.csite.2025.106223","article-title":"Enhanced thermal efficiency in off-highway vehicles using a reheat organic Rankine cycle with a two-stage radial turbine and wet fluids","author":"Alshammari","year":"2025","journal-title":"Case Stud Therm Eng"},{"issue":"10","key":"10.1016\/j.ress.2026.112762_bib0005","first-page":"1","article-title":"Energy-efficient torque distribution strategy for four wheel drive electric vehicles based on traffic zone","volume":"58","author":"Achdad","year":"2024","journal-title":"IFAC-Pap"},{"key":"10.1016\/j.ress.2026.112762_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2022.123543","article-title":"Energy recovery strategy optimization of dual-motor drive electric vehicle based on braking safety and efficient recovery","volume":"248","author":"He","year":"2022","journal-title":"Energy"},{"key":"10.1016\/j.ress.2026.112762_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2025.123161","article-title":"Research on drag reduction and energy saving of M-bow amphibious vehicle based on configuration optimization","volume":"342","author":"Liu","year":"2025","journal-title":"Ocean Eng"},{"key":"10.1016\/j.ress.2026.112762_bib0008","article-title":"Prescribed-time fault-tolerant tracking control for aquatic-aerial unmanned amphibious vehicles","author":"Meng","year":"2025","journal-title":"Aerosp Sci Technol"},{"issue":"28","key":"10.1016\/j.ress.2026.112762_bib0009","first-page":"456","article-title":"Aerial and ground vehicles collaboration for automated target tracking using reinforcement learning","volume":"58","author":"Zanone","year":"2024","journal-title":"IFAC-Pap"},{"key":"10.1016\/j.ress.2026.112762_bib0010","article-title":"An energy-efficient distributed computation offloading algorithm for ground-air cooperative networks","volume":"52","author":"Shao","year":"2025","journal-title":"Veh Commun"},{"key":"10.1016\/j.ress.2026.112762_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.112657","article-title":"A novel model for analyzing meshing characteristics in a non-uniformly distributed gear system with irregular tooth edge spalling fault","volume":"230","author":"Yue","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112762_bib0012","doi-asserted-by":"crossref","first-page":"242","DOI":"10.1016\/j.precisioneng.2024.09.015","article-title":"Numerical investigation on tooth surface waviness in continuous generating grinding of electric vehicle gears considering the main shaft vibration and grinding worm wear","volume":"91","author":"Tao","year":"2024","journal-title":"Precis Eng"},{"key":"10.1016\/j.ress.2026.112762_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.engfailanal.2024.107980","article-title":"Vibration nonstationary representation of the railway vehicle's gear system considering spalling failure under variable speed conditions: numerical modeling and field testing","volume":"158","author":"Zhao","year":"2024","journal-title":"Eng Fail Anal"},{"key":"10.1016\/j.ress.2026.112762_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2025.118456","article-title":"Fault diagnosis method for rolling bearings under a small sample dataset based on improved WGAN-GP","author":"Qin","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2026.112762_bib0015","article-title":"Fault diagnosis of rolling bearing based on two-dimensional composite multi-scale ensemble gramian dispersion entropy","author":"Ding","year":"2025","journal-title":"Chin J Mech Eng"},{"key":"10.1016\/j.ress.2026.112762_bib0016","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.aej.2024.12.007","article-title":"Enhanced fault diagnosis and remaining useful life prediction of rolling bearings using a hybrid multilayer perceptron and LSTM network model","volume":"115","author":"Bharatheedasan","year":"2025","journal-title":"Alex Eng J"},{"key":"10.1016\/j.ress.2026.112762_bib0017","doi-asserted-by":"crossref","DOI":"10.1016\/j.etran.2025.100488","article-title":"External pressure effects on thermal runaway in prismatic LiFePO4 batteries: mechanistic insights for safer battery systems in electric vehicles","author":"Chen","year":"2025","journal-title":"eTransportation"},{"key":"10.1016\/j.ress.2026.112762_bib0018","article-title":"A multi-scale modeling approach for predicting and mitigating thermal runaway in electric vehicle batteries","volume":"56","author":"Hemakumar","year":"2024","journal-title":"Therm Sci Eng Prog"},{"key":"10.1016\/j.ress.2026.112762_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.115194","article-title":"Unsupervised learning for lithium-ion batteries fault diagnosis and thermal runaway early warning in real-world electric vehicles","volume":"109","author":"Yu","year":"2025","journal-title":"J Energy Storage"},{"key":"10.1016\/j.ress.2026.112762_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.est.2024.112812","article-title":"Multiscale feature fusion approach to early fault diagnosis in EV power battery using operational data","volume":"98","author":"Wang","year":"2024","journal-title":"J Energy Storage"},{"key":"10.1016\/j.ress.2026.112762_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114191","article-title":"An adaptive feature mode decomposition based on a novel health indicator for bearing fault diagnosis","volume":"226","author":"Chauhan","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2026.112762_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113237","article-title":"An upsampling framework for vehicle low-frequency sampling data based on human-machine-environment fusion","volume":"238","author":"Wang","year":"2025","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112762_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.108503","article-title":"A prior knowledge-enhanced self-supervised learning framework using time-frequency invariance for machinery intelligent fault diagnosis with small samples","volume":"133","author":"Tang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.112762_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.115668","article-title":"Deep subdomain adversarial network with self-supervised learning for aero-engine high speed bearing fault diagnosis with unknown working conditions","volume":"241","author":"Shi","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2026.112762_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.114338","article-title":"Diesel engine fault diagnosis for multiple industrial scenarios based on transfer learning","volume":"228","author":"Zhang","year":"2024","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2026.112762_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111875","article-title":"Gas path fault detection and isolation for aero-engine based on LSTM-DAE approach under multiple-model architecture","volume":"202","author":"Wang","year":"2022","journal-title":"Measurement"},{"issue":"2","key":"10.1016\/j.ress.2026.112762_bib0027","first-page":"1282","article-title":"Norm-based data labelling in supervised learning for fault detection and diagnostics of rotating elements towards maintenance servitisation","volume":"56","author":"Polenghi","year":"2023","journal-title":"IFAC-Pap"},{"key":"10.1016\/j.ress.2026.112762_bib0028","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103876","article-title":"Physics-guided self-supervised learning for rotating-machinery fault diagnosis: role of average energy density equation","volume":"69","author":"Lee","year":"2026","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.125594","article-title":"Domain reinforcement feature adaptation methodology with correlation alignment for compound fault diagnosis of rolling bearing","volume":"262","author":"Wang","year":"2025","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112762_bib0030","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2019.100977","article-title":"Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: a deep reinforcement learning approach","volume":"42","author":"Ding","year":"2019","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0031","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2025.126669","article-title":"A convolutional-transformer reinforcement learning agent for rotating machinery fault diagnosis","volume":"271","author":"Li","year":"2025","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112762_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.108873","article-title":"Application of deep reinforcement learning for extremely rare failure prediction in aircraft maintenance","volume":"171","author":"Dangut","year":"2022","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112762_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2023.121001","article-title":"An adaptive fault diagnosis framework under class-imbalanced conditions based on contrastive augmented deep reinforcement learning","volume":"234","author":"Zhao","year":"2023","journal-title":"Expert Syst Appl"},{"key":"10.1016\/j.ress.2026.112762_bib0034","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110783","article-title":"Environment adaptive deep reinforcement learning for intelligent fault diagnosis","volume":"151","author":"Liu","year":"2025","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.112762_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2021.101480","article-title":"A reinforcement ensemble deep transfer learning network for rolling bearing fault diagnosis with multi-source domains","volume":"51","author":"Li","year":"2022","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109644","article-title":"Gearbox fault diagnosis based on temporal shrinkage interpretable deep reinforcement learning under strong noise","volume":"139","author":"Wei","year":"2025","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2026.112762_bib0037","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2022.101818","article-title":"Transfer reinforcement learning method with multi-label learning for compound fault recognition","volume":"55","author":"Wang","year":"2023","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0038","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110570","article-title":"A novel reinforcement learning agent for rotating machinery fault diagnosis with data augmentation","volume":"253","author":"Li","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2026.112762_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103297","article-title":"Continual contrastive reinforcement learning: towards stronger agent for environment-aware fault diagnosis of aero-engines through long-term optimization under highly imbalance scenarios","volume":"65","author":"Wu","year":"2025","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2024.111192","article-title":"Contrastive feature-based learning-guided elevated deep reinforcement learning: developing an imbalanced fault quantitative diagnosis under variable working conditions","volume":"211","author":"He","year":"2024","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112762_bib0041","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2023.101900","article-title":"Reinforcement learning-based distant supervision relation extraction for fault diagnosis knowledge graph construction under industry 4.0","volume":"55","author":"Chen","year":"2023","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0042","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2022.109848","article-title":"Fault diagnosis of rotating machinery based on graph weighted reinforcement networks under small samples and strong noise","volume":"186","author":"Yu","year":"2023","journal-title":"Mech Syst Signal Process"},{"key":"10.1016\/j.ress.2026.112762_bib0043","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2021.101315","article-title":"Intelligent fault recognition framework by using deep reinforcement learning with one dimension convolution and improved actor-critic algorithm","volume":"49","author":"Wang","year":"2021","journal-title":"Adv Eng Inform"},{"key":"10.1016\/j.ress.2026.112762_bib0044","unstructured":"Sun, Zhiqing, et al. \"Rotate: knowledge graph embedding by relational rotation in complex space.\" arxiv preprint arxiv:1902.10197 (2019)."}],"container-title":["Reliability Engineering & System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026005739?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832026005739?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T16:00:43Z","timestamp":1780934443000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832026005739"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,11]]},"references-count":44,"alternative-id":["S0951832026005739"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2026.112762","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2026,11]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Likelihood-driven reinforcement learning synergizing knowledge graph embedding and supervised pre-training for system-level fault localization","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2026.112762","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"112762"}}