{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T10:48:13Z","timestamp":1785840493009,"version":"3.56.0"},"reference-count":52,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,4,1]],"date-time":"2026-04-01T00:00:00Z","timestamp":1775001600000},"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":[[2026,4]]},"DOI":"10.1016\/j.ress.2025.111982","type":"journal-article","created":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T11:11:21Z","timestamp":1763637081000},"page":"111982","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"special_numbering":"C","title":["FBSTFnet: A full-band spatiotemporal fusion network for machine remaining useful life prediction"],"prefix":"10.1016","volume":"268","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2020-0715","authenticated-orcid":false,"given":"Zhixiang","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ress.2025.111982_bib0001","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2025.113015","article-title":"A comprehensive review and evaluation framework for data-driven prognostics: uncertainty, robustness, interpretability, and feasibility","volume":"237","author":"Salinas-Camus","year":"2025","journal-title":"Mech Syst Signal Proc"},{"key":"10.1016\/j.ress.2025.111982_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2021.107652","article-title":"An integrated deep multiscale feature fusion network for aeroengine remaining useful life prediction with multisensor data","volume":"235","author":"Li","year":"2022","journal-title":"Knowl-Based Syst"},{"key":"10.1016\/j.ress.2025.111982_bib0003","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.113098","article-title":"Global attention mechanism based deep learning for remaining useful life prediction of aero-engine","volume":"217","author":"Xu","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2025.111982_bib0004","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2024.112408","article-title":"Data-driven predictive maintenance framework considering the multi-source information fusion and uncertainty in remaining useful life prediction","volume":"303","author":"Xu","year":"2024","journal-title":"Knowl-Based Syst"},{"key":"10.1016\/j.ress.2025.111982_bib0005","doi-asserted-by":"crossref","first-page":"149","DOI":"10.1109\/TPAMI.2024.3465535","article-title":"A robust, bounded, sparse, and smooth loss function for supervised learning","volume":"47","author":"Akhtar","year":"2025","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.ress.2025.111982_bib0006","doi-asserted-by":"crossref","first-page":"17447","DOI":"10.1109\/JSEN.2020.3045501","article-title":"Extracting the forest type from remote sensing images by random forest","volume":"21","author":"Linhui","year":"2021","journal-title":"IEEE Sens J"},{"key":"10.1016\/j.ress.2025.111982_bib0007","doi-asserted-by":"crossref","DOI":"10.1016\/j.clet.2025.101088","article-title":"Artificial Neural network and genetic algorithm for optimization of helical blade vertical axis wind turbine","author":"Sanaye","year":"2025","journal-title":"Clean Eng Technol"},{"key":"10.1016\/j.ress.2025.111982_bib0008","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.jmsy.2023.02.006","article-title":"Tool wear identification and prediction method based on stack sparse self-coding network","volume":"68","author":"Qin","year":"2023","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2025.111982_bib0009","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.110719","article-title":"Milling tool wear prediction using multi-sensor feature fusion based on stacked sparse autoencoders","volume":"190","author":"He","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2025.111982_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.conengprac.2023.105840","article-title":"A change point detection integrated remaining useful life estimation model under variable operating conditions","volume":"144","author":"Arunan","year":"2024","journal-title":"Control Eng Pract"},{"key":"10.1016\/j.ress.2025.111982_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.ymssp.2023.110644","article-title":"On-line tool wear monitoring under variable milling conditions based on a condition-adaptive hidden semi-Markov model (CAHSMM)","volume":"200","author":"Yan","year":"2023","journal-title":"Mech Syst Signal Proc"},{"key":"10.1016\/j.ress.2025.111982_bib0012","first-page":"1","article-title":"Remaining useful life prediction via a double convolutional attention-based CNN-GRU model","volume":"74","author":"Miao","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2025.111982_bib0013","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102462","article-title":"MST-GAT: a multi-perspective spatial-temporal graph attention network for multi-sensor equipment remaining useful life prediction","volume":"110","author":"Zhou","year":"2024","journal-title":"Inf Fusion"},{"key":"10.1016\/j.ress.2025.111982_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2025.114623","article-title":"Operating condition invariant representation learning for machine prognostics","author":"Kim","year":"2025","journal-title":"Knowl-Based Syst"},{"key":"10.1016\/j.ress.2025.111982_bib0015","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.jmsy.2025.05.009","article-title":"Multi-view fully connected graph to fuse multi-sensor signals for mechanical equipment remaining useful life prediction","volume":"80","author":"Wu","year":"2025","journal-title":"J Manuf Syst"},{"key":"10.1016\/j.ress.2025.111982_bib0016","article-title":"Explainable spatiotemporal graph neutral network with dynamic SHAP enhancement for aeroengine RUL prediction","author":"Wen","year":"2025","journal-title":"Aerosp Sci Technol"},{"key":"10.1016\/j.ress.2025.111982_bib0017","first-page":"1","article-title":"Heterogeneous dynamic-aware GNN for RUL prediction of aeroengine","volume":"74","author":"Jin","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2025.111982_bib0018","first-page":"1","article-title":"Dual-channel degradation monitoring based on graph neural network for aero-engine remaining useful life prediction","volume":"74","author":"La","year":"2025","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2025.111982_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.aei.2025.103231","article-title":"Enhanced deep learning framework for accurate near-failure RUL prediction of bearings in varying operating conditions","volume":"65","author":"Kumar","year":"2025","journal-title":"Adv Eng Inf"},{"key":"10.1016\/j.ress.2025.111982_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2025.137253","article-title":"A spatial\u2013temporal graph structure automatic feedback learning system with tensor fusion and its application on engine RUL prediction","volume":"334","author":"Liu","year":"2025","journal-title":"Energy"},{"key":"10.1016\/j.ress.2025.111982_bib0021","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109186","article-title":"Digital twin-driven partial domain adaptation network for intelligent fault diagnosis of rolling bearing","volume":"234","author":"Zhang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0022","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109891","article-title":"A novel generalized source-free domain adaptation approach for cross-domain industrial fault diagnosis","volume":"243","author":"Tian","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110079","article-title":"Source-free domain adaptation for transferable remaining useful life prediction of machine considering source data absence","volume":"246","author":"Cao","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0024","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106491","article-title":"Res-HSA: residual hybrid network with self-attention mechanism for RUL prediction of rotating machinery","volume":"124","author":"Zhu","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2025.111982_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2021.107813","article-title":"A novel temporal convolutional network with residual self-attention mechanism for remaining useful life prediction of rolling bearings","volume":"215","author":"Cao","year":"2021","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0026","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109605","article-title":"PAOLTransformer: pruning-adaptive optimal lightweight transformer model for aero-engine remaining useful life prediction","volume":"240","author":"Zhang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0027","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109662","article-title":"Trend-augmented and temporal-featured Transformer network with multi-sensor signals for remaining useful life prediction","volume":"241","author":"Zhang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0028","doi-asserted-by":"crossref","first-page":"5942","DOI":"10.1109\/TNNLS.2023.3257038","article-title":"DLformer: a dynamic length transformer-based network for efficient feature representation in remaining useful life prediction","volume":"35","author":"Ren","year":"2024","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"10.1016\/j.ress.2025.111982_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109247","article-title":"Attention-augmented recalibrated and compensatory network for machine remaining useful life prediction","volume":"235","author":"Huang","year":"2023","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0030","first-page":"1","article-title":"Structural-guided interaction and attention-enhancing compensation network for machine remaining useful life prediction","volume":"73","author":"Liu","year":"2024","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2025.111982_bib0031","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1109\/TSC.2023.3335296","article-title":"Generative-adversarial-based feature compensation to predict quality of service","volume":"17","author":"Zhang","year":"2024","journal-title":"IEEE Trans v Comput"},{"key":"10.1016\/j.ress.2025.111982_bib0032","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2024.115721","article-title":"Corrupted point cloud classification based on feature compensation and cross attention mechanism","volume":"241","author":"He","year":"2025","journal-title":"Measurement"},{"key":"10.1016\/j.ress.2025.111982_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110652","article-title":"Machine remaining useful life prediction method based on global-local attention compensation network","volume":"255","author":"Chen","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0034","doi-asserted-by":"crossref","first-page":"4068","DOI":"10.1109\/JSEN.2023.3342884","article-title":"Spatial\u2013Temporal attention and information reinforcement network for machine remaining useful life prediction","volume":"24","author":"Li","year":"2024","journal-title":"IEEE Sens J"},{"key":"10.1016\/j.ress.2025.111982_bib0035","first-page":"1","article-title":"Spatio-temporal Fusion attention: a novel approach for remaining useful life prediction based on graph neural network","volume":"71","author":"Kong","year":"2022","journal-title":"IEEE Trans Instrum Meas"},{"key":"10.1016\/j.ress.2025.111982_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2025.110978","article-title":"DCAGGCN: a novel method for remaining useful life prediction of bearings","volume":"260","author":"He","year":"2025","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0037","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.ins.2022.05.127","article-title":"Attention based spatiotemporal graph attention networks for traffic flow forecasting","volume":"607","author":"Wang","year":"2022","journal-title":"Inf Sci"},{"key":"10.1016\/j.ress.2025.111982_bib0038","doi-asserted-by":"crossref","first-page":"6825","DOI":"10.1109\/JSEN.2024.3523176","article-title":"Multiscale spatiotemporal attention network for remaining useful life prediction of mechanical systems","volume":"25","author":"Gao","year":"2025","journal-title":"IEEE Sens J"},{"key":"10.1016\/j.ress.2025.111982_bib0039","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2024.110409","article-title":"Neural-transformer: a brain-inspired lightweight mechanical fault diagnosis method under noise","volume":"251","author":"Wang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.105982","article-title":"Hybrid VMD-CNN-GRU-based model for short-term forecasting of wind power considering spatio-temporal features","volume":"121","author":"Zhao","year":"2023","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2025.111982_bib0041","series-title":"2023 IEEE\/CVF winter conference on applications of computer vision (WACV)","first-page":"5457","article-title":"Discrete cosin TransFormer: image modeling from frequency domain","author":"Li","year":"2023"},{"key":"10.1016\/j.ress.2025.111982_bib0042","doi-asserted-by":"crossref","unstructured":"Shen X., Yang J., Wei C., Deng B., Huang J., Hua X. DCT-mask: discrete cosine transform mask representation for instance segmentation. IEEE\/CVF conference on computer vision and pattern recognition (CVPR), Nashville, TN, USA. (2021) 8716\u201325, https:\/\/doi.org\/10.1109\/CVPR46437.2021.00861.","DOI":"10.1109\/CVPR46437.2021.00861"},{"key":"10.1016\/j.ress.2025.111982_bib0043","doi-asserted-by":"crossref","first-page":"9454","DOI":"10.1109\/TPAMI.2023.3243048","article-title":"Conformer: local features coupling global representations for recognition and detection","volume":"45","author":"Peng","year":"2023","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"10.1016\/j.ress.2025.111982_bib0044","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2022.108707","article-title":"Harmonic convolutional networks based on discrete cosine transform","volume":"129","author":"Ulicny","year":"2022","journal-title":"Pattern Recognit"},{"key":"10.1016\/j.ress.2025.111982_bib0045","first-page":"1","article-title":"Attention is all you need","author":"Ashish","year":"2017","journal-title":":1706.03762."},{"key":"10.1016\/j.ress.2025.111982_bib0046","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 convolution neural networks","volume":"172","author":"Li","year":"2018","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0047","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107241","article-title":"An attention-based temporal convolutional network method for predicting remaining useful life of aero-engine","volume":"127","author":"Zhang","year":"2024","journal-title":"Eng Appl Artif Intell"},{"key":"10.1016\/j.ress.2025.111982_bib0048","doi-asserted-by":"crossref","DOI":"10.1016\/j.ress.2023.109666","article-title":"Health indicators for remaining useful life prediction of complex systems based on long short-term memory network and improved particle filter","volume":"241","author":"Zhang","year":"2024","journal-title":"Reliab Eng Syst Saf"},{"key":"10.1016\/j.ress.2025.111982_bib0049","doi-asserted-by":"crossref","first-page":"209","DOI":"10.1016\/j.isatra.2025.05.010","article-title":"Aeroengine remaining useful life prediction via integrating enhanced inverted transformer and spatiotemporal graph learning","volume":"163","author":"Sun","year":"2025","journal-title":"ISA Trans"},{"key":"10.1016\/j.ress.2025.111982_bib0050","first-page":"1","article-title":"Review of PHM data competitions from 2008 to 2017 methodologies and analytics","author":"Jia","year":"2018","journal-title":"Health Manag Soc"},{"key":"10.1016\/j.ress.2025.111982_bib0051","doi-asserted-by":"crossref","first-page":"3026","DOI":"10.1109\/TKDE.2014.2316504","article-title":"Highly comparative feature-based time-series classification","volume":"26","author":"Fulcher","year":"2014","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"10.1016\/j.ress.2025.111982_bib0052","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1007\/s00170-009-2482-0","article-title":"Current status of machine prognostics in condition-based maintenance: a review","volume":"50","author":"Peng","year":"2010","journal-title":"Int J Adv Manuf Technol"}],"container-title":["Reliability Engineering & System Safety"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832025011822?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0951832025011822?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,1,18]],"date-time":"2026-01-18T10:09:24Z","timestamp":1768730964000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0951832025011822"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4]]},"references-count":52,"alternative-id":["S0951832025011822"],"URL":"https:\/\/doi.org\/10.1016\/j.ress.2025.111982","relation":{},"ISSN":["0951-8320"],"issn-type":[{"value":"0951-8320","type":"print"}],"subject":[],"published":{"date-parts":[[2026,4]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"FBSTFnet: A full-band spatiotemporal fusion network for machine remaining useful life prediction","name":"articletitle","label":"Article Title"},{"value":"Reliability Engineering & System Safety","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ress.2025.111982","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"111982"}}