{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,4]],"date-time":"2026-05-04T06:25:43Z","timestamp":1777875943905,"version":"3.51.4"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,7,1]],"date-time":"2026-07-01T00:00:00Z","timestamp":1782864000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T00:00:00Z","timestamp":1773187200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007706","name":"Government of Italy Ministry of Economic Development","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100007706","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100005758","name":"Universit\u00e0 Politecnica delle Marche","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100005758","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Advanced Engineering Informatics"],"published-print":{"date-parts":[[2026,7]]},"DOI":"10.1016\/j.aei.2026.104592","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T17:39:10Z","timestamp":1773250750000},"page":"104592","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A novel multi-task multi-view approach with custom multi-label loss for fault detection in complex industrial apparatus"],"prefix":"10.1016","volume":"73","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3288-638X","authenticated-orcid":false,"given":"Riccardo","family":"Rosati","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1471-092X","authenticated-orcid":false,"given":"Lucia","family":"Pepa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1707-0147","authenticated-orcid":false,"given":"Luca","family":"Romeo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"13","key":"10.1016\/j.aei.2026.104592_b1","doi-asserted-by":"crossref","first-page":"11160","DOI":"10.1109\/JIOT.2023.3246100","article-title":"An evaluative study on IoT ecosystem for smart predictive maintenance (IoT-SPM) in manufacturing: Multiview requirements and data quality","volume":"10","author":"Liu","year":"2023","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.aei.2026.104592_b2","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106139","article-title":"Multi-fault diagnosis of industrial rotating machines using data-driven approach: A review of two decades of research","volume":"123","author":"Gawde","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.aei.2026.104592_b3","doi-asserted-by":"crossref","first-page":"2441","DOI":"10.1007\/s10845-023-02165-6","article-title":"Fault diagnosis and self-healing for smart manufacturing: a review","volume":"35","author":"Aldrini","year":"2024","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.aei.2026.104592_b4","doi-asserted-by":"crossref","first-page":"528","DOI":"10.1016\/j.procir.2019.02.098","article-title":"Log-based predictive maintenance in discrete parts manufacturing","volume":"79","author":"Gutschi","year":"2019","journal-title":"Procedia CIRP"},{"issue":"1","key":"10.1016\/j.aei.2026.104592_b5","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s10845-022-01960-x","article-title":"From knowledge-based to big data analytic model: a novel IoT and machine learning based decision support system for predictive maintenance in industry 4.0","volume":"34","author":"Rosati","year":"2023","journal-title":"J. Intell. Manuf."},{"key":"10.1016\/j.aei.2026.104592_b6","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"15485","article-title":"Efficient training of large-scale industrial fault diagnostic models through federated opportunistic block dropout","volume":"vol. 37","author":"Chen","year":"2023"},{"key":"10.1016\/j.aei.2026.104592_b7","series-title":"Machine Learning and Probabilistic Graphical Models for Decision Support Systems","first-page":"34","article-title":"Decision support systems for anomaly detection with the applications in smart manufacturing: a survey and perspective","author":"Nguyen","year":"2022"},{"issue":"16","key":"10.1016\/j.aei.2026.104592_b8","doi-asserted-by":"crossref","first-page":"8081","DOI":"10.3390\/app12168081","article-title":"On predictive maintenance in industry 4.0: Overview, models, and challenges","volume":"12","author":"Achouch","year":"2022","journal-title":"Appl. Sci."},{"key":"10.1016\/j.aei.2026.104592_b9","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/j.cirpj.2022.11.004","article-title":"Challenges in predictive maintenance\u2013a review","volume":"40","author":"Nunes","year":"2023","journal-title":"CIRP J. Manuf. Sci. Technol."},{"key":"10.1016\/j.aei.2026.104592_b10","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2024.106210","article-title":"Multi-node knowledge graph assisted distributed fault detection for large-scale industrial processes based on graph attention network and bidirectional LSTMs","volume":"173","author":"Li","year":"2024","journal-title":"Neural Netw."},{"key":"10.1016\/j.aei.2026.104592_b11","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109503","article-title":"Explainable artificial intelligence of tree-based algorithms for fault detection and diagnosis in grid-connected photovoltaic systems","volume":"139","author":"Noura","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104592_b12","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106463","article-title":"A hybrid feature learning approach based on convolutional kernels for ATM fault prediction using event-log data","volume":"123","author":"Vargas","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104592_b13","doi-asserted-by":"crossref","first-page":"2031","DOI":"10.1007\/s00521-013-1362-6","article-title":"A survey of multi-view machine learning","volume":"23","author":"Sun","year":"2013","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.aei.2026.104592_b14","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1016\/j.neucom.2021.03.090","article-title":"Deep multi-view learning methods: A review","volume":"448","author":"Yan","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104592_b15","doi-asserted-by":"crossref","unstructured":"B. Jiang, J. Xiang, X. Wu, W. He, L. Hong, W. Sheng, Robust adaptive-weighting multi-view classification, in: Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 3117\u20133121.","DOI":"10.1145\/3459637.3482173"},{"key":"10.1016\/j.aei.2026.104592_b16","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"17617","article-title":"Collaborative similarity fusion and consistency recovery for incomplete multi-view clustering","volume":"vol. 39","author":"Jiang","year":"2025"},{"key":"10.1016\/j.aei.2026.104592_b17","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.inffus.2023.03.002","article-title":"Adaptive collaborative fusion for multi-view semi-supervised classification","volume":"96","author":"Jiang","year":"2023","journal-title":"Inf. Fusion"},{"issue":"3889","key":"10.1016\/j.aei.2026.104592_b18","article-title":"Bearing fault diagnosis based on discriminant analysis using multi-view learning","volume":"10","author":"Tong","year":"2022","journal-title":"Mathematics"},{"key":"10.1016\/j.aei.2026.104592_b19","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.111159","article-title":"Multiview enhanced fault diagnosis for wind turbine gearbox bearings with fusion of vibration and current signals","volume":"196","author":"Jiang","year":"2022","journal-title":"Measurement"},{"key":"10.1016\/j.aei.2026.104592_b20","doi-asserted-by":"crossref","DOI":"10.1016\/j.apacoust.2022.108814","article-title":"Multi-component fault classification of a wind turbine gearbox using integrated condition monitoring and hybrid ensemble method approach","volume":"195","author":"Pichika","year":"2022","journal-title":"Appl. Acoust."},{"key":"10.1016\/j.aei.2026.104592_b21","article-title":"MVGNet: Multi-view graph network with interactive shared fusion for fault diagnosis of wind turbines","author":"Gao","year":"2023","journal-title":"Appl. Soft Comput. J."},{"key":"10.1016\/j.aei.2026.104592_b22","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.106138","article-title":"Multi-view rotating machinery fault diagnosis with adaptive co-attention fusion network","volume":"122","author":"Liu","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104592_b23","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.119057","article-title":"Multi-view and multi-level network for fault diagnosis accommodating feature transferability","volume":"213","author":"Lu","year":"2023","journal-title":"Expert Syst. Appl."},{"key":"10.1016\/j.aei.2026.104592_b24","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"6430","article-title":"Multi-label causal feature selection","volume":"vol. 34","author":"Wu","year":"2020"},{"issue":"4","key":"10.1016\/j.aei.2026.104592_b25","doi-asserted-by":"crossref","first-page":"4964","DOI":"10.1109\/TPAMI.2022.3199784","article-title":"Multi-target Markov boundary discovery: Theory, algorithm, and application","volume":"45","author":"Wu","year":"2022","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"10","key":"10.1016\/j.aei.2026.104592_b26","doi-asserted-by":"crossref","first-page":"6740","DOI":"10.1109\/TNNLS.2023.3249767","article-title":"Feature selection in the data stream based on incremental markov boundary learning","volume":"34","author":"Wu","year":"2023","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"issue":"2","key":"10.1016\/j.aei.2026.104592_b27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3486252","article-title":"A hybrid deep learning framework for intelligent predictive maintenance of cyber-physical systems","volume":"6","author":"Shcherbakov","year":"2022","journal-title":"ACM Trans. Cyber-Physical Syst. (TCPS)"},{"key":"10.1016\/j.aei.2026.104592_b28","doi-asserted-by":"crossref","DOI":"10.1016\/j.oceaneng.2021.109723","article-title":"Multi-label classification for simultaneous fault diagnosis of marine machinery: A comparative study","volume":"239","author":"Tan","year":"2021","journal-title":"Ocean Eng."},{"key":"10.1016\/j.aei.2026.104592_b29","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1016\/j.neucom.2020.05.064","article-title":"A deep domain adaption model with multi-task networks for planetary gearbox fault diagnosis","volume":"409","author":"Cao","year":"2020","journal-title":"Neurocomputing"},{"key":"10.1016\/j.aei.2026.104592_b30","doi-asserted-by":"crossref","first-page":"301","DOI":"10.1016\/j.jmsy.2021.12.003","article-title":"End to end multi-task learning with attention for multi-objective fault diagnosis under small sample","volume":"62","author":"Xie","year":"2022","journal-title":"J. Manuf. Syst."},{"key":"10.1016\/j.aei.2026.104592_b31","doi-asserted-by":"crossref","first-page":"5393","DOI":"10.1007\/s00521-020-05345-0","article-title":"Multi-label fault diagnosis of rolling bearing based on meta-learning","volume":"33","author":"Yu","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.aei.2026.104592_b32","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.aei.2026.104592_b33","doi-asserted-by":"crossref","DOI":"10.1109\/TIM.2021.3091504","article-title":"Degradation state partition and compound fault diagnosis of rolling bearing based on personalized multilabel learning","volume":"70","author":"Ma","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.aei.2026.104592_b34","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.isatra.2020.07.002","article-title":"Early and extremely early multi-label fault diagnosis in induction motors","volume":"106","author":"Juez-Gil","year":"2020","journal-title":"ISA Trans."},{"key":"10.1016\/j.aei.2026.104592_b35","doi-asserted-by":"crossref","first-page":"113557","DOI":"10.1109\/ACCESS.2020.3002826","article-title":"A deep multi-label learning framework for the intelligent fault diagnosis of machines","volume":"8","author":"Shen","year":"2020","journal-title":"IEEE Access"},{"key":"10.1016\/j.aei.2026.104592_b36","doi-asserted-by":"crossref","DOI":"10.1016\/j.compind.2019.103132","article-title":"Compound fault diagnosis of gearboxes via multi-label convolutional neural network and wavelet transform","volume":"113","author":"Liang","year":"2019","journal-title":"Comput. Ind."},{"issue":"9","key":"10.1016\/j.aei.2026.104592_b37","doi-asserted-by":"crossref","first-page":"10946","DOI":"10.1109\/JSEN.2021.3061595","article-title":"A multi-input and multi-task convolutional neural network for fault diagnosis based on bearing vibration signal","volume":"21","author":"Wang","year":"2021","journal-title":"IEEE Sensors J."},{"key":"10.1016\/j.aei.2026.104592_b38","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2022.112085","article-title":"M2FN: An end-to-end multi-task and multi-sensor fusion network for intelligent fault diagnosis","volume":"204","author":"Cui","year":"2022","journal-title":"Measurement"},{"issue":"9","key":"10.1016\/j.aei.2026.104592_b39","doi-asserted-by":"crossref","first-page":"8005","DOI":"10.1109\/TIE.2019.2942548","article-title":"Multitask convolutional neural network with information fusion for bearing fault diagnosis and localization","volume":"67","author":"Guo","year":"2020","journal-title":"IEEE Trans. Ind. Electron."},{"issue":"7","key":"10.1016\/j.aei.2026.104592_b40","doi-asserted-by":"crossref","DOI":"10.23915\/distill.00011","article-title":"Feature-wise transformations","volume":"3","author":"Dumoulin","year":"2018","journal-title":"Distill"},{"issue":"4","key":"10.1016\/j.aei.2026.104592_b41","article-title":"Learning multiple tasks with kernel methods.","volume":"6","author":"Evgeniou","year":"2005","journal-title":"J. Mach. Learn. Res."},{"key":"10.1016\/j.aei.2026.104592_b42","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1007\/s13748-014-0060-7","article-title":"Optimizing different loss functions in multilabel classifications","volume":"3","author":"D\u00edez","year":"2015","journal-title":"Prog. Artif. Intell."},{"key":"10.1016\/j.aei.2026.104592_b43","series-title":"Xgboost: extreme gradient boosting","first-page":"1","author":"Chen","year":"2015"},{"key":"10.1016\/j.aei.2026.104592_b44","article-title":"ERF-XGB: An edge-IoT-based explainable model for predictive maintenance","author":"Xiao","year":"2024","journal-title":"IEEE Trans. Consum. Electron."},{"key":"10.1016\/j.aei.2026.104592_b45","series-title":"2018 14th IEEE\/ASME International Conference on Mechatronic and Embedded Systems and Applications","first-page":"1","article-title":"Machine learning approach for predictive maintenance in industry 4.0","author":"Paolanti","year":"2018"},{"key":"10.1016\/j.aei.2026.104592_b46","series-title":"Ethics guidelines for trustworthy AI","author":"on Artificial Intelligence","year":"2019"}],"container-title":["Advanced Engineering Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626002843?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1474034626002843?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,30]],"date-time":"2026-04-30T22:10:46Z","timestamp":1777587046000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1474034626002843"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,7]]},"references-count":46,"alternative-id":["S1474034626002843"],"URL":"https:\/\/doi.org\/10.1016\/j.aei.2026.104592","relation":{},"ISSN":["1474-0346"],"issn-type":[{"value":"1474-0346","type":"print"}],"subject":[],"published":{"date-parts":[[2026,7]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A novel multi-task multi-view approach with custom multi-label loss for fault detection in complex industrial apparatus","name":"articletitle","label":"Article Title"},{"value":"Advanced Engineering Informatics","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.aei.2026.104592","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"104592"}}