{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T15:45:40Z","timestamp":1782488740694,"version":"3.54.5"},"reference-count":35,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100014472","name":"Scientific Research Foundation of Hunan Provincial Education Department","doi-asserted-by":"publisher","award":["22B0471"],"award-info":[{"award-number":["22B0471"]}],"id":[{"id":"10.13039\/100014472","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2026JJ90289"],"award-info":[{"award-number":["2026JJ90289"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004735","name":"Hunan Provincial Natural Science Foundation","doi-asserted-by":"publisher","award":["2024JJ5162"],"award-info":[{"award-number":["2024JJ5162"]}],"id":[{"id":"10.13039\/501100004735","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002338","name":"Ministry of Education of the People&apos;s Republic of China","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100002338","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62203164"],"award-info":[{"award-number":["62203164"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Information Fusion"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.inffus.2026.104552","type":"journal-article","created":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T16:34:37Z","timestamp":1781714077000},"page":"104552","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["Time-frequency-spatial fusion-driven local-global contrastive discrepancy learning for industrial multisensor time-series anomaly detection"],"prefix":"10.1016","volume":"136","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8000-7872","authenticated-orcid":false,"given":"Lei","family":"Chen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuhao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4427-4981","authenticated-orcid":false,"given":"Ming","family":"Lu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ying","family":"Zou","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4080-3532","authenticated-orcid":false,"given":"Mingyang","family":"Lv","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6198-8593","authenticated-orcid":false,"given":"Jiaao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"issue":"3","key":"10.1016\/j.inffus.2026.104552_bib0001","doi-asserted-by":"crossref","first-page":"5031","DOI":"10.1109\/JSEN.2024.3512857","article-title":"Anomaly detection in industrial networks: current state, classification, and key challenges","volume":"25","author":"Kuchar","year":"2025","journal-title":"IEEE Sens. J."},{"key":"10.1016\/j.inffus.2026.104552_bib0002","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.102996","article-title":"A review on full-, zero-, and partial-knowledge based predictive models for industrial applications","volume":"119","author":"Zampini","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104552_bib0003","series-title":"Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining v.2","first-page":"6151","article-title":"Advances in time-series anomaly detection: algorithms, benchmarks, and evaluation measures","author":"Paparrizos","year":"2025"},{"issue":"9","key":"10.1016\/j.inffus.2026.104552_bib0004","doi-asserted-by":"crossref","first-page":"279","DOI":"10.1007\/s10462-025-11287-7","article-title":"A survey of deep learning for industrial visual anomaly detection","volume":"58","author":"Li","year":"2025","journal-title":"Artif. Intell. Rev."},{"key":"10.1016\/j.inffus.2026.104552_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103008","article-title":"MulGad: multi-granularity contrastive learning for multivariate time series anomaly detection","volume":"119","author":"Xiao","year":"2025","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104552_bib0006","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2025.103618","article-title":"Multi-sensor data fusion via a cortical gap network for time series large data gap filling under uncertainties","volume":"126","author":"Gudla","year":"2026","journal-title":"Inf. Fusion"},{"issue":"21","key":"10.1016\/j.inffus.2026.104552_bib0007","doi-asserted-by":"crossref","first-page":"44318","DOI":"10.1109\/JIOT.2025.3585884","article-title":"Deep learning advancements in anomaly detection: a comprehensive survey","volume":"12","author":"Huang","year":"2025","journal-title":"IEEE Internet Things J."},{"key":"10.1016\/j.inffus.2026.104552_bib0008","doi-asserted-by":"crossref","first-page":"93","DOI":"10.1016\/j.inffus.2022.10.008","article-title":"Deep learning for anomaly detection in multivariate time series: approaches, applications, and challenges","volume":"91","author":"Li","year":"2023","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104552_bib0009","series-title":"Proceedings of the 31st ACM International Conference on Information & Knowledge Management","first-page":"2497","article-title":"TFAD: a decomposition time series anomaly detection architecture with time-Frequency analysis","author":"Zhang","year":"2022"},{"key":"10.1016\/j.inffus.2026.104552_bib0010","doi-asserted-by":"crossref","DOI":"10.1016\/j.cose.2024.103961","article-title":"STFT-TCAN: a TCN-attention based multivariate time series anomaly detection architecture with time-frequency analysis for cyber-industrial systems","volume":"144","author":"Tu","year":"2024","journal-title":"Comput. Secur."},{"key":"10.1016\/j.inffus.2026.104552_bib0011","doi-asserted-by":"crossref","DOI":"10.1016\/j.inffus.2024.102255","article-title":"Graph spatiotemporal process for multivariate time series anomaly detection with missing values","volume":"106","author":"Zheng","year":"2024","journal-title":"Inf. Fusion"},{"key":"10.1016\/j.inffus.2026.104552_bib0012","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1016\/j.inffus.2022.08.011","article-title":"MST-GAT: A multimodal spatial-temporal graph attention network for time series anomaly detection","volume":"89","author":"Ding","year":"2023","journal-title":"Inf. Fusion"},{"issue":"9","key":"10.1016\/j.inffus.2026.104552_bib0013","doi-asserted-by":"crossref","first-page":"11802","DOI":"10.1109\/TNNLS.2023.3325667","article-title":"Correlation-aware spatial-temporal graph learning for multivariate time-series anomaly detection","volume":"35","author":"Zheng","year":"2024","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.inffus.2026.104552_bib0014","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.108494","article-title":"Time-frequency contrastive learning with context modeling for time series anomaly prediction","volume":"197","author":"Li","year":"2026","journal-title":"Neural Netw."},{"key":"10.1016\/j.inffus.2026.104552_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.neucom.2025.130611","article-title":"TSAD: temporal-spatial association differences-based unsupervised anomaly detection for multivariate time-series","volume":"648","author":"Zhu","year":"2025","journal-title":"Neurocomputing"},{"key":"10.1016\/j.inffus.2026.104552_bib0016","first-page":"1","article-title":"Multivariate time series anomaly detection based on multiple spatiotemporal graph convolution","volume":"74","author":"He","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"6","key":"10.1016\/j.inffus.2026.104552_bib0017","doi-asserted-by":"crossref","first-page":"4435","DOI":"10.1109\/TII.2025.3538127","article-title":"Privacy-preserving lightweight time-series anomaly detection for resource-limited industrial IoT edge devices","volume":"21","author":"Chen","year":"2025","journal-title":"IEEE Trans. Ind. Inform."},{"key":"10.1016\/j.inffus.2026.104552_bib0018","series-title":"Proceedings of the ACM Web Conference 2024","first-page":"4204","article-title":"Breaking the time-frequency granularity discrepancy in time-series anomaly detection","author":"Nam","year":"2024"},{"key":"10.1016\/j.inffus.2026.104552_bib0019","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.112775","article-title":"Time series anomaly detection based on time-frequency domain with masking strategy and contrastive learning","volume":"162","author":"Wang","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.inffus.2026.104552_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.ins.2025.122790","article-title":"GDCMAD: graph-based dual-contrastive representation learning for multivariate time series anomaly detection","volume":"728","author":"He","year":"2026","journal-title":"Inf. Sci."},{"key":"10.1016\/j.inffus.2026.104552_bib0021","series-title":"Joint European Conference on Machine Learning and Knowledge Discovery in Databases","first-page":"145","article-title":"Self-supervised spatial-temporal normality learning for time series anomaly detection","author":"Chen","year":"2024"},{"issue":"9","key":"10.1016\/j.inffus.2026.104552_bib0022","doi-asserted-by":"crossref","first-page":"17295","DOI":"10.1109\/TNNLS.2025.3565807","article-title":"Lightweight and fast time-series anomaly detection via point-level and sequence-level reconstruction discrepancy","volume":"36","author":"Chen","year":"2025","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"10.1016\/j.inffus.2026.104552_bib0023","series-title":"International Conference on Learning Representations","article-title":"Anomaly transformer: time series anomaly detection with association discrepancy","author":"Xu","year":"2022"},{"key":"10.1016\/j.inffus.2026.104552_bib0024","series-title":"Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining","first-page":"3033","article-title":"DCdetector: dual attention contrastive representation learning for time series anomaly detection","author":"Yang","year":"2023"},{"key":"10.1016\/j.inffus.2026.104552_bib0025","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.110874","article-title":"CARLA: self-supervised contrastive representation learning for time series anomaly detection","volume":"157","author":"Darban","year":"2025","journal-title":"Pattern Recognit."},{"issue":"6","key":"10.1016\/j.inffus.2026.104552_bib0026","doi-asserted-by":"crossref","first-page":"3460","DOI":"10.1109\/TBDATA.2025.3596745","article-title":"PatchAD: a lightweight patch-based MLP-mixer for time series anomaly detection","volume":"11","author":"Zhong","year":"2025","journal-title":"IEEE Trans. Big Data"},{"key":"10.1016\/j.inffus.2026.104552_bib0027","first-page":"1","article-title":"Frequency-domain spectrum discrepancy-based fast anomaly detection for IIoT sensor time-series signals","volume":"74","author":"Chen","year":"2025","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"1","key":"10.1016\/j.inffus.2026.104552_bib0028","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1007\/s10586-025-05772-5","article-title":"GRU contrast discrepancy-based anomaly detection for multi-sensor signals from temporal and spatial perspectives","volume":"29","author":"Chen","year":"2026","journal-title":"Clust. Comput."},{"key":"10.1016\/j.inffus.2026.104552_bib0029","doi-asserted-by":"crossref","DOI":"10.1016\/j.neunet.2025.107400","article-title":"A lightweight all-MLP time-frequency anomaly detection for IIoT time series","volume":"187","author":"Chen","year":"2025","journal-title":"Neural Netw."},{"key":"10.1016\/j.inffus.2026.104552_bib0030","first-page":"1","article-title":"Multiscale wavelet graph autoencoder for multivariate time-series anomaly detection","volume":"72","author":"Wang","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"issue":"9","key":"10.1016\/j.inffus.2026.104552_bib0031","doi-asserted-by":"crossref","first-page":"15548","DOI":"10.1109\/JSEN.2025.3549220","article-title":"A novel spatiotemporal correlation anomaly detection method based on time-frequency- domain feature fusion and a dynamic graph neural network in wireless sensor network","volume":"25","author":"Ye","year":"2025","journal-title":"IEEE Sens. J."},{"issue":"3","key":"10.1016\/j.inffus.2026.104552_bib0032","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1007\/s00778-025-00907-x","article-title":"VUS: effective and efficient accuracy measures for time-series anomaly detection","volume":"34","author":"Boniol","year":"2025","journal-title":"The VLDB J."},{"issue":"11","key":"10.1016\/j.inffus.2026.104552_bib0033","doi-asserted-by":"crossref","first-page":"2774","DOI":"10.14778\/3551793.3551830","article-title":"Volume under the surface: a new accuracy evaluation measure for time-series anomaly detection","volume":"15","author":"Paparrizos","year":"2022","journal-title":"Proc. VLDB Endow."},{"key":"10.1016\/j.inffus.2026.104552_bib0034","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1016\/j.neucom.2017.04.070","article-title":"Unsupervised real-time anomaly detection for streaming data","volume":"262","author":"Ahmad","year":"2017","journal-title":"Neurocomputing"},{"key":"10.1016\/j.inffus.2026.104552_bib0035","series-title":"The Eleventh International Conference on Learning Representations","article-title":"TimesNet: temporal 2D-variation modeling for general time series analysis","author":"Wu","year":"2023"}],"container-title":["Information Fusion"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526004306?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1566253526004306?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,26]],"date-time":"2026-06-26T14:45:43Z","timestamp":1782485143000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1566253526004306"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":35,"alternative-id":["S1566253526004306"],"URL":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104552","relation":{},"ISSN":["1566-2535"],"issn-type":[{"value":"1566-2535","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Time-frequency-spatial fusion-driven local-global contrastive discrepancy learning for industrial multisensor time-series anomaly detection","name":"articletitle","label":"Article Title"},{"value":"Information Fusion","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.inffus.2026.104552","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104552"}}