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To address these issues, this paper proposes temporal fusion network (TFN), a novel data fusion algorithm designed for processing industrial time series data. TFN integrates variational mode decomposition (VMD) for denoising, reconstruction, and gap-filling with a hybrid neural network architecture. This architecture combines a temporal convolutional network (TCN) for capturing hierarchical patterns and a gated recurrent unit (GRU) for modeling long-term dependencies. This approach effectively mitigates the influence of high noise and overcomes the limitations of deep convolutional neural network (DCNN) algorithms in handling long-term dependencies. The effectiveness of TFN is demonstrated through experiments on real-world datasets for Industrial Component Degradation Prediction and Predictive Maintenance of Industrial Motors, showcasing its potential for enhancing predictive capabilities in industrial applications. <\/jats:p>","DOI":"10.1142\/s0218126625502135","type":"journal-article","created":{"date-parts":[[2025,1,18]],"date-time":"2025-01-18T05:16:29Z","timestamp":1737177389000},"source":"Crossref","is-referenced-by-count":0,"title":["Temporal Fusion Network for Noisy Long-Time Series Industrial Data Processing"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-7689-1151","authenticated-orcid":false,"given":"Gongwen","family":"Li","sequence":"first","affiliation":[{"name":"School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230000, P. R. China"},{"name":"Wuhu Ahpu Robot Technology Research Institute Co. LTD, Wuhu 241000, P. R. 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