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None the less, attaining precise RUL predictions often encounters challenges due to the scarcity of historical condition monitoring data. This paper introduces a multiscale deep transfer learning framework via integrating domain adaptation principles. The framework encompasses three integral components: a feature extraction module, an encoding module, and an RUL prediction module. During pre-training phase, the framework leverages a multiscale convolutional neural network to extract distinctive features from data across varying scales. The ensuing parameter transfer adopts a domain adaptation strategy centered around maximum mean discrepancy. This method efficiently facilitates the acquisition of domain-invariant features from the source and target domains. The refined domain adaptation Transformer-based multiscale convolutional neural network model exhibits enhanced suitability for predicting RUL in the target domain under the condition of limited samples. Experiments on the C-MAPSS dataset have shown that the proposed method significantly outperforms state-of-the-art methods.<\/jats:p>","DOI":"10.1093\/jcde\/qwae018","type":"journal-article","created":{"date-parts":[[2024,2,20]],"date-time":"2024-02-20T02:02:40Z","timestamp":1708394560000},"page":"343-355","source":"Crossref","is-referenced-by-count":13,"title":["Enhancing aircraft engine remaining useful life prediction via multiscale deep transfer learning with limited data"],"prefix":"10.1093","volume":"11","author":[{"given":"Qi","family":"Liu","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Southwest Jiaotong University , Chengdu, 610031 , China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2705-6608","authenticated-orcid":false,"given":"Zhiyao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Southwest Jiaotong University , Chengdu, 610031 , 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