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However, the complex operating conditions and various fault modes make it difficult to extract features containing more degradation information with existing prediction methods. We propose a self-supervised learning method based on variational automatic encoder (VAE) to extract features of data\u2019s operating conditions and fault modes. Then the clustering algorithm is applied to the extracted features to divide data from different failure modes into different categories and reduce the impact of complex working conditions and fault modes on the estimation accuracy. In order to verify the effectiveness of the proposed method, we conduct experiments with different network structures on the C-MAPSS dataset, and the results verified that our method can effectively improve the feature extraction capability of the model. In addition, the experimental results further demonstrate the superiority and necessity of using hidden features for clustering rather than raw data.<\/jats:p>","DOI":"10.1115\/1.4062599","type":"journal-article","created":{"date-parts":[[2023,5,23]],"date-time":"2023-05-23T08:04:34Z","timestamp":1684829074000},"update-policy":"https:\/\/doi.org\/10.1115\/crossmarkpolicy-asme","source":"Crossref","is-referenced-by-count":11,"title":["Feature Extraction Based on Self-Supervised Learning for Remaining Useful Life Prediction"],"prefix":"10.1115","volume":"24","author":[{"given":"Zhenjun","family":"Yu","sequence":"first","affiliation":[{"name":"Tsinghua University Department of Automation, , Beijing 100084 , China ;"},{"name":"Tsinghua Shenzhen International Graduate School Division of Information Science, , Shenzhen 518055 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ningbo","family":"Lei","sequence":"additional","affiliation":[{"name":"China Nuclear Power Engineering, Co. 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