{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:15:16Z","timestamp":1777706116397,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,8,1]]},"abstract":"<jats:p>In order to address the timing problem, invalid data problem and deep feature extraction problem in the current deep learning based aero-engine remaining life prediction, a remaining life prediction method based on time-series residual neural networks is proposed. This method uses a combination of temporal feature extraction layer and deep feature extraction layer to build the network model. First, the temporal feature extraction layer with multi-head structure is used to extract rich temporal features; then, the spatial attention mechanism is applied to improve the weights of important data; finally, the deep feature extraction layer is used to process the deep features of the data. To verify the effectiveness of the proposed method, experiments are conducted on the C-MAPSS dataset provided by NASA. The experimental results show that the method proposed in this paper can make accurate predictions of the remaining service life under different sub-datasets and has outstanding performance advantages in comparison with other outstanding networks.<\/jats:p>","DOI":"10.3233\/jifs-223971","type":"journal-article","created":{"date-parts":[[2023,5,30]],"date-time":"2023-05-30T11:14:10Z","timestamp":1685445250000},"page":"2437-2448","source":"Crossref","is-referenced-by-count":5,"title":["Aero-engine residual life prediction based on time-series residual neural networks"],"prefix":"10.1177","volume":"45","author":[{"given":"Ping","family":"Yu","sequence":"first","affiliation":[{"name":"College of Electrical and Information Engineering, Lanzhou University of Technology, Lanzhou, China"},{"name":"Key Laboratory of Industrial Process Control of Gansu Province, Lanzhou, China"},{"name":"National Experimental Teaching Demonstration Center for Electrical and Control Engineering, Lanzhou 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