Summarization of Remaining Life Prediction Methods for Special Power Plants

• Published in: Applied sciences Vol. 13; no. 16; p. 9365
• Main Authors: Liang, Weige, Li, Chi, Zhao, Lei, Yan, Xiaojia, Sun, Shiyan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.08.2023
• Subjects: Analysis; data-driven; data–model fusion; dynamic model; Electric power-plants; Methods; Power plants; remaining useful life prediction; Sensors; special power plants; China
• ISSN: 2076-3417; 2076-3417
• DOI: 10.3390/app13169365

With continuous improvements in integration, totalization and automation, remaining useful life predictions of mechanical equipment have become a key feature of technology and core element of equipment prediction and health management. The traditional method based on degradation mechanisms is not fully capable of predicting remaining useful life, especially for special power plants that use industrial transmissions, barrel launchers, etc. The expected service requirements are higher for condition monitoring and remaining service life prediction. The effective prediction of the remaining useful life of such special power plants is a major challenge and technical bottleneck in the industrial field and national defense equipment construction. This paper analyzes and expands on the research on the remaining life prediction methods for special power plants and analyzes the remaining life prediction methods of existing dynamic models, as well as data-driven and data–model fusion drives, and specific ideas for future research and development in four aspects, including remaining useful life prediction tests supplemented with soft measurements. Additionally, future research directions for the remaining life prediction of special power plants are provided.