Abstract
Remaining useful life (RUL) prediction is one of the most challenging problems for modern engineering systems, which is of great significance to avoiding catastrophic failures and reducing economic losses. In an effort to make use of multi-sensor monitoring data and enhance prediction accuracy, a novel RUL prediction approach is proposed based on long and short term memory (LSTM) network with an attention mechanism in this paper. By using the input attention mechanism, the proposed network can selectively focus on certain important inputs without any prior knowledge. Experimental results have illustrated that the proposed approach can achieve superior RUL prediction performance compared with other conventional networks.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
