Two stage deep learning for prognostics using multi-loss encoder and convolutional composite features
作者：
Highlights：
• Proposed two-stage deep learning separates feature generation and RUL prediction.
• A multi-loss objective function generates relevant information maximizing features.
• Generated features capture degradation trend with low-noise and low-redundancy.
• Benchmark performance is achieved for C-MAPSS dataset and blade-wear prognostics.
• Analysis of data features reveal the benefits of relevant information maximization.
摘要
•Proposed two-stage deep learning separates feature generation and RUL prediction.•A multi-loss objective function generates relevant information maximizing features.•Generated features capture degradation trend with low-noise and low-redundancy.•Benchmark performance is achieved for C-MAPSS dataset and blade-wear prognostics.•Analysis of data features reveal the benefits of relevant information maximization.
论文关键词：Deep learning,Multi-loss encoder,Predictive maintenance,Remaining useful life
论文评审过程：Received 26 August 2020, Revised 28 November 2020, Accepted 2 January 2021, Available online 12 January 2021, Version of Record 5 February 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.114569
