상세정보
학술대회
An Empirical Study of Remaining Useful Life Prediction using Deep Learning Models
- 발행일
- 202210
- 출처
- International Conference on Information and Communication Technology Convergence (ICTC) 2022, pp.1639-1642
- 협약과제
- 22ZK1100, 호남권 지역산업 기반 ICT 융합기술 고도화 지원사업, 강현서
- 초록
- An accurate remaining useful life (RUL) prediction is critical for successful operation of a target system. In pursuing a better method for RUL prediction, in this paper, we consider three well-known deep learning models: long short-term memory (LSTM), Transformer, and denoising autoencoder (DAE). In particular, we conduct empirical study with various combinations of the three deep learning models. The experiment results first show that DAE is useful to remove noise from the raw input data. The experiment results also show that the combination of DAE and Transformer leads to the best performance.
- KSP 제안 키워드
- Best performance, Empirical study, Experiment results, Long-short term memory(LSTM), Remaining Useful Life (RUL) prediction, Remaining useful life prediction, deep learning(DL), deep learning models, denoising autoencoder, input data, remove noise