Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning-Reference-Cited by-同舟云学术

Optimizing prior distribution parameters for probabilistic prediction of remaining useful life using deep learning

Published:2024-02 Issue: Volume:242 Page:109793
ISSN:0951-8320
Container-title:Reliability Engineering & System Safety
language:en
Short-container-title:Reliability Engineering & System Safety

Author:

Keshun You,Guangqi Qiu,Yingkui Gu

Funder

Natural Science Foundation of Jiangxi Province

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Safety, Risk, Reliability and Quality

Reference45 articles.

1. Selected prognostic indicator for bearings remaining useful life prediction with genetic algorithm and Weibull proportional hazards model;Guangqi;Measurement,2020

2. A failure analysis of floating offshore wind turbines using AHP-FMEA methodology;Li;Ocean Eng,2021

3. Machinery health prognostics: a systematic review from data acquisition to RUL prediction;Lei;Mech Syst Signal Process,2018

4. An efficient lightweight neural network using BiLSTM-SCN-CBAM with PCA-ICEEMDAN for diagnosing rolling bearing faults;Keshun;Meas Sci Technol,2023

5. Remaining useful life prediction of aero-engine enabled by fusing knowledge and deep learning models;Li;Reliab Eng Syst Saf,2023

Cited by 27 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. An analytical method for reliability evaluation of power distribution system with time-varying failure rates;Reliability Engineering & System Safety;2024-10

2. Threshold-based earthquake early warning for high-speed railways using deep learning;Reliability Engineering & System Safety;2024-10

3. A performance-interpretable intelligent fusion of sound and vibration signals for bearing fault diagnosis via dynamic CAME;Nonlinear Dynamics;2024-08-24

4. Adaptive staged remaining useful life prediction of roller in a hot strip mill based on multi-scale LSTM with multi-head attention;Reliability Engineering & System Safety;2024-08

5. A novel RUL prediction method for rolling bearings based on dynamic control chart and adaptive incremental filtering;Measurement Science and Technology;2024-08-01