Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems
Published 2022 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Probabilistic deep learning methodology for uncertainty quantification of remaining useful lifetime of multi-component systems
Authors
Keywords
Prognostics, Uncertainty management, Remaining useful life time, System reliability, LSTM, Lognormal distribution, Multi-component systems
Journal
RELIABILITY ENGINEERING & SYSTEM SAFETY
Volume 222, Issue -, Pages 108383
Publisher
Elsevier BV
Online
2022-02-26
DOI
10.1016/j.ress.2022.108383
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process
- (2021) Sen-Ju Zhang et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- System reliability analysis based on dependent Kriging predictions and parallel learning strategy
- (2021) Ning-Cong Xiao et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Multicellular LSTM-based deep learning model for aero-engine remaining useful life prediction
- (2021) Sheng Xiang et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Time-dependent structural system reliability analysis model and its efficiency solution
- (2021) Yingshi Hu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- An automated health indicator construction methodology for prognostics based on multi-criteria optimization
- (2020) Khanh T.P. Nguyen et al. ISA TRANSACTIONS
- Dynamic maintenance strategy with iteratively updated group information
- (2020) Tianyi Wu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- A Bayesian Deep Learning Framework for Interval Estimation of Remaining Useful Life in Complex Systems by Incorporating General Degradation Characteristics
- (2020) Minhee Kim et al. IISE Transactions
- Remaining useful life prediction for auxiliary power unit based on particle filter
- (2020) Jiachen Guo et al. PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING
- An improved similarity-based prognostic algorithm for RUL estimation using an RNN autoencoder scheme
- (2020) Wennian Yu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Deep learning for prognostics and health management: State of the art, challenges, and opportunities
- (2020) Behnoush Rezaeianjouybari et al. MEASUREMENT
- Remaining Useful Life Prediction based on a Multi-Sensor Data Fusion Model
- (2020) Naipeng Li et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Time series chain graph for modeling reliability covariates in degradation process
- (2020) Huyang Xu et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- A deep belief network based health indicator construction and remaining useful life prediction using improved particle filter
- (2019) Kaixiang Peng et al. NEUROCOMPUTING
- Remaining useful life estimation using a bidirectional recurrent neural network based autoencoder scheme
- (2019) Wennian Yu et al. MECHANICAL SYSTEMS AND SIGNAL PROCESSING
- A new dynamic predictive maintenance framework using deep learning for failure prognostics
- (2019) Khanh T.P. Nguyen et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Bayesian Deep-Learning-Based Health Prognostics Toward Prognostics Uncertainty
- (2019) Weiwen Peng et al. IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS
- Controlling the accuracy and uncertainty trade-off in RUL prediction with a surrogate Wiener propagation model
- (2019) Yingjun Deng et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
- Ensemble of optimized echo state networks for remaining useful life prediction
- (2018) Marco Rigamonti et al. NEUROCOMPUTING
- Multiobjective Deep Belief Networks Ensemble for Remaining Useful Life Estimation in Prognostics
- (2017) Chong Zhang et al. IEEE Transactions on Neural Networks and Learning Systems
- A survey on feature selection methods
- (2013) Girish Chandrashekar et al. COMPUTERS & ELECTRICAL ENGINEERING
- Investigation of uncertainty treatment capability of model-based and data-driven prognostic methods using simulated data
- (2012) Piero Baraldi et al. RELIABILITY ENGINEERING & SYSTEM SAFETY
Publish scientific posters with Peeref
Peeref publishes scientific posters from all research disciplines. Our Diamond Open Access policy means free access to content and no publication fees for authors.
Learn MoreAsk a Question. Answer a Question.
Quickly pose questions to the entire community. Debate answers and get clarity on the most important issues facing researchers.
Get Started