Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial

V Nemani, L Biggio, X Huan, Z Hu, O Fink… - … Systems and Signal …, 2023 - Elsevier
On top of machine learning (ML) models, uncertainty quantification (UQ) functions as an
essential layer of safety assurance that could lead to more principled decision making by …

Recent advances and trends of predictive maintenance from data-driven machine prognostics perspective

Y Wen, MF Rahman, H Xu, TLB Tseng - Measurement, 2022 - Elsevier
In the Engineering discipline, prognostics play an essential role in improving system safety,
reliability and enabling predictive maintenance decision-making. Due to the adoption of …

Prognostics and Health Management (PHM): Where are we and where do we (need to) go in theory and practice

E Zio - Reliability Engineering & System Safety, 2022 - Elsevier
We are performing the digital transition of industry, living the 4th industrial revolution,
building a new World in which the digital, physical and human dimensions are interrelated in …

A prognostic driven predictive maintenance framework based on Bayesian deep learning

L Zhuang, A Xu, XL Wang - Reliability Engineering & System Safety, 2023 - Elsevier
Recent years have witnessed prominent advances in predictive maintenance (PdM) for
complex industrial systems. However, the existing PdM literature predominately separates …

A calibration-based hybrid transfer learning framework for RUL prediction of rolling bearing across different machines

Y Deng, S Du, D Wang, Y Shao… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
The effective remaining useful life (RUL) prediction of rolling bearings could guarantee
mechanical equipment reliability and stability. The hybrid physical and data-driven …

Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE

Y Zhang, Y **n, Z Liu, M Chi, G Ma - Reliability Engineering & System …, 2022 - Elsevier
Prognostics and health management (PHM) is a critical work to ensure the reliable operation
of industrial equipment, in which health status (HS) assessment and remaining useful life …

A comprehensive review on convolutional neural network in machine fault diagnosis

J Jiao, M Zhao, J Lin, K Liang - Neurocomputing, 2020 - Elsevier
With the rapid development of manufacturing industry, machine fault diagnosis has become
increasingly significant to ensure safe equipment operation and production. Consequently …

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

Y Keshun, Q Guangqi, G Yingkui - Reliability Engineering & System Safety, 2024 - Elsevier
In this study, a deep learning-based probabilistic remaining useful life (RUL) prediction
model is proposed to improve the strong prior limitations of traditional probabilistic RUL …

Deep learning for prognostics and health management: State of the art, challenges, and opportunities

B Rezaeianjouybari, Y Shang - Measurement, 2020 - Elsevier
Improving the reliability of engineered systems is a crucial problem in many applications in
various engineering fields, such as aerospace, nuclear energy, and water declination …

Bayesian deep-learning for RUL prediction: An active learning perspective

R Zhu, Y Chen, W Peng, ZS Ye - Reliability Engineering & System Safety, 2022 - Elsevier
Deep learning (DL) has been intensively exploited for remaining useful life (RUL) prediction
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …