Small data challenges for intelligent prognostics and health management: a review
Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …
and reducing maintenance costs, and research on intelligent PHM has made significant …
Uncertainty quantification in machine learning for engineering design and health prognostics: A tutorial
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 …
essential layer of safety assurance that could lead to more principled decision making by …
A prognostic driven predictive maintenance framework based on Bayesian deep learning
Recent years have witnessed prominent advances in predictive maintenance (PdM) for
complex industrial systems. However, the existing PdM literature predominately separates …
complex industrial systems. However, the existing PdM literature predominately separates …
[HTML][HTML] Dynamic predictive maintenance for multiple components using data-driven probabilistic RUL prognostics: The case of turbofan engines
The increasing availability of condition-monitoring data for components/systems has
incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the …
incentivized the development of data-driven Remaining Useful Life (RUL) prognostics in the …
[HTML][HTML] Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study
This paper examines the integration of Industry 5.0 principles with advanced predictive
maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's …
maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's …
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 …
model is proposed to improve the strong prior limitations of traditional probabilistic RUL …
[HTML][HTML] Self-adaptive optimized maintenance of offshore wind turbines by intelligent Petri nets
With the emerging monitoring technologies, condition-based maintenance is nowadays a
reality for the wind energy industry. This is important to avoid unnecessary maintenance …
reality for the wind energy industry. This is important to avoid unnecessary maintenance …
A 3D attention-enhanced hybrid neural network for turbofan engine remaining life prediction using CNN and BiLSTM models
Y Keshun, Q Guangqi, G Yingkui - IEEE Sensors Journal, 2023 - ieeexplore.ieee.org
As the most popular power source equipment in commercial aviation, turbofan engines face
problems such as difficulties in data acquisition and unbalanced data sets. In addition, it is …
problems such as difficulties in data acquisition and unbalanced data sets. In addition, it is …
Reinforcement and deep reinforcement learning-based solutions for machine maintenance planning, scheduling policies, and optimization
O Ogunfowora, H Najjaran - Journal of Manufacturing Systems, 2023 - Elsevier
Abstract Systems and machines undergo various failure modes that result in machine health
degradation, so maintenance actions are required to restore them back to a state where they …
degradation, so maintenance actions are required to restore them back to a state where they …
A hybrid CNN-LSTM model for joint optimization of production and imperfect predictive maintenance planning
This paper deals with the problem of dynamically integrating tactical production planning
and predictive maintenance in the context of a rolling horizon approach. At the production …
and predictive maintenance in the context of a rolling horizon approach. At the production …