Physics-informed machine learning for reliability and systems safety applications: State of the art and challenges
The computerized simulations of physical and socio-economic systems have proliferated in
the past decade, at the same time, the capability to develop high-fidelity system predictive …
the past decade, at the same time, the capability to develop high-fidelity system predictive …
A comprehensive review of digital twin—part 1: modeling and twinning enabling technologies
As an emerging technology in the era of Industry 4.0, digital twin is gaining unprecedented
attention because of its promise to further optimize process design, quality control, health …
attention because of its promise to further optimize process design, quality control, health …
The challenge and opportunity of battery lifetime prediction from field data
Accurate battery life prediction is a critical part of the business case for electric vehicles,
stationary energy storage, and nascent applications such as electric aircraft. Existing …
stationary energy storage, and nascent applications such as electric aircraft. Existing …
[HTML][HTML] Potential, challenges and future directions for deep learning in prognostics and health management applications
Deep learning applications have been thriving over the last decade in many different
domains, including computer vision and natural language understanding. The drivers for the …
domains, including computer vision and natural language understanding. The drivers for the …
Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
[HTML][HTML] Deep reinforcement learning for predictive aircraft maintenance using probabilistic remaining-useful-life prognostics
The increasing availability of sensor monitoring data has stimulated the development of
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
Remaining-Useful-Life (RUL) prognostics and maintenance planning models. However …
A calibration-based hybrid transfer learning framework for RUL prediction of rolling bearing across different machines
The effective remaining useful life (RUL) prediction of rolling bearings could guarantee
mechanical equipment reliability and stability. The hybrid physical and data-driven …
mechanical equipment reliability and stability. The hybrid physical and data-driven …
Bayesian transfer learning with active querying for intelligent cross-machine fault prognosis under limited data
Most existing deep learning (DL)-based health prognostic methods assume that the training
and testing datasets are from identical machines operating under similar conditions …
and testing datasets are from identical machines operating under similar conditions …
Aircraft engine run-to-failure dataset under real flight conditions for prognostics and diagnostics
A key enabler of intelligent maintenance systems is the ability to predict the remaining useful
lifetime (RUL) of its components, ie, prognostics. The development of data-driven …
lifetime (RUL) of its components, ie, prognostics. The development of data-driven …
Bayesian deep-learning for RUL prediction: An active learning perspective
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 …
in the recent decade. Although with high precision and flexibility, DL methods need sufficient …