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 …
Challenges in predictive maintenance–A review
Predictive maintenance (PdM) aims the reduction of costs to increase the competitive
strength of the enterprises. It uses sensor data together with analytics techniques to optimize …
strength of the enterprises. It uses sensor data together with analytics techniques to optimize …
Deep learning models for predictive maintenance: a survey, comparison, challenges and prospects
Given the growing amount of industrial data in the 4th industrial revolution, deep learning
solutions have become popular for predictive maintenance (PdM) tasks, which involve …
solutions have become popular for predictive maintenance (PdM) tasks, which involve …
Bearing remaining useful life prediction based on regression shapalet and graph neural network
Remaining useful life (RUL) prediction of bearing is essential to guarantee its safe
operation. In recent years, deep learning (DL)-based methods attract a lot of research …
operation. In recent years, deep learning (DL)-based methods attract a lot of research …
A survey of the advancing use and development of machine learning in smart manufacturing
Abstract Machine learning (ML)(a subset of artificial intelligence that focuses on autonomous
computer knowledge gain) is actively being used across many domains, such as …
computer knowledge gain) is actively being used across many domains, such as …
Transfer learning with deep recurrent neural networks for remaining useful life estimation
Prognostics, such as remaining useful life (RUL) prediction, is a crucial task in condition-
based maintenance. A major challenge in data-driven prognostics is the difficulty of …
based maintenance. A major challenge in data-driven prognostics is the difficulty of …
Data alignments in machinery remaining useful life prediction using deep adversarial neural networks
Recently, intelligent data-driven machinery prognostics and health management have been
attracting increasing attention due to the great merits of high accuracy, fast response and …
attracting increasing attention due to the great merits of high accuracy, fast response and …
A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and …
Develo** an accurate and reliable multi-step ahead prediction model is a key problem in
many Prognostics and Health Management (PHM) applications. Inevitably, the further one …
many Prognostics and Health Management (PHM) applications. Inevitably, the further one …
Transfer learning for prognostics and health management: Advances, challenges, and opportunities
As failure data is usually scarce in practice upon preventive maintenance strategy in
prognostics and health management (PHM) domain, transfer learning provides a …
prognostics and health management (PHM) domain, transfer learning provides a …
Physics-based prognostics of lithium-ion battery using non-linear least squares with dynamic bounds
Real-time health diagnostics/prognostics and predictive maintenance/control of lithium-ion
(Li-ion) batteries are essential to reliable and safe battery operation. This paper presents a …
(Li-ion) batteries are essential to reliable and safe battery operation. This paper presents a …