Overview of fault prognosis for traction systems in high-speed trains: A deep learning perspective
K Zhong, J Wang, S Xu, C Cheng, H Chen - Engineering Applications of …, 2023 - Elsevier
As the “heart” of high-speed train, traction systems play an important role in the safe
operation of trains, of which the operation and maintenance level is still unable to meet the …
operation of trains, of which the operation and maintenance level is still unable to meet the …
Filter-informed spectral graph wavelet networks for multiscale feature extraction and intelligent fault diagnosis
Intelligent fault diagnosis has been increasingly improved with the evolution of deep
learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also …
learning (DL) approaches. Recently, the emerging graph neural networks (GNNs) have also …
A lightweight and adaptive knowledge distillation framework for remaining useful life prediction
For prognostics and health management of industrial systems, machine remaining useful life
(RUL) prediction is an essential task. While deep learning-based methods have achieved …
(RUL) prediction is an essential task. While deep learning-based methods have achieved …
Transfer-learning-based state-of-health estimation for lithium-ion battery with cycle synchronization
Accurately estimating a battery's state of health (SOH) helps prevent battery-powered
applications from failing unexpectedly. With the superiority of reducing the data requirement …
applications from failing unexpectedly. With the superiority of reducing the data requirement …
A federated learning-based industrial health prognostics for heterogeneous edge devices using matched feature extraction
Data-driven industrial health prognostics require rich training data to develop accurate and
reliable predictive models. However, stringent data privacy laws and the abundance of edge …
reliable predictive models. However, stringent data privacy laws and the abundance of edge …
A critical review on system architecture, techniques, trends and challenges in intelligent predictive maintenance
Traditional maintenance strategies risk unforeseen failure, sophisticated physics-based
modeling, and manual feature extraction. Early detection and accurate predictions of …
modeling, and manual feature extraction. Early detection and accurate predictions of …
An explainable artificial intelligence approach for remaining useful life prediction
G Youness, A Aalah - Aerospace, 2023 - mdpi.com
Prognosis and health management depend on sufficient prior knowledge of the degradation
process of critical components to predict the remaining useful life. This task is composed of …
process of critical components to predict the remaining useful life. This task is composed of …
Spatiotemporal capsule neural network for vehicle trajectory prediction
Through advancement of the Vehicle-to-Everything (V2X) network, road safety, energy
consumption, and traffic efficiency can be significantly improved. An accurate vehicle …
consumption, and traffic efficiency can be significantly improved. An accurate vehicle …
Prognostics for the Sustainability of Industrial Cyber-Physical Systems: From an Artificial Intelligence Perspective
As industrial cyber-physical systems (ICPS) play an increasingly pivotal role in the new
industrial paradigm, their sustainability has become the current research focus. Remaining …
industrial paradigm, their sustainability has become the current research focus. Remaining …
Lithium-ion battery state of health estimation by matrix profile empowered online knee onset identification
Lithium-ion batteries (LiBs) degrade slightly until the knee onset, after which the
deterioration accelerates to end of life (EOL). The knee onset, which marks the initiation of …
deterioration accelerates to end of life (EOL). The knee onset, which marks the initiation of …