Remaining useful life (RUL) prediction, known as 'prognostics', has long been recognized as
one of the key technologies in prognostics and health management (PHM) to maintain the …

Prognostics and health management (PHM) is critical for enhancing equipment reliability
and reducing maintenance costs, and research on intelligent PHM has made significant …

Accurate estimations in state of health (SOH) and remaining useful life (RUL) are significant
for safe and efficient operation of batteries. With the development of big data and deep …

By informing the onset of the degradation process, health status evaluation serves as a
significant preliminary step for reliable remaining useful life (RUL) estimation of complex …

Lithium-ion battery prognostics and health management (BPHM) systems are vital to the
longevity, economy, and environmental friendliness of electric vehicles and energy storage …

Accurate assessment and prediction of lithium-ion batteries'(LIBs') state-of-health (SOH) are
crucial for the safety and timely maintenance of LIB-powered systems. During the aging …

The requirement of sufficient run-to-failure data poses a significant challenge in developing
deep learning (DL)-based health prognostics for lithium-ion batteries. In practice, numerous …

Deploying a large number of smart cameras and training their models is a very time-
consuming and labor-intensive process. Although there have been studies that utilized …

Federated learning (FL) is a promising solution for addressing information security sharing
challenges in the Internet of Vehicles (IOV). It enables individual-level capacity prediction of …

Time series data widely exist in public services, industrial environments, and military
applications. Traditionally, the transmission of a huge volume of data for analytic tasks poses …