Batteries are prevalent energy providers for modern systems. They can also be regarded as
storage units for renewable and sustainable energy. Failures of batteries can bring huge …

Abstract A Dynamic Bayesian network (DBN) model for solar power generation forecasting
in photovoltaic (PV) solar plants is proposed in this paper. The key idea is to fuse sensor …

The subsea production system is essential for the subsea production of oil and gas. Real-
time monitoring can ensure safe production. The subsea production control system is the …

In recent years, advances in computer technology and the emergence of big data have
enabled deep learning to achieve impressive successes in bearing condition monitoring …

Fault diagnosis plays an important role in actual production activities. As large amounts of
data can be collected efficiently and economically, data-driven methods based on deep …

Fault diagnosis is important to ensure the safety and efficience of mechanical equipment. In
recent years, data-driven fault diagnosis methods have received extensive attention and …

In this study, a novel deep convolutional neural network-bootstrap-based integrated
prognostic approach for the remaining useful life (RUL) prediction of rolling bearing is …

Permanent magnet synchronous motor (PMSM) is one of the common core power
components in modern industrial systems. Early fault diagnosis can avoid major accidents …

The Bayesian network (BN) is a powerful model for probabilistic knowledge representation
and inference and is increasingly used in the field of reliability evaluation. This paper …