Industrial process data are usually mixed with missing data and outliers which can greatly
affect the statistical explanation abilities for traditional data-driven modeling methods. In this …

Data mining and analytics have played an important role in knowledge discovery and
decision making/supports in the process industry over the past several decades. As a …

Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …

Efficient and valuable strategies provided by large amount of available data are urgently
needed for a sustainable electricity system that includes smart grid technologies and very …

This paper discusses the monitoring of complex nonlinear and time-varying processes.
Kernel principal component analysis (KPCA) has gained significant attention as a …

Dynamic principal component analysis (DPCA) and dynamic independent component
analysis (DICA), as the frequently-used dimensional reduction methods, have been widely …

Multimode process monitoring has recently attracted much attention both in academy and
industry. Conventional methods assume that either the process data are Gaussian in each …

This paper presents a new technique for the detection of islanding conditions in electrical
power systems. This problem is especially prevalent in systems with significant penetrations …

Process monitoring can be considered as a one-class classification problem, the aim of
which is to differentiate the normal data samples from the faulty ones. This paper introduces …

This article develops statistics based on the Kullback–Leibler (KL) divergence to monitor
large-scale technical systems. These statistics detect anomalous system behavior by …