Support vector machine (SVM) is one of the most studied paradigms in the realm of machine
learning for classification and regression problems. It relies on vectorized input data …

In this paper, the Conditional Gaussian Networks (CGNs), a form of Bayesian Networks
(BN), are used as a statistical process monitoring approach to detect and diagnose faults …

Fault diagnosis is crucial for the stable and reliable operation of chemical processes.
However, faced with the complexity of chemical processes, conventional diagnosis methods …

Conventional Bayesian fault diagnosis assumes that all measurements are available
synchronously; however, this condition does not always hold in practical industry because a …

Driverless trucks have the potential to contribute to a more sustainable freight transport
system. However, the role of the human driver is crucial when the truck experiences a fault …

Dynamic principal component analysis (DPCA) is generally employed in monitoring
dynamic processes and typically incorporates all measured variables. However, for a large …

Many performance monitoring algorithms (or monitors) have been developed to assess
control performance and detect problems with specific components; however, these …

Traditional outlier detection methods assume that the sampling time and interval are the
same. However, for plant-wide processes, since the signal change rate of different devices …

The transportation system is undergoing a transformation to enable socially,
environmentally, and economically sustainable transport solutions, and driverless trucks are …

The drivers play an essential role in detecting and handling vehicle faults, and they are an
important actor in the diagnosis service system. When developing self-driving vehicles …