Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …

Timely detection and diagnosis of process abnormality in industries is crucial for minimizing
downtime and maximizing profit. Among various process monitoring and fault detection …

Maintenance strategies have been playing an increasingly important role in improving
engineering systems' performance, supporting the growth of availability and reliability, and …

Kernel principal component analysis (KPCA), which is a nonlinear extension of principal
component analysis (PCA), has gained significant attention as a monitoring method for …

Incipient fault monitoring is becoming very important in large industrial plants, as the early
detection of incipient faults can help avoid major plant failures. Recently, Canonical Variate …

Abstract Fixed-wing Unmanned Aerial Vehicles (FW-UAVs) are intelligent aircrafts. It is of
significance to carry out fault diagnosis of FW-UAVs to improve reliability and safety. An …

Fault detection and diagnosis (FDD) based on data-driven techniques play a crucial role in
industrial process monitoring. It intends to promptly detect and identify abnormalities and …

The industrial process often shows nonstationary characteristic, such as time-varying mean
and variance, due to the unmeasured disturbances, adjustments of production plans …

For nearly a decade, quality-related fault detection algorithms have been widely used in
industrial systems. However, the majority of these detection strategies rely on static …

Traditional process monitoring methods, such as PCA, PLS, ICA, MD et al., are strongly
dependent on continuous variables because most of them inevitably involve Euclidean or …