Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …

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 …

In this paper, a decentralized fault diagnosis approach of complex processes is proposed
based on multiblock kernel partial least squares (MBKPLS). To solve the problem posed by …

The article presents a novel non-linear multivariate and multiscale statistical process
monitoring and signal denoising method which combines the strengths of the Kernel …

Traditional kernel principal component analysis (KPCA) concentrates on the global structure
analysis of data sets but omits the local information which is also important for process …

This paper proposes a wavelet-based statistical signal detection approach for monitoring
and diagnosis of bearing compound faults at an early stage. The bearing vibration signal is …

Process monitoring techniques are of paramount importance in the chemical industry to
improve both the product quality and plant safety. Small or incipient irregularities may lead to …

In this paper, a nonlinear fault diagnosis scheme is established for the hot strip mill process
(HSMP). In HSMP, the faults affecting quality index are denoted as quality-related faults …

REVIEW an earlier paper of special significance is referenced. The key aspects of this
review include advances in measurement technologies that are applicable for at-line or …

There has been an increased use of soft-sensors in process industry in recent years. These
soft-sensors are computer programs that are used as a relatively cheap alternative to …