The issue of how to improve product quality and product yield in a brief period of time
becomes more critical in many industries. Even though industrial processes are totally …

In this age of globalization, the realization of production innovation and highly stable
operation is the chief objective of the process industry in Japan. Obviously, modern …

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 …

A novel multivariate statistical process monitoring (MSPM) method based on modified
independent component analysis (ICA) is proposed. ICA is a multivariate statistical tool to …

Most multivariate statistical monitoring methods based on principal component analysis
(PCA) assume implicitly that the observations at one time are statistically independent of …

Many of the current multivariate statistical process monitoring techniques (such as principal
component analysis (PCA) or partial least squares (PLS)) do not utilize the non-Gaussian …

Virtual sensing technology is crucial for high product quality and productivity in any industry.
This review aims to clarify the trend of research and application of virtual sensing technology …

In this paper, some drawbacks of original kernel independent component analysis (KICA)
and support vector machine (SVM) algorithms are analyzed for the purpose of multivariate …

In process industries, early detection and diagnosis of faults is crucial for timely identification
of process upsets, equipment and/or sensor malfunctions. Machine learning techniques …

In this paper, a new non‐linear process monitoring method based on kernel independent
component analysis (KICA) is developed. Its basic idea is to use KICA to extract some …