In this final part, we discuss fault diagnosis methods that are based on historic process
knowledge. We also compare and evaluate the various methodologies reviewed in this …

With increasing demands for efficiency and product quality plus progress in the integration of
automatic control systems in high-cost mechatronic and safety-critical processes, the field of …

Early and accurate fault detection and diagnosis for modern manufacturing processes can
minimise downtime, increase the safety of plant operations, and reduce costs. Such process …

Multiscale principal‐component analysis (MSPCA) combines the ability of PCA to
decorrelate the variables by extracting a linear relationship with that of wavelet analysis to …

Schemes for monitoring the operating performance of large continuous processes using
multivariate statistical projection methods such as principal component analysis (PCA) and …

Much of group and organization research is constrained by either limited sample sizes
and/or nascent theoretical development. Wold developed the partial least squares (PLS) …

Many applications of principal component analysis (PCA) can be found in recently published
papers. However principal component analysis is a linear method, and most engineering …

Even though there has been a recent interest in the use of principal component analysis
(PCA) for sensor fault detection and identification, few identification schemes for faulty …

One of the main difficulties in using principal component analysis (PCA) is the selection of
the number of principal components (PCs). There exist a plethora of methods to calculate …

Modern chemical plants are large scale, highly complex, and operate with a large number of
variables under closed loop control. Early and accurate fault detection and diagnosis for …