Fault prognostic determines whether a failure is impending and estimates how soon an
incident will occur; it is nowadays recognized as a key feature in maintenance strategies. For …

Traditional quality-related process monitoring mainly focuses on the magnitude change of
the quality variables caused by additive faults. However, the abnormal fluctuations in the …

Fisher discriminant analysis (FDA), as a very important method for feature extraction, has
been widely used in different applications. However, some drawbacks of the conventional …

The conventional fault detection in general focuses on reactively detecting the abnormal
changes and failure of the plant state as indicated by confidence limit violation, which …

Multimode processes may be operating under different statuses, revealing different process
characteristics and variations. It can be an interesting issue to check how the process …

Neural networks have been successfully used to model nonlinear dynamic systems.
However, when a static neural network model is used in system fault detection and the …

In the era of technological advances and Industry 4.0, massive data collection and analysis
is a common approach followed by many industries and companies worldwide. One of the …

Local Fisher discriminant analysis (LFDA) has been widely applied to dimensionality
reduction and fault classification fields. However, it often suffers from small sample size …

Background The dynamic and nonlinear characteristics of process data have become the
major issue in data-driven process monitoring. Traditional data-driven methods are often …

Reconstruction-based fault isolation, which explores the underlying fault characteristics and
uses them to isolate the cause of the fault, has attracted special attention. However, it does …