By incorporating the serial correlations of the process data, a new dimensionality reduction
method, named time neighborhood preserving embedding (TNPE) is proposed and applied …

In batch processes, it is crucial to ensure safe production by fault detection. However, the
long batch duration, limited runs, and strong nonlinearity of the data pose challenges …

Abstract Probabilistic Weighted Neighborhood Preserving Embedding and Support Vector
Data Description (WNPE-SVDD) is proposed to improve chemical process monitoring …

Low-rank representation (LRR) is widely utilized in image feature extraction, as it can reveal
the underlying correlation structure of data. However, the subspace learning methods based …

Technological advances in the process industries during the past decade have resulted in
increasingly complicated processes, systems and products. Therefore, recent researches …

Batch process generally has varying dynamic characteristic that causes low fault detection
rate and high false alarm rate, and it is necessary and urgent to monitor batch process. This …

In the practical batch process, the duration of each batch is probably different, and the key
event happened may also vary from batch to batch. This paper proposes an effective fault …

Due to the high dimensionality, non‐linearity and dynamic characteristics of chemical
process data, a fault monitoring method based on temporal extension orthogonal …

The principal component analysis (PCA) is a well-known technique to detect, isolate and
estimate faults affecting a system. However, PCA identifies only linear structures in a given …