Process monitoring is crucial for maintaining favorable operating conditions and has
received considerable attention in previous decades. Currently, a plant-wide process …

Partial least squares (PLS) and canonical correlation analysis (CCA) are two most popular
key performance indicators (KPI) monitoring algorithms, which have shortcomings in dealing …

In this paper, we highlight the basic techniques of multivariate statistical process control
(MSPC) under the dimensionality criteria, such as Multiway Principal Component Analysis …

In order to ensure the long-term stable operation of a large-scale industrial process, it is
necessary to detect and solve the minor abnormal conditions in time. However, the large …

Multivariate statistical process control (MSPC) has been widely employed for process fault
detection. Recently, deep neural networks (DNNs), ie, stacked autoencoder (SAE) enjoys its …

Industrial processes generally have various operation modes, and fault detection for such
processes is important. This paper proposes a method that integrates a variational Bayesian …

In order to improve the performance of fault isolation and diagnosis of principal component
analysis (PCA) based methods, this article proposes a novel fault detection and isolation …

This article proposes a distributed parallel modeling and monitoring framework for plant-
wide processes with big data. The “distributed” contains two layers of meaning. One is the …

Deep neural network (DNN) extracts hierarchical representations from process data and is
promising for nonlinear process monitoring. Obtaining meaningful representations and …

Blast furnaces are the most crucial equipment in ironmaking processes. Stable operation of
the blast furnace is a prerequisite for personnel safety and production efficiency. Therefore …