Data-driven modeling and applications in plant-wide processes have recently caught much
attention in both academy and industry. This paper provides a systematic review on data …

Data-driven methods have been regarded as desirable methods for fault detection and
diagnosis (FDD) of practical chemical processes. However, with the big data era coming …

In this paper, a new approach for fault detection and diagnosis based on One-Class Support
Vector Machines (1-class SVM) has been proposed. The approach is based on a non-linear …

Monitoring complex industrial plants is a very important task in order to ensure the
management, reliability, safety and maintenance of the desired product quality. Early …

With the growing complexity of the modern industrial process, monitoring large-scale plant-
wide processes has become quite popular. Unlike traditional processes, the measured data …

In this work, a distributed predictive modeling framework is proposed for prediction and
diagnosis of key performance indices in plant-wide processes. With block division of the …

In a typical large-scale chemical process, hundreds of variables are measured. Since
statistical process monitoring techniques typically involve dimensionality reduction, all …

For large scale and complex processes, data-driven analysis methods are receiving
increasing attention for fault detection and diagnosis to improve process operation by …

An enhanced intelligent diagnosis method is proposed based on a joint pairwise graph
embedded sparse deep belief network with partial least square fine-tuning (J-PDBN). In this …

Multivariate statistical projection methods such as principal component analysis (PCA) are
the most common strategy for process monitoring in wastewater treatment plants (WWTPs) …