Fault diagnosis is a critical task in the daily operation of chemical processes. In this paper, a hybrid fault diagnosis method is proposed that combines a process-knowledge-based qualitative reasoning technique with fault detection based on a …
With the emergence of Industry 4.0 and Big Data initiatives, there is a renewed interest in leveraging the vast amounts of data collected in (bio)chemical processes to improve their operations. The objective of this article is to provide a …
Process monitoring is of importance to maintain process safety, reliability, performance and cost efficiency. This work presents a hybrid fault detection approach that combines process knowledge such as first-principles and process causal relations …
This paper proposes a novel batch process monitoring method called adjoined time series principal component analysis (AdTsPCA). In this method, a modified GG clustering is used for phase identification and data segmentation and multiple time-ordered …
Data-driven approaches to fault detection and isolation are widely used for various process systems. The purpose of this paper is to present a new method to improve the performance of fault diagnosis of chemical plant. This method combines simple …
This paper presents a fault detection methodology based on the Fisher discriminant analysis (FDA) and individuals control charts (XmR control charts). As the first step, FDA is used to find the optimal discriminant direction between the normal …
This paper proposes a new fault detection method using an absolute-value based Fisher discriminant analysis combined with the individuals and moving range chart. Two fault identification methods are also proposed by using the discriminant model, …
Two methods for reducing dimensions in order to address the classification problem are shown here. Both methods are filters using information gain ratio to select feature subset from the original data. These methods are applied to fault detection for …