Bridging data-driven and model-based approaches for process fault diagnosis and health monitoring: A review of researches and future challenges
Abstract Fault Diagnosis and Health Monitoring (FD-HM) for modern control systems have
been an active area of research over the last few years. Model-based FD-HM computational …
been an active area of research over the last few years. Model-based FD-HM computational …
A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems
N Md Nor, CR Che Hassan… - Reviews in Chemical …, 2020 - degruyter.com
Fault detection and diagnosis (FDD) systems are developed to characterize normal
variations and detect abnormal changes in a process plant. It is always important for early …
variations and detect abnormal changes in a process plant. It is always important for early …
Review of recent research on data-based process monitoring
Data-based process monitoring has become a key technology in process industries for
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
safety, quality, and operation efficiency enhancement. This paper provides a timely update …
On paradigm of industrial big data analytics: From evolution to revolution
The arrival of the intelligent manufacturing and industrial internet era brings more and more
opportunities and challenges to modern industry. Specifically, the revolution of the …
opportunities and challenges to modern industry. Specifically, the revolution of the …
Survey on the theoretical research and engineering applications of multivariate statistics process monitoring algorithms: 2008–2017
Multivariate statistical process monitoring (MSPM) methods are significant for improving
production efficiency and enhancing safety. However, to the authors' best knowledge, there …
production efficiency and enhancing safety. However, to the authors' best knowledge, there …
Review of adaptation mechanisms for data-driven soft sensors
In this article, we review and discuss algorithms for adaptive data-driven soft sensing. In
order to be able to provide a comprehensive overview of the adaptation techniques …
order to be able to provide a comprehensive overview of the adaptation techniques …
A review on data‐driven learning approaches for fault detection and diagnosis in chemical processes
Fault detection and diagnosis for process plants has been an active area of research for
many years. This review presents a concise overview on supervised and unsupervised data …
many years. This review presents a concise overview on supervised and unsupervised data …
A review of kernel methods for feature extraction in nonlinear process monitoring
Kernel methods are a class of learning machines for the fast recognition of nonlinear
patterns in any data set. In this paper, the applications of kernel methods for feature …
patterns in any data set. In this paper, the applications of kernel methods for feature …
Fault monitoring using novel adaptive kernel principal component analysis integrating grey relational analysis
Y Han, G Song, F Liu, Z Geng, B Ma, W Xu - Process Safety and …, 2022 - Elsevier
The kernel principal component analysis (KPCA) is widely used as a fault monitoring tool for
complex nonlinear chemical processes in recent years. The cumulative contribution rate that …
complex nonlinear chemical processes in recent years. The cumulative contribution rate that …
On the application of PCA technique to fault diagnosis
In this paper, we briefly address the application of the standard principal component
analysis (PCA) technique to fault detection and identification. Based on an analysis of the …
analysis (PCA) technique to fault detection and identification. Based on an analysis of the …