Journal ArticleDOI
Statistical process monitoring: basics and beyond
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TLDR
It is demonstrated that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring.Abstract:
This paper provides an overview and analysis of statistical process monitoring methods for fault detection, identification and reconstruction. Several fault detection indices in the literature are analyzed and unified. Fault reconstruction for both sensor and process faults is presented which extends the traditional missing value replacement method. Fault diagnosis methods that have appeared recently are reviewed. The reconstruction-based approach and the contribution-based approach are analyzed and compared with simulation and industrial examples. The complementary nature of the reconstruction- and contribution-based approaches is highlighted. An industrial example of polyester film process monitoring is given to demonstrate the power of the contribution- and reconstruction-based approaches in a hierarchical monitoring framework. Finally we demonstrate that the reconstruction-based framework provides a convenient way for fault analysis, including fault detectability, reconstructability and identifiability conditions, resolving many theoretical issues in process monitoring. Additional topics are summarized at the end of the paper for future investigation. Copyright © 2003 John Wiley & Sons, Ltd.read more
Citations
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Journal ArticleDOI
A Review on Basic Data-Driven Approaches for Industrial Process Monitoring
TL;DR: A basic data-driven design framework with necessary modifications under various industrial operating conditions is sketched, aiming to offer a reference for industrial process monitoring on large-scale industrial processes.
Journal ArticleDOI
Survey on data-driven industrial process monitoring and diagnosis
TL;DR: A state-of-the-art review of the methods and applications of data-driven fault detection and diagnosis that have been developed over the last two decades are provided to draw attention from the systems and control community and the process control community.
Journal ArticleDOI
A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark Tennessee Eastman process
TL;DR: A comparison study on the basic data-driven methods for process monitoring and fault diagnosis (PM–FD) based on the original ideas, implementation conditions, off-line design and on-line computation algorithms as well as computation complexity are discussed in detail.
Journal ArticleDOI
Review of Recent Research on Data-Based Process Monitoring
TL;DR: The natures of different industrial processes are revealed with their data characteristics analyzed and a corresponding problem is defined and illustrated, with review conducted with detailed discussions on connection and comparison of different monitoring methods.
Journal ArticleDOI
Data Mining and Analytics in the Process Industry: The Role of Machine Learning
TL;DR: The state-of-the-art of data mining and analytics are reviewed through eight unsupervisedLearning and ten supervised learning algorithms, as well as the application status of semi-supervised learning algorithms.
References
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Principal component analysis
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Book
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TL;DR: In this paper, the authors present a directory of Symbols and Definitions for PCA, as well as some classic examples of PCA applications, such as: linear models, regression PCA of predictor variables, and analysis of variance PCA for Response Variables.
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TL;DR: In this article, the authors review the state of the art of fault detection and isolation in automatic processes using analytical redundancy, and present some new results with emphasis on the latest attempts to achieve robustness with respect to modelling errors.
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Cross-Validatory Estimation of the Number of Components in Factor and Principal Components Models
TL;DR: In this article, the rank estimation of the rank A of the matrix Y, i.e., the estimation of how much of the data y ik is signal and how much is noise, is considered.
Journal ArticleDOI
Process fault detection based on modeling and estimation methods-A survey
TL;DR: This contribution presents a brief summary of some basic fault detection methods, followed by a description of suitable parameter estimation methods for continuous-time models.