A self-validating control system based approach to plant fault detection and diagnosis
Jun Chen,John Howell +1 more
TLDR
In this paper, an approach is proposed in which fault detection and diagnosis (FDD) tasks are distributed to separate FDD modules associated with each control system located throughout a plant.About:
This article is published in Computers & Chemical Engineering.The article was published on 2001-03-15 and is currently open access. It has received 31 citations till now. The article focuses on the topics: Control reconfiguration & Fault detection and isolation.read more
Citations
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Journal ArticleDOI
Online Steady-State Detection for Process Control Using Multiple Change-Point Models and Particle Filters
TL;DR: A robust online steady-state detection algorithm using multiple change-point model and particle filtering techniques is proposed, which is more accurate and robust than the other existing methods.
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Signed directed graph-based hierarchical modelling and fault propagation analysis for large-scale systems
TL;DR: The signed directed graph (SDG) model can be considered as a qualitative model to describe the variables and their cause-effect relations in a continuous process and can be transformed into a hierarchical model to improve search efficiency.
Journal ArticleDOI
Type-II critical values for a steady-state identifier
TL;DR: In this paper, critical values for Type-II error are reported for a variety of signal-to-noise conditions, and for non-steady state behaviors, and a critical value of 0.8 for the ratio-statistic permits acceptance of SS for processes visually judged to be at SS, and rejects processes that are visually judged not-at-SS with greater than 99.99% confidence.
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Online detection of steady-state operation using a multiple-change-point model and exact Bayesian inference
Jianguo Wu,Yong Chen,Shiyu Zhou +2 more
TL;DR: A new robust and computationally efficient online steady-state detection method using multiple change-point models and exact Bayesian inference is proposed that is much more accurate and robust than currently available methods.
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Towards distributed diagnosis of the Tennessee Eastman process benchmark
Jun Chen,John Howell +1 more
TL;DR: In this article, a distributed hybrid strategy for the isolation of faults and disturbances in the Tennessee Eastman process, which would build on existing structures for distributed control systems, so should be easy to implement, be cheap and be widely applicable.
References
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Book
Modern Control Systems
Richard C. Dorf,Naresh K. Sinha +1 more
TL;DR: This book presents a control engineering methodology that, while based on mathematical fundamentals, stresses physical system modeling and practical control system designs with realistic system specifications.
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Qualitative process theory
TL;DR: This paper describes the basic concepts of qualitative process theory, several different kinds of reasoning that can be performed with them, and discusses its implications for causal reasoning.
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A qualitative physics based on confluences
Johan de Kleer,John Seely Brown +1 more
TL;DR: A fairly encompassing account of qualitative physics, which introduces causality as an ontological commitment for explaining how devices behave, and presents algorithms for determining the behavior of a composite device from the generic behavior of its components.
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Statistical Process Control of Multivariate Processes
TL;DR: An overview of multivariate statistical methods use for the statistical process control of both continuous and batch multivariate processes and examples are provided of their use for analysing the operations of a mineral processing plant, for on-line monitoring and fault diagnosis of a continuous polymerization process and for the on- line monitoring of an industrial batch polymerization reactor.
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Assessment of control loop performance
TL;DR: In this article, an estimate of the best possible control can be obtained by fitting a univariate time series to process data collected under routine control, and the use of this technique is demonstrated with pilot plant and production data.