Application of signed digraphs-based analysis for fault diagnosis of chemical process flowsheets
01 Aug 2004-Engineering Applications of Artificial Intelligence (Pergamon)-Vol. 17, Iss: 5, pp 501-518
TL;DR: Two case studies are presented to illustrate SDG-based analysis of process flowsheets containing many units and control loops and it is shown that digraph-based steady-state analysis results in good diagnostic resolution.
About: This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2004-08-01. It has received 135 citations till now.
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TL;DR: In this paper, the authors review advances in detection and diagnosis of plant-wide control system disturbances in chemical processes and discuss new directions that look promising for the future, including the linkage of data-driven analysis with a qualitative model of the process.
178 citations
TL;DR: In this paper, the authors present a thematic review of the methods for integration of process design and control, and the evolution paths of these methods are described and the advantages and disadvantages of each method are explained.
Abstract: There is a large variety of methods in literature for process design and control, which can be classified into two main categories. The methods in the first category have a sequential approach in which, the control system is designed, only after the details of process design are decided. However, when process design is fixed, there is little room left for improving the control performance. Recognizing the interactions between process design and control, the methods in the second category integrate some control aspects into process design. With the aim of providing an exploration map and identifying the potential areas of further contributions, this paper presents a thematic review of the methods for integration of process design and control. The evolution paths of these methods are described and the advantages and disadvantages of each method are explained. The paper concludes with suggestions for future research activities.
122 citations
TL;DR: The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables.
Abstract: A novel networked process monitoring, fault propagation identification, and root cause diagnosis approach is developed in this study. First, process network structure is determined from prior process knowledge and analysis. The network model parameters including the conditional probability density functions of different nodes are then estimated from process operating data to characterize the causal relationships among the monitored variables. Subsequently, the Bayesian inference-based abnormality likelihood index is proposed to detect abnormal events in chemical processes. After the process fault is detected, the novel dynamic Bayesian probability and contribution indices are further developed from the transitional probabilities of monitored variables to identify the major faulty effect variables with significant upsets. With the dynamic Bayesian contribution index, the statistical inference rules are, thus, designed to search for the fault propagation pathways from the downstream backwards to the upstream process. In this way, the ending nodes in the identified propagation pathways can be captured as the root cause variables of process faults. Meanwhile, the identified fault propagation sequence provides an in-depth understanding as to the interactive effects of faults throughout the processes. The proposed approach is demonstrated using the illustrative continuous stirred tank reactor system and the Tennessee Eastman chemical process with the fault propagation identification results compared against those of the transfer entropy-based monitoring method. The results show that the novel networked process monitoring and diagnosis approach can accurately detect abnormal events, identify the fault propagation pathways, and diagnose the root cause variables. © 2013 American Institute of Chemical Engineers AIChE J, 59: 2348–2365, 2013
117 citations
TL;DR: This study provides a data set and evaluation measures that could be used as a framework for future comparisons and proposes and uses general performance assessment indexes based on precision and recall of each FDS.
107 citations
TL;DR: A combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis that combines the completeness property of SDG with the high diagnostic resolution property of QTA.
Abstract: In this article a combined signed directed graph (SDG) and qualitative trend analysis (QTA) framework for incipient fault diagnosis has been proposed. The SDG is the first level in this framework and provides a possible candidate set of faults based on the incipient response of the process. The search for the actual fault is performed based on a QTA (level 2), which uses the temporal evolution of the sensors for further resolution. Thus, this framework combines the completeness property of SDG with the high diagnostic resolution property of QTA. Methods to address the problem of incorrect diagnosis arising due to incorrect measurement of initial response have also been presented. The proposed approach is tested on the Tennessee Eastman (TE) case study. Correct fault diagnosis is performed in all possible single fault scenarios. It is shown that this framework provides fast, reliable and accurate incipient fault diagnosis.
93 citations
Cites background from "Application of signed digraphs-base..."
...The sign patterns corresponding to faults 9–15 and 25–33 are simply opposite to the sign patterns for faults 1–7 and 16–24 in that order, respectively (Maurya et al., 2004)....
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..., ‘þ ’ or ‘2 ’) only for faults 1–4 and 19–21 (Maurya et al., 2004)....
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References
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