A method of alarm system analysis for process plants

Modarres, Mohammad; Cadman, Theodore W.

doi:10.1016/0098-1354(86)85035-9

Cited by 27 publications

(5 citation statements)

References 2 publications

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“…After the TMI accident, various alarm processing methods such as decision tables, fault trees, alarm trees [14], goal trees [16], cause-consequence trees [17] and alarm transition tables [18] were proposed. Also, Corsberg has proposed the methods of rule-based deduction and object-oriented programming for alarm processing system [19].…”

Section: Multiple Alarm Processing Methodsmentioning

confidence: 99%

Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

Cheon

Chang

Chung

et al. 1993

IEEE Trans. Nucl. Sci.

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Absfracf-We present the feasibility study of multiple alarm processing and diagnosis using neural networks. The back-propagation network (BPN) algorithm is applied to the training of multiple alarm patterns for the identification of faults in a reactor coolant pump (RCP) system. The general mapping capability of the neural network enables to identify a fault easily. A number of case studies are performed with emphasis on the applicability of the neural network to the pattern recognition of multiple alarms. Based on the case studies, the neural network can identify the cause of multiple alarms well, although untrained, incomplete/sensor-failed or time-varying alarm symptoms are given. Also, multiple faults are easily identified with a given alarm pattern.

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Section: Multiple Alarm Processing Methodsmentioning

confidence: 99%

Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

Cheon

Chang

Chung

et al. 1993

IEEE Trans. Nucl. Sci.

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“…Among functional modeling approaches that have been successfully applied in numerous applications are the ''Goal Tree-Success Tree'' (GTST) modeling (Modarres and Cadman 1986) and Multilevel Flow Modeling (MFM) (Lind 1990). GTST is a functional decomposition framework that has been used widely in modeling complex physical systems.…”

Section: Motivation and Outline Of Proposed Workmentioning

confidence: 99%

Cognitive map-based system modeling for identifying interaction failure modes

et al. 2011

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Past few decades have seen an upsurge in failure analysis techniques capable of dealing with reliability issues up front in the early stages of the product development process. Most of these approaches are centered on component-specific failures. However, with the advent of highly complex systems that derive functionalities from multiple physical phenomena domains, more emphasis is required on identifying failures arising due to various system interactions, which is largely absent in existing failure analysis approaches. Owing to the causal nature of system interaction failures, the use of cognitive maps in system modeling and simulation for failure analysis is highly suitable. This paper proposes a structured framework for the development and use of cognitive mapbased system models capable of capturing all types of failure modes, including interaction failures. The applicability of the proposed framework is demonstrated with the example of an electric water heater.

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“…More lately, Lind's group has presented a combination of MFM and the Goal Tree-Success Tree (GTST) approach of Modarres, see Jalashgar (1997). GTST has been used for fault diagnosis, Chung and Modarres (1989), alarm analysis, Modarres and Cadman (1986), and operator support, Kim and Modarres (1987).…”

Section: Related Workmentioning

confidence: 99%

Diagnostic reasoning based on means-end models: experiences and future prospects

Larsson¹

2002

Knowledge-Based Systems

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Multilevel Flow Models (MFM) are graphical models of goals and functions of technical systems. MFM was invented by Morten Lind at the Technical University of Denmark and several new algorithms and implementations have been contributed by the group headed by Jan Eric Larsson at Lund Institute of Technology. MFM provides a good basis for computerbased supervision and diagnosis, especially in real-time applications, were fast execution and guaranteed worstcase response times are essential. The expressive power of MFM is similar to that of rule-based expert systems, while the explicit representation of means-end knowledge and the graphical nature of the models make the knowledge engineering effort less and the execution efficiency higher than that of standard expert systems. The resulting models can be used for different diagnostic tasks, such as fault diagn osis, causal explanations, and qualitative predictions. MFM has several properties which makes for a relatively easy knowledge engineering task, compared to mathematical models as used in classical control theory and compared to the rule bases used in standard expert systems. In addition, MFM allows for diagnostic algorithms with excellent real-time properties. The paper gives an overview of existing MFM algorithms, and different MFM projects which have been performed or are currently in progress.

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A method of alarm system analysis for process plants

Cited by 27 publications

References 2 publications

Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

Application of neural networks to multiple alarm processing and diagnosis in nuclear power plants

Cognitive map-based system modeling for identifying interaction failure modes

Diagnostic reasoning based on means-end models: experiences and future prospects

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