A Knowledge-Based System for Fault Diagnosis in Real-Time Engineering Applications

Martin, T. Patrick; Glasgow, Janice I.; Féret, Michel P.; Kelley, Todd G.

doi:10.1007/978-3-7091-7555-2_48

Cited by 4 publications

(2 citation statements)

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“…The Reactor Building Ventilation System is a modified model of a ventilation system for an existing nuclear power plant. 6 The ADMS collects real-time sensor data and stores them in a database to be used by a diagnostic module when necessary (Martin et al 1991).…”

Section: Implementation Of the Modelmentioning

confidence: 99%

A Formal Model for Experience–Aided Diagnosis

Féret

Glasgow

1997

Computational Intelligence

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This paper presents a novel approach to model-based diagnosis. The approach addresses the two main problems that have prevented model-based diagnostic techniques from being widely used: computational complexity of abduction and inadequacies of device models. A model for automated diagnosis is defined that combines (1) deduction to rule out hypotheses, (2) abduction to generate hypotheses, and (3) induction to recall past experiences and account for potential errors in the device models. A review of the three forms of inference is provided, as well as a detailed analysis of the relationship between case-based reasoning and induction. The proposed model for diagnosis is used to characterize diagnostic errors and relate them to different types of errors in the device models. Experimental results are then described and used to assert the practicality and the usefulness of the approach. The model presented in this paper yields a practical method for solving hard diagnostic problems at a reasonable computational cost and provides a theoretical basis for overcoming the problem of partially incorrect device models.

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Section: Implementation Of the Modelmentioning

confidence: 99%

A Formal Model for Experience–Aided Diagnosis

Féret

Glasgow

1997

Computational Intelligence

Self Cite

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“…However, the construction of such systems condenses a large amount of expert knowledge, experience, and statistical data. Therefore, using domain expert knowledge, experience, and statistical data to construct a diagnostic model intuitively and realize an efficient and reliable diagnosis is a feasible method to solve such a diagnostic system [25].…”

Section: Introductionmentioning

confidence: 99%

An Industrial Fault Diagnostic System Based on a Cubic Dynamic Uncertain Causality Graph

Nie

Zhang

et al. 2022

Sensors

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This study presents an industrial fault diagnosis system based on the cubic dynamic uncertain causality graph (cubic DUCG) used to model and diagnose industrial systems without sufficient data for model training. The system is developed based on cloud native technology. It contains two main parts, the diagnostic knowledge base and the inference method. The knowledge base was built by domain experts modularly based on professional knowledge. It represented the causality between events in the target industrial system in a visual and graphical form. During the inference, the cubic DUCG algorithm could dynamically generate the cubic causal graph according to the real-time data and perform the logic and probability calculations based on the generated cubic DUCG models, visually displaying the dynamic causal evolution of faults. To verify the system’s feasibility, we rebuild a fault-diagnosis model of the secondary circuit system of No. 1 at the Ningde nuclear power plant based on the new system. Twenty-four fault cases were used to test the diagnostic accuracy of the system, and all faults were correctly diagnosed. The results showed that it was feasible to use the cubic DUCG platform for fault diagnosis.

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