21 research outputs found

    Process Fault Diagnosis using Neural Networks and Fault Tree Analysis Information

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    Neural nets have recently become the focus of much attention, largely because of their wide range of complex and nonlinear problems. This paper presents a new integrated approach using neural networks for diagnosing process failures. The fault propagation in process is modeled by causal relationships from the fault tree and its minimal cut sets. The measurement patterns required for training and testing the neural network were obtained from fault propagation model. The network is able to diagnose even in the presence of malfunction of certain sensors. We demonstrate via a nitric acid cooler process how the neural network can learn and successfully diagnose the faults

    Fuzzy fault diagnostic system based on fault tree analysis

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    A method is presented for process fault diagnosis using information from fault tree analysis and uncertainty/imprecision of data. Fault tree analysis, which has been used as a method of system reliability/safety analysis, provides a procedure for identifying failures within a process. A fuzzy fault diagnostic system is constructed which uses the fuzzy fault tree analysis to represent a knowledge of the causal relationships in process operation and control system. The proposed method is applied successfully to a nitric acid cooler process plant</p

    Development of Environment for Logical Process Safety Management Based on the Business Process Model

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    Process Fault Diagnosis using Neural Networks and Fault Tree Analysis Information

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    SYNOPSIS Neural nets have recently become the focus of much attention, largely because of their wide range of complex and nonlinear problems. This paper presents a new integrated approach using neural networks for diagnosing process failures. The fault propagation in process is modeled by causal relationships from the fault tree and its minimal cut sets. The measurement patterns required for training and testing the neural network were obtained from fault propagation model. The network is able to diagnose even in the presence of malfunction of certain sensors. We demonstrate via a nitric acid cooler process how the neural network can learn and successfully diagnose the faults

    Syntheses of Organic Fluorescent Compounds

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