The performance of diagnostic systems based on empirical models may vary in different zones of th... more The performance of diagnostic systems based on empirical models may vary in different zones of the training space. It is, thus, important to a-priori verify whether the model is working in a zone where the performance is expected to be satisfactory. In this respect, the objective of this work is to estimate the degree of confidence in the identification of nuclear transients by a diagnostic system based on a bagged ensemble of Supervised Fuzzy C-Means (FCM) classifiers. The method has been applied for classifying simulated transients in the feedwater system of a nuclear Boiling Water Reactor (BWR). The obtained results indicate that the bagging ensemble permits to achieve satisfactory performance, with a reliable estimation of the degree of confidence in the classification.
The proper and timely fault detection and isolation of industrial plant is of premier importance ... more The proper and timely fault detection and isolation of industrial plant is of premier importance to guarantee the safe and reliable operation of industrial plants. The paper presents application of a neural networks-based scheme for fault detection and isolation, for the pressurizer of a PWR nuclear power plant. The scheme is constituted by two components: residual generation and fault isolation. The first component generates residuals via the discrepancy between measurements coming from the plant and a nominal model. The neural network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neural network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator to detect and isolate the main faults appearing in the pressurizer of a PWR.
World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making, Aug 2012
The performance of diagnostic systems based on empirical models may vary in different zones of th... more The performance of diagnostic systems based on empirical models may vary in different zones of the training space. It is, thus, important to a-priori verify whether the model is working in a zone where the performance is expected to be satisfactory. In this respect, the objective of this work is to estimate the degree of confidence in the identification of nuclear transients by a diagnostic system based on a bagged ensemble of Supervised Fuzzy C-Means (FCM) classifiers. The method has been applied for classifying simulated transients in the feedwater system of a nuclear Boiling Water Reactor (BWR). The obtained results indicate that the bagging ensemble permits to achieve satisfactory performance, with a reliable estimation of the degree of confidence in the classification.
The proper and timely fault detection and isolation of industrial plant is of premier importance ... more The proper and timely fault detection and isolation of industrial plant is of premier importance to guarantee the safe and reliable operation of industrial plants. The paper presents application of a neural networks-based scheme for fault detection and isolation, for the pressurizer of a PWR nuclear power plant. The scheme is constituted by two components: residual generation and fault isolation. The first component generates residuals via the discrepancy between measurements coming from the plant and a nominal model. The neural network estimator is trained with healthy data collected from a full-scale simulator. For the second component detection thresholds are used to encode the residuals as bipolar vectors which represent fault patterns. These patterns are stored in an associative memory based on a recurrent neural network. The proposed fault diagnosis tool is evaluated on-line via a full-scale simulator to detect and isolate the main faults appearing in the pressurizer of a PWR.
World Scientific Proc. Series on Computer Engineering and Information Science 7; Uncertainty Modeling in Knowledge Engineering and Decision Making, Aug 2012
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Papers by Roozbeh Razavi-Far