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Трофимов, Александр Геннадьевич

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Институт интеллектуальных кибернетических систем
Цель ИИКС и стратегия развития - это подготовка кадров, способных противостоять современным угрозам и вызовам, обладающих знаниями и компетенциями в области кибернетики, информационной и финансовой безопасности для решения задач разработки базового программного обеспечения, повышения защищенности критически важных информационных систем и противодействия отмыванию денег, полученных преступным путем, и финансированию терроризма.
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Александр Геннадьевич
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20192
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    Abnormal operation detection in heat power plant using ensemble of binary classifiers
    (2019) Korshikova, A. A.; Trofimov, A. G.; Kuznetsova, K. E.; Трофимов, Александр Геннадьевич
    © Springer Nature Switzerland AG 2019. The problem of abnormal operation detection is considered for prediction of malfunctions appearance and their progress in the equipment of power plant. Abnormal operation detection method based on multivariate state estimation technique (MSET) along with machine learning algorithms is proposed. The ensemble of linear regression models is used for feature construction. The ensembles of binary classifiers (logistic regressions) together with the multilayer neural network are used for the abnormal operation index calculation based on the constructed features. The method was applied to abnormal operation detection in turbo feed pump (TFP 1100-350-17-4) at Kashirskaya heat power plant (Moscow region, Kashira). It is shown that the abnormal operation index of the pump starts to increase a few days before accidents appear and stays close to zero during the normal operation periods. The obtained results demonstrate that the developed model can be used to detect and predict operation anomalies in the power plant equipment.
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    Model for Early Detection of Emergency Conditions in Power Plant Equipment Based on Machine Learning Methods
    (2019) Korshikova, A. A.; Trofimov, A. G.; Трофимов, Александр Геннадьевич
    © 2019, Pleiades Publishing, Inc. Abstract—: The article discusses a method for early detection and prediction of abnormality in operation of power-unit process equipment taking as an example the PTN 1100-350-17-4 turbine driven feedwater pump of a 300 MW power unit. The importance of the problem of predicting possible process equipment malfunctions at an early state of their occurrence is determined, and the specific features of solving it in the power industry are explained. The range of process equipment defects that can be efficiently detected using the predictive analytics methods is outlined. The fundamental assertion stating that the scope of analog and discrete measurements available in the process control system’s set of computerized automation tools is sufficient for applying the predictive analytics methods is emphasized. Modern predictive analytics methods are briefly reviewed, and the specific features of model training algorithms are mentioned. Separate attention is paid to the problems of preparing initial data for training the model. The mathematical problem of modeling an abnormality indicator taking the values from 0 (normal operation) to 1 (abnormal operation) is formulated. In turn, this problem is formulated as the binary classification problem of attribute vectors characterizing the equipment state at the given moment of time. An original approach is suggested, which combines the multivariate state estimation technique (MSET), in which the degree of abnormality in a technical state is determined from the extent to which the Hotelling criterion exceeds a threshold level (which is automatically calculated in the algorithm), and machine learning methods, the use of which makes it possible to overcome a number of difficulties inherent in the MSET. For solving the problem of determining the composition of the most informative attributes from the values of which early development of an emergency can be detected, it is proposed to use an ensemble of regression models. A method for selecting the modeled variable and the set of regressors is substantiated. An abnormality indicator calculation method based on composing an ensemble of linear regression models is proposed, and the advantage of using an ensemble over a single classifier is shown. A method for producing an alarm in response to detected abnormality in the operation of power unit process equipment is suggested. It is shown that it became possible by using the proposed model to detect the onset of the emergency development process, whereas individual indicators failed to reveal pump operation singularities in the preemergency interval of time.