Automated self-trained system of functional control and state detection of railway transport nodes
Abstract
Аннотация - Automation of data processing of contactless diagnostics (detection) of the technical condition of the majority of nodes and aggregates of railway transport (RWT) minimizes the damage from failures of these systems in operating modes. This becomes possible due to the rapid detection of serious defects at the stage of their origin. Basically, in practice, the control of the technical condition of the nodes and aggregates of the RWT is carried out during scheduled repairs. It is not always possible to identify incipient defects. Consequently, it is not always possible to warn personnel (machinists, repairmen, etc.) of significant damage to the RWT systems until their complete failure. The difficulties of obtaining diagnostic information is that there is interdependence between the main nodes of the RWT. This means that if physical damage occurs at any of the RWT nodes, in other nodes there can also occur malfunctions.
В качестве основного способа повышения эффективности обнаружения состояний узлов и агрегатов RWT мы видим направление предоставления свойства адаптивности для автоматизированной системы обработки данных из различных систем удаления бесконтактной диагностической информации. Глобальная цель может быть достигнута, в частности, за счет использования методов машинного обучения и распознавания ошибок (объектов распознавания). Для повышения эксплуатационной надежности и срока службы основных узлов и агрегатов RWT предложена соответствующая модель и алгоритм машинного обучения системы управления оператором узлов и агрегатов. Предлагается использовать нормированную меру Шеннона для энтропии и критерий информации о расстоянии Кульбака-Лейблера в качестве критерия эффективности обучения автоматизированной системы обнаружения и контроля состояния узла оператора RWT. В статье описано применение предлагаемого способа на примере системы автоматического обнаружения (АДС) состояния тягового двигателя электровоза. Приведены тестовые данные модели и алгоритма в среде MATLAB.
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