Искусственные нейронные сети прогнозирования технического состояния электродвигателей газоперекачивающих агрегатов
Аннотация
Рассмотрены вопросы проектирования эффективных и достоверных систем оперативной диагностики электродвигателей электроприводных компрессорных станций. Представлена статистика выхода из строя наиболее ответственных установок газотранспортных систем – электроприводных газоперекачивающих агрегатов. Разработана методология и архитектура искусственных нейронных сетей для получения прогнозных моделей электрических машин мегаваттного класса. Приведены примеры нейро-нечеткого прогнозирования технического состояния и ресурса статорных обмоток синхронных машин. Получены тесты селекционированных сетей, нечеткая модель Бокса – Дженкинса, модели метода анализа динамики спектральных составляющих, прогнозирование величин тока и температур статора. Сопоставительные результаты анализа ожидаемых состояний электрических машин магистрального транспорта газа, исходя из учета различных эксплуатационных факторов работы электроприводных газоперекачивающих агрегатов, позволили выработать рекомендации по применению метода искусственных нейронных сетей.
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