title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³Ð¸Ð´ÑÐ¾Ð°Ð³ÑÐµÐ³Ð°ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Develop a diagnostic model of a hydraulic unit based on machine learning
Creators: name: ÐÑÐ´ÑÑÐ²ÑÐµÐ²Ð°, ÐÐºÐ°ÑÐµÑÐ¸Ð½Ð°
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð³Ð¸Ð´ÑÐ¾Ð°Ð³ÑÐµÐ³Ð°Ñ
Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ°
Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð±Ð¸Ð±Ð»Ð¸Ð¾ÑÐµÐºÐ¸ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð¸ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ
Torch
Matplotlib
Pandas
hydraulic unit
predictive analytics
machine learning
neural networks
libraries for analytics and machine learning

description: Ð¦ÐµÐ»Ñ ÑÐ°Ð±Ð¾ÑÑ ÑÐ¾ÑÑÐ¾Ð¸Ñ Ð² ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐµ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³Ð¸Ð´ÑÐ¾Ð°Ð³ÑÐµÐ³Ð°ÑÐ° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÑÑÑÑ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð¿ÑÑÐ¸ ÑÐµÑÐµÐ½Ð¸Ñ Ð¿ÑÐ¾Ð±Ð»ÐµÐ¼Ñ Ð½ÐµÑÑÐ°ÑÐ½ÑÑ ÑÐ¸ÑÑÐ°ÑÐ¸Ð¹ Ð½Ð° ÐÐ­Ð¡ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð³Ð¸Ð´ÑÐ¾Ð°Ð³ÑÐµÐ³Ð°ÑÐ°. ÐÐ»Ñ ÑÐ´Ð¾Ð±ÑÑÐ²Ð° Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»ÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð°ÑÑÐ²Ð°ÐµÑÑÑ Ð¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÐµÐ»ÑÑÐºÐ¸Ð¹ Ð¸Ð½ÑÐµÑÑÐµÐ¹Ñ, Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑÐ¸Ð¹ Ð² Ð¿Ð¾Ð»Ð½Ð¾Ð¹ Ð¼ÐµÑÐµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°ÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÐ¸ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸.
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description: The purpose of the work is to develop a diagnostic model of a hydraulic unit based on machine learning. Main ways of solving the problem of emergency occurrence at hydro power plants are considered based on development of a diagnostic model of the hydraulic unit. For ease of use of the diagnostic model, a graphic user interface is developed.
The first chapter provides an overview of the subject area, which includes the consideration of the relevance of the work, an overview of existing diagnostic models in the energy industry, and a summary of machine learning. The second chapter deals with theoretical materials that include operation principles and structure of a hydraulic unit and information on neural networks, their activation functions and training methods. The final chapter describes the selected software, used libraries, describes the implemented diagnostic model of the hydraulic unit, its possibilities and analyses the results.