title: Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐ°ÑÐ¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ Ð´Ð»Ñ Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ ÑÐµÑ Ð½Ð¸ÑÐµÑÐºÐ¾Ð³Ð¾ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ ÐºÐ¾Ð¼Ð¿ÑÐµÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² 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: Development and analysis of a distributed system for predicting the technical condition of compressor equipment based on machine learning algorithms
Creators: name: Ð¡Ð¸ÑÐºÐ¸Ð½, ÐÐ»ÑÑ
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: 2024
Subjects: Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ°
ÐºÐ¾Ð¼Ð¿ÑÐµÑÑÐ¾ÑÐ½Ð°Ñ ÑÐµÑ Ð½Ð¸ÐºÐ°
Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ Ð¾ÑÐ¸Ð±Ð¾Ðº
ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
ÑÐ»ÑÑÐ°Ð¹Ð½ÑÐ¹ Ð»ÐµÑ
machine learning
predictive analytics
compressor technology
error prediction
efficiency
data analysis
random forest

description: Ð¦ÐµÐ»ÑÑ Ð¼Ð°Ð³Ð¸ÑÑÐµÑÑÐºÐ¾Ð¹ Ð´Ð¸ÑÑÐµÑÑÐ°ÑÐ¸Ð¸ ÑÐ²Ð»ÑÐµÑÑÑ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° ÑÐµÑÐµÐ½Ð¸Ñ Ð¿Ð¾ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ñ ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ ÐºÐ¾Ð¼Ð¿ÑÐµÑÑÐ¾ÑÐ½Ð¾Ð³Ð¾ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ Ð¾ÑÐ¸Ð±Ð¾Ðº Ð¸ Ð¿Ð¾Ð»Ð¾Ð¼Ð¾Ðº Ð½Ð° ÑÐ°Ð½Ð½Ð¸Ñ ÑÑÐ°Ð´Ð¸ÑÑ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¸ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐµ Ð¸ÑÑÐ¾ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ. ÐÐ»Ñ Ð´Ð¾ÑÑÐ¸Ð¶ÐµÐ½Ð¸Ñ ÑÐµÐ»Ð¸ Ð±ÑÐ»Ð¸ Ð¸Ð·ÑÑÐµÐ½Ñ Ð¼ÐµÑÐ¾Ð´Ñ Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ, Ð²ÑÑÐ²Ð»ÐµÐ½Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐ¸Ðµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ, Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ ÐºÐ¾ÑÐ¾ÑÑÑ ÑÑÑÐ¾Ð¸ÑÑÑ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ, ÑÐ°Ðº Ð¶Ðµ Ð¿ÑÐ¾Ð¸Ð·Ð²ÐµÐ´ÐµÐ½Ñ Ð¼Ð°Ð½Ð¸Ð¿ÑÐ»ÑÑÐ¸Ð¸ Ñ Ð´Ð°Ð½Ð½ÑÐ¼Ð¸, Ð¿Ð¾Ð¼Ð¾Ð³Ð°ÑÑÐ¸Ðµ ÑÐ»ÑÑÑÐ¸ÑÑ ÐºÐ°ÑÐµÑÑÐ²Ð¾ Ð¸ÑÑÐ¾ÑÐ¸ÑÐµÑÐºÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¿ÑÐ¾Ð´ÐµÐ»Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÑ Ð±ÑÐ»Ð° ÑÐ¿ÑÐ¾ÐµÐºÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð° Ð°ÑÑÐ¸ÑÐµÐºÑÑÑÐ° ÑÐµÑÐµÐ½Ð¸Ñ Ð¸ ÑÐµÐ°Ð»Ð¸Ð·Ð¾Ð²Ð°Ð½Ð° Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÑÑÑÐ°Ñ Ð¼Ð¾Ð´ÐµÐ»Ñ, Ð¿ÑÐ¸ Ð¿Ð¾Ð¼Ð¾ÑÐ¸ ÐºÐ¾ÑÐ¾ÑÐ¾Ð¹ Ð¼Ð¾Ð¶Ð½Ð¾ Ð·Ð°Ð±Ð»Ð°Ð³Ð¾Ð²ÑÐµÐ¼ÐµÐ½Ð½Ð¾ ÑÐ»Ð°Ð²Ð»Ð¸Ð²Ð°ÑÑ Ð¾ÑÐ¸Ð±ÐºÐ¸ ÐºÐ¾Ð¼Ð¿ÑÐµÑÑÐ¾ÑÐ½ÑÑ ÑÑÑÐ°Ð½Ð¾Ð²Ð¾Ðº. Ð¢Ð°Ðº Ð¶Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¸Ð½ÑÐµÑÑÐµÐ¹Ñ Ð´Ð»Ñ Ð»ÐµÐ³ÐºÐ¾Ð³Ð¾ Ð¸ Ð¸Ð½ÑÑÐ¸ÑÐ¸Ð²Ð½Ð¾ Ð¿Ð¾Ð½ÑÑÐ½Ð¾Ð³Ð¾ Ð²Ð·Ð°Ð¸Ð¼Ð¾Ð´ÐµÐ¹ÑÑÐ²Ð¸Ñ Ñ Ð¼Ð¾Ð´ÐµÐ»ÑÑ.â
description: The purpose of the masters thesis is to develop a solution for monitoring the condition of compressor equipment and predicting errors and breakdowns in the early stages of detection using machine learning methods and predictive analytics of historical data. To achieve this goal, data analysis methods and algorithms were studied, suitable machine learning methods were identified, with which a predictive model is built, as well as data manipulations were performed to help improve the quality of historical data. As a result of the work done, the architecture of the solution was designed and a predictive model was implemented, with which it is possible to detect errors of compressor units in advance. An interface has also been developed for easy and intuitive interaction with the model.