title: ÐÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¸Ì Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð´Ð»Ñ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐ°Ð½Ð½ÐµÐ¸Ì Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Research on the use of predictive analytics for monitoring the state of equipment and early diagnostics
Creators: name: ÐÑÑÑÑÐµÐº, ÐÑ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: 2021
Subjects: Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð°Ñ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ°
Ð½ÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
LSTM
Python
Ð°Ð½Ð°Ð»Ð¸Ð· Ð´Ð°Ð½Ð½ÑÑ
Ð¿ÑÐ¾Ð³Ð½Ð¾Ð·Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸Ðµ
predictive analytics
neural networks
data analysis
forecasting

description: ÐÐ°Ð½Ð½Ð°Ñ Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÑÐºÐ°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¿Ð¾ÑÐ²ÑÑÐµÐ½Ð° Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð´Ð»Ñ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° ÑÐ¾ÑÑÐ¾ÑÐ½Ð¸Ñ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸ ÑÐ°Ð½Ð½ÐµÐ¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸. ÐÐ»Ñ ÑÑÐ¾Ð³Ð¾ Ð¸Ð·ÑÑÐ°Ð»Ð¸ÑÑ Ð¼ÐµÑÐ¾Ð´Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÑÐ»Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð¸ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ ÐµÐ³Ð¾ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ¸, Ð° ÑÐ°ÐºÐ¶Ðµ Ð¿ÑÐ¾Ð²Ð¾Ð´Ð¸Ð»ÑÑ Ð°Ð½Ð°Ð»Ð¸Ð· ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑ ÐºÈ§Ð±ÐµÐ»ÐµÐ¹ Ð¸ ÑÐ°Ð½Ð½ÐµÐ¹ Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÐºÐ¸ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ. ÐÐ°Ð´Ð°ÑÐ¸, ÑÐµÑÐ°ÐµÐ¼ÑÐµ Ð² ÑÐ¾Ð´Ðµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ: Ð¸Ð·ÑÑÐµÐ½Ð¸Ðµ ÑÑÑÐµÑÑÐ²ÑÑÑÐµÐ¹ Ð°Ð²Ð°ÑÐ¸Ð¹Ð½Ð¾-Ð¿ÑÐµÐ´ÑÐ¿ÑÐµÐ´Ð¸ÑÐµÐ»ÑÐ½Ð¾Ð¹ ÑÐ¸ÑÑÐµÐ¼Ñ, Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ðµ ÑÐ¿Ð¾ÑÐ¾Ð±Ð¾Ð² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÑÐ»Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð¸ Ð¿ÑÐ¾Ð³ÑÐ°Ð¼Ð¼Ð½ÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð´ÑÐ»Ñ, ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¼Ð¾Ð´ÑÐ»Ñ ÐÐ. Ð Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð° Ð½Ð° Ð±Ð°Ð·Ðµ ÐÐÐ Â«Ð¨Ð½ÐµÐ¹Ð´ÐµÑ Ð­Ð»ÐµÐºÑÑÐ¸Ðº ÐÐ°Ð²Ð¾Ð´ Ð­Ð»ÐµÐºÑÑÐ¾ÐÐ¾Ð½Ð¾Ð±Ð»Ð¾ÐºÂ», Ð³Ð´Ðµ ÑÐ¾Ð±Ð¸ÑÐ°Ð»Ð¸ÑÑ Ð´Ð°Ð½Ð½ÑÐµ Ð´Ð»Ñ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ñ Ð´Ð°ÑÑÐ¸ÐºÐ¾Ð² ÑÐµÐ¼Ð¿ÐµÑÐ°ÑÑÑÑ TH110. ÐÑÐ»Ð¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ñ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ, Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð½ÑÐµ Ð½Ð° Ð²ÑÑÐ²Ð»ÐµÐ½Ð¸Ðµ Ð½Ð°Ð¸Ð±Ð¾Ð»ÐµÐµ Ð¿Ð¾Ð´ÑÐ¾Ð´ÑÑÐµÐ³Ð¾ ÑÐ¿Ð¾ÑÐ¾Ð±Ð° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð´Ð°Ð½Ð½ÑÑ. ÐÐ° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ð¹ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ Ð¼Ð¾Ð´ÑÐ»Ñ Ð¿ÑÐµÐ´Ð¸ÐºÑÐ¸Ð²Ð½Ð¾Ð¹ Ð°Ð½Ð°Ð»Ð¸ÑÐ¸ÐºÐ¸ Ð½Ð° ÑÐ·ÑÐºÐµ Python Ñ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸ÐµÐ¼ ÑÐµÐºÑÑÑÐµÐ½ÑÐ½Ð¾Ð¹ LSTM ÑÐµÑÐ¸.
description: This bachelor's thesis explores the use of predictive analytics for equipment monitoring and early diagnosis. For this, methods of implementing the predictive analytics module and the possibility of its development were studied, and also an analysis of cable temperatures and early equipment diagnostics was carried out. Tasks solved in the course of the study: study of the existing emergency warning system, study of ways to implement the predictive analytics module and software methods for implementing the module, development of the PA module. The work was carried out on the basis of LLC "Schneider Electric Plant ElectroMonoblock", where data for analysis was collected from temperature sensors TH110. Studies have been conducted to identify the most appropriate predictive data analysis method. Based on the research, a predictive analytics module was developed in Python using a recurrent LSTM network.