title: ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¾ÑÐµÐ½ÐºÐ¸ ÑÐ¸ÑÐºÐ¾Ð² Ð¾ÑÐºÐ°Ð·Ð° Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð²ÑÐµÐ¾Ð±ÑÐµÐ³Ð¾ Ð¾Ð±ÑÐ»ÑÐ¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ (TPM)
title: Application of machine learning to assess the risks of equipment failure as part of total productive maintenance (TPM)
Creators: name: ÐÐ¸ÑÐºÑÐ½Ð¾Ð², ÐÐ»ÑÑ
name: "Ð£Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ðµ Ð¸Ð½Ð½Ð¾Ð²Ð°ÑÐ¸ÑÐ¼Ð¸ Ð² ÑÑÐ»Ð¾Ð²Ð¸ÑÑ  ÑÐ¸ÑÑÐ¾Ð²Ð¾Ð¹ ÑÑÐ°Ð½ÑÑÐ¾ÑÐ¼Ð°ÑÐ¸Ð¸", Ð²ÑÐµÑÐ¾ÑÑÐ¸Ð¹ÑÐºÐ°Ñ ÑÑÑÐ´ÐµÐ½ÑÐµÑÐºÐ°Ñ ÑÑÐµÐ±Ð½Ð¾-Ð½Ð°ÑÑÐ½Ð°Ñ ÐºÐ¾Ð½ÑÐµÑÐµÐ½ÑÐ¸Ñ (2023; Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³)
Publisher: name: ÐÐÐÐÐ¢ÐÐ¥-ÐÐ ÐÐ¡Ð¡
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
Ð²ÑÐµÐ¾Ð±ÑÐµÐµ Ð¾Ð±ÑÐ»ÑÐ¶Ð¸Ð²Ð°Ð½Ð¸Ðµ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ
Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ Ð¾ÑÐºÐ°Ð·Ð° Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ
ÑÐ¸ÑÐºÐ¸
Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¼Ð½Ð¾Ð¶ÐµÑÑÐ²ÐµÐ½Ð½Ð¾Ð¹ ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸
total productive maintenance
probability of equipment failure
risks
multiple regression algorithm

description: Ð¡Ð¾Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÐµ ÐºÐ¾Ð¼Ð¿ÑÑÑÐµÑÐ½ÑÐµ ÑÐµÑÐ½Ð¾Ð»Ð¾Ð³Ð¸Ð¸ Ð¿Ð¾Ð·Ð²Ð¾Ð»ÑÑÑ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸ÑÐ¼
ÑÑÐ¾Ð²ÐµÑÑÐµÐ½ÑÑÐ²Ð¾Ð²Ð°ÑÑ ÑÐ°Ð·Ð»Ð¸ÑÐ½ÑÐµ ÑÐ°ÑÐ°ÐºÑÐµÑÐ¸ÑÑÐ¸ÐºÐ¸ Ð¿ÑÐ¾ÑÐµÑÑÐ¾Ð² ÑÐ°ÐºÐ¸Ðµ, ÐºÐ°Ðº Ð²ÑÐµÐ¼Ñ Ð²ÑÐ¿Ð¾Ð»Ð½ÐµÐ½Ð¸Ñ Ð¿ÑÐ¾ÑÐµÑÑÐ°, ÐµÐ³Ð¾ ÑÐ¾ÑÐ½Ð¾ÑÑÑ, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÑÐ¾Ð¸Ð¼Ð¾ÑÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, ÑÐµÐ¼ ÑÐ°Ð¼ÑÐ¼ ÑÐ²ÐµÐ»Ð¸ÑÐ¸Ð²Ð°Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ ÑÐ°Ð±Ð¾ÑÑ ÑÑÐ¸Ñ Ð¾ÑÐ³Ð°Ð½Ð¸Ð·Ð°ÑÐ¸Ð¹. Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð°ÑÑÐ¸Ð²Ð°ÐµÑÑÑ ÑÑÐ¾Ð²ÐµÑÑÐµÐ½ÑÑÐ²Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¿ÑÐ¾ÑÐµÑÑÐ° Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ð¸Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ Ð¾ÑÐºÐ°Ð·Ð° Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¿ÑÐ¸ Ð¿ÑÐ¾Ð²ÐµÐ´ÐµÐ½Ð¸Ð¸ FMECA-Ð°Ð½Ð°Ð»Ð¸Ð·Ð° Ð² ÑÐ°Ð¼ÐºÐ°Ñ Ð²ÑÐµÐ¾Ð±ÑÐµÐ³Ð¾ Ð¾Ð±ÑÐ»ÑÐ¶Ð¸Ð²Ð°Ð½Ð¸Ñ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ (TPM). Ð£ÑÐ¾Ð²ÐµÑÑÐµÐ½ÑÑÐ²Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¾ÑÑÑÐµÑÑÐ²Ð»ÑÐµÑÑÑ Ð¿Ð¾ÑÑÐµÐ´ÑÑÐ²Ð¾Ð¼ Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð¾Ð² Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ. ÐÐºÑÑÐ°Ð»ÑÐ½Ð¾ÑÑÑ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÑÐµÑÑÑ ÑÐµÐ¼, ÑÑÐ¾ ÑÑÐ°Ð´Ð¸ÑÐ¸Ð¾Ð½Ð½ÑÐµ ÑÐºÑÐ¿ÐµÑÑÐ½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð¾ÑÐµÐ½ÐºÐ¸ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ Ð¾ÑÐ»Ð¸ÑÐ°ÑÑÑÑ Ð½ÐµÐ²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð¸ ÑÐºÐ¾ÑÐ¾ÑÑÑÑ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸, ÑÑÐ¾ Ð¿ÑÐ¸Ð²Ð¾Ð´Ð¸Ñ Ðº Ð½ÐµÐ¿ÑÐ°Ð²Ð¸Ð»ÑÐ½Ð¾Ð¹ Ð¾ÑÐµÐ½ÐºÐµ ÐºÑÐ¸ÑÐ¸ÑÐ½Ð¾ÑÑÐ¸ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¸, ÑÐ¾Ð¾ÑÐ²ÐµÑÑÑÐ²ÐµÐ½Ð½Ð¾, ÐµÐ³Ð¾ ÑÐ±Ð¾ÑÐ¼. Ð ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ Ð¸ÑÑÐ»ÐµÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð±ÑÐ» ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½ ÐºÐ¾Ð´, Ñ Ð²ÑÑÐ¾ÐºÐ¾Ð¹ ÑÐ¾ÑÐ½Ð¾ÑÑÑÑ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ¸Ð¹ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÑ Ð¾ÑÐºÐ°Ð·Ð° Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð¸Ð¸ ÑÑÐ¾ÐºÐ° ÐµÐ³Ð¾ ÑÐºÑÐ¿Ð»ÑÐ°ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð·Ð°Ð³ÑÑÐ¶ÐµÐ½Ð½Ð¾ÑÑÐ¸. Ð¢Ð°ÐºÐ¶Ðµ Ð±ÑÐ»Ð¸ Ð¾Ð¿ÑÐµÐ´ÐµÐ»ÐµÐ½Ñ Ð½Ð°Ð¿ÑÐ°Ð²Ð»ÐµÐ½Ð¸Ñ Ð´Ð°Ð»ÑÐ½ÐµÐ¹ÑÐµÐ³Ð¾ ÑÐ°Ð·Ð²Ð¸ÑÐ¸Ñ ÑÐ°Ð±Ð¾ÑÑ.
description: Modern computer technologies allow organizations to improve various process characteristics such as process execution time, its accuracy and implementation cost, thereby increasing the efficiency of these organizations. This paper considers the improvement of the process of determining the probability of equipment failure in FMECA analysis in the framework of total productive maintenance (TPM). The improvement is carried out through the use of machine learning algorithms. The relevance of the research is determined by the fact that traditional expert methods of probability estimation are characterized by low accuracy and speed of implementation, which leads to incorrect assessment of the criticality of equipment and, consequently, its failures. As a result of the research, a code was developed that accurately predicts the probability of equipment failure based on its service life and workload. The directions of further development of the work were also identified.