title: Ð¡Ð¾Ð²ÐµÑÑÐµÐ½ÑÑÐ²Ð¾Ð²Ð°Ð½Ð¸Ðµ Ð¼ÐµÑÐ¾Ð´Ð° Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð¸Ð·Ð½Ð¾ÑÐ° Ð½Ð° Ð³Ð°Ð·Ð¾Ð¿ÑÐ¾Ð²Ð¾Ð´Ðµ
title: Improvement of the automation method for monitoring wear on the gas pipeline
Creators: name: ÐÐ¾ÑÐ½ÐµÐµÐ², Ð .
name: Ð¡ÐºÐ¾ÑÐ½Ð¸ÐºÐ¾Ð²Ð°, ÐÐ°ÑÐ³Ð°ÑÐ¸ÑÐ°
name: "ÐÐ½ÑÐµÐ»Ð»ÐµÐºÑÑÐ°Ð»ÑÐ½Ð°Ñ ÑÑÐ¸Ð±Ð¾Ð»Ð¾Ð³Ð¸Ñ Ð² Ð¼Ð°ÑÐ¸Ð½Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ð¸: BALT TRIBO 2024", Ð¼ÐµÐ¶Ð´ÑÐ½Ð°ÑÐ¾Ð´Ð½Ð°Ñ Ð½Ð°ÑÑÐ½Ð¾-Ð¿ÑÐ°ÐºÑÐ¸ÑÐµÑÐºÐ°Ñ ÐºÐ¾Ð½ÑÐµÑÐµÐ½ÑÐ¸Ñ (2024; 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: 2024
Subjects: ÐÐ°Ð·Ð¾Ð¿ÑÐ¾Ð²Ð¾Ð´Ñ
ÐÐ·Ð½Ð¾Ñ
Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ñ
automation

description: Ð ÑÐ°Ð±Ð¾ÑÐµ ÑÐ°ÑÑÐ¼Ð¾ÑÑÐµÐ½Ñ Ð¾ÑÐ½Ð¾Ð²Ð½ÑÐµ Ð¼ÐµÑÐ¾Ð´Ñ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸Ð·Ð°ÑÐ¸Ð¸ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³Ð° Ð¸Ð·Ð½Ð¾ÑÐ° Ð½Ð° Ð¿Ð¾Ð²ÐµÑÑÐ½Ð¾ÑÑÑÑ ÑÑÑÐ±Ð¾Ð¿ÑÐ¾Ð²Ð¾Ð´Ð° Ð¸ ÑÑÐ»ÐµÐ²Ð¾Ð¹ ÑÑÐ³Ð¸ Ð¾Ð±Ð¾ÑÑÐ´Ð¾Ð²Ð°Ð½Ð¸Ñ. ÐÑÐµÐ´Ð»Ð¾Ð¶ÐµÐ½ Ð¿ÑÐ¾ÑÑÐ¾Ð¹ Ð² Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ð¸ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¾Ð³Ð¾ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð²ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ð¹ Ð¼Ð°Ð³Ð¸ÑÑÑÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ ÑÑÑÐ±Ð¾Ð¿ÑÐ¾Ð²Ð¾Ð´Ð° Ð½Ð° Ð¾ÑÐ½Ð¾Ð²Ðµ Ð²Ð¸Ð±ÑÐ°ÑÐ¸Ð¸ Ð¸ Ð´Ð°Ð½Ð½ÑÑ. Ð­ÑÐ¾Ñ Ð¿Ð¾Ð´ÑÐ¾Ð´ Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð» Ð¾Ð±Ð½Ð°ÑÑÐ¶Ð¸Ð²Ð°ÑÑ Ð¿Ð¾Ð²ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð°ÐºÑÐµÐ»ÐµÑÐ¾Ð¼ÐµÑÑÐ° Ð½Ð° ÐºÐ¾Ð½ÑÑÐ¾Ð»Ð¸ÑÑÐµÐ¼Ð¾Ð¹ ÑÑÐ³Ðµ, Ð½Ðµ ÑÑÐµÐ±ÑÐµÑ Ð·Ð½Ð°Ð½Ð¸Ñ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÑ, Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, Ð¾ÑÐµÐ²Ð¾Ð¹ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸ Ð½Ð° ÑÑÐ»ÐµÐ²ÑÑ ÑÑÐ³Ñ, Ð²ÑÐµÐ¼ÐµÐ½Ð½ÑÑ Ð¿ÑÐ¾Ð²ÐµÑÐ¾Ðº Ð¸ Ð¼Ð¾Ð¶ÐµÑ Ð²ÑÐ¿Ð¾Ð»Ð½ÑÑÑÑÑ Ð±ÐµÐ· Ð¿ÑÐ¸ÑÐ¼Ð¾ÑÑÐ° ÑÐµÐ»Ð¾Ð²ÐµÐºÐ°-Ð¾Ð¿ÐµÑÐ°ÑÐ¾ÑÐ°. ÐÑÐ¸Ð¼ÐµÐ½ÐµÐ½Ð¸Ðµ Ð¿ÑÐ¾ÑÐµÐ´ÑÑÑ Ð¾ÑÐ¸ÑÑÐºÐ¸ Ð´Ð°Ð½Ð½ÑÑ, Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼ Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ñ Ð¿Ð¾Ð²ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ð¹, Ð¾ÑÐ½Ð¾Ð²Ð°Ð½Ð½ÑÐ¹ Ð½Ð° Ð¼ÐµÑÐ¾Ð´Ðµ MSD (ÐºÐ²Ð°Ð´ÑÐ°Ñ ÑÐ°ÑÑÑÐ¾ÑÐ½Ð¸Ñ ÐÐ°ÑÐ°Ð»Ð°Ð½Ð¾Ð±Ð¸ÑÐ°), Ð¿Ð¾ÑÑÑÐ¾ÐµÐ½Ð¸Ñ Ð»Ð¾Ð³Ð¸ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ ÐºÑÐ¸Ð²ÑÑ (ÑÐµÐ³ÑÐµÑÑÐ¸Ð¸) ROC, Ð¿Ð¾Ð·Ð²Ð¾Ð»Ð¸Ð»Ð¸ Ð¿Ð¾Ð»Ð½Ð¾ÑÑÑÑ Ð°Ð²ÑÐ¾Ð¼Ð°ÑÐ¸ÑÐµÑÐºÐ¸ Ð¾ÑÑÑÐµÑÑÐ²Ð¸ÑÑ Ð¼Ð¾Ð½Ð¸ÑÐ¾ÑÐ¸Ð½Ð³ ÑÑÐ»ÐµÐ²Ð¾Ð¹ ÑÑÐ³Ð¸ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¾Ð´Ð½Ð¾Ð³Ð¾ Ð´Ð°ÑÑÐ¸ÐºÐ° Ð¸ Ð¿ÑÐ¾ÑÑÐ¾Ð³Ð¾ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð°, ÐºÐ¾ÑÐ¾ÑÑÐ¹ Ð½Ðµ ÑÑÐµÐ±ÑÐµÑ Ð·Ð½Ð°Ð½Ð¸Ñ ÑÐ¸Ð·Ð¸ÑÐµÑÐºÐ¸Ñ Ð¿ÐµÑÐµÐ¼ÐµÐ½Ð½ÑÑ, Ð½Ð°Ð¿ÑÐ¸Ð¼ÐµÑ, Ð¾ÑÐµÐ²Ð¾Ð¹ Ð½Ð°Ð³ÑÑÐ·ÐºÐ¸ Ð½Ð° ÑÑÐ»ÐµÐ²ÑÑ ÑÑÐ³Ñ. ÐÐ¾ÐºÐ°Ð·Ð°Ð½Ð¾, ÑÑÐ¾ Ñ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸ÐµÐ¼ Ð¿Ð¾Ð²ÑÐµÐ¶Ð´ÐµÐ½Ð¸Ð¹ Ð¸ ÑÐ²ÐµÐ»Ð¸ÑÐµÐ½Ð¸Ñ ÐºÐ¾ÑÑÑÐ¸ÑÐ¸ÐµÐ½ÑÐ° Ð¼Ð¾Ð´Ð°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð·Ð°ÑÑÑÐ°Ð½Ð¸Ñ Ð¾Ñ =5/8 Ð´Ð¾ =8/10, Ð³ÑÐ°ÑÐ¸ÐºÐ¸ ROC-ÐºÑÐ¸Ð²Ð¾Ð¹ Ð¿ÑÐ¾ÑÐ¾Ð´ÑÑ ÑÐµÑÐµÐ· Ð²ÐµÑÑÐ½Ð¸Ð¹ Ð»ÐµÐ²ÑÐ¹ ÑÐ³Ð¾Ð», Ð³Ð´Ðµ Ð´Ð¾Ð»Ñ Ð¸ÑÑÐ¸Ð½Ð½Ð¾ Ð¿Ð¾Ð»Ð¾Ð¶Ð¸ÑÐµÐ»ÑÐ½ÑÑ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² ÑÐ¾ÑÑÐ°Ð²Ð»ÑÐµÑ 100%, Ð° Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°ÑÐµÐ»ÑÐ½Ð°Ñ ÑÐ¿Ð¾ÑÐ¾Ð±Ð½Ð¾ÑÑÑ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð²Ð¾Ð·ÑÐ°ÑÑÐ°ÐµÑ, Ð¾ÑÐ¾Ð±ÐµÐ½Ð½Ð¾, ÐºÐ¾Ð³Ð´Ð° ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÑ Ð¿ÑÐµÐ´ÑÑÐ°Ð²Ð»ÐµÐ½Ñ ÑÐ¾ ÑÐºÐ¾Ð»ÑÐ·ÑÑÐ¸Ð¼ ÑÑÐµÐ´Ð½Ð¸Ð¼.
description: The paper considers the main methods of automation of wear monitoring on the surfaces of pipelines and tie rods of equipment. An easy-to-use algorithm for automatic detection of damage to the main pipeline based on vibration and data is proposed. This approach made it possible to detect damage using a single accelerometer on controlled thrust, does not require knowledge of physical variables, for example, axial load on the steering rod, temporary checks and can be performed without the supervision of a human operator. The application of the data cleaning procedure, the damage detection algorithm based on the MSD (Mahalanobis distance square) method, the construction of logistic curves (regression) ROC, made it possible to fully automatically monitor the steering rod using a single sensor and a simple algorithm that does not require knowledge of physical variables, for example, the axial load on the steering rod. It is shown that with an increase in damage and an increase in the modal attenuation coefficient from =5/8 to =8/10, the graphs of the ROC curve pass through the upper left corner, where the proportion of truly positive results is 100%, and the predictive ability of the model increases, especially when the results are presented with a moving average.