Predictive Maintenance Framework for Fault Detection in Remote Terminal Units

被引:1
|
作者
Lekidis, Alexios [1 ]
Georgakis, Angelos [2 ]
Dalamagkas, Christos [2 ]
Papageorgiou, Elpiniki I. [1 ]
机构
[1] Univ Thessaly, Dept Energy Syst, Gaiopolis Campus, Larisa 41500, Greece
[2] Publ Power Corp, Chalkokondili 22, Athens 10432, Greece
来源
FORECASTING | 2024年 / 6卷 / 02期
基金
欧盟地平线“2020”;
关键词
predictive maintenance; remote terminal unit; time-series forecasting; anomaly detection; DIGITAL-TWIN; ANOMALY DETECTION; CHALLENGES;
D O I
10.3390/forecast6020014
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
The scheduled maintenance of industrial equipment is usually performed with a low frequency, as it usually leads to unpredicted downtime in business operations. Nevertheless, this confers a risk of failure in individual modules of the equipment, which may diminish its performance or even lead to its breakdown, rendering it non-operational. Lately, predictive maintenance methods have been considered for industrial systems, such as power generation stations, as a proactive measure for preventing failures. Such methods use data gathered from industrial equipment and Machine Learning (ML) algorithms to identify data patterns that indicate anomalies and may lead to potential failures. However, industrial equipment exhibits specific behavior and interactions that originate from its configuration from the manufacturer and the system that is installed, which constitutes a great challenge for the effectiveness of ML model maintenance and failure predictions. In this article, we propose a novel method for tackling this challenge based on the development of a digital twin for industrial equipment known as a Remote Terminal Unit (RTU). RTUs are used in electrical systems to provide the remote monitoring and control of critical equipment, such as power generators. The method is applied in an RTU that is connected to a real power generator within a Public Power Corporation (PPC) facility, where operational anomalies are forecasted based on measurements of its processing power, operating temperature, voltage, and storage memory.
引用
收藏
页码:239 / 265
页数:27
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