基于机器学习的数据挖掘技术在火电厂锅炉管泄漏故障检测中的应用

Kyu-Han Kim, Heung-seok Lee, Jung-Hwan Kim, Juneho Park
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引用次数: 6

摘要

蒸汽锅炉管道泄漏会降低火电厂的整体效率,最终导致电厂停运。本文提出了一种基于机器学习的火电厂锅炉管泄漏故障检测方法。首先介绍了基于聚类的故障检测方法,并对结果进行了解释。其次,提出了基于神经网络的故障检测方法。最后,将所提出的故障检测方法应用于锅炉管泄漏故障案例,验证了检测方法的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Detection of Boiler Tube Leakage Fault in a Thermal Power Plant Using Machine Learning Based Data Mining Technique
Tube leakage of steam boiler can decrease the whole efficiency of thermal power plant, and eventually cause an outage. In this paper, we propose a fault detection method based on machine learning for boiler tube leakage fault in thermal power plant. First, we introduce the clustering-based fault detection method and explain results. Second, we propose neural network based fault detection method. Finally, to verify the performance, the proposed fault detection method is applied to faults cases due to boiler tube leakage and validate effectiveness of detection.
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