Development of features for blade rubbing defect classification in machine learning

被引：1

作者：

Park, Dong Hee ^{[1
]}

Lee, Jeong Jun ^{[1
]}

Cheong, Deok Yeong ^{[1
]}

Eom, Ye Jun ^{[1
]}

Kim, Seon Hwa ^{[2
]}

Choi, Byeong Keun ^{[1
]}

机构：

[1] Gyeongsang Natl Univ, Dept Energy & Mech Engn, 2 Tongyeonghaean Ro, Tongyeong Si 53064, South Korea

[2] Korea Energy Technol Grp, 17 Techno,4 Ro, Daejeon, South Korea

来源：

JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY | 2024年 / 38卷 / 01期

关键词：

Condition diagnosis; Fault feature; Phase of vibration; Machine learning; Condition monitoring; Fault detection; Blade rubbing; PREVENTIVE MAINTENANCE; VIBRATION;

D O I：

10.1007/s12206-023-1201-3

中图分类号：

TH [机械、仪表工业];

学科分类号：

0802 ;

摘要：

This study has developed new features necessary for condition monitoring and diagnosis of rotating machinery. These features are developed using the phase change of vibration signal, which is characteristic of blade rubbing fault. These developed features are intended to identify the fault's correct condition and severity of the rotating machinery. The difference between normal and blade rubbing fault was compared through experiments. The experimental model was produced to simulate a blade rubbing fault. The data were acquired through the experimental model and calculated using the developed features. Fault detection was confirmed by using genetic algorithm and machine learning that failure detection was possible using the developed features, it is expected that such study can evaluate the health of the rotating machinery.

引用

页码：1 / 9

页数：9

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