Motor Fault Diagnosis Based on Scale Invariant Image Features

被引：47

作者：

Long, Zhuo ^{[1
]}

Zhang, Xiaofei ^{[1
]}

He, Min ^{[1
]}

Huang, Shoudao ^{[1
]}

Qin, Guojun ^{[1
]}

Song, Dianyi ^{[2
]}

Tang, Yao ^{[1
]}

Wu, Gongping ^{[1
]}

Liang, Weizhi ^{[1
]}

Shao, Haidong ^{[3
]}

机构：

[1] Hunan Univ, Coll Elect & Informat Engn, Changsha 410082, Hunan, Peoples R China

[2] Natl Univ Def Technol, Changsha 410073, Peoples R China

[3] Hunan Univ, Coll Mech & Vehicle Engn, Changsha 410082, Hunan, Peoples R China

来源：

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS | 2022年 / 18卷 / 03期

基金：

美国国家科学基金会;

关键词：

Feature extraction; Dictionaries; Fault diagnosis; Time-frequency analysis; Visualization; Data mining; Transforms; Motor fault diagnosis; scale-invariant feature transform (SIFT); symmetrized dot pattern (SDP); visual knowledge;

D O I：

10.1109/TII.2021.3084615

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Traditional fault diagnosis methods are easy to be affected by different working conditions. This article proposed a motor fault diagnosis method based on visual knowledge, to reduce the impact of changes in working conditions and improve the feature extraction ability. The mapping relationship between actual faults and image intuitive features by symmetrized dot pattern and scale-invariant feature transform is established in this article. The fault state is obtained by statistics of the matching point with the dictionary templates generated from signals of normal and unnormal motors. Compared with other machine learning algorithms, this method does not need too much data training and learning. The efficiency of this method is validated by experiments, and the data image processing technology has great industrial application value in the field of motor fault detection or monitoring in the age of intelligence.

引用

页码：1605 / 1617

页数：13

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