Motor Fault Diagnosis Based on Scale Invariant Image Features
Zhuo Long, Xiaofei Zhang, Min He, Shoudao Huang, Guojun Qin, Dianyi Song, Yao Tang, Gongping Wu, Weizhi Liang, Haidong Shao
Abstract
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.