Back to Search
Start Over
Motor Fault Diagnosis Using Image Visual Information and Bag of Words Model
- Source :
- IEEE Sensors Journal. 21:21798-21807
- Publication Year :
- 2021
- Publisher :
- Institute of Electrical and Electronics Engineers (IEEE), 2021.
-
Abstract
- In order to solve the tough issue that is difficult to extract the representative fault features of the hybrid vibration signals from the various industrial applications, a motor fault diagnosis method based on image visual information and bag of words model (BoW) are proposed in this paper. Based on the redundancy information of raw signals, the mapping which is between the original fault signal and the visual information is obtained by the methods of SDP (Symmetrized Dot Pattern) image and dense SIFT (Scale Invariant Feature Transform) features. The bag of visual word (BoVW) and pyramid histogram intersection kernel support vector machine (PHIK-SVM) are applying to complete the fault diagnosis and state recognition of related motors. The test results reveal that the diagnostic accuracy of four fault states could reach 99.50%, moreover, the diagnostic accuracy of different operating conditions under each fault could reach 91.83% as well. Compared with the traditional methods, the proposed method is able to obtain the detailed characteristics of various motors from the perspective of visual knowledge, furthermore, it is more robust and without complex data processing.
- Subjects :
- Computer science
business.industry
Feature extraction
Scale-invariant feature transform
Pattern recognition
Fault (power engineering)
Support vector machine
Kernel (image processing)
Bag-of-words model
Pyramid (image processing)
Visual Word
Artificial intelligence
Electrical and Electronic Engineering
business
Instrumentation
Subjects
Details
- ISSN :
- 23799153 and 1530437X
- Volume :
- 21
- Database :
- OpenAIRE
- Journal :
- IEEE Sensors Journal
- Accession number :
- edsair.doi...........6e03d50f354e6e37d4fe6fdcee42644f