期刊
JOURNAL OF MECHANICAL SCIENCE AND TECHNOLOGY
卷 28, 期 8, 页码 2947-2952出版社
KOREAN SOC MECHANICAL ENGINEERS
DOI: 10.1007/s12206-014-0701-6
关键词
Condition monitoring; Fault diagnosis; Rotating machine; Support vector machine (SVM); Thermal image
资金
- National Research Foundation of Korea [31Z20130012971, 21A20131712486] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
Feature-based classification techniques consist of data acquisition, preprocessing, feature representation, feature calculation, feature selection, and classifiers. They are useful for online, real-time condition monitoring and fault diagnosis / features, which are now available with the development of information technologies and various measurement techniques. In this paper, an intelligent feature-based fault diagnosis is suggested, developed, and compared with vibration signals and thermal images. Fault diagnosis is performed using thermal imaging along with support vector machine (SVM) classification to simulate machinery faults, resulting in an accuracy level comparable to vibration signals. The observed results show that fault diagnosis using thermal images for rotating machines can be applied to industrial areas as a novel intelligent fault diagnostic method with plausible accuracy. It can be also proposed as a unique non-contact method to analyze rotating systems in mass production lines within a short time.
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