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http://dx.doi.org/10.7734/COSEIK.2021.34.1.25

Fault Classification Model Based on Time Domain Feature Extraction of Vibration Data  

Kim, Seung-il (School of Mechanical Engineering, Pusan National University)
Noh, Yoojeong (School of Mechanical Engineering, Pusan National University)
Kang, Young-jin (Research Institute of Mechanical Engineering, Pusan National University)
Park, Sunhwa (H&A Research Center, LG Electronics)
Ahn, Byungha (H&A Research Center, LG Electronics)
Publication Information
Journal of the Computational Structural Engineering Institute of Korea / v.34, no.1, 2021 , pp. 25-33 More about this Journal
Abstract
With the development of machine learning techniques, various types of data such as vibration, temperature, and flow rate can be used to detect and diagnose abnormalities in machine conditions. In particular, in the field of the state monitoring of rotating machines, the fault diagnosis of machines using vibration data has long been carried out, and the methods are also very diverse. In this study, an experiment was conducted to collect vibration data from normal and abnormal compressors by installing accelerometers directly on rotary compressors used in household air conditioners. Data segmentation was performed to solve the data shortage problem, and the main features for the fault classification model were extracted through the chi-square test after statistical and physical features were extracted from the vibration data in the time domain. The support vector machine (SVM) model was developed to classify the normal or abnormal conditions of compressors and improve the classification accuracy through the hyperparameter optimization of the SVM.
Keywords
fault diagnosis; twin rotary compressor; health index; intersection area; data augmentation; support vector machine (SVM);
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