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http://dx.doi.org/10.14775/ksmpe.2021.20.03.001

Classification of Inverter Failure by Using Big Data and Machine Learning  

Kim, Min-Seop (Department of Mechanical Engineering, Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology)
Shifat, Tanvir Alam (Department of Mechanical Engineering, Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology)
Hur, Jang-Wook (Department of Mechanical Engineering, Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology)
Publication Information
Journal of the Korean Society of Manufacturing Process Engineers / v.20, no.3, 2021 , pp. 1-7 More about this Journal
Abstract
With the advent of industry 4.0, big data and machine learning techniques are being widely adopted in the maintenance domain. Inverters are widely used in many engineering applications. However, overloading and complex operation conditions may lead to various failures in inverters. In this study, failure mode effect analysis was performed on inverters and voltages collected to investigate the over-voltage effect on capacitors. Several features were extracted from the collected sensor data, which indicated the health state of the inverter. Based on this correlation, the best features were selected for classification. Moreover, random forest classifiers were used to classify the healthy and faulty states of inverters. Different performance metrics were computed, and the classifiers' performance was evaluated in terms of various health features.
Keywords
Failure Classification; Machine Learning; Inverter; Big Data;
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