Journal of the Korean Society of Manufacturing Process Engineers (한국기계가공학회지)

The Korean Society of Manufacturing Process Engineers (한국기계가공학회)
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Classification of Inverter Failure by Using Big Data and Machine Learning

빅데이터와 머신러닝 기반의 인버터 고장 분류

  • 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)
  • 김민섭 (금오공과대학교 기계공학과 항공기계전자융합공학전공) ;
  • ;
  • 허장욱 (금오공과대학교 기계공학과 항공기계전자융합공학전공)
  • Received : 2020.07.20
  • Accepted : 2020.12.14
  • Published : 2021.03.31
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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.

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References

  1. Choi, H., Kim, T. K., Heo, G. R., Hur, J. W. and Choi, S. D., "A Study on the Prediction of Failure of Fuel Pump Based on Big Data and Machine Learning", Journal of the Korean Society of Manufacturing Process Engineers, Vol. 2019, No. 4, pp. 231-231, 2019.
  2. Yoon, Y. H., "Smart Monitoring System for Improving Solar Power System Efficiency", The Journal of the Institute of Internet, Broadcasting and Communication, Vol. 19, No. 1, pp. 219-224, 2019. https://doi.org/10.7236/JIIBC.2019.19.1.219
  3. Lee, S. T., Cho, J. H. and Kim, D. K., "Development Trends in Electric Vehicle Driving Systems", Journal of the Korean Institute of Power Electronics, Vol. 16, No. 2, pp. 32-37, 2013.
  4. Min, J. K., Ra, B. H. and Huang, U. S., "Failure Mode Design of PV Inverter Accoding to Grid Conditions", Journal of the The Korean Institute of Power Electronics, Vol. 2016, No. 7, pp. 24-25, 2016.
  5. Sun, Z., Xing, W. and Guo, W., "A Survey on Dimension Reduction Algorithms in Big Data Visualization", Cloud Computing, Smart Grid and Innovative Frontiers in Telecommunications, pp. 375-395, 2019.
  6. Song, W., Wang, L., Liu, P., "Improved T-SNE Based Maniford Dimensional Reduction for Remote Sensing Data Processing", Multimedia Tools and Applications, Vol. 78, No. 4, pp. 4311-4326, 2019 https://doi.org/10.1007/s11042-018-5715-0
  7. Shifat, T. A. and Hur, J. W., "An Improved Stator Winding Short-circuit Fault Diagnosis using AdaBoost Algorithm", In 2020 International Conference on Artificial Intelligence in Information and Communication(ICAIIC), pp. 382-387, 2020.
  8. Shin, K. S and Baek, J. G., "Machine System Failure Prediction using Multivariate State Estimation Technique(MSET) and Real Time Contrast(RTC)", Journal of the Korean Institute of Industrial Engineers, Vol. 2017, No. 11, pp. 2203-2208, 2017.
  9. Shifat T. A. and Hur, J. W., "EEMD assisted supervised learning for the fault diagnosis of BLDM motor using vibration signal", Renewable Energy, pp. 3981-3990, 2020.