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2020 Vol.39, Issue 4 Preview Page

Research Article

A study on the fault diagnosis of rotating machine by machine learning

기계학습을 적용한 회전체 고장진단에 관한 연구

Hang-Kyu Jeon1
,
Ji-Sun Kim1
,
Bong-Ju Kim1
,
Won-Jin Kim12∗
전 항규1
,
김 지선1
,
김 봉주1
,
김 원진12∗
1계명대학교 기계공학과
2계명대학교 기계자동차공학과
∗Corresponding Author
31 July 2020. pp. 263-269
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Abstract

In this study, a rotating machine that can reproduce normal condition and 8 fault conditions were produced, and vibration data was acquired. Feature is calculated from the acquired data, and accuracy is analyzed through fault diagnosis using artificial neural networks and genetic algorithms. In order to achieve optimal timing and higher accuracy, features by three domains were applied to the fault diagnosis. The learning number was selected as a setting variable. As a result of the rotating machine fault diagnosis, high precision was found in the frequency domain than in others, and precise fault diagnoses were accomplished through all of 10 operations, at the learning number of 5000 and 8000. Given the efficiency of time, it was estimated to be the most efficient when the number of learning was 5000.

Keywords
Rotating machine
Machine learning
Artificial neural network
Genetic algorithm
Fault diagnosis

본 논문에서는 정상상태와 8가지의 고장이 재현가능한 회전체를 제작하고 진동 데이터를 취득하였다. 취득한 데이터로 특징을 계산하여 인공신경망과 유전알고리즘을 적용한 고장진단을 통해 정확성을 분석한다. 최적의 시간과 높은 정확성의 구현을 위해 특징을 3가지 영역으로 구분하여 고장진단에 적용하였다. 설정변수는 학습수로 설정하였다. 회전체 고장진단의 결과는 다른 영역보다 주파수영역에서 높은 정확성을 보였으며 학습수 5000, 8000회에서 10회의 구동 모두 정확한 고장진단을 하였다. 시간의 효율성을 고려하였을 경우, 학습수가 5000회일 때 가장 우수하다고 판단하였다.

키워드
회전체
기계학습
인공신경망
유전알고리즘
고장진단
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Information
  • Publisher :The Acoustical Society of Korea
  • Publisher(Ko) :한국음향학회
  • Journal Title :The Journal of the Acoustical Society of Korea
  • Journal Title(Ko) :한국음향학회지
  • Volume : 39
  • No :4
  • Pages :263-269
  • Received Date : 2020-05-28
  • Revised Date : 2020-06-25
  • Accepted Date : 2020-07-16
  • DOI :https://doi.org/10.7776/ASK.2020.39.4.263
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