| 제목 |
음향신호를 이용한 모터 기어 박스 모듈의 CNN기반 2단계 불량 검출 기법 |
| 분야 |
진동ㆍ소음 |
| 언어 |
Korean |
| 저자 |
최건영(에스엠알오토모티브모듈코리아), 장일식(서울과학기술대학교), 이영화(서울과학기술대학교), 강현석(서울과학기술대학교), 박구만(서울과학기술대학교) |
| Key Words |
Fault detection(불량진단), CNN(합성곱 신경망), Audio classification(오디오 분류), Mel-spectrogram(멜 스펙트로그램), Motor gear box(모터 기어 박스) |
| 초록 |
A motor gear box is used to control tilting of the mirror and folding of the side mirror wings in a vehicle. Gear box fault detection in the production line is very important because faulty production returns considerable cost when undertaking vehicle maintenance. Fault detection based on acoustic sounds is widely used because it is simple and efficient. Sound-based method offers the advantage of non-destructive inspection, but it also provides limited classification performance. In this article, we propose a two-stage classification algorithm based on CNN. This method detects anomaly in the first stage, then subsequently classifies the faulty class. Mel-spectrogram is used to extract the features of the acoustic motor sounds. Since the proposed method classified the types of defects after determining faulty components, it is expected not only to improve accuracy, but also to reduce the time required to figure out the cause of failure. |
| 원문(PDF) |
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