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Scalogram과 Switchable 정규화 기반 합성곱 신경망을 활용한 베이링 결함 탐지

Scalogram and Switchable Normalization CNN(SN-CNN) Based Bearing Falut Detection

김윤수|석종원|미아그마르 델레그마
한국전기전자학회 (Institute of Korean Electrical and Electronics Engineers)|2022.06|전기전자학회논문지 (Journal of IKEEE)|제26권 제2호|pp. 319-328|DOI 10.7471/ikeee.2022.26.2.319

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키워드

Bearjng fault detecaondeep learnjngC鬪U datasetswjtchable normaJjzatjonscalogramconvoluaonaJ neural network
주제분류전자/정보통신공학

초록

베어링은 기계가 작동할때 중요한 역할을 한댜 때문에, 베어링에 결함이 발생하면 기계전체의 치명적인 결함을 발생시킨다. 그러 므로 베어링 결함은 조기에 발견되어야한댜 본 논문에서는 연속 웨이불릿 변환과 Switchable 정규회를 기반으로 한 합성곱 신경망 (SN-CNN)을 이용한 방법을 베어링 결함 감지 모델에 대해 설명한다. 모델의 정확도는 Case Western Reserve University (CWRU) 베어링 데이터 집합을 사용히여 측정되었다. 또한 배치 정규희{BN, Batch Normalization)[l] 방법과 스펙트로그램 이미 지가 모델 성능의 비교를 위해 시용되었댜

Abstract

Bearing plays an important role in the operation of most machinery, Therefore, when a defect occurs in the bearing, a fatal defect throughout the machine is generated. In this reason, bearing defects should be detected early. In this paper, we describe a method using Convolutional Neural Networks (SN-CNNs) based on continuous wavelet transformations and Switchable normalization for bearing defect detection models. The accuracy of the model was measured using the Case Western Reserve University (CWRU) bearing dataset. In addition, batch normalization methods and spectrogram images are used to compare model performance. The proposed model achieved over 99% testing accuracy in CWRU dataset.

참고문헌 (37)

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