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Architecture & Urban Research Institute

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한국소음진동공학회|한국소음진동공학회논문집 2021년 10월

논문명 합성곱 오토인코더를 이용한체인 전동 장치의 고장 결함 감지 및 진단 / Fault Detection and Diagnosis of Chain Transmission System Using Convolutional Auto-encoder
저자명 이창훈 ; 이상권 ; 김풍일
발행사 한국소음진동공학회
수록사항 한국소음진동공학회 논문집, Vol.31 No.05 (2021-10)
페이지 시작페이지(563) 총페이지(11)
ISSN 1598-2785
주제분류 환경및설비
주제어 고장 검출; 고장 진단; 딥러닝; 합성곱 오토인코더; 비지도학습; 합성곱 신경망 ; Fault Detection; Fault Diagnosis; Deep Learning; Convolutional Auto-Encoder; Unsupervised Learning; Convolutional Neural Network
요약2 This paper presents a method to detect the mechanical faults of a chain drive power transmission system (CDPTS) using a convolutional auto-encoder (CAE). In previous research, it was known that the methods to detect faults of the CDPTS based on an artificial neural network (ANN) and convolutional neural network (CNN) were useful. In this paper, an advanced application of CNN, the CAE function of CNN is employed to detect faults. This method uses the characteristics of reconstruction of CAE. Difference of input images of the CNN and reconstructed images extracted by CAE were used as the guideline of fault detection. In the fault condition of the system, the difference was larger than the predetermined threshold of error. The encoder of CAE can be fine-tuned to classify the fault types of CDPTS. Finally, this method was well applied to diagnose the fault types of the test CDPTS installed in the laboratory.
소장처 한국소음진동공학회
언어 한국어
DOI https://doi.org/10.5050/KSNVE.2021.31.5.563