볼 베어링 고장진단 기법 비교 및 XAI Grad-CAM을 이용한 분류결과 해석 연구 (A Comparison Study of Ball Bearing Fault Diagnosis and Classification Analysis Using XAI Grad-CAM)

서지정보

· 발행기관 : 대한전기학회
· 수록지 정보 : 전기학회논문지 / 71권 / 9호 / 1315 ~ 1325페이지
· 저자명 : 김영근, 김예진, 전현직

초록

Various machine learning and deep learning methods were proposed to monitor and classify the bearing's health state using vibration signals since bearing faults are one of the most causes of failure of rotationary machine. The process of diagnosing bearing faults using machine learning is as follows. First, the features, including the fault characteristic of the vibration signals, are extracted, and these features are selected to reduce the dimension of the features. These features are input into the machine learning classifier to diagnose the system's health. In addition to machine learning methods, CNN, one of the deep learning methods, is widely used.
Since the deep learning model extracts features by itself, only the preprocessing process of converting the bearing signals into 2D is needed. The fault classification accuracy of two vibration signal transformation methods as preprocessing methods for the CNN model was compared. This paper compares the bearing fault classification performance of several machine learning commonly used and the CNN model for the lab-made wind turbine machinery testbed. By comparing different feature extraction, feature selection, and classification methods, the most appropriate pipeline is selected for the testbed. Also, grad-cam, an explainable AI(XAI) technique, is applied to interpret the CNN based classification in terms of interested frequency bandwidth. The XAI analysis was verified by designing preprocessing filters based on the grad-cam outputs for enhancing classification performance.

영어초록

Various machine learning and deep learning methods were proposed to monitor and classify the bearing's health state using vibration signals since bearing faults are one of the most causes of failure of rotationary machine. The process of diagnosing bearing faults using machine learning is as follows. First, the features, including the fault characteristic of the vibration signals, are extracted, and these features are selected to reduce the dimension of the features. These features are input into the machine learning classifier to diagnose the system's health. In addition to machine learning methods, CNN, one of the deep learning methods, is widely used.
Since the deep learning model extracts features by itself, only the preprocessing process of converting the bearing signals into 2D is needed. The fault classification accuracy of two vibration signal transformation methods as preprocessing methods for the CNN model was compared. This paper compares the bearing fault classification performance of several machine learning commonly used and the CNN model for the lab-made wind turbine machinery testbed. By comparing different feature extraction, feature selection, and classification methods, the most appropriate pipeline is selected for the testbed. Also, grad-cam, an explainable AI(XAI) technique, is applied to interpret the CNN based classification in terms of interested frequency bandwidth. The XAI analysis was verified by designing preprocessing filters based on the grad-cam outputs for enhancing classification performance.

참고자료

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