Intelligent Fault Diagnosis of Robotic Strain Wave Gear Reducer Using Area-Metric-Based Sampling

Yeong Rim Noh, Salman Khalid, Heung Soo Kim () and Seung-Kyum Choi ()
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Yeong Rim Noh: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea
Salman Khalid: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea
Heung Soo Kim: Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul, 30 Pil-dong 1 Gil, Jung-gu, Seoul 04620, Republic of Korea
Seung-Kyum Choi: George W. Woodruff School of Mechanical Engineering, Georgia Institute of Technology, North Ave NW, Atlanta, GA 30332, USA

Mathematics, 2023, vol. 11, issue 19, 1-22

Abstract: The main challenge with rotating machine fault diagnosis is the condition monitoring of machines undergoing nonstationary operations. One possible way of efficiently handling this situation is to use the deep learning (DL) method. However, most DL methods have difficulties when the issue of imbalanced datasets occurs. This paper proposes a novel framework to mitigate this issue by developing an area-metric-based sampling method. In the proposed process, the new sampling scheme can identify which locations of the datasets can potentially have a high degree of surprise. The basic idea of the proposed method is whenever significant deviations from the area metrics are observed to populate more sample points. In addition, to improve the training accuracy of the DL method, the obtained sampled datasets are transformed into a continuous wavelet transform (CWT)-based scalogram representing the time–frequency component. The dilated convolutional neural network (CNN) is also introduced as a classification process with the altered images. The efficacy of the proposed method is demonstrated with fault diagnosis problems for welding robots. The obtained results are also compared with existing methods.

Keywords: industrial robot; area metric; explainable artificial intelligence (XAI); fault diagnosis; data imbalance (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
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