잔차 합성곱 신경망을 통한 산업용 로봇 기어박스의 동작 적응형 퓨샷 고장 감지 방법

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 윤병동.Nowadays, industrial robots are indispensable equipment for automated manufacturing processes because they can perform repetitive tasks with consistent precision and accuracy. However, when faults occur in the industrial robot, it can lead to the unexpected shutdown of the production line, which brings significant economic losses, so the fault detection is important. The gearbox, one of the main drivetrain components of an industrial robot, is often subjected to high torque loads, and faults occur frequently. When faults occur in the gearbox, the amplitude and frequency of the torque signal are modulated, which leads to changes in the characteristics of the torque signal. Although several previous studies have proposed fault detection methods for industrial robots using torque signals, it is still a challenge to extract fault-related features under various environmental and operating conditions and to detect faults in the complex motions used in industrial sites To overcome such difficulties, in this paper, we propose a novel motion-adaptive few-shot (MAFS) fault detection method of industrial robot gearboxes using torque ripples via a one-dimensional (1D) residual-convolutional neural network (Res-CNN) and binary-supervised domain adaptation (BSDA). The overall procedure of the proposed method is as follows. First, applying the moving average filtering to the torque signal to extract the data trend, and the torque ripples of the high-frequency band are obtained as a residual value between the original signal and the filtered signal. Second, classifying the state of pre-processed torque ripples under various operating and environmental conditions. It is shown that Res-CNN network 1) distinguishes small differences between normal and fault torque ripples effectively, and 2) focuses on important regions of the input data by the attention effect. Third, after constructing the Siamese network with a pre-trained network in the source domain, which consisted of simple motions, detecting the faults on the target domain, which consisted of complex motions through BSDA. As a result, 1) the similarities of the jointly shared physical mechanisms of torque ripples between simple and complex motions are learned, and 2) faults of the gearbox are adaptively detected while the industrial robot executes complex motions. The proposed method showed the most superior accuracy over other deep learning-based methods in few-shot conditions where only one cycle of each normal and fault data of complex motions is available. In addition, the transferable regions on the torque ripples after domain adaptation was highlighted using 1D guided grad-CAM. The effectiveness of the proposed method was validated with experimental data of multi-axial welding motions in constant and transient speed, which are commonly executed in real-industrial fields such as the automobile manufacturing line. Furthermore, it is expected that the proposed method is applicable to other types of motions, such as inspection, painting, assembly, and so on. The source code is available on my GitHub page of https://github.com/oyt9306/MAFS.Chapter 1. Introduction 1 1.1 Research Motivation 1 1.2 Scope of Research 4 1.3 Thesis Layout 5 Chapter 2. Research Backgrounds 6 2.1 Interpretations of Torque Ripples 6 2.1.1. Causes of torque ripples 6 2.1.1. Modulations on torque ripples due to gearbox faults 8 2.2 Architectures of Res-CNN 11 2.2.1 Convolutional Operation 11 2.2.2 Pooling Operation 12 2.2.3 Activation 13 2.2.4 Batch Normalization 13 2.2.5 Residual Learning 15 2.3 Domain Adaptation (DA) 17 2.3.1 Few-shot domain adaptation 18 Chapter 3. Motion-Adaptive Few-Shot (MAFS) Fault Detection Method 20 3.1 Pre-processing 23 3.2 Network Pre-training 28 3.3 Binary-Supervised Domain Adaptation (BSDA) 31 Chapter 4. Experimental Validations 37 4.1 Experimental Settings 37 4.2 Pre-trained Network Generation 40 4.3 Motion-Adaptation with Few-Shot Learning 43 Chapter 5. Conclusion and Future Work 52 5.1 Conclusion 52 5.2 Contribution 52 5.3 Future Work 54 Bibliography 55 Appendix A. 1D Guided Grad-CAM 60 국문 초록 62Maste