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RISS 인기검색어
- 검색키워드
- 저자 : Yunhan Kim(김윤한) (검색결과 14 건)
- 무료
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포락선 평균법을 활용한 향상된 복조 분석 기반 유성기어박스 고장 진단 방법
김윤한(Yunhan Kim),박정호(Jungho Park),나규민(Kyumin Na),윤병동(Byeng D. Youn) 대한기계학회 2019 대한기계학회 춘추학술대회 Vol.2019 No.11
We propose envelope averaging for fault diagnostics of planetary gearboxes. The proposed method improves the performance of the previous demodulation analysis. The previous demodulation analysis requires the decomposition to obtain the mono-component signals. However, the previous demodulation methods are applied in the time domain. In the proposed method, we use bandpass filter bank to decompose the signals in the frequency domain because a gear fault produces the modulation sideband around the gear mesh frequency (GMF) and its harmonics. Then, we use a Hilbert transform (HT) to demodulate the selected signals based on the power spectrum entropy. Finally, we average the envelope from HT to enhance the fault-related signals. To demonstrate our proposed method, we use the vibration signals from a real testbed. From the results, we conclude that the proposed envelope averaging shows better performance than the previous demodulation analysis for fault diagnostics of planetary gearboxes.
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다양한 산업용 로봇 작동조건에 따른 강건 고장 진단 기법
김윤한(Yunhan Kim),박정호(Jungho Park),하종문(Jong Moon Ha),윤병동(Byeng Dong Youn),박진균(Jin-Gyun Park) 대한기계학회 2017 대한기계학회 춘추학술대회 Vol.2017 No.11
Industrial robots are the crucial system for manufacturing automation and smart factory. However, the unexpected failures of the industrial robots cause a large amount of downtime and loss in the production line. Thus, condition monitoring system for the industrial robots receives considerable attention in recent years. This paper presents fault detection methods of an industrial robot. For robust fault detection under various operating conditions, two methods are developed based on acceleration signal and control signal. The performance of the proposed methods is demonstrated using a 6-DOF industrial robot with normal and faulty cases under various operating conditions. The method based on acceleration signal shows the robust performance in terms of rotating speed, loading condition and different postures of robot arms.
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위상 정보 활용 시간 영역 평균법을 이용한 산업용 로봇 고장 진단
김윤한(Yunhan Kim),박정호(Jungho Park),나규민(Kyumin Na),윤병동(Byeng D. Youn) 대한기계학회 2018 대한기계학회 춘추학술대회 Vol.2018 No.12
Recently, industrial robots play the crucial roles in the smart factory. However, the unexpected failure of the industrial robots causes a large amount of downtime and economic loss. Therefore, it is required to develop the fault detection method for the industrial robots. In this study, we focus on the mechanical fault from the gearbox, which works as the reducer in the industrial robots. Vibration signals are effectively used for detecting the fault on the gearbox. Vibration signals from the gearbox have strong deterministic signals due to the gear meshing. Therefore, the deterministic signals can be removed for improving the sensitivity of fault detection. Time domain average (TDA) is a method to obtain the deterministic signals from the gearbox. However, the signals from the gearbox in the industrial robots are not usually synchronized and the result of TDA is erroneous. To solve this problem, we propose TDA with phase. The phase is obtained from Fourier transform. The proposed method is demonstrated with the industrial robot.
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비정상 속도 조건에서의 유성기어 고장 진단을 위한 조인트 분해 분석법
비카스 샤마(Vikas Sharma),김윤한(Yunhan Kim),박정호(Jungho Park),윤병동(Byeng D. Youn) 대한기계학회 2018 대한기계학회 춘추학술대회 Vol.2018 No.12
The vibration signals acquired from the planetary gearboxes are multi-component, amplitude modulated and require more attention to diagnose the localized gear tooth faults. Under nonstationary operating conditions, fault diagnosis of gear becomes more challenging. In this manuscript, a decomposing approach has been proposed to detect the localized gear tooth faults on the basis of oscillations generated by a gear system. The proposed fault diagnosis approach involves joint decomposition using tunable Q-factor wavelet transform (TQWT) and empirical mode decomposition (EMD). The proposed approach has been implemented to detect the planet gear tooth faults from experimentally acquired vibration signals. Adaptive decomposition by both TQWT and EMD has shown the possible extraction of oscillatory tooth vibrations from the raw vibrations. Further, FFT plots of intrinsic mode functions have also revealed the increased.
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