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정준하(Joonha Jung),전병철(Byungchul Jeon),윤병동(Byeng D. Youn),김연환(Yeon-Whan Kim),배용채(Yong-Chae Bae) 대한기계학회 2014 대한기계학회 춘추학술대회 Vol.2014 No.11
Data-driven diagnosis systems for journal bearings frequently use vibration data acquired from sensors. However, a small disruption in the system leads to perturbation in the rotating speed, which results in unstable vibration signal. In this research, a datum unit for feature extraction is proposed to embrace a sudden change in vibration data. It presents the angular resampling method, common for ball bearing systems but not for journal bearing systems, and definition of datum for extracting features. The proposed approach enables the raw vibration data to be processed consistently despite the variations in signals, and defines effective cycles for extr acting features. The data is acquired from analytic signal from a calculation program as well as experiment data from journal bearing test-bed. Overall, the increased separability measure values shows that proposed idea is effective and the improved classification result adds reliability in diagnosis system for journal bearings.
저널베어링 상태 진단을 위한 최적의 데이터 분석 기준 설정
윤병동(Byeng D. Youn),정준하(Joonha Jung),전병철(Byungchul Jeon),김연환(Yeon-Whan Kim),배용채(Yong-Chae Bae) 한국소음진동공학회 2014 한국소음진동공학회 학술대회논문집 Vol.2014 No.10
Data-driven method for fault diagnostics system often use machine learning technique. To use such technique proper signal processing should be implemented such as time synchronous averaging (TSA) for ball bearing systems. However, for journal bearing diagnostics systems not much has been researched, and yet a proper signal processing method has not been studied. Therefore, in this research an optimal datum unit for a reliable journal bearing diagnostics system along with angular resampling process is being suggested. Before extracting time and frequency domain features, angular resampling is applied to each cycle of vibration data. As to preserve the characteristics of vibration signal, averaging method is replaced by finding the optimal datum unit which strengthens statistical characteristics of vibration signal. Then 20 features were extracted for various cases, and those features are being evaluated by two criteria, separability and classification accuracy.
전병철(Byungchul Jeon),정준하(Joonha Jung),윤병동(Byeng D. Youn),김연환(Yeon-Whan Kim),배용채(Yong-Chae Bae) 대한기계학회 2015 大韓機械學會論文集A Vol.39 No.8
저널베어링은 회전하는 축과 베어링 지지부 사이에 유막을 형성하여 회전체를 지지하는 구조물이며, 고속 및 고하중 조건에서도 안정적이기 때문에 발전소와 같은 대형 시스템에 널리 사용되고 있다. 본 연구에서는 저널베어링 시스템의 신뢰성을 확보하기 위한 감독학습 기반의 상태진단 알고리즘을 연구하였다. 기존에는 진동신호 특성인자들의 정의에 대한 연구가 주로 진행되었으나, 본 연구에서는 정의된 특성인자의 추출단위인 데이텀의 적용 기준에 대한 연구가 수행되었다. 데이텀의 효용성 평가를 통해 저널베어링 회전체 특성인자의 추출기준은 시간영역에서 1 회전, 주파수영역에서 60 회전 기준이 타당하다는 결론을 도출하였다. Journal bearings support rotors using fluid film between the rotor and the stator. Generally, journal bearings are used in large rotor systems such as turbines in a power plant, because even in high-speed and load conditions, journal bearing systems run in a stable condition. To enhance the reliability of journal-bearing systems, in this paper, we study health-diagnosis algorithms that are based on the supervised learning method. Specifically, this paper focused on defining the unit of features, while other previous papers have focused on defining various features of vibration signals. We evaluate the features of various lengths or units on the separable ability basis. From our results, we find that one cycle datum in the time-domain and 60 cycle datum in the frequency domain are the optimal datum units for real-time journal-bearing diagnosis systems.