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Jong-Duk Son(손종덕),Bo-Suk Yang(양보석),A.C.C. Tan,J. Mathew 한국동력기계공학회 2004 한국동력기계공학회 학술대회 논문집 Vol.- No.-
많은 기계에서 전달 매체에 의한 원래 신호의 감쇠와 노이즈로 인해 결함을 미리 발견하기 어렵다. 적응 필터와 고차 통계를 이용한 진보된 신호처리기술이 기계 표면에서 측정된 신호로부터 원래의 신호를 추출하는데 사용되었다. 이 논문에서는 고유벡터 알고리듬을 이용한 Blind Deconvolution 기술이 측정된 신호로부터 결함 베어링 신호를 복원하는데 이용되었다. 결함 신호는 다양한 노이즈를 포함하고 있다. 신호와 노이즈 비율은 초기 신호를 발견하는데 이 기술의 효율을 결정하고 필터의 최적의 길이를 결정하는데 이용되었다. 결과로부터 이 기술이 0.21과 같이 낮은 신호 대 노이즈 비율에서 원래의 신호를 결정하는데 효과적이라는 나타낸다. 그러나 상대적으로 큰 필터 길이가 요구된다.
MEMS 가속도계 기반의 기계 상태감시용 스마트센서 개발
손종덕(Son, Jong-Duk),심민찬(Shim, Min-Chan),양보석(Yang, Bo-Suk) 한국소음진동공학회 2008 한국소음진동공학회 논문집 Vol.18 No.8
Many industrial operations require continuous or nearly-continuous operation of machines, interruption of which can result in significant cost loss. The condition monitoring of these machines has received considerable attentions in recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called 'smart' sensors which are capable of wireless communication with a remote site. The purpose of this research is to develop a new type of smart sensor for on-line condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This system is capable for signal preprocessing task and analog to digital converter which is controlled by CPU. This sensor communicates with a remote site PC using TCP/IP protocols. The developed sensor executes performance tests for data acquisition accuracy estimations.
손종덕(Jong-Duk Son),양보석(Bo-Suk Yang),A.C.C Tan(A.C.C. Tan),J.Mathew(J. Mathew) 대한기계학회 2004 대한기계학회 춘추학술대회 Vol.2004 No.11
Many machine failures are not detected well in advance due to the masking of background noise and attenuation of the source signal through the transmission mediums. Advanced signal processing techniques using adaptive filters and higher order statistics have been attempted to extract the source signal from the measured data at the machine surface. In this paper, blind deconvolution using the eigenvector algorithm (EVA) technique is used to recover a damaged bearing signal using only the measured signal at the machine surface. A damaged bearing signal corrupted by noise with varying signal-to-noise (s/n) was used to determine the effectiveness of the technique in detecting an incipient signal and the optimum choice of filter length. The results show that the technique is effective in detecting the source signal with an s/n ratio as low as 0.21, but requires a relatively large filter length.
손종덕(Jong Duk Son),뉴강(Gang Niu),양보석(Bo Suk Yang),황돈하(Don Ha Hwang),강동식(Dong Sik Kang) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.10
Fault detection and diagnosis is the most important in condition-based maintenance (CBM) in industry system that usually begins from collecting signatures of running machines using multiple sensors for subsequent accurate analysis. With the quick development in industry, there is an increasing requirement of selecting special sensors that are cheap, robust, and easy-installation. This paper experimentally investigated performances of four types of sensors used in induction motors faults diagnosis, which are vibration, current, voltage and flux. In addition, Diagnostic effects of five popular classifiers also were evaluated. First, the raw signals from the four types of sensors are collected at the same time. Then the features are calculated from collected signals. Next, these features are classified through five classifiers using artificial intelligence (AI) techniques. Finally, conclusions are given based on the experiment results.
다중 센서를 이용한 유도전동기의 결함진단 분류기의 성능 평가
손종덕(Jong Duk Son),뉴강(Gang Niu),양보석(Bo Suk Yang),황돈하(Don Ha Hwang),강동식(Dong Sik Kang) 대한기계학회 2006 대한기계학회 춘추학술대회 Vol.2006 No.11
Fault detection and diagnosis is the most important in condition-based maintenance (CBM) in industry system that usually begins from collecting signatures of running machines using multiple sensors for subsequent accurate analysis. With the quick development in industry, there is an increasing requirement of selecting special sensors that are cheap, robust, and easy-installation. This paper experimentally investigated performances of four types of sensors used in induction motors faults diagnosis, which are vibration, current, voltage and flux. In addition, Diagnostic effects of five popular classifiers also were evaluated. First, the raw signals from the four types of sensors are collected at the same time. Then the features are calculated from collected signals. Next, these features are classified through five classifiers using artificial intelligence (AI) techniques. Finally, conclusions are given based on the experiment results.