• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 1997년도 추계학술대회논문집; 한국과학기술회관; 6 Nov. 1997
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• pp.210-215
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• 1997

Safety diagnosis on the driving system of electric train is performed using the vibration and noise signals of running railway train. Safety diagnosis is tried on the viewpoints of the appreciation of superannuation and the fault diagnosis of motor, reduction gear and boggie. The appreciation of superannuation is checked by the rms vibration levels of driving parts and the fault diagnosis is done by analyzing the frequencies of the vibration signals. The methods of measuring and analyzing the signals are decided on the basis of field 1-measured signals. The results shows that the vibration levels of each parts increase as the train goes older and each parts have their own frequency patterns of the vibration. As the results, the vibration and noise can be utilized successfully for the safety diagnosis of the driving part of electric train.

• Journal of Advanced Marine Engineering and Technology
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• 제32권1호
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• pp.42-49
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• 2008

Most of vessels are not evaluated for their vibration and noise effects to human body. The vibration and noise generated by engine and auxiliary machine in vessel is a negative element for seamen. Therefore, in this paper, the diagnosis and evaluation of vibration and noise from vessel is accomplished by a shipbuilding corporation. The vibration and noise transferred from engine room and auxiliary machine was measured during the steady-state operation, and the vibration and noise map of vessel was made. Also, in order to evaluate the ship environment for human, the diagnosis is carried out on the base of measurement results.

• 한국음향학회지
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• 제38권3호
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• pp.363-369
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• 2019

복잡한 회전 기계의 소음 상태 진단을 위한 다양한 연구가 수행되고 있다. 본 연구에서는 진동신호를 이용하여 블로워 모터의 효율적이고 객관적인 소음 상태진단을 위한 지수를 개발하였다. 블로워 모터의 이상 시 가장 흔히 나타나는 소음으로 틱/클릭 소음과 톤 소음이 있다. 발생 원인과 소음 특성에 따라 틱/클릭 소음의 상태 진단에는 시간-주파수 분석법을 그리고 톤 소음 상태 진단에는 주파수 영역에서의 평활화 기법을 이용하였다. 개발한 지수를 이용하여 블로워 모터의 소음 상태 진단을 수행하고 이를 전문가에 의한 진단 결과와 비교하였다. 그 결과 약 95 %의 일치율을 보였다.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1046-1050
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. If rotating machinery has fault, we can detect fault using vibration or noise. But, in diagnosing rotating machinery, the end user who doesn't have expert knowledge needs the help of vibration diagnosis expert. However, vibration diagnosis experts who well satisfy the demand of end user are rare. So, this paper propose a development of the case-based reasoning system for abnormal vibration diagnosis of rotating machinery we construct the past experiences of vibration diagnosis expert into case base and shear the experiences of diagnosis expert with the end user. In this paper, we describe that process of structured system and adapting result of abnormal vibration diagnosis of electric motor.

• 조명전기설비학회논문지
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• 제26권8호
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• pp.35-41
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• 2012

As automation equipment and electronic device progresses, the importance of power quality is more increasing. This paper represents the analysis and suppression about the causes of trouble by the inverter's ON/OFF noise in plant in order to prevent damage resulting in a secondary damage to conduct precise diagnosis and effective noise reduction. The countermeasure as a reduced carrier frequency and the LC resonant filter had been applied and confirmed the effective results to solve the trouble of noise.

• 한국소음진동공학회논문집
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• 제23권9호
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• pp.814-822
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• 2013

For the fault diagnosis of a mechanical system, pattern recognition methods have being used frequently in recent research. Hidden Markov model(HMM) and artificial neural network(ANN) are typical examples of pattern recognition methods employed for the fault diagnosis of a mechanical system. In this paper, a hybrid method that combines HMM and ANN for the fault diagnosis of a mechanical system is introduced. A rotating blade which is used for a wind turbine is employed for the fault diagnosis. Using the HMM/ANN hybrid model along with the numerical model of the rotating blade, the location and depth of a crack as well as its presence are identified. Also the effect of signal to noise ratio, crack location and crack size on the success rate of the identification is investigated.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 춘계학술대회 논문집
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• pp.769-770
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• 2012

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 춘계학술대회논문집
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• pp.39-52
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• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1114-1118
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• 2000

Impulsive sound and vibration signals in gear are often associated with their faults. Thus these impulsive sound and vibration signals can be used as indicators in the diagnosis of gear fault. The early detection of impulsive signal due to gear fault prevents from complete failure in gear. However it is often difficult to make objective measurement of impulsive signals because of background noise signals. In order to ease the detection of impulsive signals embedded in background noise, we enhance the impulsive signals using adaptive signal processing.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.