• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 1997.10a
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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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• v.32 no.1
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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.

• The Journal of the Acoustical Society of Korea
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• v.38 no.3
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• pp.363-369
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• 2019

Various studies have been conducted for the diagnosis of noise condition of complex rotary machines. In this study, diagnosis index using vibration signal is developed for the efficient and objective assessment of noise condition of a blower motor. The noise most commonly caused by the abnormal blower motor are Tick/Click noise and Tone noise. According to cause and noise characteristics, time-frequency analysis is used to diagnose Tick/Click noise, and smoothing in frequency domain is used to diagnose tone noise condition. The noise condition of the blower motors were diagnosed using the developed index and these results are compared with the diagnostic results by the experts. As a result, the agreement rate was about 95 %.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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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.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.26 no.8
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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.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.9
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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.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.769-770
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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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)

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.21 no.11
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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.

• Journal of Information Processing Systems
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• v.16 no.3
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• pp.530-540
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• 2020

In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.