• 한국소음진동공학회논문집
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• 제20권5호
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• pp.486-491
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• 2010

In this paper, it is identified that high-level vibration of the single phase main power transformers shut down due to the mechanical fault. vibration sources of the SPR in the transformer's are identified and the SPR vibrations are reduced by structural modification. For vibration characteristic identification, vibration signals were measured with an accelerometer when the transformer is driving. These signals are presented in a FFT analysis in order to find the dependency of frequency components on the transformer. From finite element analyses and some experiments, it is also found that resonances occur because the natural frequencies of the SPR exist in usual driving range. To shift the natural frequencies outside the driving range, the SPR is modified by increasing stiffness. It is verified that considerable amounts of vibration are reduced by the structural modification.

• Journal of Electrical Engineering and Technology
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• 제13권2호
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• pp.781-789
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• 2018

This paper proposes a new approach utilizing empirical mode decomposition (EMD) reconstruction to process vibration signals of a transformer under DC bias caused by high voltage direction transmission (HVDC), which is the potential cause of additional vibration and noise from transformer. Firstly, the Calculation Method is presented and a 3D model of transformer is simulated to analyze transformer deformation characteristic and the result indicate the main vibration is produced along axial direction of three core limbs. Vibration test system has been built and test points on the core and shell of transformer have been measured. Then, the signal reconstruction method for transformer vibration based on EMD is proposed. Through the EMD decomposition, the corrupted noise can be selectively reconstructed by the certain frequency IMFs and better vibration signals of transformer have been obtained. After EMD reconstruction, the vibrations are compared between transformer in normal work and with DC bias. When DC bias occurs, odd harmonics, vibration of core and shell, behave as a nonlinear increase and the even harmonics keep unchanged with DC current. Experiment results are provided to collaborate our theoretical analysis and to illustrate the effectiveness of the proposed EMD method.

• 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.

• 한국인터넷방송통신학회논문지
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• 제12권6호
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• pp.303-309
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• 2012

본 연구는 인체의 활동량과 음성 신호를 기반으로 에너지 소모량을 추정하고자 수행하였으며, 3축 가속도 센서와 PVDF(polyvinylidene fluoride) 필름을 이용하여 활동량과 음성 신호를 검출하였다. 인체의 움직임과 음성 신호, 그리고 실제 에너지 소모량 값은 MP-150 시스템과 호흡가스 분석기를 이용하여 수집하였다. 음성신호의 전력 값과 피험자의 체중을 독립 변수로 활용하였을 때 0.918의 R 제곱 값을 보였으며, 활동량 분석에 있어서는 SVM(signal vector magnitude), BMI(body mass index), 키, 그리고 나이를 독립 변수로 활용하였을 때 가장 높은 상관성을 나타냈다. 음성과 활동량을 토대로 하는 에너지 소모량 추정은 활동량만을 이용하여 추정하는 기존의 기기보다 정확한 결과값을 제공할 수 있음을 확인하였다.

• Steel and Composite Structures
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• 제21권6호
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• pp.1183-1210
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• 2016

Viscoelastic sandwich structures (VSSs) are widely used in mechanical equipment, but in the service process, they always suffer from aging which affect the whole performance of equipment. Therefore, aging state recognition of VSSs is significant to monitor structural state and ensure the reliability of equipment. However, non-stationary vibration response signals and weak state change characteristics make this task challenging. This paper proposes a novel method for this task based on adaptive second generation wavelet packet transform (ASGWPT) and multiwavelet support vector machine (MWSVM). For obtaining sensitive feature parameters to different structural aging states, the ASGWPT, its wavelet function can adaptively match the frequency spectrum characteristics of inspected vibration response signal, is developed to process the vibration response signals for energy feature extraction. With the aim to improve the classification performance of SVM, based on the kernel method of SVM and multiwavelet theory, multiwavelet kernel functions are constructed, and then MWSVM is developed to classify the different aging states. In order to demonstrate the effectiveness of the proposed method, different aging states of a VSS are created through the hot oxygen accelerated aging of viscoelastic material. The application results show that the proposed method can accurately and automatically recognize the different structural aging states and act as a promising approach to aging state recognition of VSSs. Furthermore, the capability of ASGWPT in processing the vibration response signals for feature extraction is validated by the comparisons with conventional second generation wavelet packet transform, and the performance of MWSVM in classifying the structural aging states is validated by the comparisons with traditional wavelet support vector machine.

• 오토저널
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• 제14권5호
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• pp.117-127
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• 1992

In this work, the possibility to diagnose valve lashes of an automotive diesel engine via cylinder head vibration/noise analysis is studied. First of all the measurement signals and conditions are selected after considering which signals and conditions are most suitable to diagonse valve lashes. Both accelerometer and microphone are used to measure cylinder head accelerations and acoustic pressure due to valve impact on cylinder head. The signals are measured in both cranking and engine firing conditions. Finally, it was found that acceleration signal obtained in engine operating condition is the most reliable signal to diagnose the valve lash condition. The valve closing angle and the peak acceleration due to valve close are chosen to analyze the valve lash condition. The measured cylinder head acceleration signals are statistically tested to derive information which are useful to judge the valve lash. In conclusion, it was found that the developed technique can be one of feasible methods to diagnose the valve conditions while the engine is in operation.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2000년도 춘계학술대회논문집
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• pp.1487-1492
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• 2000

To identify forklift's booming noise in cabin under idling engine revolution, we discussed and applied conditioned input analysis. Acceleration signals at engine mounts and front window and rear window were considered as candidate input signals and output signal was sound pressure signal at driver's ear position in cabin. To reduce the numbers of the input signals, one idea were applied; Each one input signal from each input groups was selected, respectively because input signals in the same group were highly correlated. And Hilbert transform was used to determine the ordering of three selected inputs. Partial coherence functions, multiple coherence function and conditioned spectral density functions were investigate to the effects of booming noise by partial inputs.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2000년도 춘계학술대회논문집 - 한국공작기계학회
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• 한국소음진동공학회논문집
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• 제26권2호
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• pp.172-178
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• 2016

The location template matching (LTM) method is a technique of identifying an impact location on a structure, and is often applied to structural health monitoring and large scale human-computer interface (HCI) systems. The LTM method utilizes a certain measure of similarity between two time signals. The correlation coefficient is most widely used for this purpose, and the group delay based method is recently proposed to improve the accuracy of finding the best matching pair of signals. In practice, one of key essential consideration for implementing the LTM method is to guarantee that a sufficient number of vibration modes must be contained in the measured signal, and yet the lower sampling rate is needed for a real-time implementation. In this paper, the properties of correlation coefficient and group delay with respect to the number of vibration modes are investigated. A few important results are obtained through extensive computer simulations and experiments. If the number of vibration modes contained in the measured signal is more than four it is sufficient for the correlation based LTM method, while the group delay based LTM method requires smaller number of vibration modes.

• 소음진동
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• 제11권1호
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• pp.125-131
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• 2001

There is a clearance between the parts of a machine due to design tolerance, manufacturing error, wear, looseness, or misalignment. If the clearance is large, the vibration and noise of the machine is generally large. Therefore, the analysis on the nitration and noise of a machine can tell the clearance of the machine, which reveals the condition of the machine, i.e., the existence of faults and the safety of the machine. The investigation of this kind of research should be on the basis of experimental results. A link mechanism with a clearance at a joint between the coupler and locker is made for the investigation of the condition monitoring of a machine due to clearance. The vibration and sound are measured from the link driving system during the operation. The signals are clarified using line enhancement technique. The noise removed signals are used to develop the dynamic model of the system for a model based fault diagnosis. Also this study showed that the clarified signals can be used for the calculation of the joint forces between the coupler and rocker and for the correlation between the vibration and sound levels and the clearance sizes.