• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.1
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• pp.80-86
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• 2008

In this study. the noise sources were identified and the noise and vibration were reduced for an industrial forklift. To identify the noise sourses, noise signals were measured by a microphone on a driver seat and these signals were analyzed with a waterfall plot. For this purpose, the gear mesh frequencies from the gear box of a reducer were not only investigated but noise/vibration sourses of an electric motor were also examined. Furthermore, the frequency response functions were obtained to confirm the vibration and noise sourses. It was found that severe vibration and noise were generated in the casing and the connecting part of the reducer. The severe vibration and noise could be reduced by a structure modification.

• Journal of KSNVE
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• v.9 no.2
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• pp.393-400
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• 1999

Defect frequencies of rolling element bearings are experimentally investigated utilizing the two-sided directional spectra of the complex-valued vibration signals measured from the outer ring of defective bearings. The directional spectra make it possible to discern backward and forward defect frequencies. The experimental results show that the directional zoom spectrum is superior to the conventional spectrum in identification of bearing defect frequencies, in particular the inner race defect frequencies.

• Current Optics and Photonics
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• v.6 no.2
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• pp.137-142
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• 2022

Leak detection is one of the most important challenges in condition monitoring of water pipelines. Fiber Bragg grating (FBG) sensors offer an attractive technique to detect leak signals. In this paper, leak measurements were conducted on a water distribution pilot plant with a length of 270 m and a diameter of 100 mm. FBG sensors were installed on the pipeline surface and used to detect leak vibration signals. The leak was demonstrated with 1-, 2-, 3-, and 4-mm diameter leak holes in four different pipe types. The frequency response of leak signals was analyzed by fast Fourier transform analysis in real time. In the experiment, the frequency range of leak signals was approximately 340-440 Hz. The frequency shifts of leak signals according to the pipe type and the size of the leak hole were demonstrated at a pressure of 1.8 bar and a flow rate of 25.51 m³/h. Results show that frequency shifts detected by FBG sensors can be used to detect leaks in pipelines.

• International Journal of Safety
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• v.5 no.2
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• pp.1-5
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• 2006

A data acquisition and processing system for measuring the current collection signals of the Korean High-speed Railway is developed. The current collection system is composed of a pantograph and the overhead catenary that supplies electrical power to the train through the pantograph. The system simultaneously measures the signals generated at the interface between the catenary and the pantograph through the accelerometers, load cells and strain gauges placed at various locations. The on-track test data are processed to evaluate the current collection reliability. The fiequency analysis of the signals reveals the presence of several structural vibration modes in the pantograph, as well as the components arising from the periodicity in the structure of the catenary and pantograph at the interface. The feasibility of predicting the contact performance from the measured signals is also demonstrated.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.11
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• pp.1091-1100
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• 2004

This paper presents an approach for the monitoring and detection of faults in induction machine by using data fusion technique and Dempster-Shafer theory Features are extracted from motor stator current and vibration signals. Neural network is trained and Hosted by the selected features of the measured data. The fusion of classification results from vibration and current classifiers increases the diagnostic accuracy. The efficiency of the proposed system is demonstrated by detecting motor electric and mechanical faults originated from the induction motors. The results of the test confirm that the proposed system has potential for real time application.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.28-33
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• 2000

The monitoring of the chatter vibration is necessarily required to do automatic manufacturing system. To this study, we constructed a sensing system using tool dynamometer in order to the chatter vibration on cutting process. And a approach to a neural network using the feature of principal cutting force signals is proposed. with the error back propagation training process, the neural network memorized and classified the feature of principal cutting force signals. As a result, it is shown by neural network that the chatter vibration can be monitored effectively.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.9 s.102
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• pp.1070-1076
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• 2005

Vibration test using the MAST(multi axial simulation table) provide a more reliable testing environment than any conventional one. The multi axial simulation could be possible with a advanced control algorithm and hardware supports so that most of the operation is automatically conducted by MAST system itself except the input information that is synthesized by the measured response signals. That means the reliability of the vibration test is highly depended on the quality of the input profile. In this paper, the optimal algorithm based on the energy method is introduced to construct a best combination of candidated input PSD data could be constructed. Since the optimal algorithm renders time information, the nitration fatigue test is completely possible for any measured signals one wants. The proposed method is explained with representing acquired road signals from the candidate input PSD obtained from a proving ground.

• International Journal of Precision Engineering and Manufacturing
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• v.6 no.4
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• pp.26-30
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• 2005

A rotor run-up or run-down process provide more useful information for modal analysis than normal operation conditions. A traditional difficulty associated with rotor run-up or run-down analysis is the non-stationary nature of vibration data. This paper compares Short-Time Fourier Transform (STFT) and the wavelets analysis in these non-stationary signal analyses. An Adaptive Wavelet Analysis (AWT) is proposed to analyze these signals. Although simulations and experiments in a simple rotor-bearing system show that both STFT and AWT can be used to analyze non-stationary vibration signals in rotor dynamics, proposed AWT provides better results than STFT analysis. From the amplitude-frequency curve obtained by AWT, the modal frequency and damping ratio are calculated. This paper also analyzes the characteristics of signals when the shaft touches the outer hoop in a run-up process. The AWT can give a good result in this complex dynamic analysis of the touching process.

• Journal of Korean Society for Quality Management
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• v.36 no.2
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• pp.36-44
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• 2008

In this paper a novel feature extraction and selection is carried out in order to improve the discriminating capability between healthy and damaged structure using vibration signals. Although many feature extraction and selection algorithms have been proposed for vibration signals, most proposed approaches don't consider the discriminating ability of features since they are usually in unsupervised manner. We proposed a novel feature extraction and selection algorithm selecting few wavelet coefficients with higher class discriminating capability for damage detection and class visualization. We applied three class separability measures to evaluate the features, i.e. T test statistics, divergence, and Bhattacharyya distance. Experiments with vibration signals from truss structure demonstrate that class separabilities are significantly enhanced using our proposed algorithm compared to other two algorithms with original time-based features and Fourier-based ones.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.411-416
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• 2006

Fault detection and diagnosis is the most important technology in condition-based maintenance(CBM) 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 techniques. Finally, conclusions are given based on the experiment results.