• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.1612-1617
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• 2004

Unbalance analysis is essential in the rotor system. However, some problems still remain in the aspects of computational efficiency and accuracy. In the present paper a new method is proposed for estimating the mass unbalance of a rotating shaft by using the vibration signals. This is an advanced new method for the detection of a mass unbalance and its phase position. Based on the signal processing with FFT, an estimator is designed to detect the mass of unbalance. And an improved Lissajous diagram is also introduced with statistical analysis, which make it possible to compute the phase position of the mass unbalance efficiently and arranged at a certain location of the shaft. The proposed method is demonstrated and validated through several test examples.

• International Journal of Precision Engineering and Manufacturing
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• v.7 no.2
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• pp.36-40
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• 2006

Diagnosis and repair tasks of an unbalanced rigid rotor reduce the chances of unexpected failure and the consequent losses in production, time, and money. This paper presents investigation of a balancing system for equilibration of rigid rotor unbalance. A practical vibration signal based method is developed for unbalance diagnosis using wavelet technology and a Lissajous diagram. This paper shows that a mass unbalance can be efficiently estimated through an appropriate narrow-band filter used to extract the required spectra component. The wavelet technology is used to design specified narrow filter bank. A modified Lissajous diagram is also introduced with statistical analysis to compute the phase position. Several experimental tests demonstrate the effectiveness in balancing the mass unbalance of a rigid rotor.

• Journal of Mechanical Science and Technology
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• v.19 no.6
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• pp.1243-1252
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• 2005

This paper describes a new digital signal processor (DSP) imbalance measurement system dedicated to real-time vibration analysis on rotating machine. To accomplish real-time analysis, the vibration signals are on-line acquired and processed to analyze the mass imbalance and phase position. This is achieved through the use of FFT and Lissajous diagram. The method followed to analyze the mass imbalance with the chosen hardware and software solutions are described in detail in this paper. Several experimental tests demonstrate the efficiency and accuracy in imbalance analysis performance of the DSP system.

• Journal of the Korean Society of Industry Convergence
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• v.23 no.5
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• pp.825-833
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• 2020

Currently, the K-Water uses various valves that can be remotely controlled for optimal water management. Valve system fault can be classified into rotor defects, stator defects, bearing defects, and gear defects of induction motors. If the valve cannot be operated due to a gear fault, the water management operation can be greatly affected. For effective water management, there is an urgent need for preemptive repairs to determine whether gear is damaged through failure prediction diagnosis.. Recently, deep learning algorithms are being applied for valve failure diagnosis. However, the method currently applied has a disadvantage of attaching a vibration sensor to the valve. In this paper, propose a new algorithm to determine whether a fault exists using a convolutional neural network (CNN) based on the voltage and current information of the valve without additional sensor mounting. In particular, a normalized Lisasjous diagram was used to maximize the fault classification performance in the CNN-based diagnostic system.

• Journal of the Korea Safety Management & Science
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• v.4 no.2
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• pp.1-10
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• 2002

In order to protect the head, baseball helmet must to have proper strength and to absorb the kinetic energy. The purposes of this study are to validate whether the helmet have the protecting ability or not. We performed three kinds of experiment to know about the this ability. To find out the limit of displacement at 4 points(front, rear, right side, and left side), the static load by magnetic dial gauges were used, and to validate the ability of absorption, drop tests were peformed from 0.5 and 1.0 meter. Futhermore, we calculated natural frequency of the helmets by the principle of Lissajous Diagram and we performed FEM(Finite Element Method) analysis. From the results of these experiments, we found that the displacement of helmet was largest at rear point and it was smallest at left-side point(ear-covered part). The ability of absorption was better at the left-side point than the other points.