• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.852-855
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• 1997

In this paper, the vibration diagnosis system of a rotating machinery is introduced, in which the vibration signals of multiple accelerometers and displacement sensors are used combinedly as input parameters and their characteristics of the vibration response and mutual relationships between each sensor signal are considered to improve the reliability of the diagnosis system. The fuzzy logic is utilized for inferencing the fault from the vibration signal patterns.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.271-274
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• 2005

For noise and vibration order-analysis on rotating machinery, it is compulsory to measure the RPM of the rotating part of the machine. Normally the RPM is measured using dedicated tacho-probes. In this paper we describe a new method that in real-time synthesizes a tacho signal from the measured noise or vibration signal thus eliminating the tacho probe. The method strengths and weaknesses are evaluated on practical signals.

• Journal of Mechanical Science and Technology
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• v.19 no.3
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• pp.846-859
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• 2005

Support vector machines (SVMs) have become one of the most popular approaches to learning from examples and have many potential applications in science and engineering. However, their applications in fault diagnosis of rotating machinery are rather limited. Most of the published papers focus on some special fault diagnoses. This study covers the overall diagnosis procedures on most of the faults experienced in rotating machinery and examines the performance of different SVMs strategies. The excellent characteristics of SVMs are demonstrated by comparing the results obtained by artificial neural networks (ANNs) using vibration signals of a fault simulator.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.5
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• pp.1411-1418
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• 1996

Large scale plants are equipped with a number of the rotating machineries which ocuupy important positions in the plant system. Therefore, the most important one is a vibraiton diagnostic thchnology which can detect quickly any abnormal symptom of operating malfunction and guve operational and inspection guides adequately. A new diagnosis method is developed in this paper, in which the fuzzy set theory is introduced to diagnose the defects of ratating machinery. The selection of memgership function and the fuzzy operation model are discussed in datail here. The systme is sucessfully used for various defacts diagnosis of rotating machinery. The result indicate that realixtic application can be builtusing this approach.

• International Journal of Fluid Machinery and Systems
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• v.7 no.3
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• pp.94-100
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• 2014

In double-suction centrifugal pumps, it was found that cavitation instabilities occur with vibration and a periodic chugging noise. The present study attempts to identify cavitation instabilities in the double-suction centrifugal pump by the experiment and Computational Fluid Dynamics (CFD). Cavitation instabilities in the tested pump were classified into three types of instabilities. The first one, in a range of cavitation number higher than breakdown cavitation number, is cavitation surge with a violent pressure oscillation. The second one, in a range of cavitation number higher than the cavitation number of cavitation surge, is considered to be rotating cavitation and causes the pressure oscillation due to the interaction of rotating cavitation with the impeller. Last one, in a range of cavitation number higher than the cavitation number of rotating cavitation, is considered to be a surge type instability.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.453-459
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• 2002

In this paper. the correlation among vibration. wear and temperature are experimentally investigated when rubbing is caused by static and dynamic forces. Each measurement reflects the characteristics of the system and is useful in detecting and diagnosing the current status of rotating machinery. For experiment, the rotor system with lubricating equipment such as trochoid pump, oil tank and wear detecting sensor is implemented to simulate the rubbing condition. Experimental results show that significant change in wear quantity can be notified when vibration signal is changed by rubbing. The results can be applied to system monitoring and fault diagnosis in rotating machinery.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.950-955
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• 2003

This paper introduces an enhanced condition monitoring and diagnosis system recently developed for rotating machinery. In the system, the data aquisition/monitoring signal processing, machine condition classifier, case-based reasoning and demonstration modules are effectively integrated with user-friendliness so that machine operators can easily monitor and diagnose the status of rotating machinery in operation. Some of the new features include the directional spectrum, case-based reasoning and neural network techniques. And the demonstrator modules for fault diagnosis of a Bear driving system and for basic understanding of the rotor dynamics are provided to help the potential users better understand the system.

• Journal of Magnetics
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• v.22 no.1
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• pp.122-132
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• 2017

This study dealt with the design process of the 78 kW permanent magnet synchronous generator for engines. After the calculation of the basic dimensions through a theoretical method in the process of initial model design, FEA (finite-element analysis) and a d,q-axis equivalent circuit were used to identify the generator characteristics depending on the number of poles. With the use of the space harmonic analysis method, the back-EMF (electromotive force) and THD were checked, and then the number of slots was determined. In addition, the most optimized generator dimensions were determined through a sizing optimization technique. Based on this, the optimum model with enhanced efficiency, material costs, and temperature characteristics was derived, and the availability of the design method was confirmed through a comparative analysis of the initial and optimum models.

• Smart Structures and Systems
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• v.10 no.1
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• pp.15-31
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• 2012

This study proposes an active control method to suppress beam-rotating machinery system vibrations. The present control method is a combination of the fuzzy input estimation method (FIEM) and linear quadratic Gaussian problem (LQG) algorithms. The FIEM can estimate the unknown input and optimal states by measuring the dynamic displacement, the optimal estimated states into the feedback control; thereby obtaining the optimal control force for a random linear system. Active vibration control of a beam-rotating machinery system is performed to verify the feasibility and effectiveness of the proposed algorithm. The simulation results demonstrate that the proposed method can suppress vibrations in a beam-machine system more efficiently than the conventional LQG method.