• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.15 no.11 s.104
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• pp.1303-1310
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• 2005

Stabilization error of target aiming systems mounted on moving vehicles is an important performance because the error directly affects hit Probability. In a heavy load targetaiming system, the disturbance torque comes from mass unbalance and linear acceleration is a main source of stabilization error. This study suggests an experimental design method of disturbance feedforward compensation control to improve the stabilization performance of heavy load target aiming systems. The filtered_x least square(FxLMS) algorithm is used to estimate the compensator coefficients adaptively. The proposed control is applied to a simple experimental set-up which simulates dynamic characteristics of a real target aiming system. The feasibility of the proposedtechnique is illustrated, along with results of experiments.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.56 no.6
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• pp.1039-1046
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• 2007

This paper presents the analysis for power consumption, mechanical vibration and acoustic noise characteristics of the Coreless and Slotless Brushless DC motor in Digital Lightening Processor(DLP) Motor with the Air-Dynamic Bearing. The Coreless BLDC motor has not the stator yoke as well as the stator slot to remove the unbalance force by the interaction between the stator yoke and Air-Dynamic Bearing clearance. The assembling tolerance and the processing error make the air-gap difference between the magnet and the stator yoke .which occurs the unbalanced electro-magnetic force in the Slotless BLDC motor. It imposes the air-dynamic bearing on the disturbance force and makes the Air-Dynamic Bearing vibrated and noised. Also, The attractive force between the magnet and the silicon steel stator yoke increases the power consumption. In this paper, the power consumption, mechanical vibration and acoustic noise of the Coreless BLDC motor and the Slotless BLDC motor with the silicon steel stator yoke are simulated, analyzed, and tested using the manufactured proto-type motors with Air-Dynamic bearing. The simulated and tested results present that the Coreless BLDC motor without the silicon steel stator yoke has the lower mechanical vibration and noise ,and lower power consumption than the Slotless BLDC motor with the silicon steel stator yoke in Digital Lightening Processor Motor with Air-Dynamic Bearing.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.60 no.2
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• pp.82-90
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• 2011

The small precision spindle motors in the high value-added products including the visible home appliances such as DLP projector require not only the energy conversion devices but also high efficiency, low vibration and sound operation. However, the spindle motors using the conventional ball bearing and sintered porous metal bearing have following problems, respectively: the vibration by the irregularity of balls and the short motor life cycle by the ball's abrasion and higher sound noises by dry contact between shaft and sleeve. In this paper, it is proposed that the spindle motor with a fluid dynamic bearing is suitable for the motor to drive the color wheel of the DLP(digital lightening processor) in the visible home appliances. The proposed spindle motor is composed of the fluid dynamic bearing with both the radial force and the thrust force. The fluid dynamic bearing is solved by the finite element analysis of the mechanical field with the Reynolds equations. The magnetic part of spindle motor, which is a type of Brushless DC Motor, is designed by the electro-magnetic field analysis coupled with the Maxwell equation. And the load capacity and the friction loss of fluid dynamic bearing are analyzed to bearing clearance variation by the fabrication error in designed motor. The design of the proposed motor is implemented by the load torque caused by the eccentricity and the unbalance of the fluid dynamic bearing when the motors are fabricated in error. The prototype of the motor with the fluid dynamic bearing is manufactured, and experiment results show the vibration, sound, and phase current at no load and color wheel load of the motors in comparison. The high performance characteristics with the low vibration, the low acoustic noise and the optimal mechanical structure are verified by the experimental results.

• Journal of Advanced Marine Engineering and Technology
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• v.38 no.3
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• pp.253-261
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• 2014

In this paper, vibration analysis of power differential gear train for 2.5MW wind turbine system is analyzed. which system is composed of two planetary gear set, one helical gear set and main shaft that connected by flange. Planetary gear set, helical gear set, main shaft are modeled in MASTA program and housing, torque arm, carrier, flange components are modeling by finite element method. Each models are combined by component mode superposition. To analysis of natural vibration characteristic about 2.5MW wind turbine gear train was performed and check about critical speed with wind load, mass unbalance, angle misalignment excitation frequency.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2004.04a
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• pp.410-415
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• 2004

Heavy-weight head slides may cause excessive inertia impact ＆ moment on the machine tool structure when they move or stop abruptly during operation. Consequently these inertia impact and unbalanced moment bring transient vibrations and rough sliding motions on the machine structure. Machine tool engineers have tried many kind of feed-slide designs in order to solve this problem; for example, the design optimization of the moving structure for minimum weight and maximum stiffness, box-in-box type slide design, and so on. In this article, force and moment equilibrium equations regarding to the inertia force ＆ moment were derived for each one of a mold M/C's head slides. Furthermore, five different design configurations of head slide assembly were reviewed for its design improvement regarding to force ＆ moment calculations and finite element structural analysis results.

• Journal of the Korean Society for Precision Engineering
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• v.22 no.3 s.168
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• pp.123-129
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• 2005

The unbalance of the power output, noise, and vibration is happened by the disproportionate pressure variation in the cylinder. For this reason, decrease of the pressure in the cylinder and increase of the residual gas effect on the engine performance. If the abnormal combustion is continued, the crack would be occurred in the engine block. And it could be broken down. For the normal combustion of the SI engine, it is important to supply the balanced mixture by each operating condition. In this study, it was tested the combustion characteristics in the cylinder according to the abnormal adjusting of intake & exhaust valve. This test is willing to set a basic data's analysis fur developing an automotive diagnosis system by analyzing the pressure in the cylinder, the output signal of MAP sensor, the exhaust gas, etc.

• Journal of KSNVE
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• v.8 no.6
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• pp.1069-1077
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• 1998

A dynamic model of turbo compressor having helical gear pairs is developed. The model accounts for the shaft and bearing flexibilities, gyroscopic effects and the force couplings among the transverse, torsion. and axial motions due to gearings. For the mode analysis of turbo compressor, a transfer matrix method is used. The excitation sources caused by the mass unbalances of the rotors and misalignment of the shafts, the transmitted errors of the gearings. and the vane passing frequencies of the Impeller are studied qualitatively. By introducing the perturbation method, the generated forcing frequencies are defined and devided into three groups. With the field data, two critical speeds are analytically found and the corresponding modal characteristics are examined.

• Smart Structures and Systems
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• v.6 no.4
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• pp.389-403
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• 2010

By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor's normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It's demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

• 제어로봇시스템학회:학술대회논문집
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• 1994.10a
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• pp.522-526
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• 1994

There are great needs for checking machine operation status precisely in the iron and steel plants. Rotating machines such as pumps, compressors, and motors are the most important objects in the plant maintenance. In this paper back-propagation neural network is utilized in diagnosing rotating machines. Like the finger print or the voice print of human, the abnormal vibrations due to axis misalignment, shaft bending, rotor unbalance, bolt loosening, and faults in gear and bearing have their own spectra. Like the pattern recognition technique, characteristic. feature vectors are obtained from the power spectra of vibration signals. Then we apply the characteristic feature vectors to a back propagation neural net for the weight training and pattern recognition.