• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.10a
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• pp.258-263
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• 2009

This paper describes an analysis of the stability and performance of a large-capacity flywheel energy storage system (FESS) supported by active magnetic bearings. We designed and manufactured the system that can store up to 5kWh of usable energy at the maximum speed of 18,000 rpm. In order to analyze the stability of the systems accurately, we derived a rigid body rotor model, flexible rotor model using finite-element method, and a reduced-order model using modal truncation. The rotor model is combined with those of active magnetic bearings, amplifiers, and position sensors, resulting in a system simulation model. This simulation model is validated against experimental measurements. The stability of the system is checked from the pole locations of the closed-loop transfer functions. We also investigated the sensitivity function to quantify the robustness of the systems to the disturbances such as mass imbalance and sensor noises.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.76-82
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• 2021

At present, with the development of precision instruments, high dimensional accuracy of workpieces must be ensured. In particular, for the aluminum alloys used in automobiles, the surface roughness of the workpiece is extremely important. The dimensional accuracy and surface roughness of the workpiece is considerably affected by the rotational accuracy of the rotor. Therefore, to enhance the rotational accuracy, various variables such as those related to the components such as bearings, motors, and end mills, rotational speeds, and vibrations must be considered. In this study, the difference in the quality of the workpieces was compared considering the weight imbalance and rotational speed as variables.

• Proceedings of the KIPE Conference
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• 2001.10a
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• pp.341-345
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• 2001

Recently, many examples of practical applications of the motors with reluctance torque, such as IPMSM, RM, etc. are reported. However, the problems of the torque ripple produced by the IPMSM, are also presented. The main reasons of the torque ripple generation are the structural imperfectness of the IPMSM and its control system, such as the cogging torque of the motor, the dead time of inverter, sensors offset, imbalance and non-linearity, and so on. In this paper, authors propose a suppression control method of the torque ripple for IPMSM utilizing the repetitive control with the Fourier transformer and a vibration signal detected by an acceleration sensor attached to the motor frame, considering periodicity of the motor torque ripple. An experimental system to simulate the compliant mechanical frame is constructed, and the effectiveness of the proposed method is confirmed by experimental results.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.7
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• pp.940-945
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• 2018

In this study, we make a study on a system to determine a person's sitting posture by measuring the distribution of pressure in the floor of a chair or in a cushion using pressure sensors. If the wrong sitting posture is detected, a warning message is given through the vibration motor in real time to correct the imbalance of the wrong habits and posture, and prevent Bulging disc or Herniated disc.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.1116-1121
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• 2013

Mechanical imbalances are common mechanical faults in induction motors. Vibration monitoring techniques have been widely used for the diagnosis of mechanical faults in induction motors, but electrical detection methods have been preferred in recent years. For many years, researchers have concentrated on the Motor Current Signature Analysis (MCSA). This paper examines the effect of mechanical imbalances to induction machine electrical parameters. Instantaneous Power Signature Analysis (IPSA) technique used to detect these faults. In the paper, a full analysis of the proposed technique is presented, and experimental results for healthy and faulty motors have been shown and discussed.

• Tribology and Lubricants
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• v.8 no.2
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• pp.50-63
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• 1992

The minimum oil film thicknesses (MOFT) in the crankshaft main bearings of a 1.5 liter, L-4, gasoline engine are measured and calculated to study the dynamically loaded engine bearing. The MOFT are measured simultaneously at each of the five main bearings using the total capacitance method(TCM). To improve the reliability of the TCM, a reasonable determination method of bearing clearance is introduced and the effects of bearipg cavitation and aeration on the test results are analyzed. Also the crankshaft is grounded by means of a slip ring instead of the friction contact method to improve the test precision. The calculation is based on the model of statically determinate beam, short bearing approximation and Mobility method. From the comparison between the measured and calculated MOFT curves, it is found that a qualitative similarity exists between them, but in all cases, measured MOFT are smaller than that of calculated. The crankshaft vibration and the imbalance of the load distribution between the engine bearings have important influence upon the MOFT curve. So it is found that the calculation result from the model of the statically determinate beam has a limitation in predicting bearing performance.

• Journal of Broadcast Engineering
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• v.27 no.6
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• pp.885-896
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• 2022

Motor failure in manufacturing plays an important role in future A/S and reliability. Motor failure is detected by measuring sound, current, and vibration. For the data used in this paper, the sound of the car's side mirror motor gear box was used. Motor sound consists of three classes. Sound data is input to the network model through a conversion process through MelSpectrogram. In this paper, various methods were applied, such as data augmentation to improve the performance of classifying fault motors and various methods according to class imbalance were applied resampling, reweighting adjustment, change of loss function and representation learning and classification into two stages. In addition, the curriculum learning method and self-space learning method were compared through a total of five network models such as Bidirectional LSTM Attention, Convolutional Recurrent Neural Network, Multi-Head Attention, Bidirectional Temporal Convolution Network, and Convolution Neural Network, and the optimal configuration was found for motor sound classification.

• International Journal of Computer Science & Network Security
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• v.23 no.1
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• pp.147-152
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• 2023

Mostly in motor fault detection the instantaneous values 3 axis vibration and 3phase current in time domain are acquired and converted to frequency domain. Vibrations are more useful in diagnosing the mechanical faults and motor current has remained more useful in electrical fault diagnosis. With having some experience and knowledge on the behavior of acquired data the electrical and mechanical faults are diagnosed through signal processing techniques or combine machine learning and signal processing techniques. In this paper, a single-layer LSTM based condition monitoring system is proposed in which the instantaneous values of three phased motor current are firstly acquired in simulated motor in in health and supply imbalance conditions in each of three stator currents. The acquired three phase current in time domain is then used to train a LSTM network, which can identify the type of fault in electrical supply of motor and phase in which the fault has occurred. Experimental results shows that the proposed single layer LSTM algorithm can identify the electrical supply faults and phase of fault with an average accuracy of 88% based on the three phase stator current as raw data without any processing or feature extraction.

• KOREAN JOURNAL OF PACKAGING SCIENCE & TECHNOLOGY
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• v.23 no.3
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• pp.183-190
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• 2017

Carton clamp truck is widely perceived as the high-efficient handling equipment of factory premises and warehouse by its capability of palletless handling. Therefore, the significance of a lab-based handling simulation is becoming higher with the growth of clamp truck usage. In this study, preliminary FEA and design of handling test courses for the lab-based simulation of carton clamp truck handling were performed, and the PSD analyses were performed for the modified one for the test course proposed by Park et al. (2017) as well as ASTM D 6055 and ISTA 3B standards. For the vibration in all directions, the vibration energy intensity analyzed by ISTA 3B standard showed higher than that by the other two cases. A FEA was performed for the handling operation of the sudden stop of the clamps after lifting the target HCP (heavyweight refrigerator corrugated package, w=180 kgf) up to the specified height. The slip distance between the clamp arm and the target HCP was 0.85 mm. The simulation result of 0.85 mm was 3.7 times lower than the experimental result (3.2 mm) obtained by Park et al. (2017), and it was estimated that the deviation comes from both the experimental error by weight imbalance of target HCP, and excessive simplification during the FE modelling of target HCP.

• Nuclear Engineering and Technology
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• v.53 no.1
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• pp.148-163
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• 2021

Timely fault identification is important for safe and reliable operation of the electric valve system. Many research works have utilized different data-driven approach for fault diagnosis in complex systems. However, they do not consider specific characteristics of critical control components such as electric valves. This work presents an integrated shallow-deep fault diagnostic model, developed based on signals extracted from DN50 electric valve. First, the local optimal issue of particle swarm optimization algorithm is solved by optimizing the weight search capability, the particle speed, and position update strategy. Then, to develop a shallow diagnostic model, the modified particle swarm algorithm is combined with support vector machine to form a hybrid improved particle swarm-support vector machine (IPs-SVM). To decouple the influence of the background noise, the wavelet packet transform method is used to reconstruct the vibration signal. Thereafter, the IPs-SVM is used to classify phase imbalance and damaged valve faults, and the performance was evaluated against other models developed using the conventional SVM and particle swarm optimized SVM. Secondly, three different deep belief network (DBN) models are developed, using different acoustic signal structures: raw signal, wavelet transformed signal and time-series (sequential) signal. The models are developed to estimate internal leakage sizes in the electric valve. The predictive performance of the DBN and the evaluation results of the proposed IPs-SVM are also presented in this paper.