• Transactions of the Korean Society of Mechanical Engineers A
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• v.32 no.3
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• pp.232-239
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• 2008

The position sensors used in a magnetic bearing system are desirable to provide some degree of fault-tolerance as the rotor position is necessary for the feedback control to overcome the open-loop instability. In this paper, we propose an inductive position sensor that can cope with a partial fault in the sensor. The sensor has multiple poles which can be combined to sense the in-plane motion of the rotor. When a high-frequency voltage signal drives each pole of the sensor, the resulting current in the sensor coil contains information regarding the rotor position. The signal processing circuit of the sensor extracts this position information. In this paper, we used the magnetic circuit model of the sensor that shows the analytical relationship between the sensor output and the rotor motion. The multi-polar structure of the sensor makes it possible to introduce redundancy which can be exploited for fault-tolerant operation. The proposed sensor is applied to a magnetically levitated turbo-molecular vacuum pump. Experimental results validate the fault-tolerance algorithm.

• Journal of the Korea Society of Computer and Information
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• v.19 no.4
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• pp.55-62
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• 2014

This paper proposes a fault detection method utilizing converted images of acoustic emission signals and texture analysis for identifying bearing's multi-faults which frequently occur in an induction motor. The proposed method analyzes three texture features from the converted images of multi-faults: multi-faults image's entropy, homogeneity, and energy. These extracted features are then used as inputs of a fuzzy-ARTMAP to identify each multi-fault including outer-inner, inner-roller, and outer-roller. The experimental results using ten times trials indicate that the proposed method achieves 100% accuracy in the fault classification.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.121-125
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• 2010

The infrared thermography technology rather than traditional nondestructive methods has benefits with non-contact and non-destructive testings in measuring for the fault diagnosis of the rotating machine. In this work, condition monitoring measurements using this advantage of thermography were proposed. From this study, the novel approach for the damage detection of a rotating machine was conducted based on the spectrum analysis. As results, by adopting the ball bearing used in the rotating machine applied extensively, an spectrum analysis with thermal imaging experiment was performed. Also, as analysing the temperature characteristics obtained from the infrared thermography for in-situ rotating ball bearing under the lubrication condition, it was concluded that infrared thermography for condition monitoring in the rotating machine at real time could be utilized in many industrial fields.

• Structural Engineering and Mechanics
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• v.23 no.6
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• pp.615-634
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• 2006

Seismic response of the liquid storage tanks isolated by the elastomeric bearings and sliding systems is investigated under near-fault earthquake motions. The fault normal and parallel components of near-fault motion are applied in two horizontal directions of the tank. The continuous liquid mass of the tank is modeled as lumped masses known as sloshing mass, impulsive mass and rigid mass. The corresponding stiffness associated with these lumped masses has been worked out depending upon the properties of the tank wall and liquid mass. It is observed that the resultant response of the isolated tank is mainly governed by fault normal component with minor contribution from the fault parallel component. Further, a parametric study is also carried out to study the effects of important system parameters on the effectiveness of seismic isolation for liquid storage tanks. The various important parameters considered are: aspect ratio of tank, the period of isolation and the damping of isolation bearings. There exists an optimum value of isolation damping for which the base shear in the tank attains the minimum value under near-fault motion. The increase of damping beyond the optimum value will reduce the bearing and sloshing displacements but increases the base shear. A comparative performance of five isolation systems for liquid storage tanks is also studied under normal component of near-fault motion and found that the EDF type isolation system may be a better choice for design of isolated tank in near-fault locations. Finally, it is also observed that the satisfactory response can be obtained by analysing the base-isolated tanks under simple cycloidal pulse instead of complete acceleration history.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.24 no.6
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• pp.429-436
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• 2014

Most mechanical systems are now operating consistently and getting faster due to the development of automation systems. Peoples' dependence on machines have increased as when problems occur within the mechanical system, personal injury and production loss may come as a result, as most of the mechanical system's malfunctions are caused by the failure of the rotational bearing. What we need now is a maintenance system that can warn us when it detects abnormal conditions before significant damage occurs to the bearing. In this study, we have developed an acoustic emissions sensor that can figure if the bearing works under the normal condition. With this acoustic emissions sensor, we can inspect the bearing for defects by using the Heterodyne technique, which converts the ultrasound signal into audio, as a signal conditioning process.

• The Journal of the Acoustical Society of Korea
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• v.14 no.2
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• pp.92-100
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• 1995

In this study, the bearing defects were modeled and the vibration of ball bearing faults was presented for the defective pattern. The vibration signal was measured for the single and multiple defected ball bearing at the various defect positions and rotation speed, and then the signal components using the HFRT(high frequency resonance technique) were analyzed by FFT. The experimental data analysis has shown that the frequencies generated in the single or multiple defected ball bearings appear with the characteristic defect frequency and harmonics of ball pass frequency peak. Signal processing by HFRT makes it possible not only to detect the presence of a defect but also to diagnose the defect part of the bearing.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.9
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• 제어로봇시스템학회:학술대회논문집
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• 2002.10a
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• pp.32.1-32
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• 2002

The fault monitoring technique is presented for identifying the status of the agitator driving system in thermal reduction reactor. For identifying a fault such as bearing defect or clearance blocking, Wavelet transform (WT) is applied to vibration signals and features are extracted. For classification, the fuzzy ARTMAP is employed. With the features from WT, a single training epoch and a single learning iteration are sufficient for the fuzzy ARTMAP to classify the faults. The test results show the perfect classification though some features extracted from the test data are distorted against those in the training data

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2175-2177
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• 2001

This paper presents a faults diagnosis technique of induction motors based on a neural network. Only stator current is measured, transformed by using FFT and normalized for the training. Healthy, bearing fault, stator fault and rotor end-ring fault motors are prepared to obtain the learning data and diagnose the several faults. For more effective diagnosis, the load rate is changed by 100%, 60%, 30% of full load and the obtained are applied to the learning process. The experimental results show the proposed method is very detectable and applicable to the real diagnosis system.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.331-337
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• 2013

Acoustic Emission technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.