• Earthquakes and Structures
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• v.3 no.3_4
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• pp.315-340
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• 2012

There are many types of seismic isolation devices that are being used today for structural control of earthquake response in buildings. The most commonly used are sliding bearings and elastomeric bearings, the latter with or without lead core. An alternative solution is the use of steel springs combined with viscoelastic fluid dampers, which is the case discussed in this paper. An analytical study of a three-story building supported on helical steel springs and viscoelastic fluid dampers, GERB Control System (GCS), subjected to near-fault earthquakes is presented. Several earthquakes records have been obtained by the acceleration network installed in the isolated building and in its non-isolated twin since they were finished. These experimental results are analysed and discussed. The aim is to show that the spring-based system can be an alternative for base isolation of small building located near active faults.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.04a
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• pp.362-363
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• 2014

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2009.04a
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• pp.109-110
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• 2009

• Nuclear Engineering and Technology
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• v.55 no.6
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• pp.2096-2106
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• 2023

Rotating machinery is widely applied in important equipment of nuclear power plants (NPPs), such as pumps and valves. The research on intelligent fault diagnosis of rotating machinery is crucial to ensure the safe operation of related equipment in NPPs. However, in practical applications, data-driven fault diagnosis faces the problem of small and imbalanced samples, resulting in low model training efficiency and poor generalization performance. Therefore, a deep convolutional conditional generative adversarial network (DCCGAN) is constructed to mitigate the impact of imbalanced samples on fault diagnosis. First, a conditional generative adversarial model is designed based on convolutional neural networks to effectively augment imbalanced samples. The original sample features can be effectively extracted by the model based on conditional generative adversarial strategy and appropriate number of filters. In addition, high-quality generated samples are ensured through the visualization of model training process and samples features. Then, a deep convolutional neural network (DCNN) is designed to extract features of mixed samples and implement intelligent fault diagnosis. Finally, based on multi-fault experimental data of motor and bearing, the performance of DCCGAN model for data augmentation and intelligent fault diagnosis is verified. The proposed method effectively alleviates the problem of imbalanced samples, and shows its application value in intelligent fault diagnosis of actual NPPs.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.05a
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• pp.324-328
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• 2007

Vibration monitoring of rolling element bearings is probably the most established diagnostic technique for rotating machinery. Acoustic Emission (AE) Analysis is an extremely powerful technology that can be used within a wide range of applications of non destructive testing. Therefor, this paper investigates the detection methods using AE for rolling element bearings about low-speed. Two transducers, the accelerometer and acoustic emission sensor, are used to acquire data and the results are compared for the capacity of early fault detection.

• Journal of the korean Society of Automotive Engineers
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• v.14 no.5
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• pp.83-94
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• 1992

There has been a suggestion of many techniques as the methods of diagnosis for rotational machinery. In this study, HFRT was used as the analysis method for ball bearing of automobile and was compared with the conventional ANC technique. And this paper presented the computer simulation process about fault types and noise for the validity of the algorithm and identification of the physical meanings of HFRT. Also, experiment was performed using ball bearing and the results showed that HFRT was much more effective than the conventional methods in diagnostic process.

• Journal of Advanced Marine Engineering and Technology
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• v.12 no.2
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• pp.35-45
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• 1988

In this paper an experimental review of condition monitoring method using time domain vibration signals and statically measured wave forms of a multi-components damaged ball bearing is presented first time. Many investigators studied already about vibration characteristics of a single point damaged ball bearing but they did not make efforts to verify vibration phenomena of a multi-components damaged one. Even in case of a tripple components damaged (i.e, outer race, inner race and rolling element) one, the high frequency resonance technique (HERT) and the displacement time domain technique can be also used for its fault detection. According to experimental results undertaken a static displacement measuring method, the defect locations of components can be proposed confidently with simple calculation of the rotating angles of each component.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2000.06a
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• pp.1102-1107
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• 2000

The signals that can be obtained from a rotating machine often convey the information of machine. For example, if the machine under investigation has faults, then we can measure the signal which has a pulse train, embedded in noise. Therefore the ability to detect the fault signal in noise determines the degree of diagnosis level of rotating machine. In this paper, minimum variance cepstrum (MV cepstrum), which can easily detect impulse in noise, has been applied to detect the type of faults of ball bearing system. To test the performance of this technique, experiment has been performed for ball bearing elements that have man made faults. Results show that minimum variance cepstrum can easily detect the periodicity due to faults.

• Journal of the Korean Society for Nondestructive Testing
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• v.32 no.3
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• pp.291-295
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• 2012

In the industrial field, real-time monitoring system like a fault early detection is very important. For this, the infrared thermography technique as a new diagnosis method is proposed. This study is focused on the damage detection and temperature characteristic analysis of ball bearing using the non-destructive infrared thermography method. In this paper, thermal image and temperature data were measured by a Cedip Silver 450 M infrared camera. Based on the results, the temperature characteristics under the conditions of normal, loss lubrication, damage, dynamic loading, and damage under loading were analyzed. It was confirmed that the infrared technique is very useful for the detection of the bearing damage.

• The Journal of the Acoustical Society of Korea
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• v.7 no.4
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• pp.77-85
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• 1988

Many kinds of conditioning monitoring technique have been studied, so this study has inverstigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analyisis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in order to verify the usefulness of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of least mean square algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.