• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2014.10a
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• pp.511-512
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• 2014

In this paper, the finite element modeling of the RK4 rotor kit system (RK4) and then transient analysis, and was compared with the actual experimental results. RK4 manufactured by General Electric for the purpose of education and research. It is modeled by using the ANSYS finite element analysis program commercially available. Considering the rotor abnormal conditions(disc unbalance and shaft rubbing) and the vibration response of the analytical model were compared with experimental results.

• 유체기계공업학회:학술대회논문집
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• 1999.12a
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• Journal of the Korean Society of Industry Convergence
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• v.25 no.2_1
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• pp.169-176
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• 2022

Prognostics and health management (PHM) is recently converging throughout the industry, one of the trending issue is to detect abnormal conditions at decanter centrifuge during water treatment facilities. Wastewater treatment operation produces corrosive gas which results failures on attached sensors. This scenario causes frequent sensor replacement and requires highly qualified manager's visual inspection while replacing important parts such as bearings and screws. In this paper, we propose anomaly detection by measuring the vibration of the decanter centrifuge based on the video camera images. Measuring the vibration of the screw decanter by applying the optical flow technique, the amount of movement change of the corresponding pixel is measured and fed into the LST M model. As a result, it is possible to detect the normal/warning/dangerous state based on LSTM classification. In the future work, we aim to gather more abnormal data in order to increase the further accuracy so that it can be utilized in the field of industry.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.18 no.2
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• pp.348-369
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• 2024

With the advancement of Industry 4.0 and Industrial Internet of Things (IIoT), manufacturing increasingly seeks automation and intelligence. Temperature and vibration monitoring are essential for machinery health. Traditional abnormal state detection methodologies often overlook the intricate frequency characteristics inherent in vibration time series and are susceptible to erroneously reconstructing temperature abnormalities due to the highly similar waveforms. To address these limitations, we introduce synergistic, end-to-end, unsupervised Frequency-Time Domain Memory-Enhanced Autoencoders (FTD-MAE) capable of identifying abnormalities in both temperature and vibration datasets. This model is adept at accommodating time series with variable frequency complexities and mitigates the risk of overgeneralization. Initially, the frequency domain encoder processes the spectrogram generated through Short-Time Fourier Transform (STFT), while the time domain encoder interprets the raw time series. This results in two disparate sets of latent representations. Subsequently, these are subjected to a memory mechanism and a limiting function, which numerically constrain each memory term. These processed terms are then amalgamated to create two unified, novel representations that the decoder leverages to produce reconstructed samples. Furthermore, the model employs Spectral Entropy to dynamically assess the frequency complexity of the time series, which, in turn, calibrates the weightage attributed to the loss functions of the individual branches, thereby generating definitive abnormal scores. Through extensive experiments, FTD-MAE achieved an average ACC and F1 of 0.9826 and 0.9808 on the CMHS and CWRU datasets, respectively. Compared to the best representative model, the ACC increased by 0.2114 and the F1 by 0.1876.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.10a
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• pp.269-270
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• 2010

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.803-804
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.04a
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• pp.248-249
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.762-763
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• 2012

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.190-195
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• 2002

The intermittent high vibration has been occurred one or two times a day for a 500MW large steam turbine during 5 months. This abnormal vibration was caused by the rubbing between the rotor and the carbide accumulated on the seal tooth of oil deflector. It was found that the accumulated carbide was insulation material installed on the HIP casing from the examination of the chemical composition. Also, this paper presents the mechanism of the intermittent high vibration and the proper method to eliminate this vibration problem. This result would be good practice to find the solution of similar high vibration in the steam turbines for power plant as well as industrial rotating machineries.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.11
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• pp.897-903
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• 2002

The periodic high vibration has been occurred one or two times a day for a 500 MW large steam turbine during 5 months. This abnormal vibration was caused by the rubbing between the rotor and the carbide accumulated on the seal tooth of oil deflector. It was found that the accumulated carbide was insulation material installed on the HIP casing from the examination of the chemical composition. Also, this paper presents the mechanism of the periodic high vibration and the proper method to eliminate this vibration problem. This result would be good practice to find the solution of similar high vibration in the steam turbines for power plant as well as industrial rotating machineries.