• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Tribology and Lubricants
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• v.18 no.4
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• pp.285-290
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• 2002

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Journal of Applied Reliability
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• v.14 no.4
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• pp.208-212
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• 2014

An experimental gear-box was set-up to simulate the real situation of the wind-turbine. Artificial cracks of different sizes were machined into the gear. Vibration signals were acquired to diagnose the different crack fault conditions. Time-domain features such as root mean square, variance, kurtosis, normalized 6th central moments were used to capture the characteristics of different crack conditions. Normal condition, 1 mm crack condition, 2mm crack condition, 6mm crack condition, and tooth fault condition were compared using ANFIS and DAG-SVM methods, and three different DAG-SVM models were compared. High-pass filtering improved the success rates remarkably in the case of DAG-SVM.

• Proceedings of the KSME Conference
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• 2001.11a
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• pp.590-595
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• 2001

We must maintain the maximum operation capacity for production facilities and find properly out the fault diagnosis of the possessing equipments rapidly so as to decrease a loss caused by its failure. In this paper, we performed the fundamental study which develops a system of fault for a individually using pump widely or a pump as parts of the other machines. For each normal products, artificially transformed products, and working products under critical condition, we experimented in vibration, compared and analysed. Some faults showed into characteristic vibrations and other faults did not show consistent characters.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.47-54
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• 2009

Spin coaters are the essential instruments in micro-fabrication processes, which apply uniform thin films to flat substrates. In this research, a spin coater diagnosis system is developed to detect the abnormal operation of TFT-LCD process in real time. To facilitate the real-time data acquisition and analysis, the circular-buffered continuous data transfer and the short-time Fourier transform are applied to the fault diagnosis system. To determine whether the system condition is normal or not, a steady-state detection algorithm and a frequency spectrum comparison algorithm using confidence interval are newly devised. Since abnormal condition of a spin coater is rarely encountered, algorithm is tested on a CD-ROM drive and the developed program is verified by a function generator. Actual threshold values for the fault detection are tuned in a spin coater in process.

• Proceedings of the Korea Committee for Ocean Resources and Engineering Conference
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• 2006.11a
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• pp.46-53
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• 2006

This paper presents the fault diagnosis of induction motor based on support vector machine (SVMs). SVMs are well known as intelligent classifier with strong generalization ability. Application SVMs using kernel function is widely used for multi-class classification procedure. In this paper, the algorithm of SVMs will be combined with feature extraction and reduction using component analysis such as independent component analysis, principal component analysis and their kernel (KICA and KPCA). According to the result, component analysis is very useful to extract the useful features and to reduce the dimensionality of features so that the classification procedure in SVM can perform well. Moreover, this method is used to induction motor for faults detection based on vibration and current signals. The results show that this method can well classify and separate each condition of faults in induction motor based on experimental work.

• Journal of Institute of Control, Robotics and Systems
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• v.8 no.10
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• pp.851-855
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• 2002

This paper proposes a method of fault detection and diagnosis of agitators based on the wavelet analysis of the current and vibration signals. The wavelet transform has received considerable interest in the fields of acoustics, communication, image compression, vision. and seismic since it provides the fast and effective means of analyzing signals recorded during operation. Neural network is used to diagnose the fault. Specifically, the proposed approach consists of (i) fault detection, (ii) feature extraction, and (iii) classification of fault types. The results show an effective application of the wavelet analysis on the monitoring of an agitator.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 2000.10a
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• pp.25-28
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• 2000

In this paper, the technology for accelerating degradation & life estimation on the traction motor was introduced with the stator form-winding sample coils of the 200 Class insulation system The accelerative degradation was performed in 10 cycles, which were composed of thermal stress, fast rising surge voltage, vibration, water immersion and overvoltage applying. After aging of 10 cycles, condition diagnosis test such as insulation resistance & polarization index, capacitance & dielectric loss and partial discharge properties were investigated in the temperature range of $20{\sim}160^{\circ}C$. Relationship between degradation conditions and diagnosis results were analyzed to find an dominative degradation factor at the end-life point

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2001.11a
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• pp.252-259
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• 2001

Integrated condition monitoring is required to monitor effectively the machine conditions since machine failures could not be monitored accurately by any single measurement parameter. Application of various condition monitoring techniques is therefore preferred in many cases in order to diagnosis the machine condition. However it inevitably requires lots of maintenance cost and sometimes it could be proved to over-maintenance unnecessarily. This could happen especially when one measurement parameter closely correlates to another. Therefore correlation analysis of various monitoring parameters has to be performed to improve the reliability of diagnosis. In this work, Pearson correlation coefficient was used to analyze the correlation between condition monitoring parameters of an over-loaded machine system where the vibration, wear and temperature were monitored simultaneously. The result showed that Pearson correlation coefficient could be regarded as a good measure for evaluating the availability of condition monitoring technology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.597-608
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• 1998

A diagnosis system that provides early warnings regarding machine malfunction is very important for rolling mill so as to avoid great losses resulting from unexpected shutdown of the production line. But it is very difficult to provide early warnings in rolling mill. Because dynamics of rolling mill is non-linear. This paper proposes a new method for diagnosis of rolling mill using wavelet to solve this problem. Proposed method that measures the vibration signals of rolling mill on-line and analyze it using wavelet to acquire pattern datas. And we design a nero-fuzzy model that diagnose a rolling mill using this data. Validity of the new method is asserted by numerical simulation.