• Proceedings of the KIEE Conference
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• 2011.07a
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• pp.1850-1851
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• 2011

The faults of a electric motor cause to rise the maintenance and repair cost and to reduce the reliability of the electric power system. In this paper, the auto fault detection system for a induction motor is developed using the torque signal spectrum analysis. The spectrum of motor torque signal is used for finding a bearing fault feature frequency. A threshold value, for detecting the motor bearing fault is set by the difference of torque signal spectrum(FFT signal) between normal condition and faulted condition of the motor.

• Tribology and Lubricants
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• v.3 no.1
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• pp.52-60
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• 1987

Many kinds of condition monitoring technique as the preventive maintenance technique have been studied, so this study has investigated the possibility of chbcking the trend in the fault diagnosis of ball bearing, one of the important elements of rotating machine, by applying the cepstral analysis method. And computer simulation is conducted in order to identify obviously the physical meaning of cepstral analysis. It is identified that cepstral analysis is effective method to distinguish between the basic and reflected wave by computer simulation, and we know that it is possible to apply the cepstral analysis to the arbitrary elements of rotating machine which are different in fundamental frequency. It is verified that cepstral analysis method is more effective than the other conventional method in bearing fault diganosis.

• Proceedings of the Korean Geotechical Society Conference
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• 2004.03b
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• pp.74-81
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• 2004

In this study, static end loading tests with load transfer measurement were accomplished for large diameter drilled shaft constructed in fault zone. Yield pile capacity (or ultimate pile capacity) from load-settlement curve was determined and axial load transfer behavior was measurd. The end bearing capacity was increased 2 times due to grouting the toe ground under pile base.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.17 no.11
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• pp.1102-1111
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• 2007

The theory for a fault-tolerant control of homopolar magnetic bearings is developed. New coil winding law is utilized such that control fluxes are isolated for an 8-pole homopolar magnetic bearing. Decoupling chokes are not required for the fault tolerant magnetic bearing since C-core fluxes are isolated. If some of the coils or power amplifiers suddenly fail, the remaining coil currents change via a distribution matrix such that the same magnetic forces are maintained before and after failure. Lagrange multiplier optimization with equality constraints is utilized to calculate the optimal distribution matrix that maximizes the load capacity of the failed bearing. Some numerical examples of distribution matrices are provided to illustrate the theory. Simulations show that very much the same dynamic responses (orbits or displacements) are maintained throughout failure events while currents and fluxes change significantly.

• Journal of the Korean Society for Nondestructive Testing
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• v.33 no.4
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• pp.355-360
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• 2013

With the modern machinery towards the direction of high-speed development, the thermal issues of mechanical transmission system and its components is increasingly important. Ball bearing is one of the main parts in rotating machinery system, and is a more easily damaged part. In this paper, bearing thermal fault detection is investigated in details Using infrared thermal imaging technology to the operation state of the ball bearing, a preliminary thermal analysis, and the use of numerical simulation technology by finite element method(FEM) under thermal conditions of the bearing temperature field analysis, initially identified through these two technical analysis, bearing a temperature distribution in the normal state and failure state. It also shows the reliability of the infrared thermal imaging technology. with valuable suggestions for the future bearing fault detection.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.11b
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• pp.611-616
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• 2002

Bearing diagnostics is difficult in a gearbox because bearing signals are masked by the strong gear signals. Self adaptive noise cancellation(SANC) is useful technique to seperate bearing signals from gear signals. While gear signals are correlated with a long correlation length, bearing signals are not correlated with a short length. SANC seperates two components on the basis of correlation length. Then we can find defect frequency component in the envelope spectrum of the bearing signals.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.6
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• pp.551-558
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• 2013

A support vector machine (SVM) is a very powerful classification algorithm when a set of training data, each marked as belonging to one of several categories, is given. Therefore, SVM techniques have been used as one of the diagnostic tools in machine learning as well as in pattern recognition. In this paper, we present the results of classifying ball bearing fault types and severities using SVM with an optimized feature set based on the minimum distance rule. A feature set as an input for SVM includes twelve time-domain and nine frequency-domain features that are extracted from the measured vibration signals and their decomposed details and approximations with discrete wavelet transform. The vibration signals were obtained from a test rig to simulate various bearing fault conditions.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.4
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• pp.179-188
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• 2022

There have been many successful researches on a bearing fault diagnosis based on Deep Learning, but there is still a critical issue of the data distribution difference between training data and test data from their different working conditions causing performance degradation in applying those methods to the machines in the field. As a solution, a data adaptation method has been proposed and showed a good result, but each and every approach is strictly limited to a specific applying scenario or presupposition, which makes it still difficult to be used as a real-world application. Therefore, in this study, we have proposed a method that, using a data transformation with MFCCs and a simple CNN architecture, can perform a robust diagnosis on a target domain data without an additional learning or tuning on the model generated from a source domain data and conducted an experiment and analysis on the proposed method with the CWRU bearing dataset, which is one of the representative datasests for bearing fault diagnosis. The experimental results showed that our method achieved an equal performance to those of transfer learning based methods and a better performance by at least 15% compared to that of an input transformation based baseline method.

• Journal of IKEEE
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• v.26 no.3
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• pp.488-493
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• 2022

In manufacturing sites, bearing fault in eletrically driven motors cause the entire system to shut down. Stopping the operation of this environment causes huge losses in time and money. The reason of this bearing defects can be various factors such as wear due to continuous contact of rotating elements, excessive load addition, and operating environment. In this paper, a motor driving environment is created which is similar to the domestic manufacturing sites. In addition, based on the established environment, we propose a dataset for bearing fault detection by collecting changes in vibration characteristics that vary depending on normal and defective conditions. The sensor used to collect the vibration characteristics is Microphone G.R.A.S. 40PH-10. We used various machine learning models to build a prototype bearing fault detection system trained on the proposed dataset. As the result, based on the deep neural network model, it shows high accuracy performance of 92.3% in the time domain and 98.3% in the frequency domain.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.134-138
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• 2011

Fault detection for dynamic loading conditions of rotational machineries was considered from the contactless, non-destructive infrared thermographic method, rather than the traditional diagnosis method. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiment was performed as an alternative way proceeding the traditional fault monitoring. In addition, the thermographic experiments were compared with the vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results, it was concluded the temperature characteristics of the ball bearing under dynamic loading conditions were analyzed thoroughly.