• 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 Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1328-1333
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• 2006

For fault diagnosis of machines and equipments, knowledge-based method has been used widely but has some limitations for complex systems. These can be covered by model-based method. As one kind of model-based method, Qualitative modeling diagnosis method is developed in this research. The developed method uses output signal only. In this method quantization of the output signal mattes automata which can characterize the flow of the signal pattern to normal and fault respectively. As an example of the qualitative diagnosis method, an electric tool which has faults at gear and bearing were examined in this research. The result shows that the developed method can diagnose the fault clearly for the two fault cases.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2016.10a
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• pp.819-820
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• 2016

Electrical power distribution is consists of high voltage, low voltage and motor control center(MCC). Motor control centers involves turning the motor on and off, it is configured electronic over current relay to detect a motor overcurrent flows. Existing electronic over current relay detects electrical fault such as overcurrent, undercurrent, phase sequence, negative sequence current, current unbalance and earth fault. However, it is difficult to detect mechanical fault such as locked rotor, motor stator and rotor and bearing fault. In this paper, we propose a condition monitoring and fault diagnosis system for electrical and mechanical fault detection of rotating machinery. The proposed system is designed with signal input and control part, system interface part and data acquisition board for condition monitoring and fault diagnosis, it was possible to detect electrical fault and mechanical fault through measurement and control of insulation resistance, locked rotor, MC counter and bearing temperature.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.12
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• pp.1233-1240
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• 2004

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the nitration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Smart Structures and Systems
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• v.6 no.4
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• pp.389-403
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• 2010

By using image recognition technology, this paper presents a new fault diagnosis method for rotating machinery with artificial immune algorithm. This method focuses on the vibration state parameter image. The main contribution of this paper is as follows: firstly, 3-D spectrum is created with raw vibrating signals. Secondly, feature information in the state parameter image of rotating machinery is extracted by using Wavelet Packet transformation. Finally, artificial immune algorithm is adopted to diagnose rotating machinery fault. On the modeling of 600MW turbine experimental bench, rotor's normal rate, fault of unbalance, misalignment and bearing pedestal looseness are being examined. It's demonstrated from the diagnosis example of rotating machinery that the proposed method can improve the accuracy rate and diagnosis system robust quality effectively.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.11a
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.05a
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• pp.603-608
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• 2001

In order to distinguish fault electric motors automatically in real time. an intelligent diagnosis technique may be required. This paper presents an automatic fault detection system for electric motors by using their acoustic noises. Time signals of each candidate motor were measured in an anechoic chamber for further analysis. Spectral analysis was first carried out and they showed that two typical types of fault motors could be successfully distinguished in the frequency domain; bearing faults and scratches. Unlike the trend of normal motors that shows only a single dominant peak at around 2000 ㎐, several peaks are bunched together in bearing fault motors. On the other hand, large frequency noises at around 6500 ㎐ are newly arisen in scratchy fault motors. However, the processing time for spectral analysis was rather long for a real time application in production lines. Thus, a number of band-pass filters were used in the time domain instead for a real time application. Before applying filters, the bands of filters were set from the information of spectral analysis. By applying a set of band-pass filters, the RMS values of each filtered signal were calculated, and thus the normal and damaged motors could be successfully distinguished.

• 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.

• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.10
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• pp.1-8
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• 2023

If there is a defect in the wheel bearing, which is a major part of the car, it can cause problems such as traffic accidents. In order to solve this problem, big data is collected and monitoring is conducted to provide early information on the presence or absence of wheel bearing failure and type of failure through predictive diagnosis and management technology. System development is needed. In this paper, to implement such an intelligent wheel hub bearing maintenance system, we develop an embedded system equipped with sensors for monitoring reliability and soundness and algorithms for predictive diagnosis. The algorithm used acquires vibration signals from acceleration sensors installed in wheel bearings and can predict and diagnose failures through big data technology through signal processing techniques, fault frequency analysis, and health characteristic parameter definition. The implemented algorithm applies a stable signal extraction algorithm that can minimize vibration frequency components and maximize vibration components occurring in wheel bearings. In noise removal using a filter, an artificial intelligence-based soundness extraction algorithm is applied, and FFT is applied. The fault frequency was analyzed and the fault was diagnosed by extracting fault characteristic factors. The performance target of this system was over 12,800 ODR, and the target was met through test results.