• 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.

• Journal of the Korea Convergence Society
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• v.11 no.11
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• pp.47-53
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• 2020

There is a need for a system that provides early warning of presence and type of failure of automobile wheel bearings through the application of predictive fault analysis technologies. In this paper, we presented a sensor module mounted on a wheel bearing and a diagnostic system that collects, stores and analyzes vehicle acceleration information and vibration information from the sensor module. The developed sensor module and predictive analysis system was tested and evaluated thorough excitation test equipment and real automotive vehicle to prove the effectiveness.

• Economic and Environmental Geology
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• v.31 no.6
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• pp.463-472
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• 1998

The survey was carried out in order to delineate the occurrence of ore deposits and the mineralized characteristics in the Estancia de la Virgen area through the 1:2,000 scaled geological mapping and topographic measuring surveys. Gold-silver mineralization is in the fault block developed between the San Agustin Fault and Cabanas Fault. It is associated with ore bearing quartz veins controlled by the fault structure. The contents of Au and Ag range from traces up to 72 g/t and 180 g/t respectively. According to traversing the outcrops, the quartz veins are traced by 0.5 Km trended to north and south. In those extended part, they continue for 1,000 m intermittently. Gold-silver mineralization could be divided into three stages. In the first stage, pyrite, galena, sphalerite, and chalcopyrite were formed with the primary silver and gold associated with galena and copper sulfides respectively. In the second stage, Cu-Bi-Au-Ag bearing sulfides such as chalcocite, covellite, and linarite are formed and usually deposited on the cataclastic fractures of galena and/or chalcopyrite. In the third stage, both the carbonation of galena and sphalerite and the sulphatization of galena, took place in the surface environment. And then primary silver was carried away off and was deposited on galena and/or copper sulfides during oxidation near the water table. Low partitionings of Fe in sphalerite assist that the minerals were formed at the relatively low temperature, which is coincided with previously reported homogenization temperature of fluid inclusions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.1
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• pp.75-84
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• 2018

Increasing the operation rate of components and stabilizing the operation through timely management of the core parts are crucial for improving the efficiency of the railroad maintenance industry. The demand for diagnosis technology to assess the condition of rolling stock components, which employs history management and automated big data analysis, has increased to satisfy both aspects of increasing reliability and reducing the maintenance cost of the core components to cope with the trend of rapid maintenance. This study developed a big data platform-based system to manage the rolling stock component condition to acquire, process, and analyze the big data generated at onboard and wayside devices of railroad cars in real time. The system can monitor the conditions of the railroad car component and system resources in real time. The study also proposed a machine learning technique that enabled the distributed and parallel processing of the acquired big data and automatic component fault diagnosis. The test, which used the virtual instance generation system of the Amazon Web Service, proved that the algorithm applying the distributed and parallel technology decreased the runtime and confirmed the fault diagnosis model utilizing the random forest machine learning for predicting the condition of the bearing and wheel parts with 83% accuracy.

• The Journal of Society for e-Business Studies
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• v.23 no.2
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• pp.111-121
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• 2018

Many problems in rotating machinery such as aircraft engines, wind turbines and motors are caused by bearing defects. The abnormalities of the bearing can be detected by analyzing signal data such as vibration or noise, proper pre-processing through a few signal processing techniques is required to analyze their frequencies. In this paper, we introduce the condition monitoring method for diagnosing the failure of the rotating machines by analyzing the vibration signal of the bearing. From the collected signal data, the normal states are trained, and then normal or abnormal state data are classified based on the trained normal state. For preprocessing, a Hamming window is applied to eliminate leakage generated in this process, and the cepstrum analysis is performed to obtain the original signal of the signal data, called the formant. From the vibration data of the IMS bearing dataset, we have extracted 6 statistic indicators using the cepstral coefficients and showed that the application of the Mahalanobis distance classifier can monitor the bearing status and detect the failure in advance.

• International Journal of Control, Automation, and Systems
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• v.4 no.6
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• pp.763-768
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• 2006

The two-for-one(TFO) twister is precision machinery that twists fibers rapidly under constant tension. Since the quality of the twisted yarn is directly deteriorated by faults of the twister, such as the distortion of the spinning axis, bearing abrasion, and tension irregularity, it is important to detect faults of the TFO twister at an early stage. In this research, a new algorithm is proposed to detect faults of the TFO twister and their causes, by measuring the vibrations of the TFO twister and obtaining frequency components with a FFT algorithm. The TFO twister with faults showed increased vibrations and each fault generated vibrations at different frequencies. By analyzing changes of characteristics of vibrations, we can determine faulty twisters. The proposed fault detection algorithm can be implemented cheaply with a signal processor chip. It can be used to find when to repair a faulty TFO twister without much loss of yam on-line.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.6
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• pp.413-422
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• 2022

In this paper, the fault analysis of the momentum wheel, which is a high-speed rotary machinery of 'Control Moment Gyro' for medium and large satellite, was described. For fault diagnosis, envelope spectrum analysis was performed using Hilbert transformation method and signal demodulation method to find the impact signals periodically generated from amplitude modulated signals. Through this, the fault of the momentum wheel was diagnosed by analyzing whether there was a harmonic component of the rotational frequency and a bearing fault frequency in a specific frequency band with a high peak.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.5
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• pp.373-386
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• 2023

To analyze the prediction of geological conditions and water-bearing zones, TSP was performed in the collapse zone of the fault zone. The results of the TSP were verified by comparing them to the face mapping results of the prediction zone. The rock quality prediction result of the TSP had an error of about 3 to 10 meters compared to the face mapping result, but the overall rock quality change and ground condition were analyzed to be relatively similar. In the water-bearing zones of the face mapping results, the Vp/Vs ratio ranges from 1.79 to 2.37 and the Poisson's ratio ranges from 0.27 to 0.39. In the sections other than the water-bearing zones, the Vp/Vs ratio ranges from 1.61 to 1.89, and the Poisson's ratio ranges from 0.19 to 0.3. As a result of analyzing the Vp/Vs ratio and Poisson's ratio in the water-bearing zones, it is analyzed that the sections with a Vp/Vs ratio of 2.0 or more and a Poisson's ratio of 0.3 or more have a high possibility of being water-bearing zones.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.16 no.2
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• pp.128-139
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• 2017

Railway is one of the public transportation systems along with shipping and aviation. With the recent introduction of high speed train, its proportion is increasing rapidly, which results in the higher risk of catastrophic failures. The wheel bearing to support the train is one of the important components requiring higher reliability and safety in this aspect. Recently, many studies have been made under the name of prognostics and health management (PHM), for the purpose of fault diagnosis and failure prognosis of the bearing under operation. Among them, the most important step is to extract a feature that represents the fault status properly and is useful for accurate remaining life prediction. However, the conventional features have shown some limitations that make them less useful since they fluctuate over time even after the signal de-noising or do not show a distinct pattern of degradation which lack the monotonic trend over the cycles. In this study, a new method for feature extraction is proposed based on the observation of relative frequency energy shifting over the cycles, which is then converted into the feature using the information entropy. In order to demonstrate the method, traditional and new features are generated and compared using the bearing data named FEMTO which was provided by the FEMTO-ST institute for IEEE 2012 PHM Data Challenge competition.