• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.7 no.4
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• pp.42-49
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• 1998

This wear debris can be harvested from the lubricants of operating machinery and its morphology is directly related to the damage to the interacting surface from which the particles originated. The morphological identification of wear debris can therefore provide very early detection of a fault and can also often facilitate a diagnosis. The purpose of this study is to attempt the developement of intelligent system for moving condition diagnosis of the machine driving system. The four shape parameter(50% volumetric diameter, aspect, roundness and reflectivity) of war debris are used as inputs to the neural network and learned the moving condition of five values(material3, applied load 1, sliding distance 1). It is shown that identification results depend on the ranges of these shape parameter learned. The three kinds of the wear debris had a different pattern characteristics and recognized the moving condition and materials very well by neural network.

• Tribology and Lubricants
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• v.10 no.4
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• pp.51-58
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• 1994

Condition monitoring technology has recently been received much attention in the light of its significance on the maintenance of complex machineries such as turbines in power plants. Currently, turbines in power plants are maintained by scheduled overhaul based on the manufacturer's recommendations and the utility's experience. Although this preventive maintenance is known to be very effective, operators have less access to identify failure of elements when it happens between overhaul period. Therefore, in this study, a development of a on-line condition monitoring system through wear debris analysis of lubricating oils is aimed with a view to detecting abnormal wear behaviour of bearings and other wet-components at an early stage, allowing better outage scheduling and minimizing forced outages. For field application purposes, the on-line system developed was installed on the turbine of the No.4 unit at Ulsan Power Plant and its performance has been evaluated on site.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1997.10a
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• pp.191-199
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• 1997

Condition monitoring technology was applied for an air compressor lubricating system to achieve a proactive maintenance, which could prevent a catastrophic failure and detect root causes of the conditional failure of the system. For this work, various types of wear monitoring technology were used and compared with the results of vibration and temperature measurements. Results generally showed that every technology has a limitation to failure detection, and integrated-based condition monitoring should be performed for the best results. In this work, an idea for the implementing integrated wear monitoring was suggested and demonstrated.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• Journal of Biomedical Engineering Research
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• v.20 no.6
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• pp.601-607
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• 1999

상악(maxilla)과 하악(mandibular)의 최대 교합상태에서 하악을 기계적으로 자극(jaw jerk)할 경우 교근(masseter muscle)의 근전도 (electromyography)에서는 근신호가 일시적으로 침묵하는 형태의 휴지기(silent period) 현상이 발생한다. 턱관절 질환(temporo-mandibular joint dysfuntion)이 없는 정상인의 경우 24ms 정도의 휴지기가 나타나지만, 턱관절 질환 환자의 경우 평균 60ms 정도임을 볼 때 휴지기는 턱관절 질환을 진단하는 중요한 요소라 할 수있다. 본 논문에서는 이러한 휴지기 기간을 자동적으로 결정하기 위해 mCFAR 알고리즘을 제안하고 CLMS 적응 필터를 사용하여 근전도 신호의 왜곡을 가져오는 전원 잡음의 영향을 효과적으로 제거하였다. 실험 결과 전원 잡음에 대해 강건하며, 정확한 휴지기 기간을 결정할 수 있다.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2002.05a
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• pp.453-459
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• 2002

In this paper. the correlation among vibration. wear and temperature are experimentally investigated when rubbing is caused by static and dynamic forces. Each measurement reflects the characteristics of the system and is useful in detecting and diagnosing the current status of rotating machinery. For experiment, the rotor system with lubricating equipment such as trochoid pump, oil tank and wear detecting sensor is implemented to simulate the rubbing condition. Experimental results show that significant change in wear quantity can be notified when vibration signal is changed by rubbing. The results can be applied to system monitoring and fault diagnosis in rotating machinery.

• 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.

• Tribology and Lubricants
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• v.18 no.2
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• pp.117-126
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• 2002

Abstract - An integrated condition monitoring algorithm based on an expert system was implemented in this work in order to monitor effectively the machine conditions. The knowledge base was consisted of numeric data which meant the posterior probability of each measurement parameter for the representative machine failures. Also the inference engine was constructed as a series of statistical process, where the probable machine fault was inferred by a mapping technology of pattern recognition. The proposed algorithm was, through the user interface, applied for an air compressor system where the temperature, vibration and wear properties were measured simultaneously. The result of the case study was found fairly satisfactory in the diagnosis of the machine condition since the predicted result was well correlated to the machine fault occurred.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2003.05a
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• pp.950-955
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• 2003

This paper introduces an enhanced condition monitoring and diagnosis system recently developed for rotating machinery. In the system, the data aquisition/monitoring signal processing, machine condition classifier, case-based reasoning and demonstration modules are effectively integrated with user-friendliness so that machine operators can easily monitor and diagnose the status of rotating machinery in operation. Some of the new features include the directional spectrum, case-based reasoning and neural network techniques. And the demonstrator modules for fault diagnosis of a Bear driving system and for basic understanding of the rotor dynamics are provided to help the potential users better understand the system.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.29 no.2
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• pp.84-93
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• 2021

Airline schedule is the most basic data for flight operations and has significant importance to an airline's management. It is crucial to know the airline's current schedule status in order to effectively manage the company and to be prepared for abnormal situations. In this study, machine learning techniques were applied to actual schedule data to examine the possibility of whether the airline's fleet state could be artificially learned without prior information. Given that the schedule is in categorical form, One Hot Encoding was applied and t-SNE was used to reduce the dimension of the data and visualize them to gain insights into the airline's overall fleet status. Interesting results were discovered from the experiments where the initial findings are expected to contribute to the fields of airline schedule health monitoring, anomaly detection, and disruption management.