• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 1998.10a
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• pp.11-16
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• 1998

Condition monitoring technique used in the military is JOAP, for which the spectroscopic analysis is applied. However it is known that the technique has some problems in economical and practical point of view. This study introduces a new technique for measuring wear in real time base and applies the technique to a high power semi-automatic transmission. The results show the feasibility by measuring the concentration of ferrous wear particles from the transmission oil.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.61 no.11
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• pp.1732-1736
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• 2012

To increase speed up of train, in the field of catenary system, it is necessary to develop of new monitoring methods for dynamic interaction between pantograph and contact wire. Also, there is a need to develop technologies that constantly measure are from various railway structure such as uplift of contact wire, vibration of catenary, dynamic strain of contact line in tunnel. In this paper condition monitoring systems for dynamic performance of catenary systems in tunnel were proposed. An advanced method and results of field tests using high speed camera for monitoring of vertical upward movement of the grooved contact wire due to the force produced from the pantograph were presented. The proposed uplift measurement system of contact wire is expected to enhance precision of current collection quality performance assessment methods at high-speed lines.

• Nuclear Engineering and Technology
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• v.37 no.2
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• pp.127-138
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• 2005

Approaches to several recent issues in the operation of nuclear power plants using computational intelligence are discussed. These issues include 1) noise analysis techniques, 2) on-line monitoring and sensor validation, 3) regularization of ill-posed surveillance and diagnostic measurements, 4) transient identification, 5) artificial intelligence-based core monitoring and diagnostic system, 6) continuous efficiency improvement of nuclear power plants, and 7) autonomous anticipatory control and intelligent-agents. Several changes to the focus of Computational Intelligence in Nuclear Engineering have occurred in the past few years. With earlier activities focusing on the development of condition monitoring and diagnostic techniques for current nuclear power plants, recent activities have focused on the implementation of those methods and the development of methods for next generation plants and space reactors. These advanced techniques are expected to become increasingly important as current generation nuclear power plants have their licenses extended to 60 years and next generation reactors are being designed to operate for extended fuel cycles (up to 25 years), with less operator oversight, and especially for nuclear plants operating in severe environments such as space or ice-bound locations.

• Smart Structures and Systems
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• v.14 no.2
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Tribology and Lubricants
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• v.24 no.3
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• pp.111-121
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• 2008

An integrated in-line oil monitoring detector assigned for continuous in situ monitoring multiple parameters of oil performance for predicting economically optimal oil change intervals and equipment condition control is presented in this study. The detector estimates oil deterioration based on the information about chemical degradation, total contamination, water content of oil and oil temperature. The oil oxidation is estimated by "chromatic ratio", total contamination is measured by the changes in optical intensity of oil in three optical wavebands ("Red", "Green" and "Blue") and water content is evaluated as Relative Saturation of oil by water. The detector is able to monitor oils with low light absorption (hydraulic, transformer, turbine, compressor and etc. oils) as well as oils with rather high light absorption in visible waveband (diesel and etc. oils). In a case study that the detector is applied to a diesel engine oil, it is found that the detector provides good results on oil chemical degradation as well as soot concentration.

• Proceedings of the KIEE Conference
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• 2005.10b
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• pp.264-266
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• 2005

Recently, on-line diagnosis methods through wired and wireless networks are widely adopted in the diagnosis of industrial Electric Facilities, such as generators, transformers and motors. Also smart sensors which includes sensors, signal conditioning circuits and micro-controller in one board are widely studied in the field of condition monitoring. This paper suggests an self-powered system suitable for condition-monitoring smart sensors, which uses parasitic vibrations of the facilities as energy source. First, vibration-driven noise patterns of the electric facilities are presented. And then, an electromagnetic generator which uses mechanical mass-spring vibration resonance are suggested and designed. Finally energy consumption of the presented smart sensor, which consists of MEMS vibration sensors, signal conditioning circuits, a low-power consumption micro-controller, and a ZIGBEE wireless tranceiver, are presented. The usefulness and limits of the presented electromagnetic generators in the field of electric facility monitoring are also suggested.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.14 no.2
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• pp.119-127
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• 2004

Butterfly valves are popularly used in service in the industrial and water works pipeline systems with large diameter because of its lightweight, simple structure and the rapidity of its manipulation. Sometimes cavitation can occur. resulting in noise, vibration and rapid deterioration of the valve trim, and do not allow further operation. Thus, the monitoring of cavitation is of economic interest and is very importance in industry. This paper proposes a condition monitoring scheme using statistical feature evaluation and support vector machine (SVM) to detect the cavitation conditions of butterfly valve which used as a flow control valve at the pumping stations. The stationary features of vibration signals are extracted from statistical moments. The SVMs are trained, and then classify normal and cavitation conditions of control valves. The SVMs with the reorganized feature vectors can distinguish the class of the untrained and untested data. The classification validity of this method is examined by various signals that are acquired from butterfly valves in the pumping stations and compared the classification success rate with those of self-organizing feature map neural network.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2012.10a
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• pp.388-393
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• 2012

Vibration condition monitoring at low rotational speeds is still a challenge. Acoustic emission (AE) is the most used technique when dealing with low speed bearings. At low rotational speeds, the energy induced from surface contact between raceway and rolling elements is very weak and sometimes buried by interference frequencies. This kind of issue is difficult to solve using vibration monitoring. Therefore some researchers utilize artificial damage on inner race or outer race to simplify the case. This paper presents vibration signal analysis of low speed slewing bearings running at a low rotational speed of 15 rpm. The natural damage data from industrial practice is used. The fault frequencies of bearings are difficult to identify using a power spectrum. Therefore the relatively improved method of empirical mode decomposition (EMD), ensemble EMD (EEMD) is employed. The result is can detect the fault frequencies when the FFT fail to do it.

• 한국지구물리탐사학회:학술대회논문집
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• 2007.12a
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• pp.31-42
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• 2007

Dc resistivity monitoring has been increasingly used in order to understand the changes of subsurface conditions in terms of conductivity. The commonly adopted interpretation approach which separately inverts time-lapse data may generate inversion artifacts due to measurement error. Eventually the contaminated error amplifies the artifacts when reconstructing the difference images to quantitatively estimate the change of ground condition. In order to alleviate the problems, we defined the subsurface structure as four dimensional (4-D) space-time model and developed 4-D inversion algorithm which can calculate the reasonable subsurface structure continuously changing in time even when the material properties change during data measurements. In this paper, we discussed two case histories of resistivity monitoring to study the ground condition change when the properties of the subsurface material were artificially altered by injecting conductive materials into the ground: (1) dye tracer experiment to study the applicability of electrical resistivity tomography to monitoring of water movement in soil profile and (2) the evaluation of cement grouting performed to reinforce the ground. Through these two case histories, we demonstrated that the 4-D resistivity imaging technique is very powerful to precisely delineate the change of ground condition. Particularly owing to the 4-D inversion algorithm, we were able to reconstruct the history of the change of subsurface material property.