• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.481-486
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• 2006

In this paper, a new on-line high-voltage motor monitoring system is proposed. This system monitors the insulation condition of the stator winding by on-line measurements of partial discharge (PD). And this system displays magnitude and phase angle distribution of PD. Sensor, CC (Ceramic Coupler) is used for PD measurement. PD signals are continuously measured and digitized with a A/D converter to build the database of the high-voltage motor's insulation condition. Also, this system can communicate with the central monitoring system via RS-232, 422, and TCP/IP. This helps more efficient operation and maintenance of the high-voltage motor.

• Structural Monitoring and Maintenance
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• v.3 no.1
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• pp.51-69
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• 2016

Monitoring the performance and estimating the remaining useful life of aging civil infrastructure in the United States has been identified as a major objective in the civil engineering community. Structural health monitoring has emerged as a central tool to fulfill this objective. This paper presents a review of the major structural monitoring programs that have been recently implemented in the United States, focusing on the integrity and performance assessment of large-scale structural systems. Applications where response data from a monitoring program have been used to detect and correct structural deficiencies are highlighted. These applications include (but are not limited to): i) Post-earthquake damage assessment of buildings and bridges; ii) Monitoring of cables vibration in cable-stayed bridges; iii) Evaluation of the effectiveness of technologies for retrofit and seismic protection, such as base isolation systems; and iv) Structural damage assessment of bridges after impact loads resulting from ship collisions. These and many other applications show that a structural health monitoring program is a powerful tool for structural damage and condition assessment, that can be used as part of a comprehensive decision-making process about possible actions that can be undertaken in a large-scale civil infrastructure system after potentially damaging events.

• Journal of the Korea institute for structural maintenance and inspection
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• v.3 no.3
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• pp.223-236
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• 1999

The need to monitor and undertake remidial works on large structures has greatly increased in recent years due to the appearance of widespread faults in large structures such as bridges and buildings, etc, of 20 or more years of age. The health condition of structures must be monitored continuously to maintenance the structures. In order to do in-situ monitoring, the sensor is necessary to be embedded in the structures. Fiber optic sensors can be embedded in the structures to get the health information in the structures. The fiber sensor was constructed with $3{\times}3$ fiber couplers to sense the multi-point strains and failure instants. The 4 RC (reinforced concrete) beams were made to 2 of A type, 2 of B type beams. These beams were reinforced by the reinforcing bars, and were tested under the flexural loading. The behavior of the beams was simultaneously measured by the fiber optic sensors, electrical strain gages, and LVDT. The states of the beams were interpreted by these all signals. By these experiments, There were verified that the fiber optic sensors could measure the structural strains and failure instants of the RC beams, The fiber sensors were well operated until the failure of the beams. It was shown that the strains of the reinforcing steel bar can be used to monitor the health condition of the beams through the flexural test of RC beams. On the other words, the results were arrived that the two strains in the reinforcing bar measured at the same point can give the information of the structural health status. Also, the failure instants of beams were well detected from the fiber optic filtered signals.

• Proceedings of the KIEE Conference
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• 2006.11a
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• pp.396-398
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• 2006

Predictive maintenance is not an unfamiliar concept because it has been used to predict the failures of electrical equipment such as transformers, motors and so on. By thoroughly monitoring the status of individual equipment and tracing how the various characteristics change over time, we can be aware of its exact condition and prevent the impending failure by taking appropriate actions. In this paper, I will extend the concept of predictive maintenance for individual electrical equipment to the power distribution system and show how to use the data obtained from power quality monitors to improve the power system.

• International conference on construction engineering and project management
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• 2011.02a
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• pp.376-381
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• 2011

In Korea Water Resources Corporation (K-Water) has seen four problems rising in four aspects of property management of approximately 1,300 buildings scattered through put to country. To solve these, ground data for repair and replacement works to be conducted for prevention will be prepared and building maintenance system (hereinafter referred to as PBMS) intended to record related repair and replacement work histories and calculate LCC of the related these items will be developed. PBMS, a web-based system, will be developed for users' convenience and data monitoring in real time. To sum up, PBMS are expected to maximize efficiency in four aspects including the establishment of repair and replacement work plans for prevention, history management, DB for predicting future work to be occurred and enable the determination of priorities by being developing into facility condition assessment systems through the results of analysis of repair and replacement histories and LCC.

• Nuclear Engineering and Technology
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• v.52 no.9
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• pp.1998-2008
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• 2020

Solenoid operated valves (SOV) play important roles in industrial process to control the flow of fluids. Solenoid valves can be found in so many industries as well as the nuclear plant. The ability to be able to detect the presence of faults and predicting the remaining useful life (RUL) of the SOV is important in maintenance planning and also prevent unexpected interruptions in the flow of process fluids. This paper proposes a fault diagnosis method for the alternating current (AC) powered SOV. Previous research work have been focused on direct current (DC) powered SOV where the current waveform or vibrations are monitored. There are many features hidden in the AC waveform that require further signal analysis. The analysis of the AC powered SOV waveform was done in the time and frequency domain. A total of sixteen features were obtained and these were used to classify the different operating modes of the SOV by applying a machine learning technique for classification. Also, a deep neural network (DNN) was developed for the prediction of RUL based on the failure modes of the SOV. The results of this paper can be used to improve on the condition based monitoring of the SOV.

• Tribology and Lubricants
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• v.35 no.5
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• pp.317-322
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• 2019

In spite of progress in tribological research, machine component failure due to friction and wear has been reported frequently. This failure may lead to secondary damage that can cause huge expense for maintenance and repair. To prevent economic loss, it is important to detect and predict the initial failure point. In this sense, various researchers have been tried to develop Condition Monitoring (CM) method using Acoustic Emission (AE) generated while the materials undergo failure. In this study, effect of particles on friction and wear was investigated using the pin-on-plate friction test and AE signal was recorded with a band-width type AE sensor. The experiments were performed in dry and lubricant conditions using steel and glass as specimens. After the experiment, 3D laser microscope image was captured to evaluate the wear behavior quantitatively. The AE signal was analyzed in time-domain and frequency-domain. The amplitude was compared with the frictional results. The results of this study showed that particle generation accelerate wear, generate high magnitude AE signal and change the frequency characteristics of the signal. Also, lubricant condition test results showed low coefficient of friction, low wear rate, and low magnitude of AE signal compared to the dry condition. It is expected that the results of this study will aid in better assessment of wear in CM technology

• Journal of the Korean Society of Propulsion Engineers
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• v.17 no.2
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• pp.71-83
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• 2013

Recently, the advanced condition monitoring methods such as the model-based method and the artificial intelligent method have been applied to maximize the availability as well as to minimize the maintenance cost of the aircraft gas turbines. Among them the non-linear GPA(Gas Path Analysis) method and the GA(Genetic Algorithms) have lots of advantages to diagnose the engines compared to other advanced condition monitoring methods such as the linear GPA, fuzzy logic and neural networks. Therefore this work applies both the non-linear GPA and the GA to diagnose AE3007 turbofan engine for an aircraft, and in case of having sensor noise and bias it is confirmed that the GA is better than the GPA through the comparison of two methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.5
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• pp.923-930
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• 2016

Various pipes are used in the many industrial field such as water supply, drainage system and marine plants, so a maintenance of these pipes is essential. Especially, the maintenance of the piping in the industrial field, some professional staffs enter and clean the pipe. If the professional staffs can not enter and clean the pipe, the workers has to use the method of inserting a scraper connected to wire inside the pipe. However, this method demands huge budget and causes a number of problems such as traffic congestion. To solve these problems, pipe cleaning robot has been researching and developing. Many Pipe cleaning robots have a problem, that is impossible to confirm the operating condition of the robot in a real time. This paper suggest pipe cleaning robot with RS-485 which transmit operating and cleaning condition of robot and inner pipe filmed by camera, that user can check.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.18 no.1
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• pp.42-49
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• 2009

This study proposes a web-based remote monitoring system for evaluating degradation of machine tools using ART2(Adaptive Resonance Theory 2) neural network. A number of studies on the monitoring of machine tools using neural networks have been reported. However, when normal condition is changed due to factors such as maintenance, tool change etc., or a new failure signal is generated, such algorithms need to be entirely retrained in order to accommodate the new signals. To cope with such problems, this study develops a remote monitoring system using ART2 in which new signals when required are simply added to the classes previously trained. This system can monitor degradation as well as failure of machine tools. To show the effectiveness of the proposed approach, the system is experimentally applied to monitoring a simulator similar to the main spindle of a machine tool, and the results show that the proposed system can be extended to monitoring of real industrial machine tools and equipment.