• Journal of the Korea Institute of Information Security & Cryptology
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• v.29 no.6
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• pp.1403-1412
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• 2019

Effect of damage and loss cost for downtime is huge, if physical devices such as turbines, pipe, and storage tanks are in the abnormal state originated from not only aging, but also cyber attacks on the control and monitoring system like PLC (Programmable Logic Controller). To improve availability and dependability of the physical devices, we design and implement an indirect health monitoring system which sense temperature, acceleration, current, etc. indirectly, and put sensor data into Influx DB in real-time. Then, the actual performance of detecting abnormal state is shown using the indirect health monitoring system. Analyzing data are acquired using the real-time indirect health monitoring system, abnormal state and security threats can be double-monitored and lower maintenance cost utilizing prognostics and health management.

• Journal of the Korea institute for structural maintenance and inspection
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• v.4 no.3
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• pp.185-195
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• 2000

Detection of cracks in concrete beams using optical fiber sensors is useful for monitoring of concrete structures. In this study, optical time domain reflectometry (OTDR) is used to detect cracks. Resolution of OTDR is the main contributor to detect cracks in concrete structures. The OTDR used in this study can detect cracks with high precision of 0.5 m. Two mortar beams, reinforced with a 19 mm diameter steel bar, are made with the dimensions of 140 mm (width) ${\times}$ 200 mm (depth) ${\times}$ 2.000 mm (length). Two fibers are embedded inside each beam and two fibers are attached under the beams. The application of measurement system which consists of fiber and FC/PC connecter is studied. For this, theory of optics, resolution, crack moment, and size of specimens are investigated. From the measured data, it is verified that fibers which are attached under the beam can detect the crack in beams effectively. However, fibers embedded inside the beam are unable to detect cracks in beams using the OTDR in this study.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.4
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• pp.751-756
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• 2018

In smart factories and industrial IoT, all facilities in a factory are monitored over the Internet, thereby facility can reduce the downtime and increase the availiability by preventive maintenance before it breaks down. The abnormal conditions of the major facilities in the plant are caused by abnormal temperature rise, vibration, and variations in noise. Consequently, it is critical to develop a very small smart device that is easily installed in a small space to enable real-time monitoring of the vibration status of the facility. In this study, smart devices were developed for smart factory fault prediction and robustness management using ultra small micro-controllers with WiFi capabilities and MEMS acceleration sensors.

• Journal of the Korea institute for structural maintenance and inspection
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• v.16 no.2
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• pp.1-9
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• 2012

Reinforced concrete buildings will be deteriorated as passed time or effect of an earthquake, etc in main elements. In order to manage such cracks, time and efforts, expense, etc, are required. So micro lead switch sensors are embedding or bonding in flexible specimens, and these are smart elements for diagnostic crack damages by external force such as physical load, dynamic load, etc in this study. The monitoring to crack damages are studied using radio frequency system. If load is received on the center of flexible specimens, embedded and bonded lead switch sensors will be destroyed, and these become to send signals of damages at radio frequency system connected with lead switch sensors. This study is fundamental research of the diagnostic system for architectural elements using radio frequency.

• Journal of the Korea institute for structural maintenance and inspection
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• v.22 no.3
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• pp.69-74
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• 2018

Fiber-Reinforced Cementitious Composites (FRCCs) have electrical conductivity by inserting reinforced conductive fibers into a cementitious matrix. Such characteristic allows us to utilize FRCCs for crack monitoring of a structure by measuring electrical responses without sensor installation. However, the electrical responses are often sensitively altered by temperature variation as well as crack initiation. The temperature variation may disturb crack detection on the measured electrical responses. Moreover, as sensing probes for measuring electrical reponses increase, undesired contact noises are often augmented. In this paper, a self-sensing impedance circuit is specially designed for reducing the number of sensing probes. The crack initiation and temperature variation effects on the self-sensing impedance responses of FRCCs are experimentally investigated using the self-sensing impedance circuit. The experiment results reveal that the electrical impedance response are more sensitively changed due to temperature variation than crack initiation.

• Ceramist
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• v.23 no.1
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• pp.54-88
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• 2020

Global effort has resulted in tremendous progress with energy harvesters that extract mechanical energy from ambient sources, convert it to electrical energy, and use it for systems such as wrist watches, mobile electronic devices, wireless sensor nodes, health monitoring, and biosensors. However, harvesting a single energy source only still pauses a great challenge in driving sustainable and maintenance-free monitoring and sensing devices. Over the last few years, research on high-performance mechanical energy harvesters at the micro and nanoscale has been directed toward the development of hybrid devices that either aim to harvest mechanical energy in addition to other types of energies simultaneously or to exploit multiple mechanisms to more effectively harvest mechanical energy. Herein, we appraise the rational designs for multiple energy harvesting, specifically state-of-the-art hybrid mechanical energy harvesters that employ multiple piezoelectric and triboelectric mechanisms to efficiently harvest mechanical energy. We identify the critical material parameters and device design criteria that lead to high-performance hybrid mechanical energy harvesters. Finally, we address the future perspectives and remaining challenges in the field.

• 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

• Smart Structures and Systems
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• v.21 no.6
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• pp.741-749
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• 2018

Structural health monitoring (SHM) systems are necessary to achieve smart predictive maintenance and repair planning as well as they lead to a safe operation of mechanical structures. In the context of vibration-based SHM the measured structural responses are employed to draw conclusions about the structural integrity. This usually leads to a mathematically illposed inverse problem which needs regularization. The restriction of the solution set of this inverse problem by using prior information about the damage properties is advisable to obtain meaningful solutions. Compared to the undamaged state typically only a few local stiffness changes occur while the other areas remain unchanged. This change can be described by a sparse damage parameter vector. Such a sparse vector can be identified by employing $L_1$-regularization techniques. This paper presents a novel framework for damage parameter identification by combining sparse solution techniques with an Extended Kalman Filter. In order to ensure sparsity of the damage parameter vector the measurement equation is expanded by an additional nonlinear $L_1$-minimizing observation. This fictive measurement equation accomplishes stability of the Extended Kalman Filter and leads to a sparse estimation. For verification, a proof-of-concept example on a quadratic aluminum plate is presented.

• Journal of Institute of Control, Robotics and Systems
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• v.14 no.1
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• pp.36-41
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• 2008

Cooling tower is an important equipment of the cooling systems for large buildings like factory and department store. Water used for cooling in cooling tower is reused continuously. If the water is polluted, corrosion and scale can happen at equipments and pipes. In order to prevent this problem, it is necessary to control the water quality using chemicals. To control the water quality, an automatic control system is designed, fabricated, and experimented. The control system is based on an imbedded microcontroller. Relays are used for power driving, an LCD and LED for display, and RS485 for remote data acquisition. Monitoring program is also developed for easy man-machine interface and extraction of data stored in the imbedded processor and EEPROM. The control system calculates amounts of chemicals necessary using sensor data and injects the chemicals into the cooling tower on proper time. The developed water quality control system is expected to reduce cost of maintenance and extend the lifetime of the cooling systems with low cost.

• Proceedings of the Korean Nuclear Society Conference
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• 1998.05a
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• pp.264-270
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• 1998

This research broadens the prime concern of nuclear power plant operations from safe performance to both economic and safe performance. First emergency diesel generator is identified as one of main contributors for the lost plant availability through the review of plants forced outage records. The framework of an integrated architecture for performing modern on-line condition for operational availability improvement is configured in this work. For the development of the comprehensive sensor networks for complex target systems, an integrated methodology incorporating a structural hierarchy, a functional hierarchy, and a fault-system matrix is formulated. The second part of our research is development of intelligent diagnosis and maintenance advisory system, which employs Bayesian Belief networks (BBNs) as a high level reasoning tool incorporating inherent uncertainty use in probabilistic inference. Our prototype diagnosis algorithms are represented explicitly through topological symbols and links between them in a causal direction. As new evidence from sensor network development is entered into the model especially, our advisory of system provides operational advice concerning both availability and safety, so that the operator is able to determine the likely modes, diagnose the system state, locate root causes, and take the most advantageous action. Thereby, this advice improves operational availability