• International Journal of Fluid Machinery and Systems
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• v.5 no.4
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• pp.143-151
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• 2012

Predicting the future condition of machine and assessing the remaining useful life are the center of prognostics. This paper contributes a new prognostic method based on grey model and survival probability. The first step of the method is building a normal condition model then determining the error indicator. In the second step, the survival probability value is obtained based on the error indicator. Finally, grey model coupled with one-step-ahead forecasting technique are employed in the last step. This work has developed a modified grey model in order to improve the accuracy of prediction. For evaluating the proposed method, real trending data of low methane compressor acquired from condition monitoring routine were employed.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.101-102
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• 2022

The current facility condition monitoring has disadvantages such as a slow inspection cycle, a risk of human casualties, and the need for a lot of time and money as the size of the structure is larger, because human access is required with limited use. Drones can reduce the risk of human casualties due to their good accessibility, and can compensate for the shortcomings of the current method by enabling monitoring on a wide scale. The goal of this study is to provide the current domestic monitoring process through benchmarking according to the recent research case of the US Department of Transportation (DOT) to suggest a process suitable for the domestic situation and the direction of future improvement measures.

• Proceedings of the KSR Conference
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• 2008.11b
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• pp.235-240
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• 2008

Since urban railway transit is one of the most essential transportation systems, its power facilities must ensure high reliability and safety. Currently, urban railway operating organizations perform TBM (Time Based Maintenance) on power facilities. However, in order to improve management efficiency and system safety, CBM (Condition Based Maintenance) is preferred. Among various power facilities, mold transformers has been chosen as the object of study since it is widely used for the purpose of minimizing volume and weight, and due to safety against fire. In this paper, various transformer failure cases due to electric, thermal, mechanical and environmental factors have been collected and analyzed. In addition, investigation on national and international condition based maintenance cases and the characteristics of sensors widely used for transformer monitoring has been performed to suggest the optimal condition based maintenance technique for urban railway systems.

• Structural Monitoring and Maintenance
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• v.5 no.1
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• pp.129-150
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• 2018

This paper presents the structural performance monitoring of an urban footbridge located in Hangzhou, China. The structural health monitoring (SHM) system is designed and implemented for the footbridge to monitor the structural responses of the footbridge and to ensure the structural safety during the period of operation. The monitoring data of stress and displacement measured by the fiber Bragg grating (FBG)-based sensors installed at the critical locations are used to analyze and assess the operation performance of the footbridge. A linear regression method is applied to separate the temperature effect from the stress monitoring data measured by the FBG-based strain sensors. In addition, the static vertical displacement of the footbridge measured by the FBG-based hydrostatic level gauges are presented and compared with the dynamic displacement remotely measured by a machine vision-based measurement system. Based on the examination of the monitored stress and displacement data, the structural safety evaluation is executed in combination with the defined condition index.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.8
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• pp.872-878
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• 2008

Many industrial operations require continuous or nearly-continuous operation of machines, interruption of which can result in significant cost loss. The condition monitoring of these machines has received considerable attentions in recent years. Rapid developments in semiconductor, computing, and communication with a remote site have led to a new generation of sensor called "smart" sensors which are capable of wireless communication with a remote site. The purpose of this research is to develop a new type of smart sensor for on-line condition monitoring. This system is addressed to detect conditions that may lead to equipment failure when it is running. Moreover it will reduce condition monitoring expense using low cost MEMS accelerometer. This system is capable for signal preprocessing task and analog to digital converter which is controlled by CPU. This sensor communicates with a remote site PC using TCP/IP protocols. The developed sensor executes performance tests for data acquisition accuracy estimations.

• Tribology and Lubricants
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• v.26 no.1
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• pp.73-77
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• 2010

One of the important trends for condition monitoring in the 21st century is the development of smart sensors that will permit the cost-effective continuous monitoring of key machine equipments. In this study, an integrated in-line oil monitoring sensor assigned for continuous in situ monitoring multiple parameters of oil performance is presented. The sensor 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 density of oil in three optical wave-bands ('Red', 'Green' and 'Blue') and water content is evaluated as relative saturation of oil by water. In order to evaluate the sensor's effectiveness, the sensor was applied to several used oil samples in steel making industry and the results were compared with those measured by standard test methods.

• International Journal of Aeronautical and Space Sciences
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• v.15 no.2
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• pp.123-137
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• 2014

The aviation gas turbine is composed of many expensive and highly precise parts and operated in high pressure and temperature gas. When breakdown or performance deterioration occurs due to the hostile environment and component degradation, it severely influences the aircraft operation. Recently to minimize this problem the third generation of predictive maintenance known as condition based maintenance has been developed. This method not only monitors the engine condition and diagnoses the engine faults but also gives proper maintenance advice. Therefore it can maximize the availability and minimize the maintenance cost. The advanced gas turbine health monitoring method is classified into model based diagnosis (such as observers, parity equations, parameter estimation and Gas Path Analysis (GPA)) and soft computing diagnosis (such as expert system, fuzzy logic, Neural Networks (NNs) and Genetic Algorithms (GA)). The overview shows an introduction, advantages, and disadvantages of each advanced engine health monitoring method. In addition, some practical gas turbine health monitoring application examples using the GPA methods and the artificial intelligent methods including fuzzy logic, NNs and GA developed by the author are presented.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.493-505
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• 2023

Comprehensive condition monitoring of large industry systems such as nuclear power plants (NPPs) is essential for safety and maintenance. In this study, we developed novel system-scale diagnostic technology based on deep-learning and IR thermography that can efficiently and cost-effectively classify system conditions using compact Raspberry Pi and IR sensors. This diagnostic technology can identify the presence of an abnormality or accident in whole system, and when an accident occurs, the type of accident and the location of the abnormality can be identified in real-time. For technology development, the experiment for the thermal image measurement and performance validation of major components at each accident condition of NPPs was conducted using a thermal-hydraulic integral effect test facility with compact infrared sensor modules. These thermal images were used for training of deep-learning model, convolutional neural networks (CNN), which is effective for image processing. As a result, a proposed novel diagnostic was developed that can perform diagnosis of components, whole system and accident classification using thermal images. The optimal model was derived based on the modern CNN model and performed prompt and accurate condition monitoring of component and whole system diagnosis, and accident classification. This diagnostic technology is expected to be applied to comprehensive condition monitoring of nuclear power plants for safety.

• Journal of the Korean Society of Propulsion Engineers
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• v.15 no.3
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• pp.86-93
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• 2011

Recently, development and application of condition monitoring and diagnostic system for improvement of durability and reliability and reduction of operating cost is generalized in the aircraft propulsion system. Expecially, for reliable operation of the high altitude and a long time and condition monitoring system to identify faults and degradations of its propulsion system should be needed. This work proposed a GUI-based On-line condition monitoring program using LabVIEW by PT6A-67 turboprop engine. The proposed on-line condition program can monitor the real engine performance as well as the trend through precise comparison between performance results calculated by the base performance simulation program and measuring engine performance signals. In the development phase of this monitoring system, a signal generation module is proposed to evaluate the proposed on-line monitoring system.

• International Journal of Automotive Technology
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• v.6 no.4
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• pp.341-350
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• 2005

In this study, fault diagnosis and monitoring of serial wound pre-engaged starter motors have been carried out. Starter motors are DC motors that enable internal combustion engine (ICE) to run. In case of breakdown of a starter motor, internal combustion engine can not be worked. Starter motors have vital importance on internal combustion engines. Kohonen network based fault diagnosis system is proposed for fault diagnosis and monitoring of starter motors. A graphical user interface (GUI) software has been developed by using Visual Basic 6.0 for fault diagnosis. Six faults, seen in starter motors, have been diagnosed successfully by using the developed fault diagnosis system. GUI software makes it possible to diagnose the faults in starter motors before they occur by keeping fault records of past occurrences.