• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.677-682
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• 1997

Vibration diagnosis of steam turbine generator is essential for safe operation. For a fast few decades, several data base systems for diagnosis of steam turbine generators have been developed and proved useful. However, there still remains a problem in using data base systems such that they require an expert engineer who has a deep insight or knowledg into the system. Moreover,such data base systems can not give any information if the input is not completely fit with data base. This paper presents an effective method for vibration diagnosis of steam turbine generators using fuzzy inference. The proposed method includes also a strategy to overcome the drawback of data base system such that one cannot obtain any information when the input is insufficient or not exact. A computer program is written to realize the entire procedure for the diagnosis. Three realistic problems are dealt with to show the effectiveness of the proposed method.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3236-3240
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• 2007

This paper describes the results of steam turbine performance tests. The objectives of performance test is to exactly evaluate the degradation(decrease in performance) of the coal-fired steam turbine generator in order to provide plant information to help performance engineers identify problems, improve performance, and make economic decisions about scheduling maintenance and optimizing operation. To achieve these goals, the periodic thermal performance tests have been carried out since the initial operation period, 1997. We made the calculation program and guidelines for the tests and developed the performance index of the turbine cycle on the basis of the ASME PTC. By comparing the performance changes throughout the whole operation period, we confirmed the performance reliabilities of the turbine and its conditions.

• Journal of KSNVE
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• v.5 no.4
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• pp.543-553
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• 1995

Modern steam turbine generators are being built as a higher power and larger system, experiencing more frequent starts and stops of operation due to a constant change of power demands. Hence, they are inevitably more vulnerable to various vibrations, and more often exposed to the danger of sudden vibration accidents than ever before. Even under the circumstances, in order to secure the system reliability of steampower plants and there by to supply safely the public electricity, it is important to prevent a sudden vibration accident in one hand and even when it happens, to raise an operating efficiency of the plants throught swift and precise treatments in the other. In this study, an interactive vibration diagnostic system has been developed to make the on-site vibration diagnosis of steam turbine generators possible and convenient, utilizing a note-book PC. For this purpose, at first the principal vibration phenomena, such as various unbalance and unstable vibrations as well as rubbing, misalignment, and shaft crack vibrations, have been systematically classified as grouped parameters of vibration frequencies, amplitudes, phases, rotating speeds at the time of accident, and operating conditions or condition changes. A new complex vibration diagnostic table has been constructed from the causal relations between the characteristic parameters and the principal vibration phenomena. Then, the diagnostic system has been developed to screen and issue the corresponding vibration phenomena by assigning to each user-selected combination of characteristic parameters a unique characteristic vector and comparing this vector with a diagnostic vector of each vibration phenomenon based on the constructed diagnostic table. Moreover, the diagnostic system has a logic whose diagnosis may be performed successfully by inputing only some of the corresponding characteristic parameters without having to input all the parameters. The developed diagnostic system has been applied to perform the diagnosis of several real cases of steam turbine vibration accidents. And the results have been quite satisfactory.

• 유체기계공업학회:학술대회논문집
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• 2001.11a
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• pp.315-321
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• 2001

The shaft system of turbine is composed of rotating shaft, blades, bearings which support the shaft, packing seal which prevent the leakage of steam, and couplings which connect the shaft. Shaft system component failure, incorrect assemblage or deflection by unexpected forces causes vibration problem. And every turbine has its own characteristics in dynamic response. In this paper we propose the three-bearing supported type rotor which is real equipment and being operated this time as commercial operation. From 1996 it has a high vibration problem and there are many kinds of trial to solve this problem. In resent outage we performed a special diagnosis and carried out appropriate work. We would like to introduce and explain about this case history.

• Journal of Welding and Joining
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• v.16 no.1
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• pp.70-76
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• 1998

Most fossil power plants and many critical components will be approaching the end of their nominal design life. At the same time, utilities are finding it economically attractive to extend the use of these plants for several more years, Especially Main steam pipe that operated under high temperature and pressure, often under the more severe operating conditions associated with cycling duty, is most important pipe system and critical component in fossil power plant. To extend the viability of older pipe system and to improve the operation and maintenance reliability, some technologies of precise diagnosis and life management have evolved out of the necessity. The purpose of this study is to descrive the related technologies and show the example of one power plants. The purpose of this study is to descrive the related technologies and show the example of one power plants. The stress analysis was done using ANSYS FEM Code. The branch area from main steam to turbine was the high stressed zone. To evaluate the degradation of the pipe material, replica, visual check, magnetic test, hardness test were done at the welding spot. The degradation level of welding point was E/F, so the remaining life of the welded area was about 0-25%.

• KEPCO Journal on Electric Power and Energy
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• v.4 no.2
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• pp.89-99
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• 2018

Downtime and malfunction of industrial rotor machines represents a crucial cost burden and productivity loss. Fault diagnosis of this equipment has recently been carried out to detect their fault(s) and cause(s) by using fault classification methods. However, these methods are of limited use in detecting rotor faults because of their hypersensitivity to unexpected and different equipment conditions individually. These limitations tend to affect the accuracy of fault classification since fault-related features calculated from vibration signal are moved to other regions or changed. To improve the limited diagnosis accuracy of existing methods, we propose a new approach for fault diagnosis of rotor machines based on the model generated by supervised learning. Our work is based on feature residual values from vibration signals as fault indices. Our diagnostic model is a robust and flexible process that, once learned from historical data only one time, allows it to apply to different target systems without optimization of algorithms. The performance of the proposed method was evaluated by comparing its results with conventional methods for fault diagnosis of rotor machines. The experimental results show that the proposed method can be used to achieve better fault diagnosis, even when applied to systems with different normal-state signals, scales, and structures, without tuning or the use of a complementary algorithm. The effectiveness of the method was assessed by simulation using various rotor machine models.

• Nuclear Engineering and Technology
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• v.46 no.6
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• pp.737-752
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• 2014

As condition-based maintenance (CBM) has risen as a new trend, there has been an active movement to apply information technology for effective implementation of CBM in power plants. This motivation is widespread in operations and maintenance, including monitoring, diagnosis, prognosis, and decision-making on asset management. Thermal efficiency analysis in nuclear power plants (NPPs) is a longstanding concern being updated with new methodologies in an advanced IT environment. It is also a prominent way to differentiate competitiveness in terms of operations and maintenance costs. Although thermal performance tests implemented using industrial codes and standards can provide officially trustworthy results, they are essentially resource-consuming and maybe even a hind-sighted technique rather than a foresighted one, considering their periodicity. Therefore, if more accurate performance monitoring can be achieved using advanced data analysis techniques, we can expect more optimized operations and maintenance. This paper proposes a framework and describes associated methodologies for in-situ thermal performance analysis, which differs from conventional performance monitoring. The methodologies are effective for monitoring, diagnosis, and prognosis in pursuit of CBM. Our enabling techniques cover the intelligent removal of random and systematic errors, deviation detection between a best condition and a currently measured condition, degradation diagnosis using a structured knowledge base, and prognosis for decision-making about maintenance tasks. We also discuss how our new methods can be incorporated with existing performance tests. We provide guidance and directions for developers and end-users interested in in-situ thermal performance management, particularly in NPPs with large steam turbines.

• Journal of the Korean Society for Nondestructive Testing
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• v.30 no.2
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• pp.110-115
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• 2010

This study was conducted to estimate the feasibility using thermal image measurement that is applicable to internal leak diagnosis for the power plant valve. Abnormal heating of valve surface associated with high temperature steam f10w toward valve outlet side in the condition of low temperature is a primary indicator of leakage problems in high temperature and pressure valves. Thermal imaging enables to see the invisible thermal radiation that may portend impending damage before their condition becomes critical. When steam flow in valve outlet side in the condition of low temperature is converted into heat transmitted through the valve body due to the internal leakage in valve. The existence of abnormally increasable leakage rate in the valve will result in abnormally high levels of heat to be generated that can be quickly identified with a thermal image avoiding energy loss or damage of valve component. From the experimental results, it was suggested that the thermal image measurement could be an effective way to precisely diagnose and evaluate internal leak situation of valve.

• Journal of the Korea Society for Simulation
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• v.29 no.3
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• pp.9-18
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• 2020

The development of a large-scale thermal power plant control simulator consists of water/steam systems, air/combustion systems, pulverizer systems and turbine/generator systems. Modeling is possible for all systems except mechanical turbines/generators. Currently, there have been attempts to develop neural network simulators for some systems of a boiler, but the development of simulator for the whole system has never been completed. In particular, autoTuning, one of the key technology developments of all power generation companies, is a technology that can be achieved only when modeling for all systems with high accuracy is completed. The simulation results show accuracy of 95 to 99% or more of the actual boiler system, so if the field PID controller is fitted to this simulator, it will be available for fault diagnosis or auto-tuning.