• Korean Journal of Air-Conditioning and Refrigeration Engineering
• /
• v.26 no.8
• /
• pp.351-356
• /
• 2014

Typical trouble patterns in solar thermal systems include working fluid leakage and freezing other than breakdown of pump. A fluid sensor for measuring electric resistance of fluid was developed and installed at the top of the collector piping in order to check the fault of solar system. Working fluid level in the pipe was determined by measuring electric resistance from a fluid sensor. On the base of this, it was confirmed that the fluid sensor diagnoses leakage of fluid. Electric resistance of propylene glycol aqueous solution was measured in the range of $0{\sim}70^{\circ}C$ and 0~40% of concentration. The response surface analysis was performed by using a central composite design, and the regression equation was derived from the relationship between electric resistance, temperature, and concentration. Through the experiment in a real solar system, we can estimate a concentration of working fluid when a pump is not operating and predict a possibility of freezing. Finally, an effective algorithm for trouble shooting was proposed to operate and maintain the solar system.

• Transactions of the Korean Society of Automotive Engineers
• /
• v.19 no.1
• /
• pp.95-100
• /
• 2011

The test was done on cars travelling at speeds of 20km/h, 60km/h and 100km/h, the performance testing mode for chassis dynamometer. In this test, the secondary waveform were measured, including those using faulty MAP sensors, oxygen sensors and spark plugs. The results from these measurements and their analysis of secondary waveform can be summarized as follows: 1) The secondary waveform measured from the faulty oxygen sensor showed a lot of noise around peak voltage and in the rising and falling sections during spark line which means that the air fuel mixture was non-homogeneous. 2) The secondary waveform from the faulty MAP sensor showed the worst shape compared to other sensors, including variation of spark line, state of air-fuel mixture and velocity of flame front. 3) The spark line time of secondary waveform using a faulty spark plug displayed the shortest and smallest energy spark line, which means that a misfire occurred.

• Journal of the Korean Society of Manufacturing Technology Engineers
• /
• v.9 no.4
• /
• pp.56-61
• /
• 2000

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skilful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workship, the other is the quantitative method which utilizes the experimental data obtained by a sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are now easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based model, which is strongly depended upon experience and intuition , is described.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
• /
• 1999.10a
• /
• pp.492-497
• /
• 1999

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skillful engineers. Grinding operations include a large number of functional parameters, since there are several ways of coping with grinding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop, the other is the quantitative method which utilizes the experimental data obtained by sensor. But, they are all difficult to accomplish from the grinding trouble-shooting system. The reason is that grinding troubles are not easily controlled in the quantitative method, and therefore, trouble-shooting has mainly relied on the knowledge of skilful engineers. Thus, there is an important issue of how a grinding trouble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper, basic strategy to develop the grinding database of rule-based rule, which is strongly depended upon experience and intuition, is described.

• Journal of the Korean Society for Precision Engineering
• /
• v.17 no.8
• /
• pp.106-112
• /
• 2000

Cognition and control of grinding trouble occurring during the grinding process are classified into a quantitative knowledge which depends on experimental data and qualitative knowledge which relies on skiful engineers. grinding operations include a large number of functional parameters since there are several ways of coping with ginding trouble. One is the qualitative method which depends on empirical knowledge utilizing the skilful experts from the workshop the other is the quantitative method which utilizes the experimental data obtained by sensor. But they are all difficult to accomplish from the grinding trouble-shooting system The reason is that grinding troubles are not accomplish from the grinding trouble-shooting system,. The rason is that grinding troubles are not easily controlled in the quantitative method and therefore trouble-shooting has mainly relied on the knoledge of skiful engineers. Thus there is an important issue of how a grinding touble-shooting system can be designed and what knowledge is utilized among the large amount of grinding trouble information. In this paper basic strategy to develop the grinding database by taking rule-based model which is strongly depended upon experience and intuition is described.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2004.07b
• /
• pp.1207-1210
• /
• 2004

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm

• Journal of the Semiconductor & Display Technology
• /
• v.19 no.1
• /
• pp.1-5
• /
• 2020

In this paper, a predictive maintenance of the robot trouble using the machine learning method, so called MT(Mahalanobis Taguchi), was studied. Especially, 'MD(Mahalanobis Distance)' was used to compare the robot arm motion difference between before the maintenance(bearing change) and after the maintenance. 6-axies vibration sensor was used to detect the vibration sensing during the motion of the robot arm. The results of the comparison, MD value of the arm motions of the after the maintenance(bearing change) was much lower and stable compared to MD value of the arm motions of the before the maintenance. MD value well distinguished the fine difference of the arm vibration of the robot. The superior performance of the MT method applied to the prediction of the robot trouble was verified by this experiments.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
• /
• 2003.11a
• /
• pp.575-578
• /
• 2003

Recently, because GIS equipment has problems on confidence according to long-time usage, development of diagnosis technique has been importantly recognized. Therefore. measurement and analysis of PD has been generally used much equipment of GIS. But, in case of measurement of PD at field, real trouble signals are difficult to classify noise. Accordingly, a variety of trouble conditions for DS were simulated, and detected signals were analyzed by the application of electrical and mechanical methods. For this analysis, detected signals were accumulated according to phase-magnitude with the application of Induction sensor, and then we analyzed the characteristics. For the simulation experiment, we made DS for 170kV GIS and analyzed the characteristics of detected singals with the application of neural network algorithm.

• Journal of the Korean Society for Precision Engineering
• /
• v.13 no.11
• /
• pp.57-63
• /
• 1996

Monitoring of grinding troble occurring during the process is classified into the quantitative data which depends upon a sensor and the qualitative knowledge which relies upon an empirical knowledge. Since grinding operation is highly related with a large amount of functional parameters, it is actually deficulty in copying wiht the grinding troubles through the process. To cope with grinding trouble, it is an effective monitoring systems when occurring the grinding process. The use of neural networks is an effective method of detection and/or monitroing on the grinding trouble. In this paper, four parameters which are derived from the AE(Acoustic Emission) signatures are identified, and grinding monitoring system utilized a back propagation learning algorithm of PDP neural networks is presented.

• Proceedings of the Korean Society of Precision Engineering Conference
• /
• 2005.06a
• /
• pp.1788-1792
• /
• 2005

Eddy current sensor is representative instrument measuring gap to base metal and sensing trouble in base metal. The existing eddy current sensor works as measuring variance of sensor coil's inductance. But, sensor coil have phenomenon that not only inductance but also real resistance varies in real action. Conductivity and Permeability are main variable in sensor coil's varying impedance(inductance, real resistance). By searching relationship between conductivity-permeability and sensor coil's impedance, eddy current sensor gain advantage of elevation of accuracy, removal of alignment to each base metal, and continuous sensing to varying base metal.