• Journal of Institute of Control, Robotics and Systems
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• v.11 no.8
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• pp.662-668
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• 2005

This paper presents a realization of check valve condition monitoring system based on fault diagnosis algorithm and Fieldbus communication. We first acquired AE(acoustic emission) sensor data at the check valve test loop, extract fault features through the teamed neural network, and send the processed data to a remote site. The overall system has been implemented and experimented results are given to show its effectiveness.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3383-3388
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• 2007

To compare the sensor performance of AE leak diagnosis system which can measure valve leak conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured on valve of the simulated test system for power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, pressure difference, valve size and fluid using both piezoelectric acoustic emission sensor and Pb-Free acoustic emission sensor. The results of this study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve.

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

• Smart Structures and Systems
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• v.8 no.3
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• pp.321-341
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• 2011

Acoustic emission (AE) monitoring was conducted for mortar specimens under three types of static loading patterns (cubic-splitting, direct-shear and pull-out). Each of the applied loading patterns was expected to produce a particular fracture process. Subsequently, the AEs generated by various fracture or damage processes carried specific information on temporal micro-crack behaviors of concrete for post analysis, which was represented in the form of detected AE signal characteristics. Among various available characteristics of acquired AE signals, frequency content was of great interest. In this study, cement-based piezoelectric sensor (as AE transducer) and home-programmed DEcLIN monitoring system were utilized for AE monitoring on mortar. The cement-based piezoelectric sensor demonstrated enhanced sensitivity and broad frequency domain response range after being embedded into mortar specimens. This broad band characteristic of cement-based piezoelectric sensor in frequency domain response benefited the analysis of frequency content of AE. Various evaluation methods were introduced and employed to clarify the variation characteristics of AE frequency content in each test. It was found that the variation behaviors of AE frequency content exhibited a close relationship with the applied loading processes during the tests.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1997.04a
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• pp.186-189
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• 1997

To monitor abnormal state of rotary compressor, methods for acquisition and processing of Acoustic Emission(AE) signal are arranged and optimal AE parameter for monitoring is determined. The detecting method of abnormal compressor in real time is suggested and special-purpose minitoring system which can be applied to automatic manufacturing line is developed using one-chip microprocessor in low cost.

• Journal of the Korean Society for Nondestructive Testing
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• v.36 no.1
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• pp.45-52
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• 2016

Blast furnaces are crucial equipment for steel production. A typical furnace risks unexpected accidents caused by contraction and expansion of the walls under an environment of high temperature and pressure. In this study, an acoustic emission (AE) monitoring system was tested for evaluating the large-scale structural health of a blast furnace. Based on the growth of shell cracks with the emission of high energy levels, severe damage can be detected by monitoring increases in the AE energy parameter. Using this monitoring system, steel mill operators can establish a maintenance period, in which actual shell cracks can be verified by cross-checking the UT. From this study, we expect that AE systems permit early fault detection for structural health monitoring by establishing evaluation criteria based on the severity of shell cracking.

• Computers and Concrete
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• v.8 no.5
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• pp.563-581
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• 2011

The signal-based acoustic emission (AE) characterization of concrete fracture process utilizing home-programmed AE monitoring system was performed for three kinds of static loading tests (Cubic-splitting, Direct-shear and Pull-out). Each test was carried out to induce a distinct fracture mode of concrete. Apart from monitoring and recording the corresponding fracture process of concrete, various methods were utilized to distinguish the characteristics of detected AE waveform to interpret the information of fracture behavior of AE sources (i.e. micro-cracks of concrete). Further, more signal-based characters of AE in different stages were analyzed and compared in this study. This research focused on the relationship between AE signal characteristics and fracture processes of concrete. Thereafter, the mode of concrete fracture could be represented in terms of AE signal characteristics. By using cement-based piezoelectric composite sensors, the AE signals could be detected and collected with better sensitivity and minimized waveform distortion, which made the characterization of AE during concrete fracture process feasible. The continuous wavelet analysis technique was employed to analyze the wave-front of AE and figure out the frequency region of the P-wave & S-wave. Defined RA (rising amplitude), AF (average frequency) and P-wave & S-wave importance index were also introduced to study the characters of AE from concrete fracture. It was found that the characters of AE signals detected during monitoring could be used as an indication of the cracking behavior of concrete.

• Proceedings of the KWS Conference
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• 1995.10a
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• pp.3-20
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• 1995

Development of Real-Time Quality Evaluation of Friction Welding by Acousitc Emission : Report 1 ABSTRACT : According as the friction welding has been increasingly applied in manufacturing various machine components because of its significant economic and technical advantages, one of the important concerns is the reliable quality monitoring method for a good weld quality with both joint strength and toughness in the process of its production. However no reliable nondestructive test method is available at present to determine the weld quality particularly in process of production. So this paper presents an experimental examination and quantitative analysis for the real-time evaluation of friction weld quality by acoustic emission, as a new approach which attempts finally to develop an on-line quality monitoring system design for friction welds using AE techniques. As one of the important results, it was confirmed, through this study, that AE techniques can be reliably applied to evaluating the friction weld qualify with 100% joint strength, as the cumulative AE counts occurring during welding period were quantitatively correlated with reliability at 95% confidence level to the joint strength of welds. Real-Time Evaluation of Automatic Production Quality Control for Friction Welding Machine : Report 2 Abstract : Both in-process quality control and high reliability of the weld is one of the major concerns in applying friction welding to the economical and qualified mass-production. No reliable nondestructive monitoring method is available at present to determine the real-time evaluation of automatic production quality control for friction welding machine. This paper, so that, presents the experimental examinations and statistical quantitative analysis of the correlation between the initial cumulative counts of acoustic emission(AE) occurring during plastic deformation period of the welding and the tensile strength of the welded joints as well as the various welding variables, as a new approach which attempts finally to develop an on-line (or real-time) quality monitoring system and a program for the process of real-time friction welding quality evaluation by initial AE cumulative counts. As one of the important results, it was well confirmed that the initial AE cumulative counts were quantitatively and cubically correlated with reliability of 95% confidence level to the joint strength of the welds, bar-to-bar (SCM4 to SUM31, SCM4 to SUM24L) and that an AE technique using initial AE counts can be reliably applied to real-time strength evaluation of the welded joints, and that such a program of the system was well developed resulting in practical possibility of real-time quality control more than 100% joint efficiency showing good weld with no micro-structural defects.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1991.04a
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• pp.150-157
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• 1991

As the system monitoring technology become required in order to imporve the system performance and the productivity, We've studied to the detection for the tool breakage using AE sensor that is able to detection of generated high frequency strss pulse at cutting. The method of spectrum analysis are used for analysis of AE signals in detection system. The experiments are carred out in a CNC lathe.

• Journal of Power System Engineering
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• v.9 no.1
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• pp.5-13
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• 2005

Acoustic emission(AE) is defined as the transient elastic waves thar are generated by the rapid release of energy. The advantage of AE is that very early crack growth can be detected well before a highly stressed component may fail. At present, an exact diagnosis is the most reliable means for determining the soundness of structures during power plant operations. AE monitoring has been applied successfully in power plants to determine mechanical problems, pressure vessel integrity and external valves leaks, vacuum leaks, the onset of cavitation in pumps and valves, the presence of flow(or no flow) in piping and heat exchange equipment, etc. Acoustic emission(AE) technology has recently strengthened its application base, and practitioners' understanding of the technique's fundamentals. This paper introduces the methods of a survey and assessment on AE monitoring applications in nuclear, fossil and hydraulic power plant. The main objective of this paper was to obtain information on various applications of AE technology in power plant.