• Smart Structures and Systems
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• 제6권3호
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• pp.197-209
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• 2010

The structural state of a bridge is currently examined by visual inspection or by wired sensor techniques, which are relatively expensive, vulnerable to inclement conditions, and time consuming to undertake. In contrast, wireless sensor networks are easy to deploy and flexible in application so that the network can adjust to the individual structure. Different sensing techniques have been used with such networks, but the acoustic emission technique has rarely been utilized. With the use of acoustic emission (AE) techniques it is possible to detect internal structural damage, from cracks propagating during the routine use of a structure, e.g. breakage of prestressing wires. To date, AE data analysis techniques are not appropriate for the requirements of a wireless network due to the very exact time synchronization needed between multiple sensors, and power consumption issues. To unleash the power of the acoustic emission technique on large, extended structures, recording and local analysis techniques need better algorithms to handle and reduce the immense amount of data generated. Preliminary results from utilizing a new concept called Acoustic Emission Array Processing to locally reduce data to information are presented. Results show that the azimuthal location of a seismic source can be successfully identified, using an array of six to eight poor-quality AE sensors arranged in a circular array approximately 200 mm in diameter. AE beamforming only requires very fine time synchronization of the sensors within a single array, relative timing between sensors of $1{\mu}s$ can easily be performed by a single Mote servicing the array. The method concentrates the essence of six to eight extended waveforms into a single value to be sent through the wireless network, resulting in power savings by avoiding extended radio transmission.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1997년도 한국자동제어학술회의논문집; 한국전력공사 서울연수원; 17-18 Oct. 1997
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• pp.387-390
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• 1997

Grinding process takes a long time that grinding machine is setted properly. It is difficult for user to judge correctly the abnormal states generated in grinding process. Air grinding has to be reduced for the improvement of productivity. In addition, it is important to monitor the dressing and the grinding process so that the grinding working maintains optimal grinding conditions. In this study, the monitoring system using the acoustic emission is developed to monitor these processes continuously. This system was able to reduce the preparation as well as the machine setting time in grinding operation.

• 동력기계공학회지
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• 제9권1호
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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.

• 한국정밀공학회지
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• 제22권4호
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• pp.60-67
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• 2005

Compared with conventional welding, laser spot welding offers a unique combination of high speed, precision and low heat distortion. This combination of advantages is attractive for manufacturing industries including automotive and electronics companies. In this paper, a real time monitoring scheme fur a pulsed Nd:YAG laser spot welding was suggested. Acoustic emission (AE) signals were collected during welding and analyzed for given process conditions such as laser power and pulse duration. A back propagation artificial neural network, with AE frequency content inputs, was used to predict the weldability of stainless steel sheets.

• 한국정밀공학회지
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• 제25권10호
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• pp.41-47
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• 2008

This study aims to obtain machining characteristics during AFM (Atomic force Microscope) machining of silicon wafers and to monitor the machining states using acoustic emission. As in micro scale machining, two distinct regimes of deformation, i. e. ploughing regime and cutting regime were observed. First, the transition between the two regimes are investigated by analyzing the "pile-up" during machining. As far as in process monitoring is concerned, in the ploughing repime, no chips have been formed and related AE RMS values are relatively low, In the mean time, in the cutting regime, the RMS values are significantly higher than the ploughing regime, with apparent chip formation. From the results, we found out that the proposed scheme can be used for the monitoring of nanomachining, especially for the characterization of nanocutting mode transition.

• Smart Structures and Systems
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• 제13권1호
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• pp.155-171
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• 2014

Various monitoring techniques are now available for structural health monitoring and Acoustic Emission (AE) is one of them. One of the major advantages of the AE technique is its capability to locate active cracks in structural members. AE crack locating approaches are affected by the signal attenuation and dispersion of elastic waves due to inhomogeneity and geometry of reinforced concrete (RC) members. In this paper, a novel technique is described based on signal processing and sensor arrangement to process multisensory AE data generated by the onset and propagation of cracks and is validated with experimental results from an in-situ load test. Considering the sources of uncertainty in the AE crack location process, a methodology is proposed to capture and locate events generated by cracks. In particular, the relationship between AE events and load is analyzed, and the feasibility of using the AE technique to evaluate the cracking behavior of two RC slab strips during loading to failure is studied.

• 비파괴검사학회지
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• 제16권2호
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• pp.79-85
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• 1996

복합 재료의 single-lap 및 double-strap adhesive bonding joint 시험편에서 피로 및 인장시험시 방출되는 AE 신호의 분석에 의한 피로 수명 예측 및 파괴 과정의 감시에 대한 연구를 수행하였다. Bonding 시험편의 피로시험시 AE event의 발생양상은 피로에 의해 축적된 elastic strain energy의 방출과 관계되며, peak amplitude가 높고 event duration이 긴 event의 발생 영역과 peak amplitude가 낮고 event duration이 짧으나 많은 event가 발생되는 영역으로 나눌 수 있었다. 그리고 두 영역 사이의 경계 cycle은 피로 수명의 70-80% 근방에 존재하였으며, 이를 이용한다면 피로 수명의 예측이 가능하리라 판단된다.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2007년도 춘계학술대회B
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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.

• 한국안전학회지
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• 제16권2호
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• pp.22-26
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• 2001

The machining process on be considered as a planned interaction of the workpiece, the tool and the machine tool. In an unmanned situation, the results of this interaction are to be continuously monitored so that any changes in the machining environment on be sensed to corrective actions. In order to design the process monitoring system for unmanned manufacturing, the identification of chip formation is proposed. The system proposes the method of using acoustic emission(AE) signal analysis to identify the chip formation during cutting.

• 대한기계학회:학술대회논문집
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• 대한기계학회 2004년도 추계학술대회
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• pp.1733-1739
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• 2004

The objective this study is to estimate the feasibility of acoustic emission method for the internal leak from the valves in nuclear power plants. From the experimental results, it was suggested that the acoustic emission method for monitoring of leak was feasible. When the background levels are higher than the acoustic signals from leak, we can detect the leak analyzing the spectrum of the remainders which take the background noise from the acoustic signals.