• Smart Structures and Systems
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• v.6 no.3
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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.

• 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.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.2
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• pp.120-125
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• 2014

Conventional handheld metal detectors use a single induction coil to detect the metallic parts of explosive objects, and the detector generates an acoustic signal from its magnetic response to a metallic object so that an operator can confirm the existence of mines. Though metal detectors have very useful detection mechanisms to find mines, it is easy to cause a high false alarm ratio due to the detection of non-explosive metallic items such as cans, nails and other pieces of metal, etc. Also, because of the physical characteristic of a metal detector it is hard to detect non-metallic objects such as mines made of wood or plastic. Furthermore, the operator must move it to the left and right slowly and repeatedly to attain enough sensor signals to confirm the existence of mines using only a monotonous acoustic signal. To resolve the disadvantages of handheld detectors, many new approaches have been attempted, such as an arrayed detector and a visualization algorithm based on metal/non-metal sensor. In this paper, we introduce a visualization algorithm with a metal/non-metal complex sensor, an arrayed metal/non-metal sensor and the their testing and evaluation.

• Smart Structures and Systems
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• v.13 no.1
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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.

• 제어로봇시스템학회:학술대회논문집
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• 1995.10a
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• pp.240-243
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• 1995

In excavating tunnels, shield tunneling machines having many cutters on their cutter planes are used. Not many observation data being available in the detection system, optimal observation policy is very important. From this viewpoint, we previously considered the optimal location of acoustic sensors on the cutter plane and also the optimal observation policy for the case where three receiving transducers were used, and showed that the optimal sensor location was given as arbitrary equally-spaced points on the cutter plane circle, and that the optimal rotating angles were also found to be arbitrary. In application, however, it is often difficult to locate sensors at arbitrary positions or to use three sensors from the viewpoints of machine structure and cost. This paper considers the optimal observation policy for detecting anomlous plane objects for the case where two receiving transducers are used and the case where three receiving transducers are located only on a diameter of the cutter plane.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.48 no.6
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• pp.97-108
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• 2011

Systems for underwater environment monitoring and natural resources can be considered as a part of digital convergence where real-time data transmission is possible with the help of underwater wireless sensor network (UWSN). One of key technologies required for the deployment of the systems is underwater acoustic micro modem. In this work, we design and implement an acoustic modem equipped with a commercial omnidirectional transducer. We also make experiments at the northern Han river for the verification of the developed modem. According to the experiments, the modem supports the working distance of 250 m and the data rate of 200 bps with a negligible bit error rate. It is expected that the acoustic modem can be used for various applications based on UWSN in a near future.

• 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.

• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
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• v.5B no.1
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• pp.78-83
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• 2005

Currently in Japan, the use of the small wind turbine is an upward trend. There are already many well established small wind turbine generators in use and their various failures have been reported. The most commonly sighted failure is blade damage. Thus the research purpose was set to develop a simple failure diagnostic system, where an Acoustic Emission (AE) signal was produced from the failure part of a blade which was measured by AE sensor. The failure diagnostic technique was thoroughly examined. Concurrently, the damage part of the blade was imitated, the AE signal was measured, and a FFT(Fast Fourier Transform) analysis was carried out, and was compared with the output characteristic. When one sheet of a blade was damaged 40mm or more, the level was computed at which failure could be diagnosed.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.2198-2202
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• 2005

In this paper we have presented a condition monitoring method of check valve using neural network. The acoustic emission sensor was used to acquire the condition signals of check valve in direct vessel injection (DVI) test loop. The acquired sensor signal pass through a signal conditioning which are consisted of steps; rejection of background noise, amplification, analogue to digital conversion, extract of feature points. The extracted feature points which represent the condition of check valve was utilized input values of fault diagnosis algorithms using pre-learned neural network. The fault diagnosis algorithm proceeds fault detection, fault isolation and fault identification within limited ranges. The developed algorithm enables timely diagnosis of failure of check valve’s degradation and service aging so that maintenance and replacement could be preformed prior to loss of the safety function. The overall process has been experimented and the results are given to show its effectiveness.

• IEMEK Journal of Embedded Systems and Applications
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• v.7 no.4
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• pp.187-191
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• 2012

In these days, there are huge increases of concerning underwater acoustic sensor networks (UWASNs) to explore marine resources and to monitor climate change. To collect information from sensor nodes which are randomly deployed in underwater, Multi-Dimensional Scaling (MDS) based locating methods have been recently introduced, which consider sound speed to be constant in underwater. However, underwater sound speed tends to vary depending on underwater environment factors, such as depth, temperature, and salinity. In this paper, we propose a method considering environment factors, can influence upon sound speed in underwater, and introduce experimental setup which can follow up environmental factors.