• KIEE International Transaction on Electrical Machinery and Energy Conversion Systems
• /
• 제5B권1호
• /
• pp.78-83
• /
• 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
• /
• 제어로봇시스템학회 2005년도 ICCAS
• /
• pp.2198-2202
• /
• 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.

• 한국기계가공학회지
• /
• 제8권1호
• /
• pp.31-36
• /
• 2009

Micro-machining technology has been adopted for shape accuracy of micrometer and sub-micrometer scale, surface roughness of tens nanometer in industries. In micro-machining process the quality of machined surface is derived from machining condition and tooling. This paper investigates AE(acoustic emission) and cutting force signals according to machined surface quality related to machining condition. Machined surface quality was analyzed by the AE and cutting force parameter which reflect surface morphology. The characteristics of signal were extracted for process optimization by monitoring both the tool condition and the machined surface texture in micro end milling process.

• 한국정밀공학회지
• /
• 제22권10호
• /
• pp.90-98
• /
• 2005

A monitoring system is necessary to make the polishing process more reliable in order to ensure the high quality and performance of the final products. Generally, AE (Acoustic Emission) is known to be closely related to the material removal rate (MRR). As the surface becomes rougher, the MRR and AE increase. Therefore, the surface roughness can be indirectly estimated using the AE signal measured during the polishing. In this study, an AE sensor-based monitoring system was fabricated to detect the very small AE signal resulting from the friction between a tool and a workpiece during polishing. The performance of this monitoring system was estimated according to polishing conditions, the relation between the level of the AE RMS and the surface roughness during the polishing was investigated.

• 한국해양공학회지
• /
• 제16권2호
• /
• pp.67-71
• /
• 2002

Damage process of CFRP laminates was characterized by Acoustic Emission (AE). The main objective of this study is to determine if the sources of AE in CERP laminates could be identified from the characteristics of the waveform signals recorded during monotonic tensile test. The time history and power spectrum of each individual wave signal recorded during test were examined and classified according to their special characteristics. The wave from and frequency of AE signal from a specimens is an aid to the determination of the extent of the different fracture mechanism such as matrix crack, debonding, fiber pull-out and fiber fracture as load is increased. Four distinct types of signals were observed regardless of specimen condition. The result showed that the AE method could be effectively used for analysis of fracture mechanism in CFRP laminates.

• 한국정밀공학회:학술대회논문집
• /
• 한국정밀공학회 1993년도 추계학술대회 논문집
• /
• pp.58-63
• /
• 1993

The charactistics of AE(Acoustic Emission) signal is related to cutting conditions, tool materials and tool geometry in metal cutting. The tool geometry change which is derived from tool wear affects the source of AE signal in machining process. The relationship between AE signal and tool wear was experimentally investigated. THe value of RMS(Root Mean Sequare) and Amplitude of AE signal were increased in tool wear condition. Also the high value of Count per Hit and Count vs. Frequency was observed in this condtion. As a result, tool wear can be effectively detected by AE signal during cutting operation.

• 대한전기학회:학술대회논문집
• /
• 대한전기학회 2003년도 하계학술대회 논문집 C
• /
• pp.1659-1661
• /
• 2003

Recently, diagnostic techniques have been investigated to defect a partial discharge in high voltage electrical equipment. We have studied the characteristics of the acoustic partial discharge originating from the electrical defects in cubicle GIS(C-GIS). An acoustic emission(AE) sensor is used on the enclosure to detect partial discharge source because the sensor is sensitive to stress waves in its frequency range that may not be from a partial discharge source. AE signal is analyzed with phase-magnitude-frequency number(${\Phi}$-V-n) and pulse per second(PPS). Experience result has shown that the omitted acoustic signal has phase dependency and phase shift characteristic according to increase with applied voltage. These result will be helpful to the pattern recognition of the acoustic partial discharge in a C-GIS.

• 한국정밀공학회지
• /
• 제23권1호
• /
• pp.64-71
• /
• 2006

Ultraprecision machining and MEMS technology have been taken more and more important position in machining of microparts. Micro endmilling is one of the prominent technology that has wide spectrum of application field ranging from macro parts to micro products. Also, the method of micro-grooving using micro endmill is used widely owing to many merit, but has problems of precision and quality of products due to tool wear and tool fracture. This investigation deals with state monitoring using acoustic emission(AE) signal in the micro-grooving. Characteristic evaluation of AE raw signal, AE hit and frequency analysis for condition monitoring is presented. Also, the feature extraction of AE signal directly related to machining process is executed. Then, the distinctive micro endmill state according to the each tool condition is classified by the fuzzy C-means algorithm.

• 한국정밀공학회지
• /
• 제12권4호
• /
• pp.100-110
• /
• 1995

This paper aims at reviewing the possibility of application over normal or abnormal, detection used by AE, and the characteristics of grinding processes. In this study, when WA-vitri-fied ' resinoid bond grinding wheels:36 kinds of grinding wheel and grinding depth were tuned at the surface grinding, the zone of AE signal generation is theoretically modelled and reviewed by grinding processes. The variation of grinding resistance( F$n^{9}$ $F_{t}$) and AE signal is detected in-process by the use of AE measuring system. The tests are carried out in accordance with grain size and grade of grinding wheels, and work-pieces-STD11 and STD61. According to the experiment's results, the following can be expected;as grinding time passes by, the relation of grinding depth and quantity of AE signal, observing on AE signal and grinding burn suggest the characteristics of grinding processes and evalution on the possibility of control of grinding machine, and monitoring abnormal conditions.e, and monitoring abnormal conditions.

• 한국정밀공학회지
• /
• 제13권11호
• /
• pp.25-31
• /
• 1996

In this paper, three different types of commercial tools -P20, NC123K and ceramic- have been used to cut austempered ductile iron(ADI). In the austempered condition the materials are hard, strong and difficult to machine. Thus, we selected a optimum tool material among three different types of used tools in machining of austempered ductile iron. It was used acoustic emission (AE) to know cutting characteristic for selected tool and investigate characteristic of AE signal according to cutting condition and relationship between AE signal and flank wear land of the ceramic tool. The obtained results are as follows ; (1) The ceramic tool among three different types of tools is the best in machining austempered ductile iron. (2) In case of ceramic tool, the amplitude level of AE signal(AErms) is mainly affected by cutting condition and it is proportional to cutting speed. (3)There have been the relationship of direct proportion between the amplitude level of AE signal and flank wear land of the tool. (4) It was observed that the value of AErms was only affected by cutting speed. Therefore it is possible to in-process detec- tion of ceraic tool's wear in case the initial value of AErms at each cutting speed decided.