• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.18 no.12
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• pp.1243-1249
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• 2008

Gear system has been widely used in industrial applications and unexpected failures of gears are not only extremely damaging but also leading to economic losses. So, early detection of fault is important for diagnosis machine condition. And acoustic emission is an efficient non-destructive testing technique fur the diagnosis of machine health and is useful technique far early detection of fault because it can find low-amplitude and high-frequency signal on account of high sensibility. Therefore, in this paper, the AE signal was measured and preprocessed using envelope analysis for gearbox with misalignment between pinion and gear. And then the gear misalignment's vibration characteristic were analyzed.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.4
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• pp.69-76
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• 1994

AE(Acoustic Emission) signal is correlated to workpiece material, cutting conditions and tool geometry during metal cutting. The relationship between AE signal and cutting parameters can be obtained by theoretical model and experiments. The value of CR(Count Rate) is nearly constant in stable cutting, but when the chatter vibration occours, the value of CR is rapidly increased due to the vibration deformation zone. By experimental signal processing of AE, it is more effective than by RMS(Root Mean Square) measurement to detect the threshold of chatter vibration by CR measurement.

• Journal of the Korean Society of Manufacturing Technology Engineers
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• v.6 no.4
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• pp.46-53
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• 1997

This paper presents a method of detection localized defects on tapered roller bearing in main spindle of machine tool system. The feature vectors, i.e. statistical parameters, in time-domain analysis technique have been calculated to extract useful features from acoustic emission signals. These feature vectors are used as the input feature of an neural network to classify and detect bearing defects. As a results, the detection of bearing defect conditions could be sucessfully performed by using an neural network with statistical parameters of acoustic emission signals.

• Proceedings of the KSME Conference
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• 2000.04a
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• pp.67-72
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• 2000

Demand for now nondestructive evaluation are growing to detect fatigue crack growth behavior to predict long term performance of materials and structure in aggressive environments especially when they are In non-visible area. Acoustic emission technique is well suited to these problems and has drawn a keen interests because of its dynamic detection ability, extreme sensitivity and location of growing defects. In this study, we analysed acoustic emission signals obtained in fatigue and tensile test of high strength fire resistance steel for frame structure with time-frequency analysis methods. The main frequency range is different in the noise and the fatigue crack propagation. It could be classified that it were also generated by composite fracture mechanics of cleavage, dimple, inclusion separation etc.

• Journal of the Korean Institute of Electrical and Electronic Material Engineers
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• v.20 no.10
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• pp.907-911
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• 2007

This paper dealt with the frequency spectrum analysis of acoustic signal produced by partial discharge (PD) in insulation oil and the positioning of PD occurrence to apply in diagnosis of oil insulated transformers. Three types of electrode system ; the needle-plane, the plane-plane, and the wire-wire structure were assembled to simulate partial discharges in oil insulated transformers. A low-noise amplifier and a decoupling circuit were designed to detect acoustic signal with high sensitivity The frequency spectrum of the acoustic signal were 50 kHz ${\sim}$ 260 kHz in the needle-plane, 50 kHz ${\sim}$ 250 kHz in the plane-plane, and 45 kHz${\sim}$195 kHz in the wire-wire electrode system. Their peak frequencies were 145 kHz, 130 kHz and 114 kHz, respectively The position of PD occurrence was calculated by the time difference of arrival (TOA) using three acoustic emission (AE) sensors, and we could find the position within the error of 1 % in the experimental apparatus.

• Smart Structures and Systems
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• v.23 no.2
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• pp.173-184
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• 2019

The main objective of this study is to evaluate the true fracture energy and monitor the damage progression in steel fibre reinforced concrete (SFRC) specimens using acoustic emission (AE) features. Four point bending test is carried out using pre-notched plain and fibre reinforced (0.5% and 1% volume fraction) - concrete under monotonic loading. AE sensors are affixed at different locations of the specimens and AE parameters such as rise time, AE energy, hits, counts, amplitude and duration etc. are obtained. Using the captured and processed AE event data, fracture process zone is identified and the true fracture energy is evaluated. The AE data is also employed for tracing the damage progression in plain and fibre reinforced concrete, using both parametric- and signal- based techniques. Hilbert - Huang transform (HHT) is used in signal based processing for evaluating instantaneous frequency of the acoustic events. It is found that the appropriately processed and carefully analyzed acoustic data is capable of providing vital information on progression of damage on different types of concrete.

• Journal of Advanced Marine Engineering and Technology
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• v.36 no.2
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• pp.258-266
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• 2012

Recently, Acoustic Emission (AE) technique is widely applied to develop the early fault detection system, and the problem about a signal processing method for AE signal is mainly focused on. In the signal processing method, envelope analysis is a useful method to evaluate the rolling element bearing problems and Wavelet transform is a powerful method to detect faults occurred on gearboxes. However, exact method for AE signal is not developed yet. Therefore, in this paper, two methods, which is Hilbert transforms (HT) and Hilbert-Huang transforms (HHT), will be compared for development a signal processing method for early fault detection system by using AE. AE signals were measured through a fatigue test. HHT has better advantages than HT because HHT can show the time-frequency domain result. But, HHT needs long time to process a signal, which has a lot of data, and has a disadvantage in de-noising filter.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.361-364
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• 1995

Wavelet Transform is a new tools for signal processing, such as data compressing extraction of parameter for Reconition and Diagnostics. This transform has an advandage of a good resolution compared to Fast Fourier Transform (FFT) In this study, we employ the wavelet transform for analysis of Acoustic Emission raw signal generated form rotary compressor. In abnormal condition of rotary compressor, the state of operating condition can be classified by analizing coefficient of wavelet transformed signal.

• Korean Journal of Materials Research
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• v.17 no.5
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• pp.239-243
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• 2007

The evaluation and comparison have been made for the EMI noise which was included in the signal from the sensors in the acoustic emission testing for the bottom plate of ground tank at full. The EMI signal has been classified into two types. One is the signal with very short AE count, and this signal possibly can be filtered by front end filter setting of the channel count with low level of 4 and high level of $10^8$. The other EMI signal occurred from CH 1, CH 3 and CH 10, and had high and constant duration with high energy and count (maximun duration > $10^5\;{\mu}s$), and has characteristic gradient of accumulation amplitude distribution. This signal should be removed in the AE signal evaluation by filtering, because this may affect to the total gradient.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.05a
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• pp.34-39
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• 2002

The surfaces of machine components in sliding contact such as bearing, gears and pistons etc. frequently operate under the condition of mixed lubrication due to high load, high speed and slip. These machine components often undergo the inception of scuffing in practical application. The scuffing failure is a critical problem in modern machine components, especially for the requirement of high efficiency and small size. However, it is difficult to find a universal mechanism to explain all scuffing phenomena because there are so many factors affecting the onset of scuffing. In this study, scuffing experiments are conducted using Acoustic Emission(AE) measurement by an indirect sensing approach to detect scuffing failure. Acoustic Emission(AE) signal has been widely utilized to monitor the interaction at the friction interface. Using AE signals we can get an indication about the state of the friction processes, about the quality of solid and liquid layers eon the contacting surfaces in real time. The FFT(Fast Fourier Transform)analyses of the AE signal are used to understand the interfacial interaction and the relationship between the AE signal and the state of contact is presented