• Proceedings of the KSME Conference
• /
• 2001.11a
• /
• pp.590-595
• /
• 2001

We must maintain the maximum operation capacity for production facilities and find properly out the fault diagnosis of the possessing equipments rapidly so as to decrease a loss caused by its failure. In this paper, we performed the fundamental study which develops a system of fault for a individually using pump widely or a pump as parts of the other machines. For each normal products, artificially transformed products, and working products under critical condition, we experimented in vibration, compared and analysed. Some faults showed into characteristic vibrations and other faults did not show consistent characters.

• International Journal of Precision Engineering and Manufacturing
• /
• v.7 no.3
• /
• pp.14-17
• /
• 2006

To diagnosis abnormal compressor conditions in an air-conditioner, the acoustic emission (AE) signal, which is derived from wear condition, compressed air, and assembly error, was analyzed experimentally. Burst and continuous type AE signals resulted from metal contact and compressed air, and the raw AE signal of compressors was acquired in the production line. After extracting samples using waveforms, the Early Life Test (ELT) was conducted and the waveform was classified as normal or abnormal. Efficient parameters in the waveform pattern were investigated in time and frequency domains and a diagnosis algorithm for air-conditioners using Neural Network estimation is suggested.

• Proceedings of the KIEE Conference
• /
• 1999.07e
• /
• pp.2345-2348
• /
• 1999

Existing $\psi$-AEA-t (phase-AEA-time) characteristic in polymer materials for power cable is good in general deterioration characteristic according to time variation, but it is difficult to clearly distinguish from deterioration state and diagnosis of deterioration is not enough to some extent. This paper is interpreted AE discharge statistics quantities measuring phase-amplitude variation of acoustic emission characteristic obtained from treeing breakdown experiment. Besides it can know useful discharge statistics quantities (AE average inception phase/amplitude, AE average maximum phase/amplitude) about so many for diagnosis of treeing deterioration.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
• /
• 2007.11a
• /
• pp.891-894
• /
• 2007

This study presents fault diagnosis of low speed bearing using support vector machine (SVM). The data used in the experiment was acquired using acoustic emission (AE) sensor and accelerometer. The aim of this study is to compare the performance of fault diagnosis based on AE signal and vibration signal with same load and speed. A low speed test rig was developed to simulate various defects with shaft speeds as low as 10 rpm under several loading conditions. In this study, component analysis was also performed to extract the feature and reduce the dimensionality of original data feature. Moreover, the classification for fault diagnosis was also conducted using original data feature without feature extraction. The result shows that extracted feature from AE sensor gave better performance in faults classification.

• Proceedings of the KIEE Conference
• /
• 1998.11c
• /
• pp.910-912
• /
• 1998

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime, by introduction a neural network. In the proposed method, Ire use acoustic emission sensing system and calculate a fixed quantity statistic operator by pulse number and amplitude. Using statically operators such as the center of gravity(G) and the gradient of the discharge distribute(C), we analyzed the early stage and the middle stage. the fixed quantity statistic operators are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• The Journal of Information Technology
• /
• v.2 no.2
• /
• pp.13-22
• /
• 1999

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime by introduction a neural network. In the proposed method, we use AE(acoustic emission) sensing system and calculate a quantitative statistic parameter by pulse number and amplitude. Using statically parameters such as the center of gravity(G) and the gradient if the discharge distribute(C), we analyzed the early stage and the middle stage. the quantitative statistic parameters are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• The Journal of Korean Academy of Prosthodontics
• /
• v.54 no.4
• /
• pp.413-422
• /
• 2016

Recently, there are cases where anterior esthetic prostheses are fabricated for better esthetics, but biologic, mechanical factors could be overlooked, too focusing on esthetic factor. This leads to changes in neutral zone, dentition, position of tongue and lips, occlusion and anterior guidance causing inaccurate pronunciation. Therefore, consideration of systematic diagnosis and treatment procedure are required. In this case, prosthesis was refabricated through a systematic diagnosis and treatment procedure using four factor (acoustic analysis, esthetic analysis, occlusion, neutral zone) for the patient who complained of inaccurate pronunciation and esthetics of the fixed prosthesis fabricated 10 years ago. Thus, by promoting functional, esthetic recovery, this case report demonstrates satisfying results to both the patient and dentist.

• The Journal of the Acoustical Society of Korea
• /
• v.27 no.2
• /
• pp.80-86
• /
• 2008

Breast ultrasound has many advantages over mammography but suffers from a shortcoming of being not suitable in detecting microcalcification. We studied on a method based on acoustic resonance and power Doppler to detect calcification of breast tissue using a typical 7.5 MHz linear probe used in breast ultrasound examination. We first constructed a breast tissue phantom made of gelatin and then observed calcified legions as external vibrations varied. Calcification injected to the breast tissue phantom being resonated different from the surrounding medium, and its acoustic resonance driven by external vibrations was visualized by differences for color brightness and area in ROI of power doppler. In low frequency regions, the acoustic resonance almost not appeared and showed a plateau in $300{\sim}600\;Hz$ and the color vanished as the frequency further increased.

• Computers and Concrete
• /
• v.25 no.1
• /
• pp.15-27
• /
• 2020

During the past decades, the application of acoustic emission techniques (AET) through the diagnosis and monitoring of the fracture process in materials has been attracting considerable attention. AET proved to be operative among the other non-destructive testing methods for various reasons including their practicality and cost-effectiveness. Concrete and rock structures often demand thorough and real-time assessment to predict and prevent their damage nucleation and evolution. This paper presents an overview of the work carried out on the use of AE as a monitoring technique to form a comprehensive insight into its potential application in brittle materials. Reported properties in this study are crack growth behavior, localization, damage evolution, dynamic character and structures monitoring. This literature review provides practicing engineers and researchers with the main AE procedures to follow when examining the possibility of failure in civil/resource structures that rely on brittle materials.

• International journal of advanced smart convergence
• /
• v.6 no.2
• /
• pp.1-8
• /
• 2017

Recently, the development of the information communication industry has made many changes in the industrial acoustic industry. Especially, it has a great influence on the change of system and equipment of acoustic system. Analog equipment is changing to digital equipment, and integrated control equipment makes it easier to operate and manage the sound system. However, the integrated control system currently on the market is only controllable for some devices. In this paper, we propose a new AoIP - based system configuration method, which enables the operation status monitoring, unmanned operation and self - diagnosis of equipment. As a result of the study, it is confirmed that the proposed system can be operated, monitored, and self - diagnosed at remote sites. It is expected that an AoIP- based sound system will be the industry standard in the future.