• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2000.04a
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• pp.20-27
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• 2000

A chatter vibration and a workpiece burn are the main phenomena to be monitored in modern grinding processes. This study describes a trouble diagnosis of the cylindrical plunge grinding process using the power and acoustic emission (AE) signals. The raw signals of the power and the AE occurred during the grinding operation were sampled and analyzed to determine the relationship between each fault and change of signals. A neural network that has a high success rate of the fault detection was used. Furthermore, an analysis on the influence of parameters to the chatter vibration and the grinding burn was conducted.

• Speech Sciences
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• v.5 no.1
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• pp.151-165
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• 1999

This paper describes a series of researches to diagnose vocal diseases using the statistical method and the acoustic signal analysis method. Speech materials are collected at the hospital. Using the pathological database, the basic parameters for the diagnosis are obtained. Based on the statistical characteristics of the parameters, valid parameters are chosen and those are used to diagnose the pathological speech signal. Cepstrum is used to extract parameters which represents characteristics of pathological speech. 3 layered neural network is used to train and classify pathological speech into normal, benign and malignant case.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2E
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• pp.64-72
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• 2010

Osteoporosis is a skeletal disease characterized by two factors: reduced bone mass and microstructure disruption of bone tissue. These symptoms increase bone fragility and can contribute to eventual fracture. In recent years, quantitative ultrasound (QUS) technologies have played a growing role in the diagnosis of osteoporosis. Most of the commercial bone sonometers measure speed of sound and/or broadband ultrasound attenuation at peripheral skeletal sites. However, QUS parameters are purely empirical measures that have not yet been firmly linked to physical parameters, such as bone strength or porosity, and the underlying physics for their variations in cancellous bone is not well understood yet. This paper reviews the QUS technologies for the diagnosis of osteoporosis and also addresses several theoretical models, such as the Biot model, the scattering model, the stratified model, and the modified Biot-Attenborough model, for ultrasonic wave propagation in bone.

• Journal of the Korean Society of Safety
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• v.18 no.2
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• pp.6-15
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• 2003

A method is presented for diagnosing the converter bearings by using acoustic emission. The flaking mechanism causing the large-scale bearing for furnace to flaw is investigated and a possibility of defect is verified by Finite Element method. he diagnosis logic is proposed fir detecting the flaw of a non-continuous rotating machine. It is proved that the acoustic emission energy can be used as a representative parameter for an acoustic event. Applying the method to the tilting bearings for steel mill in operation, the effectiveness of this logic is evaluated. It is shown that AE signal is generated only when the bearing is tilting, and the trend analysis can be focused upon this process.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2013.10a
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• pp.331-337
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• 2013

Acoustic Emission 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 bearing problems and Wavelet transform is a powerful method to detect faults occurred on rotating machinery. However, exact method for AE signal is not developed yet. Therefore, in this paper two methods which are Hilbert transform and DET for feature extraction. In addition, we evaluate the classification performance with varying the parameter from 2 to 15 for feature selection DET, 0.01 to 1.0 for the RBF kernel function of SVR, and the proposed algorithm achieved 94% classification accuracy with the parameter of the RBF 0.08, 12 feature selection.

• IEMEK Journal of Embedded Systems and Applications
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• v.19 no.1
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• pp.57-64
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• 2024

In the industrial field, robots are used to increase productivity by replacing labors with dangerous, difficult, and hard tasks. However, failures of individual industrial robots in the entire production process may cause product defects or malfunctions, and may cause dangerous disasters in the case of manufacturing parts used in automobiles and aircrafts. Although requirements for early diagnosis of industrial robot failures are steadily increasing, there are many limitations in early detection. This paper introduces methods for diagnosing robot failures using sound-based data and deep learning. This paper also analyzes, compares, and evaluates the performance of failure diagnosis using various deep learning technologies. Furthermore, in order to improve the performance of the fault diagnosis system using deep learning technology, we propose a method to increase the accuracy of fault diagnosis based on an inference window. When adopting the inference window of deep learning, the accuracy of the failure diagnosis was increased up to 94%.

• The Journal of the Acoustical Society of Korea
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• v.19 no.7
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• pp.59-65
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• 2000

This paper describes the analysis of vibration characteristics through the human body as the research for voice therapy and diagnosis. The oscillation signal is not external forces but the self-voice to be pronounced the vowels ('a', 'e', 'i', 'o', 'u'). The experiment system consists of microphones, accelerometers and amplifiers. The input data are stored by the computer. At the same time, the voice is stored by the microphone and the vibration signal of the human body is stored by accelerometer. The 63 points are appointed in head, neck, trunk of human body. The positions and number of times are changeable by the purpose. The analysis parameters are amplitude, phase, fundamental. frequency, formant and the correlation of vibration signal and voice is measured by coherence function. The results show that the vibration signals have characteristic vibration in the positions of human body.

• Journal of The Korean Association of Information Education
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• v.20 no.4
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• pp.323-332
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• 2016

A robot's recognition and diagnosis of pronunciation and its speech are the most important interactions in RALL(Robot Assisted Language Learning). This study is to verify the effectiveness of robot TTS(Text to Sound) technology in assisting Korean English language learners to acquire a native-like accent by correcting the prosodic errors they commonly make. The child English language learners' F0 range and speaking rate in the 4th grade, a prosodic variable, will be measured and analyzed for any changes in accent. We compare whether robot with the currently available TTS technology appeared to be effective for the 4th graders and 1st graders who were not under the formal English learning with native speaker from the acoustic phonetic viewpoint. Two groups by repeating TTS of RALL responded to the speaking rate rather than F0 range.

• The Journal of the Acoustical Society of Korea
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• v.19 no.4
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• pp.31-36
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• 2000

This paper describes the implementation and the evaluation of the speech recognition automatic exchange system. The system provides government or public offices, companies, educational institutions that are composed of large number of members and parts with exchange service using speech recognition technology. The recognizer of the system is a Speaker-Independent, Isolated-word, Flexible-Vocabulary recognizer based on SCHMM(Semi-Continuous Hidden Markov Model). For real-time implementation, DSP TMS320C32 made in Texas Instrument Inc. is used. The system operating terminal including the diagnosis of speech recognition DSP and the alternation of speech recognition candidates makes operation easy. In this experiment, 8 speakers pronounced words of 1,300 vocabulary related to automatic exchange system over wire telephone network and the recognition system achieved 91.5% of word accuracy.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.23 no.5
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• pp.385-393
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• 2013

In this paper, the mechanism identification and the avoidance measures on the phenomena of transient acoustic vibration amplified at the water-supply piping system to regulate the steam temperature of the boiler reheater in 500MW class supercritical power plant are presented. The pressure pulsation waves induced by the impeller passing of two feed-water pumps with five blades are coincident with the local acoustic modes of boiler reheater water-supply piping system. There are the phenomena amplified at the peaks of 5X, 10X, 15X and 20X in spectrums of piping vibration, sound pressure, and the feed-water's pressure pulsation waves. The shut-off device is installed in the piping system for the interception of pressure pulsation waves transmitted from two feed-water pumps and the modified design change of the piping layout is applied for the acoustic resonance avoidance. The acoustic natural frequencies are separated from the harmonics of pressure pulsation waves induced by the pump impellers passing through the design change of the span length. The acoustic vibration is gone by resonance avoidance measures. As a result, more than 20 dBA reduction is achieved from 100 dBA to 80 dBA.