• Proceedings of the Korean Society of Precision Engineering Conference
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• 2002.10a
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• pp.883-887
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• 2002

We should maintain the maximum operation capacity for production facilities and find properly out the fault of each equipment rapidly in order to decrease a loss caused by its failure. The acoustic and vibration signals of a machine always carry the dynamic information of the machine. These signals are very useful fur the feature extraction and fault diagnosis. We performed a fundamental study which develops a system of fault diagnosis for a pump. We experimented vibrations by acceleration sensors and noises by microphones, compared and analysed for normal products, artificially deformed products. We tried to search a change of the dynamic signals according to machine malfunctions and analyse the type of deformation or failure. The results showed that acoustic signals as well as vibration signals can be used as a simple method lot a detection of machine malfunction or fault diagnosis.

• Journal of Power System Engineering
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• v.11 no.1
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• pp.45-50
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• 2007

Acoustic emission technology is applied to diagnosis the internal leak and operating conditions of the major valves at nuclear power plants. The purpose of this study is to verify availability of the acoustic emission as in-situ diagnosis method. In this study, acoustic emission tests are performed when the pressurized high temperature steam flowed through gate valve(1st stage reheater valve) and glove valve(main steam dump valve) on the normal size of 4 and 8". The valve internal leak diagnosis system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, signal level analysis and RMS(root mean square) analysis of acoustic signal emitted from the valve operating condition internal leak.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2008.04a
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• pp.425-430
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• 2008

To verify the system performance of portable AE leak diagnosis system which can measure with moving conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured during operation on total 11 valves of the secondary system in nuclear power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, type of valve, pressure difference, valve size and fluid. The results of this field study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve. The purpose of this study is to verify availability of the acoustic emission in-situ monitoring method to the internal leak and operating conditions of the major valves at nuclear power plants. In this study, acoustic emission tests are performed when the pressurized temperature water and steam flowed through glove valve(main steam dump valve) and check valve(main steam outlet pump check valve) on the normal size of 12 and 18 ". The valve internal leak monitoring system for practical field was designed. The acoustic emission method was applied to the valves at the site, and the background noise was measured for the abnormal plant condition. To improve the reliability, a judgment of leak on the system was used various factors which are AE parameters, trend analysis, frequency analysis, voltage analysis and amplitude analysis of acoustic signal emitted from the valve operating condition internal leak.

• Proceedings of the KSME Conference
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• 2007.05b
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• pp.3383-3388
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• 2007

To compare the sensor performance of AE leak diagnosis system which can measure valve leak conditions, AE activities such as RMS voltage level, AE signal trend, leak rate degree according to AE database, FFT spectrum were measured on valve of the simulated test system for power plant. AE activities were recorded and analyzed from various operating conditions including different temperature, pressure difference, valve size and fluid using both piezoelectric acoustic emission sensor and Pb-Free acoustic emission sensor. The results of this study are utilized to select the type of sensors, the frequency band for filtering and thereby to improve the signal-to-noise ratio for diagnosis or monitoring of valves in operation. As the final result of application study above, portable type leak diagnosis system by AE was developed. The outcome of the study can be definitely applied as a means of the diagnosis or monitoring system for energy saving and prevention of accident for power plant valve.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.13 no.5
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• pp.81-86
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• 2004

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method for a detection of machine malfuction or fault diagnosis.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.57 no.5
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• pp.881-888
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• 2008

We present a novel fault diagnosis methodology using acoustic sensor systems and neural independent component analysis for large-scaled power machines. Acoustic sensors are carried out to measure sounds generated from power machines whose signal is used to determine whether fault is occurred or not. Acoustic measurements are independently mixed and deteriorated from original source signals. We propose a demixing algorithm against such mixed signals by means of independent component analysis which is achieved based on information theory and higher-order statistics to derive learning mechanism.

• The Journal of the Society of Korean Medicine Diagnostics
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• v.13 no.1
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• pp.72-80
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• 2009

Objective: We performed this study to check relationship of Cold-Heat attribute analyzed quantitatively by questionnaire with acoustic analysis index. Method : We checked a questionnaire composed of 15 items about the contents of Cold-Heat and asked 83 subjects to answer in the form Likert-like 7-points score. And then, we extracted Cold-Heat attribute from heat score, cold score, heat index and cold index. we measured the acoustic analysis indexes of cardinal vowels by Dr. speech program. Afterward, the data were analyzed by correlation analysis. Results : All cardinal vowels is positive correlated with cold score, heat score and cold index. NNE of vowel /a/ is negative correlated with cold index. Shimmer and F0 tremor of vowel /e/ is negative correlated with cold index. Jitter of vower /u/ is positive correlated with Cold score.

• Journal of Mechanical Science and Technology
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• v.20 no.12
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• pp.2079-2086
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• 2006

A fundamental study for developing a fault diagnosis system of a pump is performed by using neural network. Acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer. And the codes of pump malfunctions were selected as units of output layer. Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. Neural network trained by acoustic signals can detect malfunction or diagnose fault of a given machine from the results.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 2003.10a
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• pp.137-142
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• 2003

A fundamental study for developing a system of fault diagnosis of a pump is performed by using neural network. The acoustic signals were obtained and converted to frequency domain for normal products and artificially deformed products. The signals were obtained in various driving frequencies in order to obtain many types of data from a limited number of pumps. The acoustic data in frequency domain were managed to multiples of real driving frequency with the aim of easy comparison. The neural network model used in this study was 3-layer type composed of input, hidden, and output layer. The normalized amplitudes at the multiples of real driving frequency were chosen as units of input layer, Various sets of teach signals made from original data by eliminating some random cases were used in the training. The average errors were approximately proportional to the number of untaught data. The results showed neural network trained by acoustic signals can be used as a simple method far a detection of machine malfunction or fault diagnosis.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.13 no.4
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• pp.68-74
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• 1999

Ths paper does acoustic detection of partial discharge using acoustic sensor in polymer. Time sequential rreasurement of acoustic emission characteristic obtained acoustic sensor deal with statistics process. and 5 characteristic quantities were introduced into this paper. Resulting fann analysis of $\psi$-AEA-n pattern (phase-acoustic emission amplitude-pulse number) and AE quantities ,it can know useful statistics quantities that AE average inception amplitude TEX>$(\overline{AEA_{inc}})$ and AE average maximum amplitude TEX>$(\overline{AEA_{max}})$ make diagnosis of the middle stage of deterioration, AE pulse number and AE average maximum phase $(\overline{\theta{max}})$ make diagnosis of the last stage of deterioration. it obtained that these AE quantities are useful for dias,mosis deterioration form experiment results.esults.