• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.4
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• pp.469-474
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• 2016

Abnormal sound was heard from a heat exchanger of condensate water system in a nuclear power plant, which was identified as impact sound of a loose part later. Nuclear power plants are normally equipped with loose part monitoring system for primary water system, but not for secondary water system. The abnormal sound was analyzed by using the impact signal-processing methodology based on the Hertz theory. The predicted results for impact location and size of the loose part showed good agreement with those of the actual loose part found during the overhaul period in the plant. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• Nuclear Engineering and Technology
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• v.55 no.2
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• pp.506-515
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• 2023

This study is to find a method to learn engine sound after the start-up of a diesel generator installed in nuclear power plant with an unsupervised deep learning algorithm (CNN autoencoder) and a new method to predict the failure of a diesel generator using it. In order to learn the sound of a diesel generator with a deep learning algorithm, sound data recorded before and after the start-up of two diesel generators was used. The sound data of 20 min and 2 h were cut into 7 s, and the split sound was converted into a spectrogram image. 1200 and 7200 spectrogram images were created from sound data of 20 min and 2 h, respectively. Using two different deep learning algorithms (CNN autoencoder and binary classification), it was investigated whether the diesel generator post-start sounds were learned as normal. It was possible to accurately determine the post-start sounds as normal and the pre-start sounds as abnormal. It was also confirmed that the deep learning algorithm could detect the virtual abnormal sounds created by mixing the unusual sounds with the post-start sounds. This study showed that the unsupervised anomaly detection algorithm has a good accuracy increased about 3% with comparing to the binary classification algorithm.

• MALSORI
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• no.49
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• pp.135-144
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• 2004

In this paper, we make a study of classification normal from abnormal - normal, asthma through analysis of thoracic sound to take use thoracic sound detection system. Thoracic sound detection system has a function to store thoracic sound and analyze the data. The wave shape of thoracic sound is similar to noise and is systematically generated by inhalation and exhalation breathing, therefore, in this paper, to classify asthma sound in thoracic sound, we could discriminate between normal and abnormal case using level crossing rate(LCR) and spectrogram energy rate.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.21 no.6
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• pp.183-190
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• 2011

In this paper, we propose a prototype system for abnormal sound detection and identification which detects and recognizes the abnormal situations by means of analyzing audio information coming in real time from CCTV cameras under surveillance environment. The proposed system is composed of two layers: The first layer is an one-class support vector machine, i.e., support vector data description (SVDD) that performs rapid detection of abnormal situations and alerts to the manager. The second layer classifies the detected abnormal sound into predefined class such as 'gun', 'scream', 'siren', 'crash', 'bomb' via a sparse representation classifier (SRC) to cope with emergency situations. The proposed system is designed in a hierarchical manner via a mixture of SVDD and SRC, which has desired characteristics as follows: 1) By fast detecting abnormal sound using SVDD trained with only normal sound, it does not perform the unnecessary classification for normal sound. 2) It ensures a reliable system performance via a SRC that has been successfully applied in the field of face recognition. 3) With the intrinsic incremental learning capability of SRC, it can actively adapt itself to the change of a sound database. The experimental results with the qualitative analysis illustrate the efficiency of the proposed method.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.05a
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• pp.1306-1311
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• 2006

The root cause of abnormal sound from a heat exchanger of condensate water system in a nuclear power plant is investigated by using the impact signal-processing methodology based on the Hertz theory. The predicted results for the location of impact force and the loose part size meet good agreement with the identified materials during the overhaul period in the plant. Nuclear power plants have experienced several loose parts and the frequency of the loose part will be increased along the aging of the plants. So, this analysis methodology for the impact signal will be widely utilized for the primary and secondary side of the nuclear power plant.

• KEPCO Journal on Electric Power and Energy
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• v.2 no.1
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• pp.43-47
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• 2016

This system is a diagnosis system that checks whether it causes a partial discharge of a power cable or not. It is to classify normal from abnormal-normal, PD (Partial Discharge) sound through analysis of RLCR (Relative Level Crossing Rate) and spectrogram energy algorithm. Partial discharge diagnostic system has a function that stores PD sound and analyzes the data. The wave shape of PD sound is similar to noise and is systematically generated by partial discharge. Therefore, in this paper, we could discreminate between normal and abnormal case using relative level crossing rate (RLCR) and spectrogram of frequency energy rate.

• Journal of Korea Multimedia Society
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• v.14 no.7
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• pp.884-894
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• 2011

We proposed a new algorithm for detection of first(S1) and second heart sound(S2). Many researches for detecting primary components and those algorithms have good performance at normal heart sound, but the performance is degraded at abnormal heart sound which is contain murmurs generated by heart disease. Therefore we proposed the S1, S2 detection algorithm using three-order Shannon energy difference. Using S1, S2's character which has large energy difference than murmurs, it is reduced noise and detected S1, S2. According to simulation results, not only normal heart sound but also abnormal heart sound, the proposed algorithm has better performance than former study at abnormal heart sound.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.36 no.7
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• pp.813-822
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• 2012

Abnormal operating sounds radiated from a moving transfer robot in LCD (liquid crystal display) product lines have been used for the fault detection line of a robot instead of other source signals such as vibrations, acoustic emissions, and electrical signals. Its advantage as a source signal makes it possible to monitor the status of multiple faults by using only a microphone, despite a relatively low sensitivity. The wavelet packet transform for feature extraction and the artificial neural network for fault classification are employed. It can be observed that the abnormal operating sound is sufficiently useful as a source signal for the fault diagnosis of mechanical components as well as other source signals.

• Journal of Physiology & Pathology in Korean Medicine
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• v.20 no.2
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• pp.320-327
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• 2006

This study was written in order to help understanding of listening diagnosis to vocal sound and speech. The purpose of listening diagnosis is that we know states of essence(精), Qi(氣) and spirit(神). Vocal sound and speech are made by Qi and spirit. Vocal sound originates from the center of the abdominal region(丹田) and comes out through vocal organs, for example lung, larynx, nose, tongue, tooth, lip and so on. Speech is expressed by vocal sound and spirit. They are controled by the Five Vital organs(五臟). Various changes of vocal sound and speech observe the rules of yinyang. For example, if we consider patient likes to say or not, we can diagnose heat and coldness of illness. If we consider he speaks loudly or quietly, we can diagnose weak and severe of illness. If we consider he speaks clearly or thick, we can diagnose inside and outside of illness. If we consider he speaks damp or dry, we can diagnose yin and yang of illness. If we consider change of voice, we can diagnose new and old illness. Symptoms of changes of five voices, five sounds, dumbness and huskiness are due to abnormal vocal sound, and symptoms of changes of mad talk, mumble, sleep talking and so on are due to abnormal speech.

• The Journal of Internal Korean Medicine
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• v.29 no.3
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• pp.621-628
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• 2008

Objectives : The aim of this study was to investigate changes of gastric vagal nerve activity and pyloric valve function after execution of combination treatments of both electroacupuncture at Zusanli(ST36) and manual acupuncture at other acupoints in patient with functional dyspepsia. Methods : Bowel sounds of 49 patients (18 male, 31 female) were recorded and their % of bowel sound (%BS) and ratio of dominant frequency (DF) were analyzed. Postprandial %BS was used to indicate the gastric vagal activity after eating. Ratio of postprandial/fasting dominant frequency was used to present the degree of pyloric valve function. According to values of %BS and DF ratio, each patient was classified into normal or abnormal (<6 %BS, hypoactivity: <1 DF ratio, dysfunction) group. For 2 weeks, patients received a treatment consisting of both electroacupuncture stimulation at Zusanli (ST36) and acupuncture at other meridian points. Variation of parameters shifting normal to abnormal or abnormal to normal was observed, and total cure rate was calculated. Results : Total cure rate of %BS was 16%, and that of DF was 37%. Patients who improved to normal value from abnormal or aggravated to abnormal level showed both significant difference in both vagal nerve hypoactivity and pyloric valve dysfunction, respectively. Conclusions : Analysis of bowel sound might be useful to evaluate both gastric vagal nerve activity and pyloric valve function. Combination effects of Zusanli (ST36) electroacupuncture and manual acupuncture of other acupoints showed a bidirectional effect in which their activity and function were in general improved. sometimes from aggravated to abnormal level.