• Journal of Power System Engineering
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• v.7 no.2
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• pp.29-35
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• 2003

The monitoring and diagnostics of the rotating machinery have been received considerable attention for many years. The objectives are to classify the machinery condition and to find out the cause of abnormal condition. This paper describes a classification method of diagnosing the small reciprocating compressor for refrigerators using the artificial neural network and the wavelet transform. In order to extract salient features, the wavelet transform are used from primary noise signals. Since the wavelet transform decomposes raw time-waveform signals into two respective parts in the time space and frequency domain, more and better features can be obtained easier than time-waveform analysis. In the training phase for classification, self-organizing feature map(SOFM) and learning vector quantization(LVQ) are applied, and the accuracies of them ate compared with each other. This paper is focused on the development of an advanced signal classifier to automatize the vibration signal pattern recognition. This method is verified by small reciprocating compressors, for refrigerator and normal and abnormal conditions are classified with high flexibility and reliability.

• Journal of the Korean Society for Precision Engineering
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• v.18 no.10
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• pp.101-108
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• 2001

In this study, the static and dynamic characteristics of endmilling process were modelled and the analytic realization of chatter mechanism was discussed. In this reward, We have discussed on the comparative assessment of recursive time series modeling algorithms that cal represent time machining process and detect the abnormal machining behaviors in precision endmilling operation. In this study, simulation and experimental works were performed to show the malfunctional behaviors. For this purpose, new recursive algorithm(RLSM) was adopted for the oil-line system identification and monitoring of a machining process, we can apply these new algorithms in real process for detection of abnormal chatter. Also, The stability lobe of chatter was analysed by varying parameter of cutting dynamics in regenerative chatter mechanics.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1249-1256
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• 2018

Recently the research on artificial intelligence is actively processing in the fields of diagnosis and prediction. In this paper, we acquire the data of electrical current, revolution per minute (RPM) and vibration that is occurred in the motor and pump where hey are installed in the industrial fields. We train the acquired data by using the k-nearest neighbors. Also, we propose the status diagnosis methods that judges normal and abnormal status of motor and pump by using the trained data. As a proposed result, we confirm that normal status and abnormal status are well judged.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.37 no.2
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• pp.249-255
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• 2013

A large-sized refrigerator has a complicated inner structure such as a shelf and a rack for product loading. Therefore, when the refrigerator door is opened and closed, the temperature inside the refrigerator varies and vibrations occur due to the physical force applied for opening and closing the door. Owing to these factors, an abnormal sound is generated by the relative distortion between the inner structures. In this study, we aimed to clarify the mechanism that generates this abnormal noise inside the refrigerator using experimental approaches, and we also investigated ways by which to reduce this noise. Toward this end, we developed an experimental setup for measuring the noise, temperature, inner pressure, as well as amount of vibration, and we analyzed the main factors causing the noise based on the experimental results. Furthermore, we suggested a way by which to reduce the noise; this method can be applied in the design stage itself.

• The Transactions of the Korea Information Processing Society
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• v.13 no.3
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• pp.130-139
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• 2024

Artificial intelligence models are being used to detect facility anomalies using physics data such as vibration, current, and temperature for predictive maintenance in the manufacturing industry. Since the types of facility anomalies, such as facility defects and failures, anomaly detection methods using autoencoder-based unsupervised learning models have been mainly applied. Normal or abnormal facility conditions can be effectively classified using the reconstruction error of the autoencoder, but there is a limit to identifying facility anomalies specifically. When facility anomalies such as unbalance, misalignment, and looseness occur, the facility vibration frequency shows a pattern different from the normal state in a specific frequency range. This paper presents an N-segmentation anomaly detection method that performs anomaly detection by dividing the entire vibration frequency range into N regions. Experiments on nine kinds of anomaly data with different frequencies and amplitudes using vibration data from a compressor showed better performance when N-segmentation was applied. The proposed method helps materialize them after detecting facility anomalies.

• Journal of Animal Environmental Science
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• v.12 no.1
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• pp.21-28
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• 2006

This study was carried out to measure and analyze the characteristics of noise and vibration, and to analyze their effects on the productivity of layers, mechanical troubles, and abnormal wear-out failure of facilities and equipment of the layer house. The measurements of noise and vibration were taken at 13 layer farms nationwide for the operations of feed supplier system, feed distribution system, automatic egg collection system, ventilation system, blot conveyer for layer feces, and fur the case of with and without their operation by a sound level meter and a vibration measuring system in the layer house equipped with upright multi-tier cages. Measurement results showed that normal times were noise(N) 82 dB and vibration(V) 0.2072 cm/s, feed supplier system were 90 dB(N) and 2.8560 cm/s(V), feed distribution system were 90 dB(N) and 2.0222 cm/s(V), automatic egg collection system were 87 dB(N) and 0.1865 cm/s(V), ventilation system 88 dB(N) and 2.5364 cm/s(V), belt conveyer fur layer feces were 88 dB(N) and 0.2387 cm/s(V), and then maximum values of noise and vibration were 90 dB and 2.8560 cm/s, respectively, when feeding systems(feed supplying system and feed distribution system) were operated. Based on these results, an experiment is being conducted to find out the effect of noise and vibration on the productivity of layers in the layer house equipped with upright multi-tier cages.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.14 no.2
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• pp.96-103
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• 2005

The early detection of the bearing defects in rotating machinery is very important since the critical failure of bearing causes a machinery shutdown. However it is not easy to detect the vibration signal caused by the initial defects of bearing because of the high level of random noise. A signal processing technique, called the adaptive line enhancer(ALE) as one of adaptive filter, is used in this study. This technique is to eliminate random noise with little a prior knowledge of the noise and signal characteristics. Also we propose the optimal methods fir selecting the three main ALE parameters such as correlation length filter order and adaptation constant. Vibration signals f3r three abnormal bearings, including inner and outer raceways and ball defects, were acquired by Anderon(angular derivative of radius on) meter. The experimental results showed that ALE is very useful f3r detecting the bearing defective signals masked by random noise.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2007.11a
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• pp.318-321
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• 2007

Since human listening is very sensitive to sound, a subjective index of a sound quality is required. Therefore, in the analysis for each situation, the sound evaluation is composed with sound quality factor. Many researchers spends their effort to make a more reliable and more accurate of sound in term of sound quality index for various system noise. The previous methods to evaluation of the SQ about vehicle interior noise are linear regression analysis of subjective SQ metrics by statistics and the estimation of the subjective SQ values by neural network. But these are so depended on jury test very much that they result in many difficulties. So, to reduce jury test weight, we suggested a new method using Mahalanobis distance for SQ evaluation. Threrefore, in this study Mahalanobis distance for the vehicle interior noise was derived using the objective SQ except jury test. Finnaly, the results of the SQ evaluation was analyzed discrimination between reference and abnormal group.

• Proceedings of the Korean Society of Tribologists and Lubrication Engineers Conference
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• 2002.05a
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• pp.53-62
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• 2002

Condition monitoring plays a vital role since it sustains reliable operation of industrial plants and machinery in the pursuit of economic whole life operation. In order to achieve this goal, it is needed to monitor various parameters of mechanical system such as vibration, wear, temperature and etc., and finally to diagnosis the root causes of any possible abnormal machine condition. In this work, a machine failure caused by mechanical looseness was experimentally simulated and on-line measurement of the vibration, wear and temperature were simultaneously measured. For the quantitative analyses of machine wear, several statistical parameters of the wear particle size distribution were obtained through the center moment method of the Weibull distribution function, and they were compared to vibrational characteristics. Results showed that the wear and vibrational characteristics did not reveal a strong correlation each other in a loosely supported proceeding bearing.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2006.11a
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• pp.69-74
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• 2006

It is known that the various noise sources which are engine, transmission, tire, intake system, etc exist at vehicle driving status. Specially noises which cannot be expected by a driver induce unpleasantness to all passengers. These noises are difficult to distinguish noise sources or specifications because of too many vehicle parts. Therefore in this paper, study on abnormal noise of bush on suspension system is performed by the measurement and analysis of the noises of bushings that are generated artificially. The measured noises are analyzed by two points-view of spectrum and sound quality. Finally, it is shown that the noise sources of bushings on the suspension system which are the pillow ball joint bush of a control arm and the rubber bush of a lower arm could be distinguished by the spectrum distribution and a index value based on tonality.