• 한국정밀공학회지
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• 제20권4호
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• pp.136-142
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• 2003

The morphological analysis of wear debris can provide early a failure diagnosis in lubricated moving system. It can be effective to analyze operating conditions of oil-lubricated tribological system with shape characteristics of wear debris in a lubricant. But, in order to predict and recognize an operating condition of lubricated machine, it is needed to analyze and to identify shape characteristics of wear debris. Therefore, If the morphological characteristics of wear debris are recognized by computer image analysis using the neural network algorithm, it is possible to recognize operating condition of hydraulic driving members. In this study, wear debris in the lubricating oil are extracted by membrane filter (0.45 ${\mu}{\textrm}{m}$), and the quantitative values of shape parameters of wear debris are calculated by the digital image processing. This shape parameters are studied and identified by the artificial neural network algorithm. The result of study could be applied to prediction and to recognition of the operating condition of hydraulic driving members in lubricated machine systems.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.199-202
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• 2001

An elevator system, which is essential equipment for vertical movement of an object, as a property of building, has been driven by various expenditures and purposes. Since developing electrical control technology, control system are highly developed. The elevator system has expanded widely, but a data accuracy acquisition technique and safety predict technique for securing system safety is still at a basic level. So, objective verification for elevator confidence condition requires an absolute accuracy measurement technique. Therefore, this study is executed in order to acquire a method of depending on sense of a manager with simple numeric measurement data, and to construct a logical, analytical foresight system for more efficient elevator management system. As an artificial intelligence for diagnosis, the fuzzy inference algorithm is used for foreseeing the system in this thesis, because the fuzzy algorithm is the most useful method for resolving subjective ideas and a vague judgment of humans. The fuzzy inference algorithm is developed for each sensor signal(i.e. vibration, velocity, current).

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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• pp.544-544
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• 2000

From ancient times. the diagnosis method of the oriental medicine has been Performed by curing diseases by means of rectifying and adjusting the unbalance in the Physiological function of the five viscera and the six bowels of a human body. Diseases have been diagnosed by the condition of blood circulation that cycles a human body through blood vessels by dint of the vitality of the heart, Based on such a systematic pulse diagnosis method, the article presents parameters that will be beneficial to clinical application on the basis of its analysis of the filtering for eliminating noises from pulse signals inputted from sensor group the digital hardware dealing with signals necessary for recognition algorithm. and the structure of diagnosis algorithm and components of pulse waveform.

• International Journal of Precision Engineering and Manufacturing
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• 제7권3호
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• pp.14-17
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• 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.

• Journal of Power Electronics
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• 제13권6호
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• pp.985-990
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• 2013

This paper proposes a novel fault diagnosis scheme using parameter estimation of the stator resistance, especially in the case of the open-phase faults of PMSM drives. The stator resistance of PMSMs can be estimated by the recursive least square (RLS) algorithm in real time. Fault diagnosis is achieved by analyzing the estimated stator resistance of each phase according to the fault condition. The proposed fault diagnosis scheme is implemented without any extra devices. Moreover, the estimated parameter information can be used to improve the control performance. The feasibility of the proposed fault diagnosis scheme is verified by simulation and experimental results.

• Journal of Mechanical Science and Technology
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• 제16권12호
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• pp.1604-1612
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• 2002

Rollers in the continuous process systems are ones of key components that determine the quality of web products. The condition of rollers (e.g. eccentricity, runout) should be consistently monitored in order to maintain the process conditions (e.g. tension, edge position) within a required specification. In this paper, a new diagnosis algorithm is suggested to detect the defective rollers based on the frequency analysis of web tension signals. The kernel of this technique is to use the characteristic features (RMS, Peak value, Power spectral density) of tension signals which allow the identification of the faulty rollers and the diagnosis of the degree of fault in the rollers. The characteristic features could be used to train an artificial neural network which could classify roller conditions into three groups (normal, warning, and faulty conditions) The simulation and experimental results showed that the suggested diagnosis algorithm can be successfully used to identify the defective rollers as well as to diagnose the degree of the defect of those rollers.

• 대한임베디드공학회논문지
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• 제14권6호
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• pp.287-294
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• 2019

With the automation of production lines in the manufacturing industry, the importance of real-time fault diagnosis of facility is increasing. In this paper, we propose a fault diagnosis algorithm of LM (Linear Motion)-guide based on deep learning using vibration signals. Generally, in order to guarantee the performance of the deep learning, it is necessary to have a sufficient amount of data, but in a manufacturing industry, it is often difficult to obtain enough data due to physical and time constraints. To solve this problem, we propose a convolutional neural networks (CNN) model based on transfer learning. In addition, the spectrogram image is input to the CNN to reflect the frequency characteristic of the vibration signals with time. The performance of fault diagnosis according to various load condition and transfer learning method was compared and evaluated by experiments. The results showed that the proposed algorithm exhibited an excellent performance.

• International journal of advanced smart convergence
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• 제9권4호
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• pp.62-73
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• 2020

In this paper, we present electronic acupuncture needles we have developed using intelligence technology based on Bluetooth in order to allow anyone to simply receive customized remote diagnosis and treatment by clicking on the menu of the smartphone regardless of time and place. In order to determine the health condition and disease of patients, we have developed a software and a hardware of electronic acupuncture needles, operating on Bluetooth which transmits biometric data to oriental medical doctors using the functions of automatically determining pulse diagnosis, tongue diagnosis, and oxygen saturation; the functions are most commonly used in herbal treatment. In addition, using fuzzy logic and reasoning based on smartphones, we present in this paper an algorithm and the results of completion of hardware implementation for electronic acupuncture needles, appropriate for the body conditions of patients; the algorithm and the hardware implementation are for a treatment time duration by electronic acupuncture needles, an automatic determinations of pulse diagnosis, tongue diagnosis, and oxygen saturation, a function implementation for automatic display of acupuncture point, and a strength adjustment of electronic acupuncture needles. As a result of our simulation, we have shown that the treatment of patients, performed using an Electronic Acupuncture Needles based on intelligence, is more efficient compared to the treatment that was performed before.

• 전기학회논문지P
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• 제59권1호
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• pp.89-95
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• 2010

A new live-line insulator tester was developed to detect faulty insulators in 154kV power transmission lines. This paper is the second part of the two-part paper and deals with its inspection algorithm development. Unlike normal condition with low pollution and low humidity, the inspection data measured in the field under high pollution or high humidity showed that the voltage distribution of an insulator string has offsets in comparison with those of others and its insulation resistances are greatly decreased, which leads to wrong results of the existing inspection algorithms under such conditions. To solve this problem, we propose new diagnosis algorithms that can exactly detect faulty insulators from measured data regardless of environmental conditions. Its effectiveness was validated by live-line field tests in actual power lines.

• 소음진동
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• 제6권3호
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• pp.325-332
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• 1996

One of the greatest shortcoming in today's predictive maintenance program is the ability to diagnose the mechanical and electrical problems within the machine when the vibration exceeds preset overall and spectral alarm levels. In this study, auto-diagnosis system is constructed by using A/D converter to convert analog to digital singal. With this device the system analyses input signal to diagonosis machine condition. Many plots, which display machine condition, and input values of every channel are calculated in this system. If the falut is found, the system diagnoses automatically using fuzzy algorithm and trend monitoring. Prediction is also performed by the grey system theory. Operator finds out eh machine operating condition intuitively based on with personal computer CRT in using this system.