• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.05a
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• pp.945-948
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• 2005

Mobile computing devices are becoming increasingly prevalent in a huge range of physical area, offering a considerable market opportunity. The focus of this paper is on the development of a platform of fault diagnosis system integrating with personal digital assistant (PDA). An improvement of induction machine rotor fault diagnosis based on AI algorithms approach is presented. This network system consists of two parts; condition monitoring and fault diagnosis by using Artificial Intelligence algorithm. LabVIEW allows easy interaction between acquisition instrumentation and operators. Also it can easily integrate AI algorithm. This paper presents a development environment fur intelligent application for PDA. The introduced configuration is a LabVIEW application in PDA module toolkit which is LabVIEW software.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2255-2258
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• 1998

Method of diagnosis in oriental medicine, the unbalance of the physiological function of the five viscers and six bowels of the human body is determined from time immemorial with the condition of blood circulation which is performed through blood vessels by the vitality of the heart. In oriental medicine, treatment is largely attempted by adjusting this unbalance. The analysis of pulse wave, which mainly measures the changes in blood flows, is to evaluate the shapes of a pulse wave rather than the quantitative changes like rates and strength of the pulse. This paper presents the development of Hardware System and Pulse Diagnosis Algorithm for automatic diagnosis of the pulse wave. This system makes the precise diagnosis and the objective recording possible.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2001.11b
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• pp.633-639
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• 2001

This paper proposes a knowledge-based expert system. which is assembled into hardware organized with sensor module. AID converter, USB. data acquisition PC and software composed of monitoring and diagnosis module combined with a frame-based method using Sohre's chart and a rule-based method. Vibration signals using various sensors are acquired by AID converter. transferred into PC and processed to obtain a continuous monitoring of the machine status displayed into several plots. Through combining frame-base which covers wide vibration causes with rule-base which gives relatively specified diagnosis results, high accuracy of fault diagnosis can be guaranteed and knowledge base can be easily extended by adding new causes or symptoms. Some examples using experimental data show the good feasibility of the proposed algorithm for condition monitoring and diagnosis of industrial rotating machinery.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1995.10a
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• pp.350-354
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• 1995

The Tension Leveller of Cold Rolling Mill In POSCO performs levelling the strip in high speed line. But minor variations in operating condition of driving machines such as motor, gear box, and support bearings, a small gap-variation of supporter and strip slip by poor roll revolutions can cause serious problems in the quality of strip. In this study, firstly, A condition monitoring standard for each sensor is made through with the detail analysis of vibration and strip slip. Secondly, An automatic monitoring and diagnosing system was developed to monitor the condition of Tension Leveller, and diagnose the cause of abnormal condition. Finally, A diagnosing algorithm for abnormal condition and man-machine interface (MMI) for easy operation are developed.

• Journal of the Korean Society for Precision Engineering
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• v.33 no.1
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• pp.23-29
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• 2016

In this paper, a real-time condition diagnosis system for the lens injection molding process is developed through the use of LabVIEW. The built-in-sensor (BIS) mold, which has pressure and temperature sensors in their cavities, is used to capture real-time signals. The measured pressure and temperature signals are processed to obtain features such as maximum cavity pressure, holding pressure and maximum temperature by the feature extraction algorithm. Using those features, an injection molding condition diagnosis model is established based on a response surface methodology (RSM). In the real-time system using LabVIEW, the front panels of the data loading and setting, feature extraction and condition diagnosis are realized. The developed system is applied in a real industrial site, and a series of injection molding experiments are conducted. Experimental results show that the average real-time condition diagnosis rate is 96%, and applicability and validity of the developed real-time system are verified.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.50 no.10
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• pp.717-727
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• 2022

An algorithm is realized for estimating the component fault and health diagnosis such as a deterioration. Based on the turbofan engine health diagnosis model, from the health parameters which are estimated by a real time tracking filter, the outliers are eliminated efficiently by an effective median filter to minimize an false alarm. The difference between the fault and deterioration trends is identified by the detection measure for abrupt change, thereby the clear diagnosis classifying the fault and the health condition is possible. The effectiveness of the algorithm for fault and health diagnosis is verified from the simulated results of engine component faults and deterioration.

• Proceedings of the KSME Conference
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• 2004.04a
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• pp.822-827
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• 2004

In this paper, an intelligent fault diagnosis system is proposed for induction motors through the combination of feature extraction, genetic algorithm (GA) and neural network (ANN) techniques. Features are extracted from motor vibration signals, while reducing data transfers and making on-line application available. GA is used to select most significant features from whole feature database and optimize the ANN structure parameter. Optimized ANN diagnoses the condition of induction motors online after trained by the selected features. The combination of advanced techniques reduces the learning time and increases the diagnosis accuracy. The efficiency of the proposed system is demonstrated through motor faults of electrical and mechanical origin on the induction motors. The results of the test indicate that the proposed system is promising for real time application.

• Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology
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• v.8 no.3
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• pp.571-578
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• 2018

The deteriorated pipline often causes the unexpected leakage and crack. Negligence and late maintenance leads the enormous damage for gas and water resource. This paper proposes early fault detection and diagnosis algorithm for pipeline using acoustic emission (AE) signals. Early fault detection method for pipeline compares the frequency amplitude of the spectrum to that of the spectrum in normal condition. Larger amplitude of the spectrum indicates abnormal condition. Early fault diagnosis algorithm uses support vector machines (SVM), which is trained for normal and abnormal conditions to diagnose the measured AE signal from the target pipeline. In the experiment, a pipeline testbed is constructed similarly to real industrial pipeline. Normal, 5mm cracked, 10mm holed pipelines are installed and tested in this study. The proposed fault detection and diagnosis technique is validated as an efficient approach to detect early faulty condition of pipeline.

• Proceedings of the IEEK Conference
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• 2002.07a
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• pp.500-503
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• 2002

Biomedical signals are ubiquitously contaminated and degraded by background noise which span nearly all frequency bandwidths. This paper proposes the MADF (multiplication free adaptive digital filter) algorithm to cancel the noise. And the convergence characteristics of the algorithm is analyzed. In the experimental results, the MADF algorithm has the advantage in which has superior to a condition of low-frequency and slow data speed. This application gives an important significance in ensuring the objectivity of clinical information and in promoting the representation and the disease diagnosis.

• Proceedings of the SAREK Conference
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• 2005.11a
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• pp.241-246
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• 2005

The objective of this study is to develop a rule-based fault detection and diagnosis algorithm and an experimental verification using air handling unit. To develop an analytical algorithm which precisely detects a faulted component, energy equations at each control volume of AHU were applied. An experimental verification was conducted in the AHU at Green Building in KIER. In the experiment conducted in hot summer condition, the rule based FDD algorithm isolated a faulted sensor from HVAC components.