• 한국산업경영시스템학회:학술대회논문집
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• 한국산업경영시스템학회 2002년도 춘계학술대회
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• pp.403-412
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• 2002

Machine condition diagnosis is the technique to perceive the machine errors and the abrasion online without overhaul. We need the steps to predict the life span and reliability of a machine for the abrasion as with perceiving the degree of the abrasion of certain machine parts to make errors. In this study we deals with the methods to check and manage periodically and to configure the judgement criteria for the state of a machine. For the applications of CDT(Condition Diagnosis Technique) we also suggest the methods to check comparing the measured vibration values with the absolute criteria and to check the abnormality by vibration level.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2001년도 춘계학술대회 논문집
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• pp.92-97
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• 2001

The major faults of CNC machine tool is operational error which is charge over 70%. This paper describes model of remote service and fault diagnosis for CNC machine tool with open architecture controller. For intelligent fault diagnosis, new model is proposed. In this paper, the three major operational faults, emergency stop error, cycle start disable and machine ready disable, are defined. Two diagnostic models based on the ladder diagram, switching function model, step switching function model, are proposed. For internet based remote service, suitable environment is proposed and implemented with web server and client.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 추계학술대회 논문집
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• pp.7-10
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• 2002

The diagnosis of faults of machine tool, which is controlled by CNC and PLC, is generally based on ladder diagram of PLC. Because sequential controls for CNC and servo motor are mostly processed in PLC. However, when fault is occurred, a searching for logical relation to fault reasons is required a lot of fault experiences and times, because PLC program has step structure. In this paper, FDS(Fault Diagnosis System) is developed and implemented to machine tool with open architecture controller in order to find the reason of fault lastly and correctly. The diagnosed reasons for fault are tele-serviced on web through developed RSS(Remote Service System). The operationability and usefulness of developed system are evaluated on specially manufactured machine tool with open architecture CNC. The results of this research can be the model of remote monitoring and fault diagnosis system of machine tool with open architecture CNC.

• Journal of information and communication convergence engineering
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• 제17권2호
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• pp.128-134
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• 2019

Based on the concept of Industry 4.0, various sensors are attached to facilities and equipment to collect data in real time and diagnose faults using analyzing techniques. Diagnostic technology continuously monitors faults or performance degradation of facilities and equipment in operation and diagnoses abnormal symptoms to ensure safety and availability through maintenance before failure occurs. In this paper, we propose a model to analyze the data and diagnose the state or failure using machine learning. The diagnosis model is based on a support vector machine (SVM)-based diagnosis model and a self-learning one-class SVM-based diagnostic model. In the future, it is expected that this model can be applied to facilities used in the entire industry by applying the actual data to the diagnostic model proposed in this paper, conducting the experiment, and verifying it through the model performance evaluation index.

• 한국컴퓨터정보학회논문지
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• 제23권12호
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• pp.131-136
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• 2018

This paper proposes a computer-aided diagnostic algorithm in a non-invasive way. Currently, clinical diagnosis of jaundice is performed through blood sampling. Unlike the old methods, the non-invasive method will enable parents to measure newborns' jaundice by only using their mobile phones. The proposed algorithm enables high accuracy and quick diagnosis through machine learning. In here, we used the SVM model of machine learning that learned the feature extracted through image preprocessing and we used the international jaundice research data as the test data set. As a result of applying our developed algorithm, it took about 5 seconds to diagnose jaundice and it showed a 93.4% prediction accuracy. The software is real-time diagnosed and it minimizes the infant's pain by non-invasive method and parents can easily and temporarily diagnose newborns' jaundice. In the future, we aim to use the jaundice photograph of the newborn babies' data as our test data set for more accurate results.

• 한국멀티미디어학회논문지
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• 제22권11호
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• pp.1233-1241
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• 2019

The goal of the sound-based mechanical fault diagnosis technique is to automatically find abnormal signals in the machine using acoustic emission. Conventional methods of using mathematical models have been found to be inaccurate due to the complexity of industrial mechanical systems and the existence of nonlinear factors such as noise. Therefore, any fault diagnosis issue can be treated as a pattern recognition problem. We propose an automatic fault diagnosis method using discrete wavelet transform and power spectrum density using multi pattern recognition. First, we perform DWT-based filtering analysis for noise cancelling and effective feature extraction. Next, the power spectral density(PSD) is performed on each subband of the DWT in order to effectively extract feature vectors of sound. Finally, each PSD data is extracted with the features of the classifier using multi pattern recognition. The results show that the proposed method can not only be used effectively to detect faults as well as apply to various automatic diagnosis system based on sound.

• 한국정보통신학회논문지
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• 제15권1호
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• pp.241-248
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• 2011

본 논문에서는 핵연료 교환기 헤드를 제어하는 진단시스템을 설계하고 구현하였다. 제안하는 핵연료 교환기 진단시스템은 신호 수집 시스템, 진단 알고리즘, 고장 시뮬레이터의 세 부분으로 구성된다. 핵연료 교환기를 직접 사용하는 실험은 원전 운영상 불가능하여 본 연구에서는 고장 시뮬레이터로 베어링 이상 상태를 생성시키고 FFT 및 웨이블릿 변환을 이용하여 고장 진단 실험을 수행하였다. 베어링 볼 이상 상태 진동 분석과 베어링 내륜 이상 상태 진동 분석을 통해 이론값과 실험값이 거의 일치함을 확인하였다.

• 대한기계학회논문집A
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• 제21권5호
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• pp.835-841
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• 1997

Microscopic examination of the morphology of wear debris is an accepted method for machine condition and fault diagnosis. However wear particle analysis has not been widely accepted in industry because it is dependent on expert interpretation of particle morphology and subjective assessment criteria. This paper was undertaken to analyze the morphology of wear debris for machine condition diagnosis of the lubricated moving surfaces by image processing and analysis. The lubricating wear test was performed under different sliding conditions using a wear test device made in our laboratory and wear testing specimen of the pin-on-disk-type was rubbed in paraffine series base oil. In order to describe characteristics of debris of various shape and size, four shape parameters (50% volumetric diameter, aspect, roundness and reflectivity) have been developed and outlined in the paper. A system using such techniques promises to obviate the need for subjective, human interpretation of particle morphology in machine condition monitoring, thus to overcome many of the difficulties in current methods and to facilitate wider use of wear particle analysis in machine condition monitoring.

• 한국정밀공학회지
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• 제19권4호
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• pp.168-178
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• 2002

The CNC machine toots has two kinds of fault. One is the fault due to degraded parts and the other is the fault due to operation disability. The phenomena of degradation is predictable but the operational fault is unpredictable because it occurred without any warning. The major faults of CNC machine tool are operational faults which are charged over 70%. This paper describes the model of remote service and the intelligent fault diagnosis system to diagnosis operational faults of CNC machine tools. To generalize fault diagnosis, two diagnosis models such as SF(Switching Function) and SSF(Step Switching Function) are proposed. The SF is static model and SSF is dynamic model for expression of fault. The SF and SSF model can be generated using SFG(Switching Function Generator) which is developed in this research. The three major operational faults such as emergency stop error, cycle start disability and machine ready disability are applied to experiment of fault modeling. To remote service of faults fur CNC machine tool, the web server and client system based internet are proposed as the suitable environment. The developed two technologies are implemented with the internal function of open architecture controller. The implemental results for two technologies are presented to validate the proposed scheme.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2003년도 추계학술대회논문집
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• pp.537-543
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• 2003

Condition monitoring and fault diagnosis of machines are gaining importance in the industry because of the need to increase reliability and to decrease possible loss of production due to machine breakdown. By comparing the vibration signals of a machine running in normal and faulty conditions, detection of faults like mass unbalance, shaft misalignment and bearing defects is possible. This paper presents a novel approach for applying the fault diagnosis of rotating machinery. To detect multiple faults in rotating machinery, a feature selection method and support vector machine (SVM) based multi-class classifier are constructed and used in the faults diagnosis. The results in experiments prove that fault types can be diagnosed by the above method.