• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2010년도 춘계학술대회 논문집
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• pp.239-240
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• 2010

• Journal of Information Processing Systems
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• 제14권3호
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• pp.751-770
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• 2018

Web applications are indispensable in the software industry and continuously evolve either meeting a newer criteria and/or including new functionalities. However, despite assuring quality via testing, what hinders a straightforward development is the presence of defects. Several factors contribute to defects and are often minimized at high expense in terms of man-hours. Thus, detection of fault proneness in early phases of software development is important. Therefore, a fault prediction model for identifying fault-prone classes in a web application is highly desired. In this work, we compare 14 machine learning techniques to analyse the relationship between object oriented metrics and fault prediction in web applications. The study is carried out using various releases of Apache Click and Apache Rave datasets. En-route to the predictive analysis, the input basis set for each release is first optimized using filter based correlation feature selection (CFS) method. It is found that the LCOM3, WMC, NPM and DAM metrics are the most significant predictors. The statistical analysis of these metrics also finds good conformity with the CFS evaluation and affirms the role of these metrics in the defect prediction of web applications. The overall predictive ability of different fault prediction models is first ranked using Friedman technique and then statistically compared using Nemenyi post-hoc analysis. The results not only upholds the predictive capability of machine learning models for faulty classes using web applications, but also finds that ensemble algorithms are most appropriate for defect prediction in Apache datasets. Further, we also derive a consensus between the metrics selected by the CFS technique and the statistical analysis of the datasets.

• International Journal of Reliability and Applications
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• 제2권1호
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• pp.57-71
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• 2001

Global competition to increase production output and to improve quality is spurring manufacturing companies to use condition monitoring and fault diagnostic systems for predictive maintenance. As monitoring, testing, and measuring techniques develop, predictive control of components and complete systems have become more practical and affordable. In this article, we will consider the computer based data acquisition system for condition monitoring and the condition parameter analysis techniques for fault detection and diagnostics in the machinery and briefly discuss reliability prediction and the limit value determination in condition monitoring.

• International Journal of Safety
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• 제11권2호
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• pp.22-25
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• 2012

This paper deals with the problem of fault diagnosis for identifying a single fault when the number of assumed faults is larger than that of predictive variables. Principal component analysis (PCA) is employed to isolate and identify a single fault. PCA is a method to extract important information as reducing the number of large dimension in a process. The patterns of all assumed faults can be recognized by PCA and these can be employed whether a new fault is one of predefined faults or not. Through PCA, empirical models for analyzing patterns can be trained. When a single fault occurs, the pattern generated by PCA can be obtained and this is used to identify a fault. The performance of the proposed approach is illustrated in the actuator benchmark problem.

• 산업경영시스템학회지
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• 제23권54호
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• pp.155-165
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• 2000

The fault diagnosis is a systematic and unified method to find based on the observing data resulting in noises. This paper presents the fault prediction and diagnosis using fuzzy expert system technique to manipulate the uncertainties efficiently in predictive perspective. We apply a fuzzy event tree analysis to the computer system, and build up the fault prediction and diagnosis using fuzzy expert system that predicts and diagnoses the error of the system in the advance of error.

• 융합신호처리학회논문지
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• 제6권3호
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• pp.143-147
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• 2005

운전 중인 기계들의 안전 운전과 예지 보전을 위한 설비의 고장 감지 및 진단과 상태감시는 산업 현장에서 중요한 역할을 담당하고 있다. 이러한 설비의 많은 기기들은 회전기기로 이루어져 있으며 회전기기의 고장진단은 오랜 기간 많은 분야에서 연구되고 있다. 본 연구에서는 회전기기의 고장신호는 주파수 영역의 신호의 변화로 나타난다는 특징을 이용하여 보다 효율적인 주파수 영역에서의 신호 해석을 위하여 Linear Predictive Coding(LPC) 계수를 이용하였다. 사용된 데이터는 회전기기의 고장 신호의 습득을 용이하게 하기 위하여 유도전동기에 인위적인 고장재현을 통하여 습득된 진동 신호를 사용하였다. 제안된 시스템은 LPC 분석을 사용하여 일반적으로 사용되는 주파수 영역 상에서의 다른 해석 방법들보다 빠른 시간에 연산 결과를 도출할 수 있는 장점을 가질 수 있었으며, 성공적인 실험 결과를 얻을 수 있었다.

• 전기학회논문지
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• 제59권10호
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• pp.1882-1888
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• 2010

It is widely known that ESA(Electric Signature Analysis) method is very useful one for fault diagnosis of an induction motor. Online fault diagnosis system of induction motors using LabVIEW is proposed to detect the fault of broken rotor bars and shorted turns in stator. This system is not model-based system of induction motor but LabVIEW-based fault diagnosis system using FFT spectrum of stator current in faulty motor without estimating of motor parameters. FFT of stator current in faulty induction motor is measured and compared with various reference fault data in data base to diagnose the fault. This paper is focused on to predict and diagnose of the health state of induction motors in steady state. Also, it can be given to motor operator and maintenance team in order to enhance an availability and maintainability of induction motors. Experimental results are demonstrated that the proposed system is very useful to diagnose the fault and to implement the predictive maintenance of induction motors.

• 한국융합학회논문지
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• 제11권11호
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• pp.47-53
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• 2020

최근 모니터링 및 예측 시스템을 이용하여 사전에 결함을 발견하고 이를 경고하는 시스템이 활발히 연구되고 있다. 차량 안전 관리에 있어서도 예측 결함 분석 기술을 적용하여 자동차 휠 베어링의 고장 유무 및 고장 유형을 조기에 경고하는 시스템이 필요하다. 본 논문에서는 휠 베어링과 결합 된 센서 모듈과 각 센서 모듈에서 차량 가속 정보 및 진동 정보를 수집, 저장 및 분석하는 진단 시스템을 제시하였다. 제안된 센서 모듈은 저비용으로 차량의 휠 베어링 상태를 모니터링하며, 이렇게 수집된 데이터를 활용하여 진단 및 고장 예측 기능을 수행하는 방안을 연구하였다. 개발된 센서 모듈과 예측 분석 시스템은 가진 테스트 장비 및 실제 차량을 이용하여 테스트하고 그 유효성을 평가하였다.

• Journal of Platform Technology
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• 제6권3호
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• pp.47-54
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• 2018

하드웨어 산업에서의 예측 분석은 생산설비의 고장을 방지하기 위해 적절한 시점에 유지보수를 수행할 수 있고 관리비용을 절감할 수 있다. 이는 고장원인분석의 자동화를 통해 보다 효율적이고 과학적인 유지보수를 수행할 수 있도록 도와준다. 그중에서도 예측 관리는 정보 기술을 활용하여 설비 상태의 수집, 분석, 과학적 데이터 관리를 통해 예측 모델을 구성하며, 이를 바탕으로 이상상태를 파악하고 개선함으로써 이상상태가 발생하는 것을 사전에 예방하는 것을 목적으로 한다. 본 연구에서는 Relex 도구를 통해 결함트리(Fault Tree)를 만들고 하드웨어들의 에러코드를 분석하여 안전성을 연구했다.

• 소음진동
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• 제4권4호
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• pp.459-467
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• 1994

Various maintenance programs and techniques have been implemented for roating machineries, since machines were invented for commerical use. The earliest type of maintenance was run-to-failure, where the machine was run until a fault caused to fail in service. It was obviously an expensive approach due to the unpredictability of the machine condition. Another type is the periodic maintenance, where machines are disassembled and overhauled on regular schedules. With the advent of reliable data collectors including FFT analyzer and developing of versatile supporting software such as ExpertALERT system, the predictive maintenance is known to be the most feasible maintenance type these days. The vibration analysis enables for a maintenance crew to find the exact cause of fault on a machine and to make a proper maintenance schedule with a trend analysis. The predicitive maintenance is considered to be the most important part of pro-active maintenance.