• 한국소음진동공학회논문집
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• 제21권2호
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• pp.146-153
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• 2011

Model based machine condition diagnosis methods are generally using a normal and many failure models which need sufficient data to train the models. However, data, especially for failure modes of interest, is very hard to get in real applications. So their industrial applications are either severely limited or impossible when the failure models cannot be trained. In this paper, continuous hidden Markov model(CHMM) with only a normal model has been suggested as a very promising machine condition diagnosis method which can be easily used for industrial applications. Generally hidden Markov model also uses many pattern models to recognize specific patterns and the recognition results of CHMM show the likelihood trend of models. By observing this likelihood trend of a normal model, it is possible to detect failures. This method has been successively applied to arc weld defect diagnosis. The result shows CHMM's big potential as a machine condition monitoring method.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2001년도 제34회 추계학술대회 개최
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• pp.43-52
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• 2001

Condition monitoring plays a vital role since it sustains the reliable operation of industrial plant 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, we constructed a rotor system where various types of functional machine failures occurred frequently in industry were induced. Characteristics of the machine failure were monitored simultaneously by the on-line measurement of vibration, wear and temperature. Result showed that these parameters responded differently to the induced functional machine failure. The availability of each parameter on effective condition monitoring was discussed in this work.

• 한국기계가공학회지
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• 제20권9호
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• pp.35-41
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• 2021

A rotor is a crucial component in various mechanical assemblies. Additionally, high-speed and high-efficiency components are required in the automotive industry, manufacturing industry, and turbine systems. In particular, the failure of high-speed rotating bearings has catastrophic effects on auxiliary systems. Therefore, bearing reliability and fault diagnosis are essential for bearing maintenance. In this work, we performed failure mode and effect analysis on bearing rotors and determined that corrosion is the most critical failure type. Furthermore, we conducted experiments to extract vibration characteristic data and preprocess the vibration data through principle component analysis. Finally, we applied a machine learning algorithm called support vector machine to diagnose the failure and observed a classification performance of 98%.

• 한국산학기술학회논문지
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• 제11권5호
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• pp.1601-1607
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• 2010

본 논문에서는 FMEA (Failure Mode Effect Analysis) 기법을 적용한 우편기계 유지보수 방법 제시하였다. 제안된 방법은 우편기계 모듈 및 부품에 대해 고장 유형을 정의하고, 고장 유형별 시스템에 주는 영향과 고장 빈도 및 검출도 등을 정의하여 고장 유형에 대한 시스템 위험도를 계산하여 그 값에 기반하여 점점 항목과 점검 주기를 조정하도록 하므로 시스템의 고장을 사전에 예방하고 시스템 가동율을 높이도록 하는 효율적인 유지보수 방법이다. 실제 현장에서 운영되고 있는 소형 통상 우편 구분 기계에 대해 제안된 방법의 적용 예를 보였다. 따라서 제안된 방법은 향후 국내 우편기계 유지보수에 적용시 유지보수 용이성과 효율성을 높일 것으로 기대한다.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2002년도 춘계학술대회 논문집
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• pp.90-93
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• 2002

In view of reliability assessment, the failure mode analysis by performance tests for machine totals is researched in this study. First, the error analysis with circular movement test data is studied. The various errors and their origins are analyzed by error equations and related parts are investigated. Second, This paper deals with analysis of vibration testing fur machine tools spindle. The various frequency components are classified by FFT and order analysis. The simple measuring devices and error evaluation programs for tests are also developed.

• 한국CDE학회논문집
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• 제2권2호
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• pp.111-121
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• 1997

This study intends to develop a fixture planning module as a part of the planning system for cutting. The fixture module uses machine learning method to reuse previous failure results so that the system can reduce the repeated failures. Machine learning is one of efforts to incorporate human reasoning ability into a computerized system. A human expert designs better than a novice does because he has a wide experience in a specific area. This study implements the machine learning algorithm to have a wide experience in the fixture planning area as a human expert does. When the fixture planner finds a setup failure for the suggested operations by a process planner, it makes the process planner store its attributes and other information for the failed setup. Then the process planner applies the learned knowledge when it meets a similar case so that the planner can reduce possibility of setup failure. Also the system can teach a novice user by showing a failed setup with a modified setup.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 2002년도 추계학술대회 논문집
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• pp.435-439
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• 2002

In view of reliability assessment, the failure mode analysis by performance tests for machine tools is researched in this study. First, the error analysis with circular movement test data is studied. The various errors and their origins are analyzed by the error equations and then related parts and failure modes are investigated. Second, This paper deals with analysis of vibration testing for machine tools spindle. The various frequency components are classified by fourier transform and order analysis. The simple measuring devices and web-based analysis programs for each test are also developed.

• 한국기계가공학회지
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• 제19권11호
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• pp.94-101
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• 2020

In the age of industry 4.0, artificial intelligence is being widely used to realize machinery condition monitoring. Due to their excellent performance and the ability to handle large volumes of data, machine learning techniques have been applied to realize the fault diagnosis of different equipment. In this study, we performed the failure mode effect analysis (FMEA) of an aluminum electrolytic capacitor by using deep learning and big data. Several tests were performed to identify the main failure mode of the aluminum electrolytic capacitor, and it was noted that the capacitance reduced significantly over time due to overheating. To reflect the capacitance degradation behavior over time, we employed the Vanilla long short-term memory (LSTM) neural network architecture. The LSTM neural network has been demonstrated to achieve excellent long-term predictions. The prediction results and metrics of the LSTM and Vanilla LSTM models were examined and compared. The Vanilla LSTM outperformed the conventional LSTM in terms of the computational resources and time required to predict the capacitance degradation.

• 한국기계가공학회지
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• 제9권1호
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• pp.8-13
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• 2010

The shaft surface machining is a popular machine for studying descale machine design and process in automobile industry. In this study, the descale design machine of cutting shaft surface was conducted for the detection of a tool failure in surface process. Induction harden surface is used as analyzing function to detect a sudden change in cutting process level. A preliminary stepped workpiece which had a hard condition was cut by the surface tool and a tool process obtained cutting force machine. At machine failure, the results were suddenly increased and the detailed surfaces were extremely obtained.

• 한국전자통신학회논문지
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• 제8권7호
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• pp.1003-1010
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• 2013

본 연구에서는 데이터통신을 사용하기 위하여 기계간의 통신을 기초로 한 TCP/IP는 CDMA/GSM을 사용한다. 이 통신방식은 근해정박부이에서는 시스템 서버에 데이터 백업을 위하여 광법위하게 사용된다. 기후나 신호적용범위 때문에 TCP/IP M2M 통신방식은 종종 데이터 전송이 실패하거나 서버에서의 데이터손실을 유발한다. 해양통신이나 기상학적인 해석을 위해서는 데이터 손실을 줄여야 한다. 본 연구에서는 데이터 전달 손실을 최소화하고 데이터복구에서 사용되는 재시도를 위하여 M2M 데이터손실을 최소화하는 소프트웨어방식을 연구하였다. 이 연구의 유용성을 입증하기 위하여 근해에 위치하는 기상용 부이에서 통신을 이용하여 연구의 유용성을 보여주었다.