• 품질경영학회지
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• 제47권4호
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• pp.783-793
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• 2019

Purpose: The purpose of this study is to apply the machine and deep learning methodology on error terms which are continuously auto-generated on the sensors with specific time period and prove the improvement effects of power generator prediction diagnosis system by comparing detection ability. Methods: The SVM(Support Vector Machine) and MLP(Multi Layer Perception) learning procedures were applied for predicting the target values and sequentially producing the error terms for confirming the detection improvement effects of suggested application. For checking the effectiveness of suggested procedures, several detection methodologies such as Cusum and EWMA were used for the comparison. Results: The statistical analysis result shows that without noticing the sequential trivial changes on current diagnosis system, suggested approach based on the error term diagnosis is sensing the changes in the very early stages. Conclusion: Using pattern of error terms as a diagnosis tool for the safety control process with SVM and MLP learning procedure, unusual symptoms could be detected earlier than current prediction system. By combining the suggested error term management methodology with current process seems to be meaningful for sustainable safety condition by early detecting the symptoms.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2012년도 추계학술대회 논문집
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• pp.394-399
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• 2012

최근 MEMS 센서는 기계상태감시에 있어서 전력소모, 크기, 비용, 이동성, 응용 등에 있어서 각광을 받고 있다. 특히, MEMS 센서는 스마트센서와 통합가능하고, 대량생산이 가능하여 가격이 저렴하다는 장점이 있다. 이와 관련한 기계상태감시를 위한 많은 실험적 연구가 수행되고 있다. 이 논문은 MEMS 센서들을 3 가지 인공지능 분류기 성능평가를 위한 비교연구에 대해 설명하고 있다. 회전기계에 MEMS 가속도와 전류센서들을 부착하여 데이터를 취득했고, 특징추출과 파라미터 최적화를 위해 Cross validation 기법을 사용하였다. MEMS 센서를 이용한 결함분류기 적용은 적합하다고 판단된다.

• 한국음향학회지
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• 제39권4호
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• pp.263-269
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• 2020

본 논문에서는 정상상태와 8가지의 고장이 재현가능한 회전체를 제작하고 진동 데이터를 취득하였다. 취득한 데이터로 특징을 계산하여 인공신경망과 유전알고리즘을 적용한 고장진단을 통해 정확성을 분석한다. 최적의 시간과 높은 정확성의 구현을 위해 특징을 3가지 영역으로 구분하여 고장진단에 적용하였다. 설정변수는 학습수로 설정하였다. 회전체 고장진단의 결과는 다른 영역보다 주파수영역에서 높은 정확성을 보였으며 학습수 5000, 8000회에서 10회의 구동 모두 정확한 고장진단을 하였다. 시간의 효율성을 고려하였을 경우, 학습수가 5000회일 때 가장 우수하다고 판단하였다.

• 드라이브 ㆍ 컨트롤
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• 제18권1호
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• pp.24-30
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• 2021

There is an increasing interest in condition-based maintenance for the prevention of economic loss due to failure. Moreover, immense research is being carried out in related technologies in the field of construction machinery. In particular, data-based failure diagnosis methods that employ AI (machine & deep learning) algorithms are in the spotlight. In this study, we have focused on the failure diagnosis and mode classification of reduction gear of excavator's travel device by using the AI algorithm. In addition, a remote monitoring system has been developed that can monitor the status of the reduction gear by using the developed diagnosis algorithm. The failure diagnosis algorithm was performed in the process of data acquisition of normal and abnormal under various operating conditions, data processing and analysis by the wavelet transformation, and learning. The developed algorithm was verified based on three-evaluation conditions. Finally, we have built a system that can check the status of the reduction gear of travel devices on the web using the Edge platform, which is embedded with the failure diagnosis algorithm and cloud.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 추계학술대회논문집 II
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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.

• 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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 1995년도 추계학술대회 논문집
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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.

• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2004년도 학술대회지
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• pp.316-322
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• 2004

Wear volume was used generally to analyze the moving state of lubricated machine. But It is difficult of getting the correct wear volume because wear volume of it is progressed always unstably with a large amplitude on working condition. If correct analysis of wear volume on working condition for lubricated machine can be possible, it can be effect on diagnosis of failure condition. The purpose of this study is carried out to analysis friction factors affecting on wear volume for prediction of failure condition of alloy steel for machine structural use by design of experiment. The results show that the most important friction factors affecting on wear volume was applied load, neat sliding distance, sliding speed and materials.

• Journal of Medicine and Life Science
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• 제20권4호
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• pp.141-157
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• 2023

Stroke is a leading cause of disability and death. The condition requires prompt diagnosis and treatment. The quality of care provided to patients with stroke can vary depending on the availability of medical resources, which in turn, can affect prognosis. Recently, there has been growing interest in using machine learning (ML) to support stroke diagnosis and treatment decisions based on large medical data sets. Current ML applications in stroke care can be divided into two categories: analysis of neuroimaging data and clinical information-based predictive models. Using ML to analyze neuroimaging data can increase the efficiency and accuracy of diagnoses. Commercial software that uses ML algorithms is already being used in the medical field. Additionally, the accuracy of predictive ML models is improving with the integration of radiomics and clinical data. is expected to be important for improving the quality of care for patients with stroke.

• 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.