• 대한안전경영과학회지
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• 제22권1호
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• pp.1-8
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• 2020

Maintenance of power distribution facilities is a significant subject in the power supplies. Fault caused by deterioration in power distribution facilities may damage the entire power distribution system. However, current methods of diagnosing power distribution facilities have been manually diagnosed by the human inspector, resulting in continuous pole accidents. In order to improve the existing diagnostic methods, a thermal image analysis model is proposed in this work. Using a thermal image technique in diagnosis field is emerging in the various engineering field due to its non-contact, safe, and highly reliable energy detection technology. Deep learning object detection algorithms are trained with thermal images of a power distribution facility in order to automatically analyze its irregular energy status, hereby efficiently preventing fault of the system. The detected object is diagnosed through a thermal intensity area analysis. The proposed model in this work resulted 82% of accuracy of detecting an actual distribution system by analyzing more than 16,000 images of its thermal images.

• 한국전자파학회논문지
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• 제28권11호
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• pp.872-879
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• 2017

본 논문은 direct digital synthesizer, 연산 증폭기 및 이득/위상 검출기로 구성된 소형 임피던스 분석기를 사용하여 차분 주파수 영역 반사 계측을 기반으로 한 케이블 결함 모니터링 시스템을 제시한다. 제안된 시스템은 주파수 영역 반사계측을 위한 고가의 벡터 네트워크 분석기를 저비용으로 대체할 수 있으므로, 장기간 동안 다중 지점을 모니터링하는 센서 네트워크 구성이 가능하다. 이 시스템의 성능은 전원 케이블의 결함 지점을 진단하는 실험을 통해 검증하였다.

• 문화기술의 융합
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• 제10권3호
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• pp.801-807
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• 2024

선형 로봇은 자동화 시스템에서 부품의 이송이나 위치 결정에 널리 사용되며 보통 높은 정밀도가 요구된다. 선형 로봇을 응용한 시스템의 제작회사에서는 로봇의 이상 유무를 작업자가 판단하는데, 작업자의 숙련도에 따라 이상 상태를 판단하는 정확도가 달라진다. 최근에는 인공지능 등의 기술을 사용하여 로봇 스스로 이상을 검출하는 방법에 관한 연구가 진행되고 있다. 본 논문에서는 전이 학습을 이용하여 선형 로봇의 볼 스크류 정렬 이상과 선형 레일 정렬 이상을 검출하는 시스템을 제안하고 가속도 센서와 토크 센서 정보를 이용한 별개의 실험을 통해 제안한 시스템의 이상 검출 성능을 검증 및 비교한다. 센서로부터 얻어진 신호를 스펙트로그램 이미지로 변환한 후, 영상 인식 인공지능 분류기를 사용하여 이상의 종류를 진단하였다. 제안한 방법은 선형 로봇뿐만 아니라 일반적인 산업용 로봇에도 적용할 수 있을 것으로 기대한다.

• 한국안전학회지
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• 제7권4호
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• pp.105-114
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• 1992

This paper presents an study on the knowledge based system for diagnosing the fire causes using the Fuzzy Possibility Measure( FPM ) about the electric-fire ignition. The Ignition values needed for causes diagnosis is computed as FPM for electric-fire ignition based on the internal scale technique that assigns numerically the characteristic difference of facts to the-tin-ear scale. For the convinience of inference, ignition sources are classified into seven types : short, ground fault, leakge of electricity, overcurrent, cord junction overheating, bad Insulation and spark. The system for causes diagnosis of electric-fire is composed of Knowledge Acquisition System, Inference Engine and Man-Machine Interface, The diagnosis system is wrritten in an artificial intelligence langusge “PROLOG” which uses depth-first search and backward chaining schemes in reasoning process.

• 소음진동
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• 제10권2호
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• pp.331-337
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• 2000

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal defect diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised learning algorithm is used to improve the quality of the classifier decision regions.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2001년도 춘계학술대회논문집
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• pp.774-779
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• 2001

If rotating machinery have a fault, we can detect it using vibration or noise signals. However some maintenance engineers who doesn't have expert knowledge, needs the help of vibration experts for diagnosing rotating machinery. But qualified experts are rare, therefore we have been developed the case based reasoning (CBR) system which is able to manipulate the past experiences of vibration diagnosis experts. In the CBR system, the maintenance engineers can retrieve too information from previous cases which are most similar to new problem and they can solve new problem using solutions from the previous cases. In this paper, we propose a new method which is the case retrieval of CBR system using Petri net and we also applied it to diagnosis for electric motors as a practical problem.

• 유체기계공업학회:학술대회논문집
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• 유체기계공업학회 1999년도 유체기계 연구개발 발표회 논문집
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• pp.317-323
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• 1999

The necessity of diagnosis of the rotating machinery which is widely used in the industry is increasing. Many research has been conducted to manipulate field vibration signal data for diagnosing the fault of designated machinery. As the pattern recognition tool of that signal, neural network which use usually back-propagation algorithm was used in the diagnosis of rotating machinery. In this paper, self-organizing feature map(SOFM) which is unsupervised learning algorithm is used in the abnormal vibration diagnosis of rotating machinery and then learning vector quantization(LVQ) which is supervised teaming algorithm is used to improve the quality of the classifier decision regions.

• 한국기계가공학회지
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• 제15권4호
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• pp.134-140
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• 2016

Main Spindle System has effect on performance of machine tools and working quality as well as is required of high reliability. Especially, it takes great importance in producing automobiles which includes a large number of working processes. However, main spindle unit in Machine tools are often cases where damage occurs do not meet the design life due to driving in harsh environments. This is when excessive maintenance and repair of machine tools or for damage stability has resulted in huge economic losses. Therefore, this studying propose a method of accelerated life test for diagnosing and prognosis the state of life assessment main spindle system. Time status monitoring of diagnostic data - through the analysis of the frequency band signals were carried out inside the main spindle bearing condition monitoring and fault diagnosis.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1995년도 하계학술대회 논문집 B
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• pp.956-958
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• 1995

This paper presents the automatic construction of fuzzy rule-based systems for diagnosing the faults of complex systems. Generally, fuzzy systems work well when we can use expert's experience to articulate fuzzy IF-THEN rules and memberships for fuzzy sets. When we cannot do this, we should generate the fuzzy rules and membership functions for fuzzy sets directly from experimental data. In this paper, we propose a new method on how to extract fuzzy sets and fuzzy rules. We also introduce an efficient fine-tunning algorithm of the parameters of membership functions.

• 한국태양에너지학회 논문집
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• 제37권4호
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• pp.23-33
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• 2017

A great deal of study for loss reduction of photovoltaic system is conducted currently. It is hard to distinct the fault of photovoltaic system with the naked eye. For that reason, it is essential to repair and maintain the PV system by monitoring the system. The fault of individual modules can cause the huge loss of the entire system because of the mismatch. Therefore, the method of diagnosing the PV array is necessary by measuring the multi-channel arrays simultaneously. In this paper, it is presented the method of measuring I-V curve of multi-channel arrays simultaneously by using the charge and discharge characteristics of capacitor. Generated DC power at PV arrays is charged and discharged at the capacitors in a moment. By measuring the charged voltage and current, it is possible to diagnose of performance of PV arrays.