• 전기학회논문지
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• 제67권3호
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• pp.365-373
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• 2018

In this paper, inter-turn fault diagnosis of the interior permanent magnet synchronous motor (IPMSM) is performed in offline state by linking the finite element analysis (FEA) tool and control simulation tool. In order to diagnose the inter-turn fault, it is important to select the current value to determine the fault. First, the basic principles for inter-turn fault diagnosis of IPMSM are explained and co-analysis model for fault diagnosis is constructed. Further, in order to select the appropriate high frequency voltage, the change of the current value to be judged as failure was analyzed at various voltage and frequency conditions, and the change of the current value according to the number of the failed windings was analyzed. Finally, the current value to be judged as failure is selected.

• 대한전자공학회논문지SD
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• 제41권5호
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• pp.51-60
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• 2004

반도체 기술의 발달로 회로의 집적도와 복잡도가 증가함에 따라 칩의 생산 과정에서 고장이 발생하는 빈도가 높아지게 되었다. 칩의 수율을 향상시키고, 생산 단가를 절감시키기 위해서 고장의 원인을 찾아내고 분석하는 과정은 매우 중요하다. 그러나 고장의 원인을 분석하는 과정 중 고장의 위치를 찾아내는 데는 많은 시간이 소요된다. 게이트 수준에서의 고장 위치 진단은 물리적 수준에서의 고장 범위를 한정해 줌으로써 고장 위치를 찾는 데 소요되는 시간을 줄 일 수 있다는 데 의미를 갖는다. 본 논문에서는 새로운 방식의 고장 딕션너리 방식과 추가적인 고장 시뮬레이션 방식을 혼합하여, 메모리의 소비를 최소화하면서도 시뮬레이션 수행 시간을 단축시킴으로써 효과적으로 고장 진단을 수행할 수 있는 고장 진단 알고리듬을 제안한다.

• 한국소음진동공학회논문집
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• 제21권11호
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• pp.1020-1028
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• 2011

As rotating machines play an important role in industrial applications such as aeronautical, naval and automotive industries, many researchers have developed various condition monitoring system and fault diagnosis system by applying artificial neural network. Since using obtained signals without preprocessing as inputs of neural network can decrease performance of fault classification, it is very important to extract significant features of captured signals and to apply suitable features into diagnosis system according to the kinds of obtained signals. Therefore, this paper proposes a neural-network-based fault diagnosis system using AR coefficients as feature vectors by LPC(linear predictive coding) and EIV(errors-in variables) analysis. We extracted feature vectors from sound, vibration and current faulty signals and evaluated the suitability of feature vectors depending on the classification results and training error rates by changing AR order and adding noise. From experimental results, we conclude that classification results using feature vectors by EIV analysis indicate more than 90 % stably for less than 10 orders and noise effect comparing to LPC.

• 한국자동차공학회논문집
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• 제19권2호
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• pp.72-77
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• 2011

In this paper, we propose effective diagnosis algorithm for hub bearing fault in driving vehicle using acceleration signal and wheel speed signal measured in hub bearing unit or knuckle. This algorithm consists of differential, envelope and power spectrum method. We developed diagnosis system for realizing proposed algorithm. This system consists of input device including acceleration sensor and wheel speed sensor, calculation device using Digital Signal Processor (DSP) and display device using Personal Digital Assistant (PDA). Using this diagnosis system, a driver can see hub bearing fault(flaking) from the vibration in driving vehicle. With early repairing, he can keep good ride feeling and prevent accident of vehicle resulting from hub bearing fault.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 추계학술대회 논문집 학회본부 C
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• pp.910-912
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• 1998

In this paper, we purpose automatic diagnosis in online, as the fundamental study to diagnose the partial discharge mechanism and to predict the lifetime, by introduction a neural network. In the proposed method, Ire use acoustic emission sensing system and calculate a fixed quantity statistic operator by pulse number and amplitude. Using statically operators such as the center of gravity(G) and the gradient of the discharge distribute(C), we analyzed the early stage and the middle stage. the fixed quantity statistic operators are learned by a neural network. The diagnosis of insulation degradation and a lifetime prediction by the early stage time are achieved. On the basis of revealed excellent diagnosis ability through the neural network learning for the patterns during degradation, it was proved that the neural network is appropriate for degradation diagnosis and lifetime prediction in partial discharge.

• 산업경영시스템학회지
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• 제42권4호
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• pp.126-134
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• 2019

Nowadays, since there are so many big data available everywhere, those big data can be used to find useful information to improve design and operation by using various analysis methods such as data mining. Especially if we have event log data that has execution history data of an organization such as case_id, event_time, event (activity), performer, etc., then we can apply process mining to discover the main process model in the organization. Once we can find the main process from process mining, we can utilize it to improve current working environment. In this paper we developed a new method to find a final diagnosis of a patient, who needs several procedures (medical test and examination) to diagnose disease of the patient by using process mining approach. Some patients can be diagnosed by only one procedure, but there are certainly some patients who are very difficult to diagnose and need to take several procedures to find exact disease name. We used 2 million procedure log data and there are 397 thousands patients who took 2 and more procedures to find a final disease. These multi-procedure patients are not frequent case, but it is very critical to prevent wrong diagnosis. From those multi-procedure taken patients, 4 procedures were discovered to be a main process model in the hospital. Using this main process model, we can understand the sequence of procedures in the hospital and furthermore the relationship between diagnosis and corresponding procedures.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2000년도 추계학술대회 논문집
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• pp.81-84
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• 2000

Rotating machine is used extensively and plays important roles in the industrial field. Therefore when rotating machine get out of order, it is necessary to know reasons then deal with the troubles immediately. So many studies far diagnosis of rotating machine are being done. However by this time most of study has an interest in gaining a high recognition But without considering error $rate^{(1)(2)(3)}$ , it is not desirable enough to apply h the actual application system. If the manager of system receives the result misjudging the condition of rotating machine and takes measures, we would lose heavily. So in order to play the creditable diagnosis, we must consider error rate. T h ~ t is. it must be able to reject the result of misjudgment. This study uses nearest neighbor classifier for diagnosis of rotating $machine^{(4)(8)}$ And the Smith's rejection $method^{(1)}$ used to recognize handwritten charter is done. Consequently creditable diagnosis of rotating machine is proposed.

• 한국정보통신학회논문지
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• 제6권2호
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• pp.294-299
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• 2002

이 논문은 신경회로망을 기반으로 한 유도전동기의 고장 진단 기법을 제시한다. 제안된 기법은 고정자전류만을 측정하여 FFT 변환 후 진단 훈련을 위해 일반화한다. 정상, 베어링고장, 고정자 권선고장 그리고 회전자 엔드-링 고장을 갖는 모터로부터 학습데이터를 획득하고 여러 고장 유형을 진단한다. 더욱 효과적인 고장 진단을 위해, 전부하의 100％, 60％, 30％로 부하율을 변화시켜서 학습절차에 적용하였다. 실험 결과들은 제안된 방법이 오차 범위 0.56％∼0.04％와 같은 높은 진단 정밀도를 가지고 있어 실제 진단시스템에 적용 가능함을 보여주고 있다.

• 대한한의진단학회지
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• 제17권2호
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• pp.90-111
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• 2013

Objectives Migratory pathogenic factor(六淫) occupies an important position in the etiology of Korean Medicine. This paper shows how Dampness, one of Migratory pathogenic factor(六淫), is explained in Donguibogam(東醫寶鑑). And, based on this, we will figure out how to make a judgement of Dampness through diagnosis. Method 1. Collect parts of Dampness mentioned in Donguibogam(東醫寶鑑). 2. From the collection, extract contents about mechanism and symptom of Dampness, which is considered necessary for diagnosis. 3. Put all the extraction together, suggest the diagnosis element which can be criteria of judgement of Dampness through diagnosis. Result & Conclusions The occurrence of Dampness come from a wet climate and environment externally, and overeating of greasy food and digestive disorder internally. There are many different kinds of symptoms throughout the body cause of poor circulation. Dampness is classed as Cold-dampness, Damp-heat, and Dampness-phlegm depending on characteristic symptoms, and mainly shows musculoskeletal disease and digestive troubles. Typical symptoms are pitting edema, distention and fullness, moderate and thready pulse, volume of perspiration increase, loose feces, urine volume decrease, pain of joint and muscle, restriction of movement, etc.

• 정보학연구
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• 제2권2호
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• pp.13-22
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• 1999

본 논문에서, 부분방전 메카니즘을 진단하고 그리고 신경망을 도입하여 수명을 예측하기 위한 기초연구로서, 온라인상에서 자동진단을 제안했다. 제안한 방법에서 우리는 음향방출 감지시스템과 그리고 펄스 수와 펄스진폭에 의해서 정량적인 통계파라메타를 사용하였다. 통계적인 파라메타인 가령, 무게중심(G)와 방전분포 경도(C)를 이용하였고 그리고 초기단계와 중기단계에 대해서 분석하였다. 정량적인 통계파라메타들은 신경망에 의해서 학습되어졌다. 초기단계에 의해서 수명예측과 절연열화의 진단이 이루어졌다. 열화가 진행하는 동안 신경망 학습을 통한 휼륭한 진단능력이 있음이 근본적으로 드러났고, 신경망이 부분방전에 있어서 절연진단 및 수명예측을 위해서 적절하다는 것이 증명되었다.