• Advances in Computational Design
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• 제8권3호
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• pp.255-271
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• 2023

The gear drive of a combat aircraft engine is responsible for power transmission to the different accessories necessary for the engine's operation. Incorrect power transmission can occur due to the presence of failure modes in the gears like bending fatigue, pitting, adhesive wear, scuffing, abrasive wear and polished wear etc. Fault diagnosis of the gear drive is necessary to get an early indication of failure of the gears. The present research is to develop an algorithm using different vibration signal processing techniques on industrial vibration acquisition systems to establish gear fault diagnosis architecture. The signal processing techniques have been used to extract various feature vectors in the development of the fault diagnosis architecture. An open-source dataset of other gear fault conditions is used to validate the developed architecture. The results is a basis for development of artificial intelligence based expert systems for gear fault diagnosis of a combat aircraft engine.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2002년도 춘계학술대회논문집
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• pp.39-52
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• 2002

Research Direction The significant research direction in mechanical fault diagnosis area: Theorles and approaches for fault feature extracting and fault classification. Identification Complicated fault generating mechanism and its model Intelligent fault diagnosis system (including the expert system and network based remote diagnosis system) One of the Key Points： Fault feature extracting techniques based on (modern) signal processing(omitted)

• Annals of Clinical Neurophysiology
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• 제10권1호
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• pp.29-32
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• 2008

Although various criteria on the diagnosis of diabetic neuropathy are applied from trial to trial, being tailored in concert with its purpose, the utmost evidences of the diagnosis are subjective symptoms and objective signs of neurologic deficit. The application and interpretation of auxiliary electrophysiological test including nerve conduction study (NCS) should be made on the context of clinical pictures. The evaluation of the functions of small, thinly myelinated or unmyelinated nerve fibers has been increasingly stressed recently with the advent of newer techniques, e.g., measurement of intraepidermal fiber density, quantitative sensory testing, and autonomic function test. And the studies with those techniques have shed light to the nature of the evolution of diabetic neuropathy. The practical application of these techniques to the diagnosis of diabetic neuropathy in the individual patients, however, should be made cautiously due to several shortcomings: limited accessibility, wide overlapping zone between norm and abnormality with resultant unsatisfactory sensitivity and specificity, difficulty in performing subsequent tests, unproven quantitative correlation with clinical deficit, and invasiveness of some technique. NCS, as an extension of clinical examination, is still the most reliable electrophysiological test in evaluating neuropathy and gives the invaluable information about the nature of neuropathy, whereas the newer techniques need more refinement of the procedure and interpretation, and the accumulation of large scaled data of application to be considered as established diagnostic tools of peripheral neuropathy.

• Asian Pacific Journal of Cancer Prevention
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• 제16권14호
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• pp.5613-5618
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• 2015

Clinicians should looking for techniques that helps to early diagnosis of cancer, because early cancer detection is critical to increase survival and cost effectiveness of treatment, and as a result decrease mortality rate. Medical images are the most important tools to provide assistance. However, medical images have some limitations for optimal detection of some neoplasias, originating either from the imaging techniques themselves, or from human visual or intellectual capacity. Image processing techniques are allowing earlier detection of abnormalities and treatment monitoring. Because the time is a very important factor in cancer treatment, especially in cancers such as the lung and breast, imaging techniques are used to accelerate diagnosis more than with other cancers. In this paper, we outline experience in use of image processing techniques for lung and breast cancer diagnosis. Looking at the experience gained will help specialists to choose the appropriate technique for optimization of diagnosis through medical imaging.

• Transactions on Electrical and Electronic Materials
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• 제14권4호
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• pp.211-215
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• 2013

Partial discharge diagnosis techniques using ultra high frequencies do not affect load movement, because there is no interruption of power. Consequently, these techniques are popular among the prevention diagnosis methods. For the first time, this measurement technique has been applied to the GIS, and has been tested by applying an extra high voltage switchboard. This particular technique makes it easy to measure in the live state, and is not affected by the noise generated by analyzing the causes of faults ? thereby making risk analysis possible. It is reported that the analysis data and the evaluation of the risk level are improved, especially for poor location, and that the measurement of Ultra high frequency (UHF) partial discharge of the real live wire in industrial switchgear is spectacular. Partial discharge diagnosis techniques by using the Ultra High Frequency sensor have been recently highlighted, and it is verified by applying them to the GIS. This has become one of the new and various power equipment techniques. Diagnosis using a UHF sensor is easy to measure, and waveform analysis is already standardized, due to numerous past case experiments. This technique is currently active in research and development, and commercialization is becoming a reality. Another aspect of this technique is that it can determine the occurrences and types of partial discharge, by the application diagnosis for live wire of ultra high voltage switchgear. Measured data by using the UHF partial discharge techniques for ultra high voltage switchgear was obtained from 200 places in Gumi, Yeosu, Taiwan and China's semiconductor plants, and also the partial discharge signals at 15 other places were found. It was confirmed that the partial discharge signal was destroyed by improving the work of junction bolt tightening check, and the cable head reinforcement insulation at 8 places with a possibility for preventing the interruption of service. Also, it was confirmed that the UHF partial discharge measurement techniques are also a prevention diagnosis method in actual industrial sites. The measured field data and the usage of the research for risk assessment techniques of the live wire status of power equipment make a valuable database for future improvements.

• 대한임베디드공학회논문지
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• 제4권1호
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• pp.23-28
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• 2009

Testing of analog(and mixed-signal) circuits has been a difficult task for test engineers and effective test techniques to solve these problems are required. This paper develops a new technique which increases fault detection and diagnosis rates for analog circuits by using extended MTSS (Modified Time Slot Specification) technique based on MTSS proposed by the author. High performance current sensors with digital outputs are used as core components for these techniques. A fault diagnosis structure with minimal hardware overhead in ATE is also described.

• International Journal of Computer Science & Network Security
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• 제22권7호
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• pp.1-12
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• 2022

The current progression in the Internet of Things (IoT) and Machine Learning (ML) based technologies converted the traditional healthcare system into a smart healthcare system. The incorporation of IoT and ML has changed the way of treating patients and offers lots of opportunities in the healthcare domain. In this view, this research article presents a new IoT and ML-based disease diagnosis model for the diagnosis of different diseases. In the proposed model, vital signs are collected via IoT-based smart medical devices, and the analysis is done by using different data mining techniques for detecting the possibility of risk in people's health status. Recommendations are made based on the results generated by different data mining techniques, for high-risk patients, an emergency alert will be generated to healthcare service providers and family members. Implementation of this model is done on Anaconda Jupyter notebook by using different Python libraries in it. The result states that among all data mining techniques, SVM achieved the highest accuracy of 0.897 on the same dataset for classification of Parkinson's disease.

• 전기전자학회논문지
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• 제25권3호
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• pp.548-555
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• 2021

본 논문에서는 산업 공정, 설비 및 모터 드라이브에 적용되는 고장 진단 및 고장 허용 제어 기술의 기본 개념, 접근법과 연구 동향에 대해서 개괄적으로 기술하였다. 산업 공정을 위한 고장 진단의 주요 역할은 공정의 결함 상태를 파악할 수 있는 효과적인 지표를 만든 후 고장이나 위험한 사고에 대해 적절한 조치를 취하는 것이다. 산업 공정에 패턴이 있는지 특정 프로세스 변수가 정상적으로 동작하는지 확인하기 위해 많은 고장 검출 및 진단 기법이 개발되었다. 먼저 본 논문에서는 데이터 기반 기법과 모델 기반 기법에 대하여 살펴본다. 두 번째로 산업 공정을 위한 고장 검출 및 진단 기법을 살펴본다. 세 번째로 수동형 및 능동형 고장 허용 제어 기법을 살펴본다. 마지막으로 AC 모터 드라이브에서 발생하는 주요 고장을 열거, 그 특성을 살펴보고 이를 위한 고장 진단 및 고장 허용 제어 기술을 살펴본다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 B
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• pp.420-422
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• 1998

All Fault Detection and Diagnosis(FDD) methods utilize classification techniques. The objective of this study was to demonstrate the application of classification techniques to the problem of diagnosing faults in data generated by a variable-air-volume(VAV) air-handling unit(AHU) simulation model and to describe the characteristics of the techniques considered. Artificial neural network classifier and fuzzy clustering classifier were considered for fault diagnostics.

• 한국산학기술학회논문지
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• 제17권1호
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• pp.538-543
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• 2016

고품질의 제품과 조업 안전을 확보하기 위해서는 적절한 실시간 공정 감시 및 진단 시스템이 설치되어있는 것이 무엇보다 중요하다. 공정 감시 시스템과 결합된 신뢰도 높은 진단 시스템은 공정에서 발생한 특별한 사건이나 사고의 근본적인 원인과 공정 변수를 알려준다. 본 연구에서는 다변량 통계 분석과 분류기법에 기반한 공정진단 체계를 제시한다. 이 진단시스템은 비선형 데이터 표현과 필터링을 통한 지능적 데이터 표현으로 구성되어 있다. 진단 성능을 평가하기 위해 사례연구를 수행하였으며 다른 방법론과의 결과를 비교하기 위하여 진단 결과와 미래값 추정 방법을 평가하였다. 그 결과 본 연구에서 비교된 진단 방법론들에 비해 신뢰도 높은 진단 결과를 얻을 수 있었다.