• 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.

• International Journal of Precision Engineering and Manufacturing
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• 제3권2호
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• pp.45-51
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• 2002

Recently the interest for the environment is increasing. So the criterion for the evaluation of the burner has changed. For efficient driving problem, if the thermal efficiency is higher and the oxygen in exhaust gas is lower, then burner is evaluated better. For environmental problem. burner must satisfy NOx limit, soot limit and CO limit. Generally the experienced operator judge of the combustion status of the burner by the color of flame. we don't still have any satisfactory solution against it. the relation of the combustion status and the color of the flame hasn't still been established. This paper is the study about the relation of the combustion status and the color of the flame. This paper describes development of real time flame diagnosis technique that evaluate and diagnose combustion state such as consistency of components in exhaust gas, stability of flame in quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using image processing algorithm, the parameter extracted from the image of the flame was used as the input variables of the flame diagnostic system. at first, linear regression algorithm and multiple regression algorithm was used to obtain linear multi-nominal expression. Using the constructed inference algorithm, the amount of NOx and CO of the combustion gas was successfully inferred. the combustion control system will be realized sooner or later.

• Journal of Information Processing Systems
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• 제18권6호
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• pp.822-829
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• 2022

According to existing study into the remote fault diagnosis procedure, the current diagnostic approach has an imperfect decision model, which only supports communication in a close distance. An Internet of Things (IoT)-based remote fault diagnostic approach for wind power equipment is created to address this issue and expand the communication distance of fault diagnosis. Specifically, a decision model for active power coordination is built with the mechanical energy storage of power generation equipment with a remote diagnosis mode set by decision tree algorithms. These models help calculate the failure frequency of bearings in power generation equipment, summarize the characteristics of failure types and detect the operation status of wind power equipment through IoT. In addition, they can also generate the point inspection data and evaluate the equipment status. The findings demonstrate that the average communication distances of the designed remote diagnosis method and the other two remote diagnosis methods are 587.46 m, 435.61 m, and 454.32 m, respectively, indicating its application value.

• International Journal of Internet, Broadcasting and Communication
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• 제13권3호
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• pp.92-103
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• 2021

With the continuous acceleration of economic and social development, people gradually pay attention to their health, improve their living environment, diet, strengthen exercise, and even conduct regular health examination, to ensure that they always understand the health status. Even so, people still face many health problems, and the number of chronic diseases is increasing. Recently, COVID-19 has also reminded people that public health problems are also facing severe challenges. With the development of artificial intelligence equipment and technology, medical diagnosis expert systems based on big data have become a topic of concern to many researchers. At present, there are many algorithms that can help computers initially diagnose diseases for patients, but they want to improve the accuracy of diagnosis. And taking into account the pathology that varies from person to person, the health diagnosis expert system urgently needs a new algorithm to improve accuracy. Through the understanding of classic algorithms, this paper has optimized it, and finally proved through experiments that the combined classification algorithm improved by latent factors can meet the needs of medical intelligent diagnosis.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.235-238
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• 2000

This article presents an adaptive decision tree algorithm for dynamically reasoning machine failure cause out of real-time, large-scale machine status database. On the basis of experiment using semiconductor etching machine, it has been verified that our model outperforms previously proposed decision tree models.

• 한국정보통신학회논문지
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• 제11권11호
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• pp.2150-2157
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• 2007

본 논문에서는 개인의 건강 상태를 일련의 과정에 따라 스스로 파악하여 전문 의료 관리에 대한 접근 방향의 결정을 돕고 전문의가 쉽게 새로운 질병 및 증상을 학습 할 수 있도록 하는 자가 질병 진단 시스템을 제안하였다. 제안된 자가 진단은 보건 복지부에 제출된 #한국인이 부담을 가지는 질병# 관련 보고서와 의료 콘텐츠 #Engel Pharm#을 참조하여 선정한 60가지의 질병과 각 질병에 대한 대표 증상 161가지를 이용하여 질병을 도출한다. 개선된 ART2 학습 알고리즘을 적용하여 질병 종류를 군집화하고 각 질병의 증상에 관련된 질의 결과를 입력 벡터로 제시하여 사용자의 건강 상태를 진단함으로써 자신의 건강에 대한 정보를 제공한다.

• 전기학회논문지P
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• 제55권4호
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• pp.190-195
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• 2006

In this paper, safety algorithm of transformer is introduced for the sake of MV/LV distribution customers by using fuzzy theory. The current-carrying capacity of transformer is usually determined by the maximum temperature at which the transformer is permitted to operate. Overload of transformer has an effect on transformer utilization rate and maximum temperature rises as well as maximum ambient temperature of insulating materials. Therefore, we proposed the safety algorithm considering the overload of transformer and ambient temperature in this paper. We introduced the correlational equations between each parameters using experimental data of IEEE std C57.91-1995, and deduced the result using fuzzy reasoning. We guessed the safety algorithm making a diagnosis for the safety status of oil-immersed transformer.

• Nuclear Engineering and Technology
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• 제55권3호
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• pp.827-838
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• 2023

Centrifugal pump is a key part of nuclear power plant systems, and its health status is critical to the safety and reliability of nuclear power plants. Therefore, fault diagnosis is required for centrifugal pump. Traditional fault diagnosis methods have difficulty extracting fault features from nonlinear and non-stationary signals, resulting in low diagnostic accuracy. In this paper, a new fault diagnosis method is proposed based on the improved particle swarm optimization (IPSO) algorithm-based variational modal decomposition (VMD) and relevance vector machine (RVM). Firstly, a simulation test bench for rotor faults is built, in which vibration displacement signals of the rotor are also collected by eddy current sensors. Then, the improved particle swarm algorithm is used to optimize the VMD to achieve adaptive decomposition of vibration displacement signals. Meanwhile, a screening criterion based on the minimum Kullback-Leibler (K-L) divergence value is established to extract the primary intrinsic modal function (IMF) component. Eventually, the factors are obtained from the primary IMF component to form a fault feature vector, and fault patterns are recognized using the RVM model. The results show that the extraction of the fault information and fault diagnosis classification have been improved, and the average accuracy could reach 97.87%.

• 한국인터넷방송통신학회논문지
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• 제24권4호
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• pp.135-140
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• 2024

태양광발전시스템은 다른 재생에너지원과 비교해서 내구수명이 길어 유지점검이 거의 필요 없다고 하지만, 실제 태양광 모듈의 음영 발생, 온도상승, 미스매치, 오염·열화, 태양광 인버터의 고장, 누설전류 및 아크 발생으로 인하여 초기 설계 시에 기대했던 성능이 나오지 않는 경우가 발생한다. 따라서 이러한 시스템의 문제점 해결을 위해 단지 발전량 및 운전 현황에 대한 조사를 통하여 정성적으로 파악하거나 태양광발전시스템의 성능지수인 성능계수(PR, Performance Ratio)로부터 성능을 비교분석하고 있다. 그러나 큰 손실을 포함하고 있으므로 단지 성능계수만으로 태양광발전시스템의 성능 저하, 고장 혹은 결한 등의 이상 유무를 정확하게 판단하기가 어렵다. 본 논문에서는 주변 환경의 변화에 따른 태양광발전시스템 발전량 최적화를 위해 태양광 모듈의 음영 발생, 인버터 고장, 누설 및 아크에 대한 상태 진단 알고리즘을 연구하였다. 또한 연구된 알고리즘을 이용하여 영역별 상태진단과 이에 따른 발전량 최적화 운전에 대한 실증시험을 통한 결과를 고찰하였다.

• 품질경영학회지
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• 제45권1호
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• pp.25-38
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• 2017

Purpose: Hot strip rolling mill consists of a lot of mechanical and electrical units. In condition monitoring and diagnosis phase, various units could be failed with unknown reasons. In this study, we propose an effective method to detect early the units with abnormal status to minimize system downtime. Methods: The early warning problem with various units is defined. K-means and PAM algorithm with Euclidean and Manhattan distances were performed to detect the abnormal status. In addition, an performance of the proposed algorithm is investigated by field data analysis. Results: PAM with Manhattan distance(PAM_ManD) showed better results than K-means algorithm with Euclidean distance(K-means_ED). In addition, we could know from multivariate field data analysis that the system reliability of hot strip rolling mill can be increased by detecting early abnormal status. Conclusion: In this paper, clustering-based monitoring and fault detection algorithm using Manhattan distance is proposed. Experiments are performed to study the benefit of the PAM with Manhattan distance against the K-means with Euclidean distance.