• 한국정보과학회논문지:시스템및이론
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• 제33권1_2호
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• pp.105-109
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• 2006

하이퍼큐브의 정규적이며 계층적인 구조적 특성을 갖고 있기 때문에 진단 알고리즘 개발에 효율적으로 적용될 수 있다. Kranakis와 Pelc[1]은 HADA/IHADA와 적응적 큐브분할 방법과는 다르게 결함을 모두 포함할 수 있는 최소의 RGC-링으로 임베딩 하여 진단을 수행하는 HYP-DIAG 알고리즘을 제안하였다. 본 논문에서는 HYP-DIAG의 첫 번째 단계에서 얻어진 RGC-링들의 신드롬을 분석함으로서 테스트 라운드를 줄일 수 있는 새로운 알고리즘을 제안하고 이를 분석하였다.

• 제어로봇시스템학회논문지
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• 제3권3호
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• pp.244-251
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• 1997

In this paper, we propose a fault diagnosis algorithm to detect and isolate multiple faults in a system. The proposed fault diagnosis algorithm is based on a multiple fault classifier which consists of two ART2 NN(adaptive resonance theory2 neural network) modules and the algorithm is composed of three main parts - parameter estimation, fault detection and isolation. When a change in the system occurs, estimated parameters go through a transition zone in which residuals between the system output and the estimated output cross the threshold, and in this zone, estimated parameters are transferred to the multiple faults classifier for fault isolation. From the computer simulation results, it is verified that when the proposed diagnosis algorithm is performed successfully, it detects and isolates faults in the position control system of a DC motor.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1987년도 한국자동제어학술회의논문집(한일합동학술편); 한국과학기술대학, 충남; 16-17 Oct. 1987
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• pp.836-841
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• 1987

Many kinds of conditioning monitoring technique have been studied, so this study has investigated the possibility of checking the trend in the fault diagnosis of ball bearing, one of the elements of rotating machine, by applying the cepstral analysis method using the adaptive noise cancelling (ANC) method. And computer simulation is conducted in oder to identify obviously the physical meaning of ANC. The optimal adaptation gain in adaptive filter is estimated, the performance of ANC according to the change of the signal to noise ratio and convergence of LMS algorithm is considered by simulation. It is verified that cepstral analysis using ANC method is more effective than the conventional cepstral analysis method in bearing fault diagnosis.

• Journal of Power Electronics
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• 제16권3호
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• pp.1012-1024
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• 2016

This study deals with an on-line software fault detection, localization, and system reconfiguration method for electrical system drives composed of three-phase AC/DC/AC converters and three-phase permanent magnet synchronous machine (PMSM) drives. Current sensor failure (outage), speed/position sensor loss (disconnection), and damaged DC-link voltage sensor are considered faults. The occurrence of these faults in PMSM drive systems degrades system performance and affects the safety, maintenance, and service continuity of the electrical system drives. The proposed method is based on the monitoring signals of "abc" currents, DC-link voltage, and rotor speed/position using a measurement chain. The listed signals are analyzed and evaluated with the generated residuals and threshold values obtained from a Sliding Mode Current-Speed-DC-link Voltage Observer (SMCSVO) to acquire an on-line fault decision. The novelty of the method is the faults diagnosis algorithm that combines the use of SMCSVO and adaptive thresholds; thus, the number of false alarms is reduced, and the reliability and robustness of the fault detection system are guaranteed. Furthermore, the proposed algorithm's performance is experimentally analyzed and tested in real time using a dSPACE DS 1104 digital signal processor board.

• Journal of Electrical Engineering and Technology
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• 제2권3호
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• pp.353-357
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• 2007

The bearing diagnostics method is presented in this paper using fuzzy inference based on vibration data. Both time-domain and frequency-domain features are used as input data for bearing fault detection. The Adaptive Network based Fuzzy Inference System (ANFIS) and Genetic Algorithm (GA) have been proposed to select the fuzzy model input and output parameters. Training results give the optimized fuzzy inference system for bearing diagnosis based on measured vibration data. The result is also tested with other sets of bearing data to illustrate the reliability of the chosen model.

• 융합보안논문지
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• 제9권2호
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• pp.1-6
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• 2009

대부분의 진단 알고리즘은 PMC 모델을 바탕으로 결함의 개수가 t개를 초과하지 않는다는 t-진단가능 시스템의 특성을 이용한다. 그러나 병렬처리 시스템의 규모가 커짐에 따라 시스템 안에 존재하는 결함의 빈도수가 높아지게 된다. 진단 알고리즘에서 가정하는 결함의 개수 t는 시스텝 안에 있는 노드의 수에 비해 상당히 작은 개수이며, 결함의 개수가 t개를 초과하는 경우에 대하여 진단에 대한 연구가 거의 이루어지지 않았다. 이 논문에서는 결함의 개수가 t개를 초과하는 경우에 대하여 진단의 정확여부를 판단할 수 없는 충분히 작은 개수의 노드가 존재한다는 것을 허락함으로서, 진단 가능한 결함의 최대 수를 증가시키는 알고리즘을 제안한다.

• 한국음향학회지
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• 제7권4호
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• pp.77-85
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• 1988

各種의 Conditioning Monitoring 技術이 硏究되고 있는데 本 硏究에서는 Cepstrum 解析法에 Adaptive Noise Cancelling (ANC) 법을 利用하여 回轉機械要素의 하나인 베어링의 缺陷을 管理하는 手段으로써의 可能性을 檢討하였으며 ANC의 物理的 意味를 正確히 把握하고자 컴퓨터 시뮬레이션을 行하였다. 컴퓨터 시뮬레이션에 衣해 Adaptive filter 에서의 最適한 適應利得을 推定하였으며 信號對雜音比에 따른 ANC의 性能과 LMS알고리즘의 收劍性을 考察하였다. 또한 ANC法을 Cepstrum 解析法에 利用한 베어링의 異常診斷은 旣存의 Cepstrum解析法보다 有效함을 알았다.

• Journal of Information Processing Systems
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• 제16권3호
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• pp.530-540
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• 2020

In order to solve the problem of low tracking accuracy caused by complex noise in the fault diagnosis of complex nonlinear system, a fault diagnosis method of high precision cost reference particle filter (CRPF) is proposed. By optimizing the low confidence particles to replace the resampling process, this paper improved the problem of sample impoverishment caused by the sample updating based on risk and cost of CRPF algorithm. This paper attempts to improve the accuracy of state estimation from the essential level of obtaining samples. Then, we study the correlation between the current observation value and the prior state. By adjusting the density variance of state transitions adaptively, the adaptive ability of the algorithm to the complex noises can be enhanced, which is expected to improve the accuracy of fault state tracking. Through the simulation analysis of a fuel unit fault diagnosis, the results show that the accuracy of the algorithm has been improved obviously under the background of complex noise.

• 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%.

• 조명전기설비학회논문지
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• 제18권6호
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• pp.70-77
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• 2004

산업 응용분야에서 유도전동기 구동시스템의 예상치 않은 고장은 전체 계통의 정지, 막대한 손실 등을 가져올 수 있다. 이러한 문제점을 해결하는 방법 중에 하나로서 본 논문은 유도전동기 구동을 위한3상 전압형 PWM 인버터에 개방-스위치 손상의 고장진단에 대하여 연구한다. 고장진단 방법으로는, 먼저 고장의 특징추출을 위하여 3상 전류를 d-q 전류로 변환한 후 평균 전류벡터를 구한다. 다음으로 여러 종류의 고장 패턴을 진단하기 위하여 한 인공지능 알고리즘을 제안한다. 제안된 기법은 일반적인 뉴로-퍼지 시스템(adaptive neuro-fuzzy algorithm)의 전제 부에 클러스터링을 도입한 기법으로 적은 계산 양과 좋은 성능을 갖는다. 최종적으로, 여러 불확실한 요소를 가진 고장계통에 대하여 제안된 알고리즘의 유용성을 모의실험에 의해 검증하였다.