• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.3
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• pp.40-47
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• 2011

This paper addresses the problem of fault detection and tolerance for asynchronous sequential machines using state feedback control. The considered asynchronous machine is affected by intermittent faults. When intermittent faults occur, the machine undergoes unauthorized state transitions and, for a finite duration, remains at the fault state, not responding to the change of the external input. In this paper, we postulate the scheme of detecting intermittent faults and present the existence condition and design algorithm for a robust state feedback controller that overcomes the adversarial effect of intermittent faults. We also undertake a comparative study between the previous control scheme for transient faults and the present strategy for intermittent faults. The design procedure for the proposed controller is described in a case study.

• The Transactions of the Korean Institute of Power Electronics
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• v.5 no.4
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• pp.409-416
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• 2000

In case of operating two converters in parallel with ZCT operation method using one current sensor for fault tolerance by system characteristics, identifying fault detection and isolation is difficult of which converter is fault since the ZCT output is a difference of two converters' supply current when a converter has fault. This thesis suggest a fault detection and isolation method of converter in case of operating two converters in parallel for fault tolerant system and verified this suggested method through an experiment.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.7
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• pp.103-109
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• 2016

Corrective control for asynchronous sequential machines is a novel automatic control theory that compensates illegal behavior or adverse effects of faults in the operation of existent asynchronous machines. In this paper, we propose a scheme of diagnosing and tolerating faults occurring to input channels of corrective control systems. The corrective controller can detect faults occurring in the input channel to the controlled machine, whereas those faults happening in the external input channel cannot be detected. The proposed scheme involves an outer operator which, upon receiving the state feedback, diagnoses a fault and sends an appropriate command signal to the controller for tolerating faults in the external input channel.

• Journal of the Korean Society for Nondestructive Testing
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• v.31 no.2
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• pp.134-138
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• 2011

Fault detection for dynamic loading conditions of rotational machineries was considered from the contactless, non-destructive infrared thermographic method, rather than the traditional diagnosis method. In this paper, by applying a rotating deep-grooved ball bearing, passive thermographic experiment was performed as an alternative way proceeding the traditional fault monitoring. In addition, the thermographic experiments were compared with the vibration spectrum analysis to evaluate the efficiency of the proposed method. Based on the results, it was concluded the temperature characteristics of the ball bearing under dynamic loading conditions were analyzed thoroughly.

• Journal of Internet Computing and Services
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• v.25 no.1
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• pp.109-121
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• 2024

In various manufacturing processes such as textiles and automobiles, when equipment breaks down or stops, the machines do not work, which leads to time and financial losses for the company. Therefore, it is important to detect equipment abnormalities in advance so that equipment failures can be predicted and repaired before they occur. Most equipment failures are caused by bearing failures, which are essential parts of equipment, and detection bearing anomaly is the essence of PHM(Prognostics and Health Management) research. In this paper, we propose a preprocessing algorithm called SWT-SVD, which analyzes vibration signals from bearings and apply it to an anomaly transformer, one of the time series anomaly detection model networks, to implement bearing anomaly detection model. Vibration signals from the bearing manufacturing process contain noise due to the real-time generation of sensor values. To reduce noise in vibration signals, we use the Stationary Wavelet Transform to extract frequency components and perform preprocessing to extract meaningful features through the Singular Value Decomposition algorithm. For experimental validation of the proposed SWT-SVD preprocessing method in the bearing anomaly detection model, we utilize the PHM-2012-Challenge dataset provided by the IEEE PHM Conference. The experimental results demonstrate significant performance with an accuracy of 0.98 and an F1-Score of 0.97. Additionally, to substantiate performance improvement, we conduct a comparative analysis with previous studies, confirming that the proposed preprocessing method outperforms previous preprocessing methods in terms of performance.

• The Journal of the Korea institute of electronic communication sciences
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• v.1 no.1
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• pp.63-69
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• 2006

The optical submarine cable has a long distance cable and the repeater for optical amplification compared to territorial optical cable so conventional OTDR utilization for the optical submarine cable is limited. in case the optical core in the optical submarine cable system cut, by using Coherent OTDR that utilize OTDR path in repeater the cable fault point can be detected and in case the faulty of the copper tube in the cable that provide power for the repeater to amplify optical signal, the ways using the current/voltage characteristic, the capacitance per Km and so on is required. this report suggest efficient fault location detecting mechanism by categorized cable fault type.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39A no.11
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• pp.637-644
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• 2014

This paper investigates the detection performance of fault location using STDR(sequence time domain reflectometry) and SSTDR(spread spectrum time domain reflectometry) with various length and types of sequences, and then, proposes an improved detection technique by eliminating the injected signal in SSTDR. The detection error rates are compared and analyzed in power line channel model with various fault locations, fault types, and spreading sequences such as m-sequence, binary Barker sequence, and 4-phase Frank sequence. It is shown that the proposed technique is able to improve the detection performance obviously when the reflected signal is weak or the fault location is extremely close.

• Journal of the Korea Convergence Society
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• v.13 no.3
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• pp.23-31
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• 2022

This study analyzed the actual frozen container operation data of Starcool provided by H Shipping. Through interviews with H's field experts, only Critical and Fatal Alarms among the four failure alarms were defined as failures, and it was confirmed that using all variables due to the nature of frozen containers resulted in cost inefficiency. Therefore, this study proposes a method for detecting failure of frozen containers through characteristic importance and PCA techniques. To improve the performance of the model, we select variables based on feature importance through tree series models such as XGBoost and LGBoost, and use PCA to reduce the dimension of the entire variables for each model. The boosting-based XGBoost and LGBoost techniques showed that the results of the model proposed in this study improved the reproduction rate by 0.36 and 0.39 respectively compared to the results of supervised learning using all 62 variables.

• Proceedings of the KIEE Conference
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• 2015.07a
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• pp.357-358
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• 2015

본 논문에서는 고온 초전도 케이블 고장점 탐지를 위해 시간-주파수 반사파 계측법(Time-Frequency Domain Reflectometry)을 적용하여 EMTP 시뮬레이션을 수행하고, 고온 초전도 케이블에 적합한 가우시안 첩 포락선 선형 기준 신호를 제시하였다. 고온 초전도 케이블을 EMTP로 모델링하여 TFDR 기법을 통해 케이블의 종단점을 추정하고 기준 신호의 전파 속도를 계산하였다. EMTP를 활용하여 국부적 결함이 발생한 고온 초전도 케이블을 모델링하였고, 결함 발생 케이블의 고장점 탐지 시뮬레이션 결과를 확인하였다.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2021.10a
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• pp.89-92
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• 2021

In a manufacturing fields, an abnormality or breakdown of equipment is a factor that causes product defects. Recently, with the spread of smart factory services, a lot of research to predict and prevent machine's failures is actively ongoing. However, there is a big difficulty in developing a classification model because the number of abnormal or failure data of the machine is severely smaller than normal data. In this paper, we present an algorithm for detecting abnormalities in an MCT at manufacturing work site depending on the differences between inputs and outputs of Autoencoder model and analyze its performance. The algorithm detects abnormalities using only features of normal data from manufacturing data of the MCT in which abnormal data does not exist.