• Title, Summary, Keyword: Failure detection

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A Study on the Fault Detection of an Integrated Servo Actuator (통합 서보 액츄에이터의 고장 감지시스템 연구)

  • 신기현;임광호
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.306-312
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    • 1996
  • The performance of the failure detection algorithm may be greatly influenced by the model uncertainty. It is very important to design a robust failure detection system to the model uncertainty. In this paper, a design procedure to generate failure detection algorithm is proposed. The design procedure suggested is based on the concept of the‘threshold selector[1]’. The H$\infty$ control algorithm is used to derive a threshold selector which is robust to the model uncertainty, The threshold selector derived can be used to develop a failure detection system together with the weighted cumulative sum algorithm[3]. Computer simulation study showed that the failure detection system designed for an ISA(Integrated Servo Actuator) system by using the proposed method is robust to the model uncertainty.

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Anomaly Detection of Big Time Series Data Using Machine Learning (머신러닝 기법을 활용한 대용량 시계열 데이터 이상 시점탐지 방법론 : 발전기 부품신호 사례 중심)

  • Kwon, Sehyug
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.43 no.2
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    • pp.33-38
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    • 2020
  • Anomaly detection of Machine Learning such as PCA anomaly detection and CNN image classification has been focused on cross-sectional data. In this paper, two approaches has been suggested to apply ML techniques for identifying the failure time of big time series data. PCA anomaly detection to identify time rows as normal or abnormal was suggested by converting subjects identification problem to time domain. CNN image classification was suggested to identify the failure time by re-structuring of time series data, which computed the correlation matrix of one minute data and converted to tiff image format. Also, LASSO, one of feature selection methods, was applied to select the most affecting variables which could identify the failure status. For the empirical study, time series data was collected in seconds from a power generator of 214 components for 25 minutes including 20 minutes before the failure time. The failure time was predicted and detected 9 minutes 17 seconds before the failure time by PCA anomaly detection, but was not detected by the combination of LASSO and PCA because the target variable was binary variable which was assigned on the base of the failure time. CNN image classification with the train data of 10 normal status image and 5 failure status images detected just one minute before.

Detection and Isolation Method for Operator Failure by Unknown Input Observer

  • Kim, Hwan-Seong;Kim, Seung-Min
    • Journal of Navigation and Port Research
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    • v.32 no.2
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    • pp.133-140
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    • 2008
  • In this paper, a fault detection method for operator failures using the observation technique is proposed. The suggested algorithm is extended using the conventional sensor/actuator fault detection method. First, it is assumed that operator failure affects human work operations, as it is an external input signal. With this assumption, a human work model with operator failure is suggested. Second, an unknown input observer with proportional and integral gains is introduced. The characteristic of this observer of estimating an external signal without an exact input is shown, and the conditions for the detection of an operator failure are proposed. Finally, by simulating the container crane operations, it is verified that the observer can accurately detect an operator failure and estimate its magnitude from the given internal signal.

Failure Detection of Multi-Sensor Navigation System (다중 센서 항법 시스템에서의 센서 측정 실패 감지 시스템에 관한 연구)

  • 오재석;이판묵;오준호
    • Proceedings of the Korean Society of Precision Engineering Conference
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    • pp.51-55
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    • 1997
  • This study is devote to developing navigation filter for detecting sensor failure in multi-sensor navigation system. In multi-sensor navigation system, Kalman filter is generally used to fuse data of each sensors. Sensor failure is fatal in case that the sensor is used as external measurement of Kalman filter therefore detection and recovery of sensor failure is one the important feature of navigation filter. Generally each sensors have its specific feature in measuring navigational information. Fuzzy theory is proposed to detect external sensor failure and provide valid external measurement to Kalman filter avoiding filter divergence and instability. This idea is applied to Autonomous Underwater Vehicle(AUV) which has two navigation sensor i. e self contained inertial sensor and acoustic external sensor. 2 dimensional simulation result shows acceptable failure detection and recovery

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A Method of Failure Detection Rate Calculation for Setting up of Guided Missile Periodic Test and Application Case (유도탄 점검주기 설정을 위한 고장 탐지율 산출 방안 및 적용 사례)

  • Choi, In-Duck
    • Journal of the Society of Korea Industrial and Systems Engineering
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    • v.42 no.2
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    • pp.28-35
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    • 2019
  • Since guided missiles with the characteristics of the one-shot system remain stored throughout their entire life cycle, it is important to maintain their storage reliability until the launch. As part of maintaining storage reliability, period of preventive test is set up to perform preventive periodic test, in this case failure detection rate has a great effect on setting up period of preventive test to maintain storage reliability. The proposed method utilizes failure rate predicted by the software on the basis of MIL-HDBK-217F and failure mode analyzed through FMEA (Failure Mode and Effect Analysis) using data generated from the actual field. The failure detection rate of using the proposed method is applied to set periodic test of the actual guided missile. The proposed method in this paper has advantages in accuracy and objectivity because it utilizes a large amount of data generated in the actual field.

Failure Detection Using Adaptive Predictor (적응예측기를 이용한 고장파악방법)

  • 이연석;이장규
    • The Transactions of the Korean Institute of Electrical Engineers
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    • v.39 no.2
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    • pp.210-217
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    • 1990
  • For the failure detection of dynamic systems, processing the residuals from the observer of the estimator is the most general method. A failure detection method which use an adaptive predictor to separate the effect of sensor failure from the additive noise in the residuals of a Kalman filter that is employed as an estimator of a dynamic system is addressed here. In the method, the property of the residuals of an optimal Kalman estimator is exploited. The simulation results of this method shows that the proposed method is superior to the sequential probability ratio test for a small failure magnitude.

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Real-time Failure Detection of Composite Structures Using Optical Fiber Sensors (광섬유 센서를 이용한 복합재 구조물의 실시간 파손감지)

  • 방형준;강현규;류치영;김대현;강동훈;홍창선;김천곤
    • Proceedings of the Korean Society For Composite Materials Conference
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    • pp.128-133
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    • 2000
  • The objective of this research is to develop real-time failure detection techniques for damage assessment of composite materials using optical fiber sensors. Signals from matrix cracking or fiber fracture in composite laminates are treated by signal processing unit in real-time. This paper describes the implementation of time-frequency analysis such as the Short Time Fourier Transform(STFT) to determine the time of occurrence of failure. In order to verify the performance of the optical fiber sensor for stress wave detection, we performed pencil break test with EFPI sensor and compared it with that of PZT. The EFPI sensor was embedded in composite beam to sense the failure signals and a tensile test was performed. The signals of the fiber optic sensor when damage occurred were characterized using STFT and wavelet transform. Failure detection system detected the moment of failure accurately and showed good sensitivity with the infinitesimal failure signal.

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Failure Detection and Resilience in HRing Overlay Network (HRing 오버레이 네트워크에서 실패 탐지 및 회복)

  • Gu, Tae-Wan;Lee, Kwang-Mo
    • Journal of Internet Computing and Services
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    • v.8 no.5
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    • pp.21-33
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    • 2007
  • An overlay network is a virtual network which is constructed on top of a physical computer network. A node in the overlay network is connected through virtual or logical links, where each link corresponds to a path of the links in the underlying physical network. Overlay networks are suitable for sharing heterogeneous resources in distributed environments, However, overlay networks are limited for achieving reliable communication that failure detection in overlay networks is a very important issue. In this paper, we review conditions of conventional failure detection and propose a new approach to failure detection and resilience which can be applied to HRing (Hierarchical Ring) overlay networks. The proposed method consists of the failure detection and the failure resilience phases. Because it utilizes the characteristics of the HRing overlay network for failure detection, it can reduce unnecessary network traffic and provide better scalability and flexibility. We also analyzed and evaluated the performance of the proposed approach through simulations.

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Fault Tolerant Control with Variable Time Weight (가변시간비중을 갖는 내고장성 제어)

  • Hee Gyoo Lee;Zeungnam Bien
    • Journal of the Korean Institute of Telematics and Electronics B
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    • v.29B no.4
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    • pp.22-30
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    • 1992
  • A redundant control scheme which can maintain its tracking capability in the case of a controller failure is proposed for the industrial applications which need high reliability with fault-tolerance. It consists of two identical controllers and a switching mechanism which includes failure detection and reconfiguration algorithm. The new detection method against controller failure using fuzzy logic enables the detection of controller failures without failure assumptions through the instability of the failed controller. The failed controller is smoothly removed from the control loop by reducing time weight of the failed controller.

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Risk Evaluation in FMEA when the Failure Severity Depends on the Detection Time (FMEA에서 고장 심각도의 탐지시간에 따른 위험성 평가)

  • Jang, Hyeon Ae;Yun, Won Young;Kwon, Hyuck Moo
    • Journal of the Korean Society of Safety
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    • v.31 no.4
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    • pp.136-142
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    • 2016
  • The FMEA is a widely used technique to pre-evaluate and avoid risks due to potential failures for developing an improved design. The conventional FMEA does not consider the possible time gap between occurrence and detection of failure cause. When a failure cause is detected and corrected before the failure itself occurs, there will be no other effect except the correction cost. But, if its cause is detected after the failure actually occurs, its effects will become more severe depending on the duration of the uncorrected failure. Taking this situation into account, a risk metric is developed as an alternative to the RPN of the conventional FMEA. The severity of a failure effect is first modeled as linear and quadratic severity functions of undetected failure time duration. Assuming exponential probability distribution for occurrence and detection time of failures and causes, the expected severity is derived for each failure cause. A new risk metric REM is defined as the product of a failure cause occurrence rate and the expected severity of its corresponding failure. A numerical example and some discussions are provided for illustration.