• International Journal of Control, Automation, and Systems
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• 제5권2호
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• pp.155-160
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• 2007

A fault detection and isolation (FDI) problem is considered for inertial sensors, such as gyroscopes and accelerometers and a new FDI method for double faults is proposed using reduced-order parity vector. The reduced-order parity vector (RPV) algorithm enables us to isolate double faults with 7 sensors. Averaged parity vector is used to reduce false alarm and wrong isolation, and to improve correct isolation. The RPV algorithm is analyzed by Monte-Carlo simulation and the performance is given through fault detection probability, correct isolation probability, and wrong isolation probability.

• International Journal of Control, Automation, and Systems
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• 제5권3호
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• pp.329-336
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• 2007

This paper considers a fault accommodation problem for inertial navigation systems (INS) that have redundant inertial sensors such as gyroscopes and accelerometers. It is wellknown that the more sensors are used, the smaller the navigation error of INS is, which means that the error covariance of the position estimate becomes less. Thus, when it is decided that double faults occur in the inertial sensors due to fault detection and isolation (FDI), it is necessary to decide whether the faulty sensors should be excluded or not. A new accommodation rule for double faults is proposed based on the error covariance of triad-solution of redundant inertial sensors, which is related to the navigation accuracy of INS. The proposed accommodation rule provides decision rules to determine which sensors should be excluded among faulty sensors. Monte Carlo simulation is performed for dodecahedron configuration, in which case the proposed accommodation rule can be drawn in the decision space of the two-dimensional Cartesian coordinate system.

• 설비공학논문집
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• 제14권7호
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• pp.552-559
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• 2002

The fault detection and diagnosis (FDD) technology may be applied in order to decrease the energy consumption and the maintenance cost of the air conditioning system. In this study, rule bases and curve fitting models were used to detect faults in an air handling unit. Gradually progressed faults, such as the fan speed degradation, the coil water leakage, the humidifier nozzle clogging, the sensor degradation and the damper stoppage, were applied to the developed FBD system. Simulation results show good detections and diagnoses of these faults. Therefore, this method may be effectively used for the fault detection and diagnosis of the air handling unit.

• 응용통계연구
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• 제29권7호
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• pp.1385-1398
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• 2016

원자력 발전소의 가압기는 1차 계통의 냉각재가 고온에서도 기화되지 않도록 압력을 가해주는 장치이다. 즉, 가압기의 고장은 원자력 발전소에 큰 영향을 미칠 수 있으며, 따라서, 가압기의 조기 고장 감지는 원자력 발전소의 안전에 매우 중요하다. 이를 위해, 본 연구에서는 마할라노비스 거리 개념과 다구찌 품질 공학 이론에 기반한 패턴 분류 인식 알고리즘 중 하나인 마할라노비스 다구찌 시스템(MTS)을 가압기 압력 제어 계통의 조기 고장 감지에 적용하였다. MTS의 고장 감지 성능을 검증하기 위해, 실제 원자력 발전소에서 발생하고 있는 가압기 압력전송기 고장 시나리오를 대상으로 하여, Full Scope 시뮬레이터를 통해 모사된 데이터에 적용하였다. 실험 결과, MTS는 단일 센서모니터링 기반의 전통적인 고장 감지에 비하여 매우 빠르게 고장을 감지할 수 있었다.

• 대한전기학회논문지:시스템및제어부문D
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• 제55권9호
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• pp.414-419
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• 2006

This paper presents a design of a multi-sensor data fusion filter for a Flight Test System. The multi-sensor data consist of positional information of the target from radars and a telemetry system. The data fusion filter has a structure of a federated Kalman filter and is based on the Singer dynamic target model. It consists of dedicated local filter for each sensor, generally operating in parallel, plus a master fusion filter. A fault detection and correction algorithms are included in the local filter for treating bad measurements and sensor faults. The data fusion is carried out in the fusion filter by using maximum likelihood estimation algorithm. The performance of the designed fusion filter is verified by using both simulation data and real data.

• Smart Structures and Systems
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• 제23권3호
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• pp.279-293
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• 2019

To ensure high quality data being used for data mining or feature extraction in the bridge structural health monitoring (SHM) system, a practical sensor fault diagnosis methodology has been developed based on the similarity of symmetric structure responses. First, the similarity of symmetric response is discussed using field monitoring data from different sensor types. All the sensors are initially paired and sensor faults are then detected pair by pair to achieve the multi-fault diagnosis of sensor systems. To resolve the coupling response issue between structural damage and sensor fault, the similarity for the target zone (where the studied sensor pair is located) is assessed to determine whether the localized structural damage or sensor fault results in the dissimilarity of the studied sensor pair. If the suspected sensor pair is detected with at least one sensor being faulty, field test could be implemented to support the regression analysis based on the monitoring and field test data for sensor fault isolation and reconstruction. Finally, a case study is adopted to demonstrate the effectiveness of the proposed methodology. As a result, Dasarathy's information fusion model is adopted for multi-sensor information fusion. Euclidean distance is selected as the index to assess the similarity. In conclusion, the proposed method is practical for actual engineering which ensures the reliability of further analysis based on monitoring data.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 1998년도 하계종합학술대회논문집
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• pp.211-214
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• 1998

A detection filter assigns a specific direction to the response with respect to each fault, by which it can detect the occurrence of the several faults. The separability of a detection filter can be determined by the orthogonality among these directions. In this paper, we define the separability of a detection filter as the orthogonality of the directions in output space, and present it mathematically by using conditions number. An algorithm to determine the optimal sensor gain to maximize separability is proposed and applied to the PWR nuclear reactor model.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 1992년도 한국자동제어학술회의논문집(국제학술편); KOEX, Seoul; 19-21 Oct. 1992
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• pp.272-275
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• 1992

A novel fault diagnosis method based on likelihood decomposition is proposed for linear stochastic systems described by autoregressive (AR) model. Assuming that at some time instant .tau. the fault of one of the following two types is occurs: innovation fault (actuator fault); and observation fault (sensor fault), the log-likelihood function is decomposed into two components based on the observations before and after .tau., respectively, Then, the type of the fault is determined by comparing the log-likelihoods corresponding two types of faults. Numerical examples demonstrate the usefulness of the proposed diagnosis method.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제10권3호
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• pp.1321-1332
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• 2016

Wireless sensor networks (WSN) are self-organized networks that typically consist of thousands of low-cost, low-powered sensor nodes. The reliability and availability of WSNs can be affected by faults, including those from radio interference, battery exhaustion, hardware and software failures, communication link errors, malicious attacks, and so on. Thus, we propose a novel multi-agent fault tolerant system for wireless sensor networks. Since a major requirement of WSNs is to reduce energy consumption, we use multi-agent and mobile agent configurations to manage WSNs that provide energy-efficient services. Mobile agent architecture have inherent advantages in that they provide energy awareness, scalability, reliability, and extensibility. Our multi-agent system consists of a resource manager, a fault tolerance manager and a load balancing manager, and we also propose fault-tolerant protocols that use multi-agent and mobile agent setups.

• 대한산업공학회지
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• 제23권1호
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• pp.95-113
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• 1997

This paper outlines a framework for a diagnosis of a complex system with uncertain information. Sensor validation ploys a vital role in the ability of the overall system to correctly determine the state of a system monitored by imperfect sensors. Here, emphases are put on the heuristic technology and post-processor for reasoning. Heuristic Sensor Validation (HSV) exploits deeper knowledge about parameter interaction within the plant to cull sensor faults from the data stream. Finally the modified probability distributions and validated data are used as input to the reasoning scheme which is the runtime version of the influence diagram. The output of the influence diagram is a diagnostic mapping from the symptoms or sensor readings to a determination of likely failure modes. Once likely failure modes are identified, a detailed diagnostic knowledge base suggests corrective actions to improve performance. This framework for a diagnostic expert system with sensor validation and reasoning under uncertainty applies in $HEATXPRT^{TM}$ a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants [1].