• Transactions of the Korean Society of Mechanical Engineers
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• v.18 no.2
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• pp.323-338
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• 1994

This paper outlines a framework for performing intelligent sensor validation for a diagnostic expert system while reasoning under uncertainty. The emphasis is on the algorithmic preprocess technique. A companion paper focusses on heuristic post-processing. Sensor validation plays a vital role in the ability of the overall system to correctly detemine the state of a plant monitored by imperfect sensors. Especially, several theoretical developments were made in understanding uncertain sensory data in statistical aspect. Uncertain information in sensory values is represented through probability assignments on three discrete states, "high", "normal", and "low", and additional sensor confidence measures in Algorithmic Sv.Upper and lower warning limits are generated from the historical learning sets, which represents the borderlines for heat rate degradation generated in the Algorithmic SV initiates a historic data base for better reference in future use. All the information generated in the Algorithmic SV initiate a session to differentiate the sensor fault from the process fault and to make an inference on the system performance. This framework for a diagnostic expert system with sensor validation and reasonig under uncertainty applies in HEATXPRT$^{TM}$, a data-driven on-line expert system for diagnosing heat rate degradation problems in fossil power plants.

• Proceedings of the KIEE Conference
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• 1993.11a
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• pp.317-319
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• 1993

The function estimation characteristics of neural networks can be used for sensor signal validation of a system. In case of applying the neural networks to signal validation, it is a important problem that the redundant sensor signals used as a input signal of neural networks should be validated. In this paper, we simplify the conventional parity space method in order to input the validated signal to the neural networks and also propose the sensor signal validation method, which estimates the reliable sensor output combining neural networks with the simplified parity space method. The acceptability of the proposed signal validation method is demonstrated by using the simulation data in safety injection accident of nuclear power plants.

• Proceedings of the KIEE Conference
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• 1999.07b
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• pp.612-614
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• 1999

In order to improve the operational reliability, it is necessary to validate the measured sensor data, isolate any failed sensor and recover the failed critical measurement. This paper describes the use of estimating equation to identify failed sensors and to recover the feedback signal for control purpose when the sensor measurement is determined to be erroneous Simulation results show that the proposed sensor validation scheme can adequately identify the failed sensor and provide reasonable estimates for control purposes.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.1
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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].

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.481-486
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• 2007

Wireless Sensor Network(WSNs) consists of small sensor nodes with sensing, computation, and wireless communication capabilities. The large number of sensor nodes in a WSN means that there will often be some nodes which give erroneous sensor data owing to several reasons such as power shortage and transmission error. Generally, these sensor data are gathered by a sink node to monitor and diagnose the current environment. Therefore, this can make it difficult to get an effective monitoring and diagnosis. In this paper, to overcome the aforementioned problems, intelligent sensor data validation method based on PCA(Principle Component Analysis) is utilized. Furthermore, a practical implementation using embedded system is given to show the feasibility of the proposed scheme.

• Proceedings of the KIEE Conference
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• 1998.07g
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• pp.2275-2277
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• 1998

This paper presents an algorithmic sensor value validation technique that can systematically explore the embedded sensor redundancies in complex physical systems and maximize their utilization in validating sensor values. The confidency criteria are developed for checking the consistency of sensor relationships called Causal Relations. Development results are applied to a tubular type supercritical pressure type thermal power plant with rated operational data to demonstrate the effectiveness of the proposed technique.

• Smart Structures and Systems
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• v.15 no.5
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• pp.1203-1214
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• 2015

This paper deals with the experimental validation of the use of PVDF Patches for the assessment of spatial derivatives of displacement field. It focuses more exactly on the shear Force Identification by using Specific forms of PVDF patcHes (FISH) on beams. An overview of the theoretical approach is exposed. The principle is based on the use of the weak form of the equation of motion of the beam which allows the shear forces to be extracted at one edge of the sensor when this last has a specific form. The experimental validation is carried out with a cantilever steel beam, excited by a shaker at its free boundary. The validation consists in comparing the shear force measured by the designed sensor glued at the free edge and the directly measured force applied by the shaker. The sensor is made of two patches, called the "stiffness" patch and the "mass" patch. The use of both patches allows one to identify correctly the shear force on a large frequency domain. The use of only the stiffness patch is valid in the low frequency domain and has the advantage to have a frequency-independent gain that allows its use in real time.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.23 no.12
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• pp.201-209
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• 2009

In power plant operations, sensor values often exhibit erroneous values due to their failures or the intrusions of various noises. However, most of the power plant monitoring and fault diagnosis systems perform their tasks based on the assumptions that the collected sensor values are correct all the times. These assumptions, which are not valid, often lead to serious consequences such as power plant trips. In this paper, we propose a power plant sensor value validation technique that can utilize the relationships existing among the sensor values as the sensor redundancy. The proposed technique is applied to the flow meters installed along boiler feed water systems of a typical tubular type boiler thermal power plant and shows a good potential of future applications.

• Journal of the Korea Institute of Military Science and Technology
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• v.17 no.3
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• pp.318-326
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• 2014

The Shipyard Test of Naval Combat System depends on external factors, such as weather conditions and availability of its sensor-weapon, due to the need of on-board sensor-weapon during the test. This paper suggests the Supportive Equipment using virtual simulator for Shipward Test, in case of the unavailability of the on-board sensor-weapon or the test support force(aircraft, surface ship etc.), to pre-check the functions of the combat system as well as to prepare the Shipyard Test. To mock the real sensor-weapon functions as similar as possible, the Supportive Equipment for Shipyard Test was verified by the Verification and Validation process, which is usually performed while developing models in the Modeling & Simulation field.

• Proceedings of the KIEE Conference
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• 1995.11a
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• pp.175-177
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• 1995

The sensor signal validation and failure detection system must be able to detect, isolate, and identify sensor degradation as well as provide a reconstruction of the measurements. In this study, this is accomplished by combining the neural network, the Generalized Consistency Checking(GCC), and the Sequential Probability Ratio Test(SPRT) method in a decision estimator module. The GCC method is a computationally efficient system for redundant sensors, while the SPRT provides the ability to make decisions based on the degradation history of a sensor. The methodology is also extended to the detection of noise degradation. The acceptability of the proposed method is demonstration by using the simulation data in safety injection accident of nuclear power plants. The results show that the signal validation and sensor failure detection system is able to detect and isolate a bias failure and noise type failures under transient conditions. And also, the system is able to provide the validated signal by reconstructing the measurement signals in the failure conditions considered.