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Korea Advanced Institute of Science and Technology JUL 5th, 2010 Seung Min Lee 1
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References Introduction Background Objectives and Scope On-line Monitoring System PEANO EPI* Center Summary & Conclusion Further Works 2
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[1] J. W. Hines and R. Seibert, (2006), Technical Review of On-Line Monitoring Techniques for Performance Assessment, NUREG/CR-6895, US Nuclear Regulatory Commission [2] P. F. Fantoni, (2000), A Neuro-Fuzzy Model Applied to Full Range Signal Validation of PWR Nuclear Power plant Data, International Journal of General Systems, Vol. 29, pp.305-320 [3] D. Roverso, (2005), Intelligent Systems Integration: Guiding Principles, Examples and Lessons Learned, Progress in Nuclear Energy, Vol. 46, pp.190- 205 [4] P. F. Fantoni, (2005), Experiences and Applications of PEANO For Online Monitoring in Power Plants, Progress in Nuclear Energy, Vol. 46, pp.206- 225 [5] S. Wegerich, (2006), Condition Based Monitoring using Nonparametric Similarity Based Modeling, Proceedings of 3 rd Conference of Japan Society of Maintenology, pp.308-313 [6] S. Wegerich, (2005), Similarity-Based Modeling of Vibration Features for Fault Detection and Identification, Sensor Review, Vol. 25, pp.114-122 3
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Background Performing event detection and recognition in an industrial plant is to rely on experienced operators. Manual calibration of nuclear instruments is performed during each nuclear power plant refueling outage. ▪These manual calibrations only validate the correct operation of the instrumentation periodically. ▪Faulty sensors may remain undetected for periods up to the calibration frequency. ▪Less than 5% of the calibrated instrument channels were in a degraded condition that required maintenance. ▪The cost of the potentially unnecessary work is significant. A Computerized Operator Support System able to detect and classify plant changes would be of great value. To understand techniques and algorithms of systems is required. 4
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Objectives To understand techniques and algorithms of systems To evaluate the applicability to NPPs 5 Scope To understand techniques and algorithms used in On-Line Monitoring(OLM) system PEANO EPI* Center
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OLM Systems Nuclear Power Utilities have investigated and developed methods that would allow them to move away from periodic maintenance strategies and towards condition-based maintenance. Early fault detection and diagnosis ▪Aladdin, EPI* Center, LogACTs, etc. Signal validation and online calibration monitoring ▪PEANO, eCM, PCSVR, etc. 6 Drawbacks Performed manually Performed once per fuel cycle Requires physical access to each instrument Detects calibration problems after they occur Advantages Reduces maintenance cost Reduces radiation exposure Reduces the potential for miscalibration Reduces equipment downtime Increases instrument reliability Improves the plant`s safety
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PEANO Signal validation and online calibration monitoring ▪To validate the plant signals needed to perform the diagnostic and predictive tasks ▪To evaluates instrument channel performance by assessing its consistency with other plant indications Process Evaluation and Analysis through Neural Operators Developed by Halden Reactor Project (HRP) Monitoring approach: Artificial Neural Network & Fuzzy Logic 7
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Clustering Technique (Fuzzy Classification) Used to make the system applicable over the whole process range : a vector in representing an input dataset : : operating region components : correlated process signals : patterns : the matrix of patterns covering the operating region : fuzzy clusters Assign each pattern to each cluster 8 Sample patterns not adequately represented in any cluster are discarded, so that they have no influence on the network weights calculation. Sample patterns possibly represented in many clusters are used in the training set of many corresponding networks.
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Artificial Neural Network Module Used to calculate the measurement estimate for each sensor ▪Each ANN contains the process knowledge of one particular operating region. ▪The overall process knowledge is shared among a number of specialized ‘expert’, ANN. the total number of input nodes in each ANN is, : the number of signals, : the number of past values used. 9 The three feedback loops ▪In-step feedback The corrected pattern is fed again into the ANN ▪Back-step feedback When an error gets larger, the corrected pattern is fed back again into the classifier module ▪One-step-ahead feedback Each input pattern is compared to the previous (validated) pattern.
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Fault Detection Capabilities Drift ▪A sensor failure where the sensor starts to deviate slowly over time. This degradation in performance is usually caused by the sensor`s exposure to the environment (ex, Dirt clogging up). ▪The system detects the sensor problem and provides a measurement estimate as an alternative to the faulty sensor value. Span Drift ▪A sensor failure similar to the drift, but the measured deviation only occurs in a certain part of the operating range of the sensor. Signal Noise ▪The origin of signal noise can be at the sensor itself of at any place in the transmitter channel. ▪Actual process noise should not be removed from the measurement, but only signal noise. ▪The system works as a loss-pass filter on the measurement, while the actual process information is retained. 10
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EPI* Center Early Warning and Early Fault Detection ▪To detect, diagnose, and prioritize impending failure Equipment Performance Improvement Developed by Smart Signal Corporation Monitoring approach: Similarity Based Modeling (SBM) 11
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12 Original systemEmpirical model-exemplarsPredicted system Similarity Based Modeling (SBM) SBM does not require complicated optimization algorithms to be trained. w = f(D, x in ) The set of measurements taken at a given time as a training vector, The state matrix D, M: the number of representative training vector and L: the number of data sources contained in each vector A vector of corresponding estimated data source values,, is determined x in : input vector, w: a set of weighting factors,
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Similarity Based Modeling (SBM) in Practice Traditional Condition Monitoring SBM Early Detection ▪Alarms are triggered off of Residuals, not Actual values. 13 Estimated value Actual value Normal behavior Anomalous behavior Residual = Actual value – Estimated value Residual
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Similarity Based Modeling (SBM) in Practice Dynamic Band 14 Early Warning Dynamic Band
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15 Nuclear Power Utilities have developed the online monitoring systems for the past two decades. OLM systems are expected to improve the plant safety by reducing unnecessary sensor calibration and miscalibration, radiation exposure of maintenance personnel. Understanding each systems and techniques is required before acceptance. PEANOEPI* Center Developed by Halden Reactor Project (HRP)Smart Signal Corporation Algorithm Fuzzy-Neuro NetworkSimilarity-Based Modeling (SBM) Purpose Signal ValidationEarly Warning and Fault Detection
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16 AladdinPEANOEPI* CenterPCSVRLogACTs Developed by Halden Reactor Project (HRP) Smart Signal Corporation KEPRIKAERI Algorithm Recurrent Neural Network Fuzzy-Neuro Network Similarity- Based Modeling (SBM) Principle Component based Support Vector Regression (PCSVR) Purpose Early Fault Detection Signal Validation Early Warning and Fault Detection Signal ValidationFault Classification Evaluation Criteria for suitability to NPPs
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