TY - GEN
T1 - A reliable data-driven method for condition monitoring in nuclear power plants
AU - Qi, Zhenfeng
AU - Li, Wei
AU - Chen, Juan
AU - Yuan, Yidan
AU - Du, Shuhong
N1 - Publisher Copyright:
Copyright © ESREL2020-PSAM15 Organizers.Published by Research Publishing, Singapore.
PY - 2020
Y1 - 2020
N2 - Condition monitoring, which is the basis of condition-based maintenance (CBM) and fault tolerant control (FTC) in nuclear power plants (NPPs), can accurately estimate the sensor measurement of the complex system, and then determine whether the system or equipment is abnormal. Data-driven method, such as Auto-Associative kernel regression (AAKR) and Auto-Associative neural network (AANN), has many advantages compare to model-based method and there are many application scenarios. But due to the uncertainty of measurement data, the incompleteness of training data and the uncertainty of model hyper-parameters, data-driven model has inherent uncertainty. That is, the predictions and residuals generated by data-driven model is not the true values. When the condition monitoring system determines that there is an abnormality in NPPs, we hope that the anomaly detection result has high reliability. Otherwise, the uncertainty of data-driven model will have a significant impact on decision-making, and even on the safety and economics of NPPs. This paper proposes a novel condition monitoring technology named Auto-Associative Kernel Ridge Regression (AAKRR) and then analyse model uncertainty using Monte Carlo method. The test results in dataset from NPPs show that AAKRR model has a small uncertainty interval and has strong anomaly detection capabilities.
AB - Condition monitoring, which is the basis of condition-based maintenance (CBM) and fault tolerant control (FTC) in nuclear power plants (NPPs), can accurately estimate the sensor measurement of the complex system, and then determine whether the system or equipment is abnormal. Data-driven method, such as Auto-Associative kernel regression (AAKR) and Auto-Associative neural network (AANN), has many advantages compare to model-based method and there are many application scenarios. But due to the uncertainty of measurement data, the incompleteness of training data and the uncertainty of model hyper-parameters, data-driven model has inherent uncertainty. That is, the predictions and residuals generated by data-driven model is not the true values. When the condition monitoring system determines that there is an abnormality in NPPs, we hope that the anomaly detection result has high reliability. Otherwise, the uncertainty of data-driven model will have a significant impact on decision-making, and even on the safety and economics of NPPs. This paper proposes a novel condition monitoring technology named Auto-Associative Kernel Ridge Regression (AAKRR) and then analyse model uncertainty using Monte Carlo method. The test results in dataset from NPPs show that AAKRR model has a small uncertainty interval and has strong anomaly detection capabilities.
KW - Anomaly detection
KW - Auto-associative kernel ridge regression
KW - Bias-variance decomposition
KW - Condition monitoring
KW - Confidence interval
KW - Model uncertainty
KW - Prediction interval
UR - https://www.scopus.com/pages/publications/85110256717
M3 - 会议稿件
AN - SCOPUS:85110256717
T3 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
SP - 1695
EP - 1702
BT - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
A2 - Baraldi, Piero
A2 - Di Maio, Francesco
A2 - Zio, Enrico
PB - Research Publishing Services
T2 - 30th European Safety and Reliability Conference, ESREL 2020 and 15th Probabilistic Safety Assessment and Management Conference, PSAM 2020
Y2 - 1 November 2020 through 5 November 2020
ER -