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A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants


Affiliations
1 University of Science and Technology Beijing (USTB), 30 Xueyuan Road, Haidian District, Beijing 100083, China
2 Department of Electrical Engineering, COMSATS Institute of Information Technology, University Road, Abbottabad 22060, Pakistan
3 COMSATS Institute of Information Technology near Officers Colony, Kamra Road, Attock 43600, Pakistan
 

Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative Neural Network (AANN) based empirical sensor models have widely been reported for sensor calibration monitoring. However, such ill-posed data driven models may result in poor generalization and robustness. To address above mentioned issues, several regularization heuristics such as training with jitter, weight decay, and cross-validation are suggested in literature. Apart from these regularization heuristics, traditional error gradient based supervised learning algorithms for multilayered AANN models are highly susceptible of being trapped in local optimum. In order to address poor regularization and robust learning issues, here, we propose a Denoised Autoassociative Sensor Model (DAASM) based on deep learning framework. Proposed DAASM model comprises multiple hidden layers which are pretrained greedily in an unsupervised fashion under denoising autoencoder architecture. In order to improve robustness, dropout heuristic and domain specific data corruption processes are exercised during unsupervised pretraining phase. The proposed sensor model is trained and tested on sensor data from a PWR type nuclear power plant. Accuracy, autosensitivity, spillover, and Sequential Probability Ratio Test (SPRT) based fault detectability metrics are used for performance assessment and comparison with extensively reported five-layer AANN model by Kramer.

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  • A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants

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Authors

Ahmad Shaheryar
University of Science and Technology Beijing (USTB), 30 Xueyuan Road, Haidian District, Beijing 100083, China
Xu-Cheng Yin
University of Science and Technology Beijing (USTB), 30 Xueyuan Road, Haidian District, Beijing 100083, China
Hong-Wei Hao
University of Science and Technology Beijing (USTB), 30 Xueyuan Road, Haidian District, Beijing 100083, China
Hazrat Ali
Department of Electrical Engineering, COMSATS Institute of Information Technology, University Road, Abbottabad 22060, Pakistan
Khalid Iqbal
COMSATS Institute of Information Technology near Officers Colony, Kamra Road, Attock 43600, Pakistan

Abstract


Sensors health monitoring is essentially important for reliable functioning of safety-critical chemical and nuclear power plants. Autoassociative Neural Network (AANN) based empirical sensor models have widely been reported for sensor calibration monitoring. However, such ill-posed data driven models may result in poor generalization and robustness. To address above mentioned issues, several regularization heuristics such as training with jitter, weight decay, and cross-validation are suggested in literature. Apart from these regularization heuristics, traditional error gradient based supervised learning algorithms for multilayered AANN models are highly susceptible of being trapped in local optimum. In order to address poor regularization and robust learning issues, here, we propose a Denoised Autoassociative Sensor Model (DAASM) based on deep learning framework. Proposed DAASM model comprises multiple hidden layers which are pretrained greedily in an unsupervised fashion under denoising autoencoder architecture. In order to improve robustness, dropout heuristic and domain specific data corruption processes are exercised during unsupervised pretraining phase. The proposed sensor model is trained and tested on sensor data from a PWR type nuclear power plant. Accuracy, autosensitivity, spillover, and Sequential Probability Ratio Test (SPRT) based fault detectability metrics are used for performance assessment and comparison with extensively reported five-layer AANN model by Kramer.

Keywords


English