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

Joint Authors

Yin, Xu-Cheng
Hao, Hong-Wei
Shaheryar, Ahmad
Ali, Hazrat
Iqbal, Khalid

Source

Science and Technology of Nuclear Installations

Issue

Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-17, 17 p.

Publisher

Hindawi Publishing Corporation

Publication Date

2016-03-22

Country of Publication

Egypt

No. of Pages

17

Abstract EN

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.

American Psychological Association (APA)

Shaheryar, Ahmad& Yin, Xu-Cheng& Hao, Hong-Wei& Ali, Hazrat& Iqbal, Khalid. 2016. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants. Science and Technology of Nuclear Installations،Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1118644

Modern Language Association (MLA)

Shaheryar, Ahmad…[et al.]. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants. Science and Technology of Nuclear Installations No. 2016 (2016), pp.1-17.
https://search.emarefa.net/detail/BIM-1118644

American Medical Association (AMA)

Shaheryar, Ahmad& Yin, Xu-Cheng& Hao, Hong-Wei& Ali, Hazrat& Iqbal, Khalid. A Denoising Based Autoassociative Model for Robust Sensor Monitoring in Nuclear Power Plants. Science and Technology of Nuclear Installations. 2016. Vol. 2016, no. 2016, pp.1-17.
https://search.emarefa.net/detail/BIM-1118644

Data Type

Journal Articles

Language

English

Notes

Includes bibliographical references

Record ID

BIM-1118644