Abstract Details
4 / 2024-05-11 11:08:50
Research on Nuclear Power Sensor Failure Detection Method Based on Autoencoder and Random Forest
Nuclear Power Sensor; Failure Detection; KPCA; Autoencoder; Random Forest
Final Paper
Sensors are essential detection components in nuclear power instrumentation and control systems. Their proper functioning plays a crucial role in the operation of nuclear power systems and equipment. Sensor failures may lead to inaccurate detection data and delayed identification of nuclear system or equipment malfunctions. Based on this premise, this paper conducts research on the failure detection method of nuclear power sensors. Firstly, using the simulation platform of the PCtran, historical operational data of sensors under both steady-state and transient conditions are collected to establish a dataset for sensor failure detection. Then, based on this dataset, the SPE statistical contribution rate of the autoencoder, and the fault detection and final location result is obtained. Finally, the random forest algorithm is used to diagnose the fault type of the sensor. Experimental analysis demonstrates that the proposed method effectively detects nuclear power sensor failures with high accuracy.
Important Date
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Conference Date
Sep 23
2024
toSep 25
2024
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Sep 24 2024
Contribution Submission Deadline
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Sep 25 2024
Registration deadline
Sponsored By
Harbin Engineering University (HEU)
Contact Information
- Prof. WANG Xiang
- xi******@hrbeu.edu.cn
Previous Conferences
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2016-09-26 China Chengdu,China
8th International Symposium on Symbiotic Nuclear Power Systems for 21st Century -
2016-09-26 China Chengdu,China
8th International Symposium on Symbiotic Nuclear Power Systems for 21st Century -
2010-08-23 China 哈尔滨市
The 3rd International Symposium on Symbiotic Nuclear Power Systems for 21st Century (ISSNP2010)
