Research on robustness of five typical data-driven fault diagnosis models for nuclear power plants

2022 ◽  
Vol 165 ◽  
pp. 108639
Author(s):  
Jiangkuan Li ◽  
Meng Lin
Keyword(s):  
Fault Diagnosis ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Data Driven ◽  
Typical Data

Robustness Analysis and Improvement of Fault Diagnosis Model for Nuclear Power Plants Based on Random Forest

2021 ◽  
Author(s):  
Jiangkuan Li ◽  
Meng Lin
Keyword(s):  
Fault Diagnosis ◽  
Random Forest ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Robustness Analysis ◽  
Noisy Data ◽  
Data Driven ◽  
Model Based ◽  
Diagnosis Model

Abstract With the development of artificial intelligence technology, data-driven methods have become the core of fault diagnosis models in nuclear power plants. Despite the advantages of high flexibility and practicability, data-driven methods may be sensitive to the noise in measurement data, which is inevitable in the process of data measurement in nuclear power plants, especially under fault conditions. In this paper, a fault diagnosis model based on Random Forest (RF) is established. Firstly, its diagnostic performance on noiseless data and noisy data set containing 13 operating conditions (one steady state condition and 12 fault conditions) is analyzed, which shows that the model based on RF has poor robustness under noisy data. In order to improve the robustness of the model under noisy data, a method named ‘Train with Noisy Data’ (TWND) is proposed, the results show that TWND method can effectively improve the robustness of the model based on RF under noisy data, and the degree of improvement is related to the noise levels of added noisy data. This paper can provide reference for robustness analysis and robustness improvement of nuclear power plants fault diagnosis models based on other data-driven methods.

Application of genetic algorithms to fault diagnosis in nuclear power plants

2000 ◽  
Vol 67 (2) ◽  
pp. 153-160 ◽  
Author(s):  
Zhou Yangping ◽  
Zhao Bingquan ◽  
Wu DongXin
Keyword(s):  
Genetic Algorithms ◽  
Fault Diagnosis ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants

Improvement of fault diagnosis efficiency in nuclear power plants using hybrid intelligence approach

2014 ◽  
Vol 76 ◽  
pp. 122-136 ◽  
Author(s):  
Yong-kuo Liu ◽  
Chun-li Xie ◽  
Min-jun Peng ◽  
Shuang-han Ling
Keyword(s):  
Fault Diagnosis ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Hybrid Intelligence ◽  
Intelligence Approach

Application of FBOLES—a prototype expert system for fault diagnosis in nuclear power plants

1994 ◽  
Vol 44 (3) ◽  
pp. 225-235 ◽  
Author(s):  
Qin Zhang ◽  
Xuegao An ◽  
Jin Gu ◽  
Binquan Zhao ◽  
Dazhi Xu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants

scholarly journals Prognostics and Health Management in Nuclear Power Plants: An Updated Method-Centric Review With Special Focus on Data-Driven Methods

2021 ◽  
Vol 9 ◽  
Author(s):  
Xingang Zhao ◽  
Junyung Kim ◽  
Kyle Warns ◽  
Xinyan Wang ◽  
Pradeep Ramuhalli ◽  
...  
Keyword(s):  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Health Management ◽  
Nuclear Industry ◽  
Data Availability ◽  
Data Driven ◽  
Special Focus ◽  
Prognostics And Health Management ◽  
Term Operation

In a carbon-constrained world, future uses of nuclear power technologies can contribute to climate change mitigation as the installed electricity generating capacity and range of applications could be much greater and more diverse than with the current plants. To preserve the nuclear industry competitiveness in the global energy market, prognostics and health management (PHM) of plant assets is expected to be important for supporting and sustaining improvements in the economics associated with operating nuclear power plants (NPPs) while maintaining their high availability. Of interest are long-term operation of the legacy fleet to 80 years through subsequent license renewals and economic operation of new builds of either light water reactors or advanced reactor designs. Recent advances in data-driven analysis methods—largely represented by those in artificial intelligence and machine learning—have enhanced applications ranging from robust anomaly detection to automated control and autonomous operation of complex systems. The NPP equipment PHM is one area where the application of these algorithmic advances can significantly improve the ability to perform asset management. This paper provides an updated method-centric review of the full PHM suite in NPPs focusing on data-driven methods and advances since the last major survey article was published in 2015. The main approaches and the state of practice are described, including those for the tasks of data acquisition, condition monitoring, diagnostics, prognostics, and planning and decision-making. Research advances in non-nuclear power applications are also included to assess findings that may be applicable to the nuclear industry, along with the opportunities and challenges when adapting these developments to NPPs. Finally, this paper identifies key research needs in regard to data availability and quality, verification and validation, and uncertainty quantification.

scholarly journals Alpha Stable Distribution Based Morphological Filter for Bearing and Gear Fault Diagnosis in Nuclear Power Plant

2015 ◽  
Vol 2015 ◽  
pp. 1-15 ◽  
Author(s):  
Xinghui Zhang ◽  
Jianshe Kang ◽  
Lei Xiao ◽  
Jianmin Zhao
Keyword(s):  
Fault Diagnosis ◽  
Power Plant ◽  
Nuclear Power Plant ◽  
Nuclear Power ◽  
Power Plants ◽  
Nuclear Power Plants ◽  
Stable Distribution ◽  
Planetary Gearbox ◽  
Alpha Stable Distribution ◽  
Gear Fault

Gear and bearing play an important role as key components of rotating machinery power transmission systems in nuclear power plants. Their state conditions are very important for safety and normal operation of entire nuclear power plant. Vibration based condition monitoring is more complicated for the gear and bearing of planetary gearbox than those of fixed-axis gearbox. Many theoretical and engineering challenges in planetary gearbox fault diagnosis have not yet been resolved which are of great importance for nuclear power plants. A detailed vibration condition monitoring review of planetary gearbox used in nuclear power plants is conducted in this paper. A new fault diagnosis method of planetary gearbox gears is proposed. Bearing fault data, bearing simulation data, and gear fault data are used to test the new method. Signals preprocessed using dilation-erosion gradient filter and fast Fourier transform for fault information extraction. The length of structuring element (SE) of dilation-erosion gradient filter is optimized by alpha stable distribution. Method experimental verification confirmed that parameter alpha is superior compared to kurtosis since it can reflect the form of entire signal and it cannot be influenced by noise similar to impulse.

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