Abstract
Nuclear power plant operating data are characterized by a large variety, strong coupling, and low data value density. When using machine learning techniques for fault diagnosis and other related research, feature selection enables dimensionality reduction while maintaining the physical meaning of the original features, thus improving the computational efficiency and generalization ability of the learning model. In this paper, a correlation-based feature selection algorithm is developed to implement feature selection of nuclear power plant operating data. The proposed algorithm is verified by experiments and compared with traditional correlation-based feature selection algorithms. The experiments and comparison results show that the proposed algorithm is effective in realizing the dimensionality reduction of nuclear power plant operating data.
Highlights
During the real-time operation of a nuclear power plant, the parameters such as temperature and pressure are monitored constantly and recorded
We propose a novel correlation-based feature selection algorithm for solving the problem of poor performance in identifying redundant features when the existing correlation-based feature selection algorithm analyzes nuclear power plant operating data
We propose the maximal information coefficient (MIC) as a correlation measure for the characteristics of nuclear power plant operating data and conduct correlation analysis experiments on the MIC and Symmetric uncertainty (SU) by combining professional knowledge
Summary
During the real-time operation of a nuclear power plant, the parameters such as temperature and pressure are monitored constantly and recorded. Since nuclear power plant operating data have many types and strong coupling characteristics, common feature selection algorithms sometimes cannot accurately identify redundant features. E mRMR is similar to the CFS algorithm in that it uses a mutual information-based evaluation function to estimate the correlation and redundancy while taking into account the performance of the feature subset as a whole. To overcome the problems of existing algorithms based on correlation and meet the needs of nuclear power plant operating data feature selection, we develop a novel algorithm that can provide a collection of features with strong correlation and low redundancy
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