摘要：AbstractThis paper presents a fault detection approach based on feature selection and ensemble machine learning technique for nuclear power plant (NPP) screen cleaner condition monitoring. Firstly, comprehensive set of statistical features are extracted fromin-fieldraw accelerometer data. Then, a seperability based feature selection metric is utilized to select relevant features in order to enhance accuracy of fault detection algorithm. Afterwards, Extreme Gradient Boosting (XGBoost), which is a decision-tree-based ensemble Machine Learning algorithm, is trained using the selected features for fault detection. The comparative analysis on fault detection is also conducted in this study using different classifiers next to XGBoost. The approach is validated on different fault types of screen cleaners. The results show that the ensemble learning outperforms other classifiers in terms of accuracy and can be effectively used for NPP screen cleaners condition monitoring.
关键词：KeywordsFeature selectionSeperabilityEnsemble learningXGBoostFault classificationNuclear power plant screen cleaners
