A Copula Entropy Enhanced mRMR Feature Selection Method for Fault Diagnosis of Hydropower Units

Abstract

To address the challenges of conventional feature extraction methods in capturing nonlinear dependencies within fault signals and reducing feature redundancy during hydropower units fault diagnosis, this paper proposes a feature selection method integrating the Minimum Redundancy Maximum Relevance (mRMR) criterion with Copula Entropy (mRMR-CE). This method utilizes Copula Entropy (CE) to capture both linear and nonlinear dependencies in vibration signals. Combined with the mRMR criterion to suppress feature redundancy, it achieves stable selection of highly discriminative features. In noisy environment and with various classifiers, the method shows strong performance and stability. To validate the effectiveness of the proposed method, five feature selection approaches-CE, mRMR, mRMR-CE, Pearson, and PCA-were applied to the training samples during the feature selection stage. Lastly, the chosen features were input into four different types of classifiers for training and testing: Support Vector Machine (SVM), Random Forest (RF), Multi-layer Perceptron (MLP), and Extreme Gradient Boosting (XGBoost). Experimental results demonstrate that the proposed method exhibits outstanding performance on both the CWRU bearing fault dataset and the Unit 3 dataset from a hydropower plant. It achieved an average accuracy of 99.87% and an F1 score of 98.25% on the CWRU dataset, and reached 100% accuracy with an F1 score of 99.31% on the Unit 3 dataset. These results significantly outperform traditional feature selection methods while demonstrating high stability and robustness.