Axis Orbit Recognition of the Hydropower Unit Based on Feature Combination and Feature Selection

Abstract

Axis-orbit recognition is an essential means for the fault diagnosis of hydropower units. An axis-orbit recognition method based on feature combination and feature selection is proposed, aiming to solve the problems of the low recognition accuracy, poor robustness, and low efficiency of existing axis-orbit recognition methods. First, various contour, moment, and geometric features of axis orbit samples are extracted from the original data and combined into a multidimensional feature set; then, Random Forest (RF)-Fisher feature selection is applied to realize feature dimensionality reduction; and finally, the selected features are set as the input of the support vector machine (SVM), which is optimized by the gravitational search algorithm (GSA) for axis-orbit recognition. The analytical results show that the proposed method has high recognition efficiency and good robustness while maintaining high accuracy for axis-orbit recognition.

Intelligent Fault Diagnosis of Rolling Bearings Based on a Complete Frequency Range Feature Extraction and Combined Feature Selection Methodology

“…The RF algorithm is capable of evaluating the importance of features, and the main idea is to calculate the contribution of different features to each decision tree model. The contribution is able to be represented using the calculation of the out-of-bag (OOB) data error rate [29], where the OOB data are unused data each time the decision tree is built. The importance of the features is measured by calculating the average contribution of each feature.…”