Health status identification of rolling bearing based on SVM and improved evidence theory

Abstract

To address the lack of health status identification and poor stability problems in the rotating machinery equipment, this paper proposes a new method for health status identification of rolling bearing based on SVM and improved evidence theory. Firstly, in order to reflect the rolling health condition, we use the empirical mode decomposition (EMD) to extract energy value and the original part of the signal statistics constitute characteristic parameters. After that we take them as the input to SVM classifier for the initial classification. Then we construct the basic probability assignment (BPA) by the SVM classification results. Finally, the results of recognition are given based on recursive dynamic combining weight distribution and decision fusion. The experimental results show that this method can effectively identify Rolling health status, which has high recognition accuracy, stability, and broad applicability.