Rolling bearing state identification method based on empirical mode decomposition (EMD) and principal component analysis (PCA)
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The invention discloses a rolling bearing state identification method based on empirical mode decomposition (EMD) and principal component analysis (PCA) and belongs to the technical field of rail transit. The method includes the following steps: (1) obtaining experiment data; (2) conducting two-category state experiment data partitioning or four-category state experiment data partitioning; (3) conducting EMD processing on each partitioned section of data respectively, obtaining intrinsic mode function (IMF) component of each section of data, and forming an IMF matrix of each section of data; (4) extracting statistical characteristic component of the rolling bearing state; (5) determining a safety margin boundary; and (6) identifying a rolling bearing operation state. The rolling bearing state identification method has the advantages of providing a rolling bearing operation state safety margin estimation method based on EMD-PCA-least square support vector machine (LSSVM) and an identification method of normal and various failure states and enabling safety margin accuracy rate and various state identification rate to be both larger than 95%. The rolling bearing state identification method can monitor and diagnose rolling bearing failure fast and effectively.