A bearing fault diagnosis scheme with statistical-enhanced covariance matrix and Riemannian maximum margin flexible convex hull classifier. (May 2021) - Li, Xin; Yang, Yu; Ping, Wang; Jian, Wang; Cheng, Junsheng
Abstract: To achieve more appropriate fault feature representation for bearing, a statistical-enhanced covariance matrix (SECM) is proposed to extract the global–local features and the interaction of them. Besides, three statistical parameters are introduced to SECM to enhance its statistical characteristics. For fully mining the Riemannian geometric information embedded in SECMs, a Riemannian maximum margin flexible convex hull (RMMFCH) classifier with Log-Euclidean metric (LEM) is designed, where a set of Riemannian kernel mapping functions map SECMs to a higher-dimensional Hilbert space. In this space, the RMMFCH can be directly solved, which reduces the extra computation cost. Hence, we design a fault diagnosis scheme of bearing with SECM and RMMFCH. Experiment results prove the promising performance of our method for bearing fault diagnosis. Highlights: A statistical-enhanced covariance matrix (SECM) is used as bearing fault feature. A RMMFCH classifier is proposed with Riemannian manifold kernels. A bearing fault diagnosis scheme is proposed with SECM and RMMFCH. Experiment results verify the great effectiveness of our proposed method.