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This paper mainly discusses the performance degradation assessment of train rolling bearings under incomplete data, by using the support vector data description (SVDD) and dynamic particle swarm optimization (DPSO).The proposed method is based on the similarity weight for the assessment of the train rolling bearings under incomplete data. Firstly, to obtain effective features of bearing performance degradation from collected vibration data, the local mean decomposition (LMD) is employed todoi:10.18293/dms2016-046 dblp:conf/dms/QinWWJZ16 fatcat:nvty6nbv5japlj7bkhb6rgpbbm