关键词: 正交邻域保持嵌入, 多核相关向量机, 滚动轴承, 早期故障, 故障诊断

Abstract: Aiming at the problem that rolling bearing early fault features are too weak to identify the rolling bearing operation state quickly and effectively,a fault diagnosis method based on orthogonal neighborhood preserving embedding and relevance vector machine for roller bearing early fault identification was proposed.The multi-domain feature set which indicated the roller bearing early fault state was constructed.The low dimensional sensitive fault features were extracted from this multi-domain feature set based on the linear manifold learning Orthogonal Neighbourhood Preserving Embedding (ONPE) for dimension reduction,and the acquired low dimensional sensitive features were input to multi-kernel Relevance Vector Machine (RVM) to identify the early fault patterns.The roller bearing early fault experiment result showed that the proposed method had good performance in dimension reduction for high dimensional complex feature set,while the diagnosis accuracy was also better than single kernel RVM.

Key words: orthogonal neighborhood preserving embedding, ulti-kernel relevance vector machine, roller bearing, early fault, fault diagnosis