Rolling bearing performance degradation assessment based on deep belief network and improved support vector data description

Rolling bearing performance degradation assessment (PDA) based on vibration signal is critical to intelligent maintenance. However, there are two problems in existing methods: 1) degradation signals are needed as building model, 2) feature extraction is dependent on experience and operation parameters. Aiming at these challenges, a PDA method based on deep belief network (DBN) and improved support vector data description (SVDD) is proposed in this paper. The normalized amplitude spectrum under normal state is used as the training sample of DBN without the classified output layer, which is used as the automatic feature extraction model. Then, the SVDD which requires appropriate penalty parameter C and kernel parameter sigma is employed to fuse these features into performance indicator (PI), and the sparrow search algorithm (SSA) is introduced into the parameters optimization process of SVDD. The results applied in two experiment datasets show that the proposed method can detect the occurrence of incipient degradation and reflect the whole bearing performance degradation. And the advantages over other methods have been shown. In addition, the anti-noise capability, compared with RMS, is discussed, and the results show that the proposed method is better than RMS to some extent, which is worth being researched further in principle.

Automated summary

This paper proposes a method for assessing the performance degradation of rolling bearings based on deep belief network and improved support vector data description. The method extracts features using the normalized amplitude spectrum of training samples and fuses them into a performance indicator using SVDD. Experimental results demonstrate that the proposed method can detect bearing degradation and reflect overall performance degradation.