Rolling Bearing Fault Diagnosis Fusion Model Based on Gene Expression Programming

摘要

Traditionally, a single sensor is always used to provide several machine components operating condition for fault diagnosis. Fault diagnosis based on this approach is difficult to obtain satisfactory results, especially in the severe operating environment. This paper proposes a new feature-level fusion model based on gene expression programming. And the new fusion model fuses machine component operating features from more than one sensor in parallel. Firstly, calculate time-domain feature parameters of each sensor signal. Secondly, construct multi-sensor feature-level fusion model by using gene expression programming. Finally, identify the integration information and make decisions for machine components fault diagnosis. Experiments show that the new approach can achieve better performance than single sensor approach, and it is able to further improve the accuracy of fault diagnosis.