Fault Diagnosis for Body-in-White Welding Robot Based on Multi-Layer Belief Rule Base

Abstract

Fault diagnosis for body-in-white (BIW) welding robots is important for ensuring the efficient production of the welding assembly line. As a result of the complex mechanism of the body-in-white welding robot, its strong correlation of components, and the many types of faults, it is difficult to establish a complete fault diagnosis model. Therefore, a fault diagnosis model for a BIW-welding robot based on a multi-layer belief rule base (BRB) was proposed. This model can effectively integrate monitoring data and expert knowledge to achieve an accurate fault diagnosis and facilitate traceability. First, according to the established fault tree, a fault mechanism was determined. Second, based on the multi-layer relationship of a fault tree, we established a multi-layer BRB model. Meanwhile, in order to improve the accuracy of the model parameters, the projection covariance matrix adaptive evolutionary strategy (P-CMA-ES) algorithm was used to optimize and update the parameters of the fault diagnosis model. Finally, the validity of the proposed model was verified by a simulation experiment for the BIW-welding robot.