Fault Diagnosis of Gearbox Based on Improved DUCG With Combination Weighting Method

• Main Authors: Ying-Kui Gu, Min Zhang, Xiao-Qing Zhou
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Gearbox; fault diagnosis; dynamic uncertain causality graph (DUCG); combination weighting method; maximum pre-selected event
• Online Access: https://ieeexplore.ieee.org/document/8758116/

To reduce the influence of uncertain factors on the results of gearbox operation condition evaluation and fault diagnosis, and to improve the reliability and stability of gearbox operation, an improved dynamic uncertain causality graph (DUCG) fault diagnosis method is proposed by combining the qualitative and quantitative information obtained. In addition, to address the lack of objectivity of correlation variables in the dynamic uncertainty causal graph, the combination weighting method is used to reassign correlation variables. The sub-DUCGs of gear, bearing, shaft, and box are established and connected with a logic gate and conditional connection variables. The DUCG is used to diagnose the faults in the gearbox, and the effectiveness and rationality of the method are verified by comparing the probabilities of the maximum pre-selected events before and after the improvement. Because the combination weighting method only makes moderate modifications for different weights, the limitations of the diagnosis accuracy and the calculation of variable weights are discussed by choosing faults with different numbers of weights. The results show that the improved DUCG can more accurately identify root faults, and the growth rate of the probability of maximum pre-selected event increases with an increase in the number of weights.