Dual-source gramian angular field method and its application on fault diagnosis of drilling pump fluid end

Under complex drilling working conditions, it is difficult to completely describe the working state of the drilling pump fluid end with a single-source signal to achieve a high fault diagnosis accuracy. This paper proposes to use dual-source signals (vibration signal and strain signal) to describe the working state of the fluid end. Especially, a dual-source gramian angular field (DS-GAF) method is proposed to fuse vibration-strain signals to provide more fault information. In DS-GAF method, the dual-source signal is converted to points in spherical coordinates, and the arccosine of the normalized amplitude of the two signals is assumed as the polar angle and the azimuthal angle, respectively. Three DS-GAF matrices are generated with the position of the points. These three matrices are respectively encoded as three colors (red, green, and blue) channels of the image to achieve signal fusion. A fault diagnosis method based on DS-GAF and ResNet for the drilling pump fluid end is proposed to improve the fault diagnosis accuracy under multiple working conditions. First, by the DS-GAF method, the vibration-strain signals of the fluid end are fused and converted into a fault diagnosis image dataset. Second, a fault diagnosis model based on ResNet is established and the effect of batch size on the performance of the fault diagnosis model is discussed. Finally, fault diagnosis experiments are carried out on the proposed fault diagnosis model and its performance is investigated. The results show that the diagnosis method proposed in this paper has the highest diagnostic accuracy of 98.97% for nine fault types under multiple operating conditions, with a maximum improvement of 18.91% compared to the original GAF method. The DS-GAF method for signal fusing proposed in this paper not only improves the accuracy of fluid end diagnosis, but also provides a reference for the signal fusion diagnosis of other equipment.