Impeller fault diagnosis method and application based on digital twin flow field contour of centrifugal pump

With the development of industrial technology, the health diagnosis and maintenance of centrifugal pumps are increasingly urgent. Combining digital twin and machine vision technology, this paper proposed an intelligent impeller fault diagnosis method for centrifugal pumps based on a digital twin flow field cloud diagram. First of all, the digital twin model of the centrifugal pump was used to simulate the evolution of the random fracture for the impeller blades, and the pressure and velocity cloud diagrams of the impeller flow field with different fault characteristics were generated. Secondly, based on the learning and training of the Yolov5 algorithm, two kinds of machine vision models, namely pressure and velocity cloud diagrams, were obtained, and the preliminary diagnosis of impeller fault was realized by combining statistical analysis. Furthermore, the complementary advantages of the two types of detection models were considered, and the two types of detection models were combined based on the idea of stack integration to improve the accuracy of impeller fault diagnosis. The experimental verification shows that the intelligent fault diagnosis method for centrifugal pumps proposed in this paper has a diagnosis accuracy of more than 0.99 for the random fracture of impeller blades. The developed intelligent impeller fault diagnosis system for centrifugal pumps makes the method developed in this paper be applied to practical scenarios.