Fault diagnosis and predictive maintenance for hydraulic system based on digital twin model

Abstract

Hydraulic system has been the mainstream choice in large engineering equipment due to its smooth transmission, large bearing capacity, and small volume. However, because of the tightness and invisibility in hydraulic equipment, it is difficult to check and predict its faults. Common fault diagnosis and maintenance methods for the hydraulic system can be divided into two types: a signal analysis based on the mathematical model and a machine learning algorithm based on artificial intelligence. The first method can only diagnose specific faults based on the mathematical model, which is not universal, and the second one must rely on abundant history fault data, which is impossible to obtain in the early running stage. In order to address these questions, a digital twin framework is proposed which combines the virtual model with the real part to solve practical problems. As a concrete realization form of a five-dimension digital twin model, this framework provides a more feasible solution mode for fault diagnosis in the hydraulic system. Meanwhile, it expands the functions of faults prediction and digital model display. A case study of a hydraulic cylinder is used to illustrate the effectiveness of the proposed framework. The experimental result shows that this method can improve diagnosis accuracy for a hydraulic cylinder greatly compared with the non-interactive simulation model. Meanwhile, with the supplement of actual fault data, the diagnosis accuracy can be further improved, which has a certain growth ability and good applicability.