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

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