Multi-Condition Digital Twin Calibration for Axial Piston Pumps : Compound Fault Simulation
This work addresses the challenge of data scarcity and poor generalization for compound fault diagnosis in hydraulic systems, advancing predictive maintenance in aerospace, marine, and heavy machinery applications.
The paper tackled the problem of diagnosing compound faults in axial piston pumps under varying conditions by proposing a multi-condition physics-data coupled digital twin calibration framework, which accurately reproduced single and compound faults and enabled robust zero-shot fault diagnosis.
Axial piston pumps are indispensable power sources in high-stakes fluid power systems, including aerospace, marine, and heavy machinery applications. Their operational reliability is frequently compromised by compound faults that simultaneously affect multiple friction pairs. Conventional data-driven diagnosis methods suffer from severe data scarcity for compound faults and poor generalization across varying operating conditions. This paper proposes a novel multi-condition physics-data coupled digital twin calibration framework that explicitly resolves the fundamental uncertainty of pump outlet flow ripple. The framework comprises three synergistic stages: in-situ virtual high-frequency flow sensing on a dedicated rigid metallic segment, surrogate model-assisted calibration of the 3D CFD source model using physically estimated ripple amplitudes, and multi-objective inverse transient analysis for viscoelastic unsteady-friction pipeline parameter identification. Comprehensive experiments on a test rig demonstrate that the calibrated digital twin accurately reproduces both single-fault and two representative compound-fault. These results establish a high-fidelity synthetic fault-generation capability that directly enables robust zero-shot fault diagnosis under previously unseen operating regimes and fault combinations, thereby advancing predictive maintenance in complex hydraulic systems.