Digital Twin-Driven Zero-Shot Fault Diagnosis of Axial Piston Pumps Using Fluid-Borne Noise Signals

TL;DR

This paper introduces a digital twin-driven zero-shot fault diagnosis framework for axial piston pumps that leverages fluid-borne noise signals and physics-informed neural networks, achieving high accuracy without extensive fault data.

Contribution

The paper presents a novel DT-driven zero-shot fault diagnosis approach that calibrates a high-fidelity digital twin and uses synthetic fault signals for training, reducing reliance on real fault data.

Findings

Diagnostic accuracy exceeds 95% on real-world benchmarks.

Calibrated digital twin significantly improves fault diagnosis performance.

Gradient-based visualization reveals physically meaningful features in neural network decisions.

Abstract

Axial piston pumps are crucial components in fluid power systems, where reliable fault diagnosis is essential for ensuring operational safety and efficiency. Traditional data-driven methods require extensive labeled fault data, which is often impractical to obtain, while model-based approaches suffer from parameter uncertainties. This paper proposes a digital twin (DT)-driven zero-shot fault diagnosis framework utilizing fluid-borne noise (FBN) signals. The framework calibrates a high-fidelity DT model using only healthy-state data, generates synthetic fault signals for training deep learning classifiers, and employs a physics-informed neural network (PINN) as a virtual sensor for flow ripple estimation. Gradient-weighted class activation mapping (Grad-CAM) is integrated to visualize the decision-making process of neural networks, revealing that large kernels matching the subsequence length in time-domain inputs and small kernels in time-frequency domain inputs enable higher diagnostic accuracy by focusing on physically meaningful features. Experimental validations demonstrate that training on signals from the calibrated DT model yields diagnostic accuracies exceeding 95\% on real-world benchmarks, while uncalibrated models result in significantly lower performance, highlighting the framework's effectiveness in data-scarce scenarios.