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.

翻译：轴向柱塞泵是流体动力系统中的关键部件，其可靠的故障诊断对于确保运行安全与效率至关重要。传统的数据驱动方法需要大量带标签的故障数据，而这在实际中往往难以获取；基于模型的方法则受限于参数不确定性。本文提出一种利用流体噪声信号的数字孪生驱动零样本故障诊断框架。该框架仅使用健康状态数据校准高保真数字孪生模型，生成合成故障信号用于训练深度学习分类器，并采用物理信息神经网络作为流量脉动估计的虚拟传感器。通过集成梯度加权类激活映射技术，实现了神经网络决策过程的可视化，结果表明：在时域输入中匹配子序列长度的大卷积核与在时频域输入中的小卷积核能够通过聚焦于物理意义明确的特征，实现更高的诊断精度。实验验证表明，基于校准数字孪生模型生成的信号进行训练，在实际基准测试中诊断准确率超过95%；而未校准模型则导致性能显著下降，这凸显了该框架在数据稀缺场景下的有效性。