Improving diesel engine efficiency, reducing emissions, and enabling robust health monitoring have been critical research topics in engine modelling. While recent advancements in the use of neural networks for system monitoring have shown promising results, such methods often focus on component-level analysis, lack generalizability, and physical interpretability. In this study, we propose a novel hybrid framework that combines physics-informed neural networks (PINNs) with deep operator networks (DeepONet) to enable accurate and computationally efficient parameter identification in mean-value diesel engine models. Our method leverages physics-based system knowledge in combination with data-driven training of neural networks to enhance model applicability. Incorporating offline-trained DeepONets to predict actuator dynamics significantly lowers the online computation cost when compared to the existing PINN framework. To address the re-training burden typical of PINNs under varying input conditions, we propose two transfer learning (TL) strategies: (i) a multi-stage TL scheme offering better runtime efficiency than full online training of the PINN model and (ii) a few-shot TL scheme that freezes a shared multi-head network body and computes physics-based derivatives required for model training outside the training loop. The second strategy offers a computationally inexpensive and physics-based approach for predicting engine dynamics and parameter identification, offering computational efficiency over the existing PINN framework. Compared to existing health monitoring methods, our framework combines the interpretability of physics-based models with the flexibility of deep learning, offering substantial gains in generalization, accuracy, and deployment efficiency for diesel engine diagnostics.

翻译：提升柴油发动机效率、降低排放并实现稳健的健康监测一直是发动机建模领域的核心研究课题。尽管近期神经网络在系统监测中的应用取得了显著进展，但此类方法通常侧重于部件级分析，且缺乏普适性与物理可解释性。本研究提出一种新颖的混合框架，将物理信息神经网络（PINNs）与深度算子网络（DeepONet）相结合，以实现均值柴油发动机模型中精确且计算高效的系统参数辨识。该方法融合基于物理的系统知识与数据驱动的神经网络训练，从而增强模型的适用性。通过引入离线训练的DeepONet来预测执行器动态，与现有PINN框架相比，显著降低了在线计算成本。为应对PINN在不同输入条件下典型的重复训练负担，我们提出两种迁移学习策略：（i）多阶段迁移学习方案，相比PINN模型的完整在线训练具有更优的运行时效率；（ii）少样本迁移学习方案，该方案冻结共享的多头网络主体，并在训练循环外部计算模型训练所需的基于物理的导数。第二种策略提供了一种计算成本低廉且基于物理的方法，用于预测发动机动态和参数辨识，在计算效率上优于现有PINN框架。与现有健康监测方法相比，本框架结合了基于物理模型的可解释性与深度学习的灵活性，在柴油发动机诊断的泛化能力、精度和部署效率方面实现了显著提升。