The industrial application of data-driven methods for fault detection of new-design systems is limited by the inevitable scarcity of real data. Physics-Informed Neural Networks (PINNs) can mitigate this problem by integrating data and physical knowledge. In this work, we develop a novel fault detection method that combines physics-based simulations for data generation with a Physics-Informed Deep Autoencoder (PIDAE) for reproducing the system behaviour in normal conditions; the Sequential Probability Ratio Test (SPRT) is, then, used for detecting abnormal conditions. The proposed method is applied to new-design electro-hydraulic servo actuators used in turbofan engine fuel systems. The results show that it can provide more satisfactory fault detection performance, in terms of false and missed alarms, than state-of-the-art methods based on traditional autoencoders only and pure physics-based models only. Furthermore, the PIDAE outcomes are physically consistent and, therefore, more acceptable and trustworthy.

中文翻译：

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用于新设计系统中故障检测的物理信息深度自动编码器

用于新设计系统故障检测的数据驱动方法的工业应用受到实际数据不可避免的稀缺的限制。物理信息神经网络 (PINN) 可以通过集成数据和物理知识来缓解这个问题。在这项工作中，我们开发了一种新颖的故障检测方法，该方法将基于物理的数据生成模拟与物理信息深度自动编码器（PIDAE）相结合，以重现正常条件下的系统行为；然后，使用顺序概率比测试 (SPRT) 来检测异常情况。所提出的方法适用于涡扇发动机燃油系统中使用的新型电液伺服执行器。结果表明，与仅基于传统自动编码器和仅基于纯物理模型的最先进方法相比，在误报和漏报方面，它可以提供更令人满意的故障检测性能。此外，PIDAE 结果在物理上是一致的，因此更容易被接受和信赖。