In modern engineering systems, reliability and safety can be conferred by efficient automatic monitoring and fault detection algorithms, allowing for the early identification and isolation of incipient faults. In case of large‐scale and complex systems, scalability issues and computational limitations make centralized monitoring and fault detection methods unapplicable. Research is therefore currently focusing on the development of distributed methods, where the computational complexity is divided among different units. In this paper, we propose a partition‐based model‐based fault detection and isolation scheme based on moving horizon estimation, able to estimate both the state variables and the possible faults, modeled as additive signals on the state and/or output equations. Its theoretical properties are analyzed, and numerical simulations are performed to witness its potentialities in a benchmark case study.

中文翻译：

---

基于运动层估计的线性系统迭代分布式故障检测与隔离

在现代工程系统中，可以通过高效的自动监视和故障检测算法来赋予可靠性和安全性，从而尽早识别和隔离初期故障。对于大型而复杂的系统，可伸缩性问题和计算限制使集中式监视和故障检测方法不适用。因此，目前的研究集中在分布式方法的开发上，在分布式方法中，计算复杂度被划分为不同的单元。在本文中，我们提出了一种基于视域估计的基于分区的基于模型的故障检测和隔离方案，能够估计状态变量和可能的故障，并在状态方程和/或输出方程上建模为加性信号。分析其理论性质，