Active model-based Fault Detection and Isolation (FDI) methods are of increasing importance in modern cyber-physical systems due to their ability to generate detections as residuals between anticipated and observed information, and, thus, isolate and identify causes of faults with greater confidence than conventional passive FDI techniques. This work focuses on evaluating the effectiveness of active FDI tests which use either steady-state or dynamic information. It is shown that transient information from active FDI tests can improve the identifiability of faults compared to steady-state testing. These tests are designed by casting FDI as an optimization problem that maximizes the Fisher Information Matrix of a system sensed outputs with respect to faults. The identifiability of faults is examined at steady-state and transient FDI tests in a plate fin heat exchanger of an aircraft environmental control system (ECS). In this system particulate fouling needs to be detected, which is challenged by multiple sources of system uncertainty. It is shown that the inclusion of transient information during fault diagnosis increases the confidence in fault identification when using optimal test designs.

基于主动模型的故障检测与隔离（FDI）方法在现代网络物理系统中的重要性日益提高，因为它们能够生成检测信息，作为预期和观察到的信息之间的残差，从而更加自信地隔离和识别故障原因比传统的被动FDI技术要好。这项工作的重点是评估使用稳态或动态信息的主动FDI测试的有效性。结果表明，与稳态测试相比，来自活动FDI测试的瞬态信息可以提高故障的可识别性。通过将FDI转换为一个优化问题来设计这些测试，该问题可以使系统检测到的有关故障的输出的Fisher信息矩阵最大化。在飞机环境控制系统（ECS）的板翅式换热器中，通过稳态和瞬态FDI测试来检查故障的可识别性。在该系统中，需要检测颗粒结垢，这受到系统不确定性多种来源的挑战。结果表明，使用最佳测试设计时，在故障诊断过程中包含瞬态信息会增加对故障识别的信心。