This paper develops a structural diagnosis approach for fault detection and isolation in hybrid systems. Hybrid systems are characterized by continuous behaviors that are interspersed with discrete mode changes in the system, making the analysis of behaviors quite complex. In this paper, we address the mode detection problem in hybrid systems as the first step in diagnoser design. The proposed method uses analytic redundancy methods to detect the operating mode of the system even in the presence of system faults. We define hybrid minimal structurally overdetermined (HMSO) sets for hybrid systems. For residual generation, we develop the HMSO selection problem, formulated as a binary integer linear programming optimization problem to minimize the number of selected HMSOs and reduce online computational costs of the diagnosis algorithm. The proposed structural approach does not require preenumeration of all possible modes in the diagnoser design step. Therefore, our approach is feasible for hybrid systems with a large number of switching elements, implying that the system can have a large number of operating modes. The case study demonstrates the effectiveness of our approach. We discuss the results of our case study, and present directions for future work. Note to Practitioners—Developing feasible approaches for online monitoring, fault detection, and fault isolation of complex hybrid and embedded systems, such as automobiles, aircraft, power plants, and manufacturing processes, is essential in securing their safe, reliable, and efficient operation. Frequent changes in the operational modes of these systems because of operator actions, such as changing gears in an automobile, or environmental changes, such as driving on a wet or icy road make the fault detection and isolation task in these systems challenging. It is important to detect and isolate faults in all the operating modes, and at the same time, not mistake a mode change as a fault in the system. In this paper, we propose an approach that exploits the equation structure of hybrid systems behavior to combine mode detection and diagnosis in nonlinear hybrid systems. The proposed algorithm is scalable and efficient. We demonstrate its effectiveness using a case study of a reverse osmosis subsystem in an advances life support system for long duration manned space missions. Important challenges that can affect the success of our approach include the need for sufficiently detailed hybrid models that capture nominal and faulty behavior, and a sufficient number of sensors to make simultaneous mode detection and fault isolation possible.

中文翻译：

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混合系统中的结构故障检测与隔离

本文提出了一种用于混合系统故障检测和隔离的结构诊断方法。混合系统的特征是连续的行为，其中散布着系统中的离散模式变化，这使得行为分析相当复杂。在本文中，我们将解决混合系统中的模式检测问题作为诊断程序设计的第一步。所提出的方法使用分析冗余方法来检测系统的运行模式，即使存在系统故障也是如此。我们为混合系统定义了混合的最小结构超定（HMSO）集。对于残差生成，我们开发了HMSO选择问题，将其表述为二进制整数线性规划优化问题，以最大程度地减少选定HMSO的数量并减少诊断算法的在线计算成本。所提出的结构方法不需要在诊断程序设计步骤中预先枚举所有可能的模式。因此，我们的方法对于具有大量开关元件的混合系统是可行的，这意味着该系统可以具有大量的操作模式。案例研究证明了我们方法的有效性。我们讨论了案例研究的结果，并提出了未来工作的方向。给从业者的注意事项-开发可行的方法来进行在线监视，故障检测以及对复杂的混合和嵌入式系统（例如汽车，飞机，发电厂和制造过程）进行故障隔离，对于确保其安全，可靠和高效的运行至关重要。由于操作员的行为（例如在汽车中换档），这些系统的运行模式经常发生变化，或环境变化（例如在潮湿或结冰的道路上行驶）使这些系统中的故障检测和隔离任务具有挑战性。重要的是要检测和隔离所有操作模式中的故障，同时，不要将模式更改误认为系统故障。在本文中，我们提出了一种方法，该方法利用混合系统行为的方程结构来组合非线性混合系统中的模式检测和诊断。所提出的算法是可扩展的和有效的。我们使用反渗透子系统的案例研究来证明其有效性，该子系统在用于长期载人航天任务的先进生命支持系统中。可能影响我们方法成功的重要挑战包括需要足够详细的混合模型来捕获名义和错误行为，