Integrated System Health Management (ISHM) is a promising technology that fuses sensor data and historical state-of-health information of components and subsystems to provide actionable information and enable intelligent decision-making regarding the operation and maintenance of aerospace systems. ISHM fundamentally relies on assessments and predictions of system health, including the early detection of failures and estimation of Remaining Useful Life (RUL). Model-based, data-driven or hybrid reasoning techniques can be utilized to maximise the timeliness and reliability of diagnosis and prognosis information. The benefits of ISHM include enhancing the maintainability, reliability, safety and performance of systems. The next evolution of the ISHM concept, Intelligent Health and Mission Management (IHMM), delves deeper into the utilization of on-line system health predictions to modify mission profiles to ensure safety and reliability, as well as efficiency through predictive integrity. This concept is particularly important for Trusted Autonomous System (TAS) applications, where an accurate assessment of the current and future system state-of-health to make operational decisions (with or without human intervention) is integral to both flight safety and mission success. IHMM systems introduce the capability of predicting degradation in the functional performance of subsystems, with sufficient time to dynamically identify which appropriate restorative or reconfiguration actions to take in order to ensure that the system can perform at an acceptable level of operational capability before the onset of a failure event. This paper reviews some of the key advancements and contributions to knowledge in the field of ISHM for the aerospace industry, with a particular focus on various architectures and reasoning strategies involving the use of artificial intelligence. The paper also discusses the key challenges faced in the development and deployment of ISHM systems in the aerospace industry and highlights the safety-critical role that IHMM will play in future cyber-physical and autonomous system applications (both vehicle and ground support systems), such as Unmanned Aircraft Systems (UAS) Traffic Management (UTM), Urban Air Mobility (UAM) and Distributed Satellite Systems (DSS).

集成系统健康管理 (ISHM) 是一项很有前途的技术，它融合传感器数据和组件和子系统的历史健康状态信息，以提供可操作的信息，并实现有关航空航天系统运行和维护的智能决策。ISHM 从根本上依赖于对系统健康状况的评估和预测，包括故障的早期检测和剩余使用寿命 (RUL) 的估计。可以利用基于模型、数据驱动或混合推理技术来最大化诊断和预后信息的及时性和可靠性。ISHM 的好处包括增强系统的可维护性、可靠性、安全性和性能。ISHM 概念的下一个演变，智能健康和任务管理 (IHMM)，深入研究利用在线系统健康预测来修改任务配置文件，以确保安全性和可靠性，以及通过预测完整性确保效率。这一概念对于可信自治系统 (TAS) 应用尤为重要，在这种应用中，准确评估当前和未来系统的健康状况以做出运营决策（有或没有人为干预）是飞行安全和任务成功不可或缺的一部分。IHMM 系统引入了预测子系统功能性能退化的能力，有足够的时间来动态识别要采取哪些适当的恢复或重新配置操作，以确保系统能够在出现故障之前以可接受的操作能力水平执行故障事件。本文回顾了航空航天工业ISHM领域知识的一些关键进步和贡献，特别关注涉及人工智能使用的各种架构和推理策略。本文还讨论了在航空航天工业中开发和部署ISHM 系统所面临的主要挑战，并强调了 IHMM 将在未来的网络物理和自主系统应用（车辆和地面支持系统）中发挥的关键安全作用，例如如无人机系统 (UAS) 交通管理 (UTM)、城市空中交通 (UAM) 和分布式卫星系统 (DSS)。特别关注涉及使用人工智能的各种架构和推理策略。本文还讨论了在航空航天工业中开发和部署ISHM 系统所面临的主要挑战，并强调了 IHMM 将在未来的网络物理和自主系统应用（车辆和地面支持系统）中发挥的关键安全作用，例如如无人机系统 (UAS) 交通管理 (UTM)、城市空中交通 (UAM) 和分布式卫星系统 (DSS)。特别关注涉及使用人工智能的各种架构和推理策略。本文还讨论了在航空航天工业中开发和部署ISHM 系统所面临的主要挑战，并强调了 IHMM 将在未来的网络物理和自主系统应用（车辆和地面支持系统）中发挥的关键安全作用，例如如无人机系统 (UAS) 交通管理 (UTM)、城市空中交通 (UAM) 和分布式卫星系统 (DSS)。