Health monitoring and fault diagnosis in traditional single spacecraft missions are mostly accomplished by human operators on ground through around-the-clock monitoring and trend analysis on huge amount of telemetry data. Future multiplatform space missions, commonly known as the formation flight missions, will utilize multiple inexpensive spacecraft in formation by distributing the functionalities of a single platform among the miniature inexpensive platforms. Current spacecraft diagnosis practices do not scale up well for multiple space platforms due to an increasing need to make the long-duration missions cost-effective by limiting the size of the operations team which will be large if traditional diagnosis is employed. An ideal solution to this problem is to incorporate an autonomous fault detection, isolation, and recovery (FDIR) mechanism. However, the effectiveness of spacecraft autonomy is yet to be demonstrated and due to the existence of perceived risks, it is often desired that the expert human operators be involved in the spacecraft operations and diagnosis processes i.e., the autonomous spacecraft actions be understandable by the human operators on ground so that intervention may be made, if necessary.ududTo address the above problems and requirements, in this research a systematic and transparent fault diagnosis methodology for ground-based operations of multi-platform space systems is developed. First, novel hierarchical fault diagnosis concepts and framework are developed. Within this framework, a multi-platform space system is decomposed hierarchically into multiple levels. The decomposition is driven by the need for supporting the development of the components/subsystems of the overall system by a number of design teams and performing integration at the end. A multi-platform system is considered to be a set of interacting components where components at different levels correspond to formation, system, sub-system, etc. depending on the location of the node in the hierarchy. Two directed graph based fault diagnosis models are developed namely, fuzzy rule based hierarchical fault diagnosis model (HFDM), and Bayesian networks (BN)-based component dependency model (CDM).ududIn HFDM, fault diagnosis of different components in the formation flight is investigated. Fuzzy rules are developed for fault diagnosis at different levels in the hierarchy by taking into account the uncertainties in the fault manifestations in a given component. In this model, the component interactions are quantified without taking the uncertainties in the component health state dependencies into account. Next, a component dependency model (CDM) based on Bayesian networks (BN) models is developed in order to take the uncertainties in component dependencies into account. A novel methodology for identifying CDM parameters is proposed. Fault evidences are introduced to the CDM when the fault modes of a component are observed via fuzzy rule activations. Advantages and limitations associated with the proposed HFDM and the CDM are also discussed. Finally, the verification and validation (V&V) of the hierarchical diagnosis models are investigated via a sensitivity analysis approach.ududIt should be noted that the proposed methodology and the fault diagnosis strategies and algorithms that are developed in this research are generic in a sense that they can be applied to any hierarchically decomposable complex systems. However, the system and domain specific knowledge they require, especially for modeling component dependencies, are mostly available in the aerospace industry where extensive system design and integration-related analysis are common due to high system building cost and failure risks involved.ud
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机译：传统的单飞船任务中的健康监测和故障诊断主要由地面操作人员通过对大量遥测数据的全天候监测和趋势分析来完成。未来的多平台太空任务（通常称为编队飞行任务）将通过在微型廉价平台之间分配单个平台的功能来利用多个廉价航天器。当前越来越多的航天器诊断实践无法很好地扩展到多个空间平台，这是因为越来越需要通过限制操作团队的规模来使长期任务具有成本效益，如果采用传统诊断的话，这将是很大的。解决此问题的理想解决方案是合并自主故障检测，隔离和恢复（FDIR）机制。然而，航天器自主性的有效性尚待证明，并且由于存在感知到的风险，人们通常希望让专业的人类操作员参与航天器的操作和诊断过程，即人类可以理解自主的航天器行为为解决上述问题和要求，在本研究中，开发了一种针对多平台空间系统地面操作的系统且透明的故障诊断方法。首先，开发了新颖的分层故障诊断概念和框架。在此框架内，将多平台空间系统分层分解为多个级别。分解是由许多设计团队支持整个系统的组件/子系统的开发并最终进行集成的需求所驱动的。多平台系统被认为是一组交互组件，其中不同层次上的组件对应于层次结构中节点的位置，对应于地层，系统，子系统等。开发了两种基于有向图的故障诊断模型，即基于模糊规则的分层故障诊断模型（HFDM）和基于贝叶斯网络（BN）的组件依赖模型（CDM）。 ud ud在HFDM中，对组件中不同组件的故障诊断编队飞行进行了调查。通过考虑给定组件中故障表现的不确定性，开发了模糊规则，用于层次结构中不同级别的故障诊断。在此模型中，在不考虑组件运行状况依赖性的不确定性的情况下，对组件交互进行了量化。接下来，开发基于贝叶斯网络（BN）模型的组件依赖模型（CDM），以考虑组件依赖的不确定性。提出了一种用于识别CDM参数的新颖方法。当通过模糊规则激活观察到组件的故障模式时，将故障证据引入CDM。还讨论了与建议的HFDM和CDM相关的优势和局限性。最后，通过敏感性分析方法对分层诊断模型的验证和确认（V＆V）进行了研究。 ud ud应注意，本研究中提出的方法和故障诊断策略与算法是通用的。可以将它们应用于任何可分层分解的复杂系统。但是，它们需要的系统和领域特定知识，尤其是用于建模组件依存关系的知识，大多数在航空航天业中可用，由于高昂的系统构建成本和涉及的故障风险，广泛进行系统设计和与集成相关的分析是普遍的。
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