Verifying diagnostic procedures for complex systems is hard and labor-intensive. Usually this verification is accomplished primarily through extensive review of the procedures by experts. We aim to augment this review process by using insights from comparing the diagnostic steps described in the procedural definitions with diagnostics information derived from existing models of the system. These comparisons offer various conformance checks between the manually developed diagnostic procedures and the diagnostic trees auto-generated from the diagnostic system models.

One of the most prominent technical challenges to effective deployment of health management systems is the vast difference in user objectives with respect to engineering development. In this paper, a detailed survey on the objectives of different users of health management systems is presented. These user objectives are then mapped to the metrics typically encountered in the development and testing of two main systems health management functions: diagnosis and prognosis.

A variety of rule-based, model-based and datadriven techniques have been proposed for detection and isolation of faults in physical systems. However, there have been few efforts to comparatively analyze the performance of these approaches on the same system under identical conditions. One reason for this was the lack of a standard framework to perform this comparison.

The design, development, and operation of complex space, lunar and planetary exploration systems require the development of general procedures that describe a detailed set of instructions capturing how mission tasks are performed. For both crewed and uncrewed NASA systems, mission safety and the accomplishment of the scientific mission objectives are highly dependent on the correctness of procedures. In this paper, we describe how to use the auto-generated diagnostic trees from existing diagnostic models to improve the verification of standard operating procedures.

Diagnostic algorithms (DAs) are key to enabling automated health management. These algorithms are designed to detect and isolate anomalies of either a component or the whole system based on observations received from sensors. In recent years a wide range of algorithms, both model based and data-driven, have been developed to increase autonomy and improve system reliability and affordability. However, the lack of support to perform systematic benchmarking of these algorithms continues to create barriers for effective development and deployment of diagnostic technologies.

Knowledge about failures and failure propagation paths in early design can benefit Prognostics and Health Management (PHM) system development by identifying expected system failures, determining adequate system monitoring, and improving system reliability through hardware configurational changes. Function-based failure analysis provides a means for early system representation that can provide meaningful results for failure analysis. Function-based failure analysis methods model failures propagating between components based on shared energy, material, and signal (EMS) flows.