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.

Prognostics performance evaluation has gained significant attention in the past few years. As prognostics technology matures and more sophisticated methods for prognostic uncertainty management are developed, a standardized methodology for performance evaluation becomes extremely important to guide improvement efforts in a constructive manner. This paper is in continuation of previous efforts where several new evaluation metrics tailored for prognostics were introduced and were shown to effectively evaluate various algorithms as compared to other conventional metrics.

This paper extends model-based diagnosis (MBD) (Reiter87,deKleer87) to systems which convert, move and process material. Examples of such systems are printers, refineries and food processing plants. Such plants present two challenges to model-based diagnosis: (1) the plant may process 100s-1000s of items per minute so retaining full details of behavior of all past objects is impractical, and (2) complex multi-way interactions can occur among components operating on the same object. We address the first challenge by synopsizing past behavior in a data structure of fixed size.

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.

Bearing envelope analysis (BEA) is a powerful technique in the detection of faults in bearings. The improper selection of the envelope window frequency and window bandwidth can render the analysis ineffective. This can reduce the ability of a health and usage monitoring system (HUMS) to correctly identify a degraded bearing. This occurred recently: a teardown analysis (TDA) of a utility helicopter oil cooler fan housing found extensive bearing damage. The HUMS did not detect the fault. This paper is an analysis of why the BEA failed to detect the damage bearing.