Fault diagnosis, which encompasses the fault isolation and identification functions, is an integral part of many system health management (SHM) applications. Diagnostic applications make use of system information from the design phase, such as safety and mission assurance analysis, failure modes and effects analysis, hazards analysis, functional models, failure effect propagation models, and testability analysis. In modern process control and equipment monitoring systems, topological and analytic models of the nominal system, derived from design documents, are also employed for failure detection, fault isolation, and identification. Depending on the complexity of the monitored signals from the physical system, diagnostic applications may involve straightforward trending and feature extraction techniques to retrieve the parameters of importance from the sensor streams. They also may involve complex analysis routines, such as signal processing, learning, and classification methods to derive the parameters of importance to diagnosis. The process that is used to diagnose anomalous conditions from monitored system signals varies widely across the different approaches to system diagnosis. Rule‐based expert systems, case‐based reasoning systems, model‐based reasoning systems, learning systems, and probabilistic reasoning systems are examples of the many diverse approaches to diagnostic reasoning.

Many engineering disciplines have specific approaches to modeling, monitoring, and diagnosing anomalous conditions. Therefore, there is no “one‐size‐fits‐all” approach to building diagnostic and health monitoring capabilities for a system. For instance, the conventional approaches to diagnosing failures in rotorcraft applications are very different from those used in communications systems. Further, online and offline automated diagnostic applications are integrated into an operations framework with flight crews, flight controllers, and maintenance teams. While the emphasis of this chapter is automation of health management functions, striking the correct balance between automated and human‐performed tasks is a vital concern.