Techniques for early warning of systematic failures of aerospace components

Fleets of aerospace equipment are managed through carefully controlled supply chain processes. When any of the planning assumptions are no longer valid, an unexpected demand on maintenance and supply can develop, reducing the availability of equipment. Earlier detection of unexpected trends in demand can mitigate their impact. Systematic failures of components in man-made fleets bear an analogy to disease outbreaks in the human community. This paper presents evidence that mathematics originally developed for public health surveillance can be effectively used in support of aerospace fleet management. The central results include the formulation of new data-driven analytics to allow maintenance and supply managers to be notified about emergence of a possible problem substantially earlier than it was possible before, the ability to routinely screen incoming data for indications of problems of a broad variety of types even if their number is very large, and the ability to pragmatically prioritize investigative efforts according to the statistical significance of the detections.