Ontological Bayesian Fleet Management

Management and sustainment of large fleets is a key driver in cost reduction within the defense

industry. The industry comprises many fleets which require a common solution for all similar systems

to avoid siloed products. Additionally, many fleets involve diverse resources including people,

machines, parts, etc. demanding a solution that maps data and domain knowledge in a flexible way.

Northrop Grumman addresses this with a knowledge driven data architecture composed of reusable

microservices and fleet management products, enabling the extraction and modeling of core structures

consistent across all fleets while enhancing scalability. The solution ingests expert knowledge

and complex data sources into its ontological data model and standardizes prognostic methods ranging

from Bayesian inference to modern machine learning. In this paper we discuss the fundamental

data elements and design principles required to model an abstract fleet. We also discuss how the

solution maps data onto these abstract objects–hydrating an ontological domain. Finally, we address

techniques used to enable the prognostic model abstraction needed to feed a data driven fleet

sustainment solution.