A First-Order Stochastic Prognostic System for the Diagnosis of Helicopter Rotor Systems for the US Navy

C We have created a diagnostic system for the US Navy to use in the analysis of the “running health” of helicopter rotor systems. Although our system is not yet deployed for real-time in-flight diagnosis, we have successfully analyzed the data sets of actual helicopter rotor failures supplied by the US Navy. We discuss both critical techniques supporting the design of our stochastic diagnostic system as well as issues related to full deployment. Our diagnostic system, called DBAYES, is composed of a logic-based, first-order, and Turing-complete set of software tools for stochastic modeling. We use this language for modeling time-series data supplied by sensors on the mechanical system. The inference scheme for these software tools is based on a variant of Pearl's loopy belief propagation algorithm. Our language contains variables that can capture general classes of situations, events, and relationships. A Turing-complete language is able to reason about potentially infinite classes and situations, similar to the analysis of Dynamic Bayesian Networks. Since the inference algorithm is based on a variant of loopy belief propagation, the language includes the Expectation Maximization type learning of parameters in the modeled domain. In this paper we briefly present the theoretical foundations for our first-order stochastic language and then demonstrate time-series modeling and learning in the context of fault diagnosis.