Prognostic enhancements to diagnostic systems for improved condition-based maintenance [military aircraft]

TLDR

US Navy health‑monitoring systems such as ICAS use fault detection and diagnostic methods, yet they lack prognostic capabilities and a plug‑and‑play framework for integrating new technologies. The paper aims to develop prognostic enhancements that enable forecasting future machine condition to improve condition‑based maintenance and reduce life‑cycle costs. The authors propose a generic, interoperable prognostic module framework (PEDS) and illustrate it with modules for gas turbine engines and gearbox systems.

Abstract

In recent years, numerous machinery health monitoring technologies have been developed by the US Navy to aid in the detection and classification of developing machinery faults for various Naval platforms. Existing Naval condition assessment systems such as ICAS (Integrated Condition Assessment System) employ several fault detection and diagnostic technologies ranging from simple thresholding to rule-based algorithms. However, these technologies have not specifically focused on the ability to predict the future condition (prognostics) of a machine based on the current diagnostic state of the machinery and its available operating and failure history data. An advanced prognostic capability is desired because the ability to forecast this future condition enables a higher level of condition-based maintenance for optimally managing total life cycle costs (LCC). A second issue is that a framework does not exist for "plug-and-play" integration of new diagnostic and prognostic technologies into existing Naval platforms. This paper outlines such prognostic enhancements to diagnostic systems (PEDS) using a generic framework for developing interoperable prognostic "modules". Specific prognostic module examples developed for gas turbine engines and gearbox systems are also provided.