Jha, A., Sahay, G., and Sivaramasastry, A., "Framework and Platform for Next Generation Aircraft Health Management System," SAE Technical Paper 2017-01-2126, 2017.
In aerospace industry, the concept of Integrated Vehicle Health Management (IVHM) has gained momentum and is becoming need of the hour for entire value chain in the industry. The expected benefits of lesser time for maintenance reduced operating cost and ever busy airports are motivating aircraft manufacturers to come up with tools, techniques and technologies to enable advanced diagnostic and prognostic systems in aircrafts.At present, various groups are working on different systems and platforms for health monitoring of an aircraft e.g. SHM (Structural Health Monitoring), PHM (Prognostics Health Monitoring), AHM (Aircraft Health Monitoring), and EHM (Engine Health Monitoring) and so on. However, these approaches are mostly restricted to federated architecture where faults and failures for standalone line replaceable units (LRUs) are logged inside the unit in fault storage area and are retrieved explicitly using maintenance based applications for fault and failure diagnostics. With the transformational growth in computing technology, one can easily visualize the possibilities of moving from present federated architecture to integrated architecture for health monitoring of aircraft in near future.The advanced analytical methods like statistical trend analyzers and estimation techniques can be used to process the data from multiple sources to predict the performance of a component, subsystem or a system for failure detection and prediction both in flight and on ground. In this paper a qualitative analysis of existing health monitoring systems is presented and an integrated architecture consisting of data acquisition, data processing, and data fusion with capabilities to perform diagnostics, prognostics and decision making is proposed.A framework and a platform required to perform computation intensive prognostics and diagnostic applications which would support decisions for condition based maintenance is presented. This paper also brings out a methodology to run on- board diagnostics and generate inflight warning to the crew.