Real Time Data Management in Prognostic Systems

The aim was to research and construct various types of data compression algorithms to provide the backbone which could be integrated by the OEM into a comprehensive data compression system. The intent is to better manage the engine usage data in real time making it efficient, manageable and effective. Health monitoring modules incorporate numerous sensor networks for measuring or estimating many system parameters. These are then mathematically related to each other to properly simulate the dynamics of the system. Engine manufacturers for example have taken advantage of this information to provide significant enhancements to engine durability and life prediction algorithms. This improved capability over previous engines comes at a cost in terms of bandwidth, data storage, and system complexity. The fundamental building blocks of data compression techniques, along with data sequencing techniques, were developed to produce intelligent compression methods with average compression ratios from 27:1 to 200:1 while still matching the original signal within tolerances and accuracy. Work clearly shows that it is possible to achieve high compression of onboard sensed or inferred data streams, compress these data and thereafter use these data to produce highly accurate prognostics results. Testing algorithm development can capitalize on the trade-offs between the various approaches to develop the most optimal solution for OEM health management.