Condition-based Failure Prognostic Health Management Systems

Ridgetop Group will develop an improved method of supporting condition-based maintenance through the analysis of historical data, and direct but non-intrusive physics-of-failure measurements. The measurements apply a unique Pseudo-Random Noise (PRN) method and autocorrelation approach to detect anomalous operation that precedes failure. The Adaptive Time-to-Failure (ATTF) algorithm will continuously collect and process incoming data from both electronic and mechanical sources to provide on-going and updated estimates on the systems’ remaining useful life (RUL). This improved RUL estimate, in turn, supports comprehensive Condition-based Maintenance (CBM) for advanced Air Force Systems. The SBIR Program work plan leverages Ridgetop’s prior work in prognostics technologies and extends the state-of-the art in effective determination of system State-of-Health (SoH) and Remaining Useful Life (RUL). BENEFIT: The untimely failure of advanced systems presents a danger to the warfighter and can be mitigated or minimized through the adoption of condition-based maintenance. These advanced systems require new techniques that detect the extent of degradation or aging, and translate that information into an updated estimate of remaining useful life so that maintenance can be scheduled for non-mission critical timeframes. In addition to Aerospace applications, electronic controls have become pervasive in critical automotive applications including anti-lock braking, active suspensions, engine controls and with electromechanical actuators. Other critical areas include medical scanners and banking systems where the cost of unscheduled down-time is prohibitive.