Software combines signal forecasting and prognostic reasoning methods to predict system failures.

This new generalized approach to prognostics can provide an automated early failure prediction of an engineering system or its components, often in time to prevent occurrence of hard failures. This approach has been demonstrated in a proof-of-concept software prototype, shown to accurately predict anomalies in the Mars Explorer Rover’s (MER) power systems using archived and model data. The approach differs from other attempted prognostic solutions in that it can interpret any sensed system trend, and not just specific failure modes with previously developed physicsof- failure models. The software employs an iterative reasoning process that implements (1) methods of forecasting signals represented by streams of sensor, telemetric, and other monitoring data and (2) new artificial intelligence methods for performing prognostic reasoning. This approach affords the following capabilities: