Abstract: A system for predicting remaining useful life of a component implements a set of estimation models that generate future damage estimates for the component. The system detects damage to the component and estimates the magnitude of the current damaged. An error processor estimates the between each future damage estimate and the magnitude of current damage. A weight calculator calculates weights for the future damage estimates, wherein each weight is inversely proportional to the error. A fusion processor applies the weights respectively to future damage estimates of the estimators and combines the weighted future damage estimates.

Abstract: A system and method utilize a first rotation sensor that provides a first signal indicative of a first rotation of a first end of a rotating coupling. The rotating coupling has an articulating member mechanically coupling the first end to a second end. A second rotation sensor provides a second signal indicative of a second rotation of the second end of the rotating coupling. A processor is coupled to the first and second rotation sensors and is operable to determine a relationship between the first and second rotations based on the first and second signals. Based on the relationship, the processor estimates at least one of a degradation of and a remaining useful life of the rotating coupling.

Abstract: A system for predicting remaining useful life of a component implements a set of estimation models that generate future damage estimates for the component. The system detects damage to the component and estimates the magnitude of the current damaged. An error processor estimates the between each future damage estimate and the magnitude of current damage. A weight calculator calculates weights for the future damage estimates, wherein each weight is inversely proportional to the error.

Abstract: The prognostic tool disclosed here decomposes the problem of estimating the remaining useful life (RUL) of a component or sub-system into two separate regression problems: the feature-to-damage mapping and the operational conditions-to-damage-rate mapping. These maps are initially generated in off-line mode. One or more regression algorithms are used to generate each of these maps from measurements (and features derived from these), operational conditions, and ground truth information. This decomposition technique allows for the explicit quantification and management of different sources of uncertainty present in the process. Next, the maps are used in an on-line mode where run-time data (sensor measurements and operational conditions) are used in conjunction with the maps generated in off-line mode to estimate both current damage state as well as future damage accumulation.

Abstract: This invention develops a mathematical model to describe battery behavior during individual discharge cycles as well as over its cycle life. The basis for the form of the model has been linked to the internal processes of the battery and validated using experimental data. Effects of temperature and load current have also been incorporated into the model. Subsequently, the model has been used in a Particle Filtering framework to make predictions of remaining useful life for individual discharge cycles as well as for cycle life. The prediction performance was found to be satisfactory as measured by performance metrics customized for prognostics for a sample case. The work presented here provides initial steps towards a comprehensive health management solution for energy storage devices.