Abstract: A method for estimating future risk of failure of a component of an industrial asset. The method includes receiving a plurality of different types of data associated with the industrial asset or a fleet of industrial assets. The plurality of different types of data includes, at least, reliability data (such as time-to-event data). The method also includes generating a failure prediction model for the component based on the reliability data and available time-series measurements. Further, the method includes applying the failure prediction model to the different types of data based on the types of data available in the received data. The applied failure prediction model includes one of a default model, a conditional survival model, or a joint conditional survival model. Thus, the method includes estimating, via the failure prediction model, the future risk of failure of the industrial asset and implementing a control action as needed.

Abstract: In an example embodiment, a method of calculating end-of-life (EOL) predictions for a physical asset is provided. A state-space model for the physical asset is obtained, the state-space model being a physics-based model describing a state of the physical asset at a particular time given measurements or observations for the physical asset. Then a current state of the physical asset is inferred. Then a long-term prediction is derived for the physical asset based on the inferred current state of the physical asset and the state-space model for the physical asset. Then an EOL probability distribution function is generated for the physical asset based on the long-term prediction, the EOL probability distribution function describing a range of estimates of EOL for the physical asset and their corresponding confidence intervals.

Abstract: In an example embodiment, a method of calculating end-of-life (EOL) predictions for a physical asset is provided. A state-space model for the physical asset is obtained, the state-space model being a physics-based model describing a state of the physical asset at a particular time given measurements or observations for the physical asset. Then a current state of the physical asset is inferred. Then a long-term prediction is derived for the physical asset based on the inferred current state of the physical asset and the state-space model for the physical asset. Then an EOL probability distribution function is generated for the physical asset based on the long-term prediction, the EOL probability distribution function describing a range of estimates of EOL for the physical asset and their corresponding confidence intervals.