Bayesian risk inference engine (BRIE)

A computer-implemented method for generating survival curves for an asset comprising:

• (a) generating, by one or more processors executing a Bayesian risk inference engine (BRIE), a predicted health distribution of an asset for a failure mode of a set of failure modes based on a prior health distribution of the asset for the failure mode and a wear rate distribution based on sensor data corresponding to the asset;
• (b) updating, by the one or more processors executing the BRIE, the predicted health distribution based on (i) an observed state distribution corresponding to an observed state associated with the asset that is indicative of a health state of the asset represented by the predicted health distribution and (ii) a normalized value representative of a probability of the observed state over one or more health values of the asset;
• (c) generating, by the one or more processors executing the BRIE, a survival curve of the asset based on the predicted health distribution and a set of wear rates, the survival curve including predictions of a survival probability for the asset at a plurality of future points;
• iteratively performing (a)-(c) for each failure mode of the set of failure modes;
• aggregating, by the one or more processors executing the BRIE, the survival curve for each failure mode into an aggregate survival curve of the asset;
• and
• causing, by the one or more processors, a client device to display on a display screen a visual representation of the aggregate survival curve of the asset, the visual representation depicting on the display screen predicted points of time when the asset is predicted to fail.

A tangible, non-transitory computer-readable medium storing instructions for generating survival curves for an asset, that when executed by one or more processors cause the one or more processors to:

• (a) generate, by executing a Bayesian risk inference engine (BRIE), a predicted health distribution of an asset for a failure mode of a set of failure modes based on a prior health distribution of the asset for the failure mode and a wear rate distribution based on sensor data corresponding to the asset;
• (b) update, by executing the BRIE, the predicted health distribution based on (i) an observed state distribution corresponding to an observed state associated with the asset that is indicative of a health state of the asset represented by the predicted health distribution and (ii) a normalized value representative of a probability of the observed state over one or more health values of the asset;
• (c) generate, by executing the BRIE, a survival curve of the asset based on the predicted health distribution and a set of wear rates, the survival curve including predictions of a survival probability for the asset at a plurality of future points;
• iteratively perform (a)-(c) for each failure mode of the set of failure modes;
• aggregate, by executing the BRIE, the survival curve for each failure mode into an aggregate survival curve of the asset;
• and
• cause a client device to display on a display screen a visual representation of the aggregate survival curve of the asset, the visual representation depicting on the display screen predicted points of time when the asset is predicted to fail.