Integrating design and field management of gas turbine engine components with a probabilistic model

A method for quantifying uncertainty during different phases of the lifecycle of a manufactured component, the method comprising, with at least one computing device:

• identifying at least two sources of uncertainty that are associated with different phases of the manufactured component lifecycle, wherein each source of uncertainty is associated with a random variable of a plurality of random variables;
• connecting the at least two sources of uncertainty by a common random variable of the plurality of random variables in a Bayesian network, wherein the Bayesian network includes an acyclic directed graph that includes a plurality of nodes and one or more edges, wherein each node corresponds to a random variable of the plurality of variables, and wherein each edge corresponds to a conditional dependency between a pair of random variables;
• computing a joint probability distribution for the Bayesian network using the common random variable and at least one random variable associated with each of the at least two sources of uncertainty;
• determining a current phase of the manufactured component lifecycle, wherein the current phase comprises a pre-production certification phase or a post-production certification phase;
• communicating the re-computed joint probability distribution to a component design subsystem in response to determining the current phase is the pre-production certification phase;
• and
• communicating the re-computed joint probability distribution to a field management subsystem in response to determining the current phase is the post-production certification phase.

A gas turbine engine lifecycle decision assistant for quantifying uncertainties during the lifecycle of a gas turbine engine component, the gas turbine engine lifecycle decision assistant comprising:

• computer program instructions embodied in one or more machine-accessible storage media and executable by at least one processor to:
• create a Bayesian network of conditional dependencies between a plurality of random variables associated with a plurality of different sources of uncertainty in the gas turbine engine component lifecycle, wherein each source of uncertainty is associated with a random variable of the plurality of random variables, and wherein the Bayesian network includes an acyclic directed graph that includes a plurality of nodes and one or more edges, wherein each node corresponds to a random variable of the plurality of variables, and wherein each edge corresponds to a conditional dependency between a pair of random variables, the Bayesian network arranged to connect at least two of the plurality of different sources of uncertainty by a common random variable of the plurality of random variables;
• compute a joint probability distribution for the Bayesian network;
• receive new evidence from one or more of the different sources of uncertainty over the course of the component lifecycle;
• in response to the new evidence, re-compute the joint probability distribution;
• determine a current phase of the gas turbine engine component lifecycle, wherein the current phase comprises a pre-production certification phase or a post-production certification phase;
• communicate the re-computed joint probability distribution to a component design subsystem in response to determining the current phase is the pre-production certification phase;
• and
• communicate the re-computed joint probability distribution to a field management subsystem in response to determining the current phase is the post-production certification phase.