Remaining life prediction for individual components from sparse data

摘要

Predicting the remaining life of individual aircraft, fleets of aircraft, aircraft components and subpopulations of these components. This is accomplished through the use of precomputed databases of response that are generated from a model for the nonlinear system behavior prior to the time that decisions need to be made concerning the disposition of the system. The database is calibrated with a few data points, to account for unmodeled system variables, and then used with an input variable to predict future system behavior. These methods also permit identification of the root causes for observed system behavior. The use of the response databases also permits rapid estimations of uncertainty estimates for the system behavior, such as remaining life estimates, particularly, when subsets of an input variable distribution are passed through the database and scaled appropriately to construct the output distribution. A specific example is the prediction of remaining life for an aircraft component where the model calculates damage evolution, input variables are a crack size and the number of cycles, and the predicted parameters are the actual stress on the component and the remaining life.

主权项

1. A method for rapid decision making for a nonlinear system, the method comprising:
providing a database of system responses, the database relating a plurality of input variables to at least one output variable predicting a future state of a component, at least one of the plurality of input variables being a current component damage level, each output variable having a nonlinear dependence on the plurality of input variables; calibrating the database's relations between the plurality of input variables to the at least one output variable predicting the future state with first empirical information about the nonlinear system; obtaining a sensor response from a sensor; estimating a current component damage level probability distribution from the sensor response and second empirical data representing a dependence of the sensor response on the damage level; inputting the current component damage level probability distribution to a multivariate inverse method; estimating a future state probability distribution using the database and the multivariate inverse method; and using the future state probability distribution to make a decision.