Inverse parametric uncertainty identification using polynomial chaos and high-order moment matching benchmarked on a wet friction clutch

Inconsistent behavior of mechatronic applications is often related to uncertainties ingrained in the system. Stochastic models spawn an output distribution for any given deterministic input. A stochastic model can be obtained by associating a probability distribution to several uncertain model parameters and by propagating the parametric input uncertainty through the deterministic model. Typically a maximum likelihood identification procedure is used to estimate the parametric input uncertainty from observations. Such inverse identification procedures require to iterate the expensive forward input uncertainty propagation. In this paper we establish an efficient forward uncertainty propagation method by combining Polynomial Chaos and moment matching. The high-order statistical moments of the output distribution are estimated using the generalized Polynomial Chaos framework and by using the maximum entropy strategy a distribution can be associated to them. This strategy is numerically very attractive due to reduced forward sampling and deterministic nature of the propagation strategy. The strategy is integrated in the inverse uncertainty identification of a wet clutch system for which certain model parameters exhibit inconsistent behaviour and are therefore considered as stochastic. The number of required model simulations to achieve the same accuracy as the brute force methodologies is decreased by one order of magnitude. The probability model identified with the high order estimates resulted into a true log-likelihood increase of about 4% since the accuracy of the estimated output probability density function could be improved up to 47%.

Publication year:2020

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