Correlated floating-pinion uncertainty set

ID: correlated-floating-pinion-uncertainty-set

A correlated floating-pinion uncertainty set describes combinations of contact-model parameters that can occur together, rather than treating every separate parameter bound as independently attainable. Here correlated means linked by the parameter model; it need not mean a particular statistical correlation coefficient.
For the floating-pinion robust contact margin, write and
The set can represent shared mounting motion, a common geometric error or another justified constraint. A rectangle based only on separate ranges includes the true set, but can include combinations that its physical cause cannot produce. Minimizing over this larger set gives a conservative lower bound.
An original floating-pinion affine uncertainty bound gives an exact result for bounded shared variables. Suppose
with every allowed vector admissible. Since , minimizing each affine expression over the box gives
The uncertainty variables are independently bounded here, but the resulting physical parameters can remain linked. Each absolute-value term follows by choosing the sign of that minimizes its coefficient times .
For example, in micrometres take , and . The two minima are and , so . Separate parameter ranges instead give and , hence rectangular bound . A displacement envelope leaves guaranteed compression under the joint model, although the rectangular test cannot certify it.
A statistical covariance matrix is not itself a hard admissible set: a high-probability region and an absolute bound answer different questions. The physical model also needs to remain valid throughout the selected set. If a contact opens, the linear two-contact force law must be replaced in the floating-pinion contact-loss check.
NASA's face-gear work motivates examining mounting freedom and measured load division, while its slow-roll observations do not establish the particular uncertainty set used in this derivation.[ref] The value of a joint set is justified constraint information, not an assumption that all uncertainties cancel.

New to topics? Read the docs here!