Robust Optimal Experimental Design Accounting for Sensor Failure

TL;DR

This paper develops a robust optimal experimental design method for vibration sensor placement that accounts for sensor failures, using a relaxation-based gradient optimization and binary penalties.

Contribution

It introduces a general robust OED formulation with a binary-inducing penalty for structural dynamics, improving sensor placement reliability.

Findings

Robust designs outperform classical ones on average over failure scenarios.

The relaxation-based approach enables efficient gradient optimization.

Binary sensor design is achieved without post-optimization heuristics.

Abstract

Optimal experimental design provides a way of determining a-priori the best locations at which to place accelerometers in vibrations analysis experiments. However, in practice, sensors often fail during experimentation due high mechanical accelerations. There have been limited works exploring the use of robust OED in the context of vibrations analysis, where design spaces (i.e. candidate sensor locations and orientations) are high-dimensional and the finite-element models are expensive to compute. Therefore, this work considers the application of more general robust OED formulations to such a structural dynamics problem. We employ a relaxation-based approach that enables the use of efficient gradient-based optimization. Furthermore, we leverage a binary-inducing penalty during optimization to provide a binary sensor design as an alternative to leveraging post-optimization rounding heuristics. We consider performance metrics based on the log-determinant of the parameter covariance as well those based on parameter and prediction mean-squared errors. We find that although robust and classical designs are similar for the structural dynamics problem of interest, robust designs outperform classical designs on average over relevant failure scenarios of interest.