Robust Optimal Experimental Design Accounting for Sensor Failure

2026-04-15

AI Summary

This study addresses the challenge of sensor failure under high mechanical accelerations in structural dynamics experiments and the consequent degradation of traditional optimal experimental design (OED) performance. To overcome these limitations, the authors propose a robust OED framework that directly yields discrete sensor configurations. The approach integrates a relaxation strategy with gradient-based optimization and incorporates a binary-inducing regularizer to circumvent post-processing rounding heuristics. Leveraging a finite element model, the method is evaluated using the log-determinant of the parameter covariance matrix and mean squared error as performance metrics. Numerical results demonstrate that the proposed framework significantly outperforms classical designs across various sensor failure scenarios and efficiently handles high-dimensional, computationally expensive sensor placement problems.

Technology Category

• Search and Optimization: Mixed Discrete/Continuous Search
• Intelligent Robots: Learning & Optimization for ROB
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

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.

Problem

• robust optimal experimental design
• sensor failure
• vibrations analysis
• structural dynamics
• accelerometer placement

Innovation

• robust optimal experimental design
• sensor failure
• gradient-based optimization
• binary-inducing penalty
• structural dynamics