# Input Design for Model Discrimination and Fault Detection via Convex Relaxation

arXiv: https://arxiv.org/abs/1310.7262 · HTML: https://mlsanity.com/paper/1310.7262/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Oct 2013.

## Built on by (most cited first, 1 of 1)

- [Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty](https://mlsanity.com/paper/2102.03782/) (2021)

## Abstract (arXiv)

This paper addresses the design of input signals for the purpose of discriminating among a finite set of models dynamic systems within a given finite time interval. A motivating application is fault detection and isolation. We propose several specific optimization problems, with objectives or constraints based on signal power, signal amplitude, and probability of successful model discrimination. Since these optimization problems are nonconvex, we suggest a suboptimal solution via a random search algorithm guided by the semidefinite relaxation (SDR) and analyze the accuracy of the suboptimal solution. We conclude with a simple example taken from a benchmark problem on fault detection for wind turbines.