通过凸松弛实现模型辨识和故障检测的输入设计
Input Design for Model Discrimination and Fault Detection via Convex Relaxation
AI总结：
本文提出通过凸松弛方法设计输入信号以区分动态系统模型，用于故障检测。通过随机搜索算法求解非凸优化问题，并分析子最优解的准确性。
摘要：
本文针对在给定有限时间区间内区分有限个动态系统模型的输入信号设计问题进行了研究。应用动机是故障检测与隔离。我们提出了若干具体的优化问题，其目标或约束基于信号功率、信号幅度和成功模型辨识的概率。由于这些优化问题是非凸的，我们建议通过随机搜索算法结合半正定松弛（SDR）来求得子最优解，并分析该子最优解的准确性。最后，我们以风力机故障检测基准问题中的一个简单例子作为结论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
