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Sensor Fault Detection and Diagnosis for Enhanced Safety of Autonomous Systems

用于增强自主系统安全性的传感器故障检测和诊断

基本信息

批准号：
2031333
负责人：
Dennis Bernstein
金额：
$ 37.67万
依托单位：
Regents of the University of Michigan - Ann Arbor
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2021
资助国家：
美国
起止时间：
2021-01-01 至 2024-12-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031333&HistoricalAwards=false
关键词：
Sensor Fault Detection Diagnosis Enhanced

项目摘要

Many modern systems such as autonomous vehicles and manufacturing systems operate under the control of a computer. All of these systems rely on sensors, which measure things like speed, temperature, and pressure. If one of these sensors fails, however, then the computer may take an incorrect action that can hurt people or damage property. Therefore, it is extremely important to ensure all of the sensors that provide measurements for the computer are working correctly. This project aims to enhance the safety of these systems by developing methods for checking on whether the measurements provided by the sensors are correct and can be used safely by the computer.Adaptive delayed left inversion (ADLI) constructs a causal, delayed left inverse of a dynamical system that represents the relationship between two sets of sensors, namely, input sensors, which are suspect, and output sensors, which are assumed to be healthy. Multiple combinations of sensors will be considered in order to determine whether the output sensors are indeed healthy. Measurements from the healthy sensors are used to drive the delayed left inverse, whose output provides estimates of the expected measurements from the suspect input sensors. By comparing these estimates with the actual measurements, it is possible to detect and diagnose sensor faults. ADLI will be applied to discretized nonlinear kinematic differential equations that relate signals from multiple sensors, thus, providing the means for sensor fault detection and diagnosis.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

许多现代系统，如自动驾驶汽车和制造系统，都是在计算机的控制下运行的。所有这些系统都依赖于传感器，传感器可以测量速度、温度和压力等。然而，如果其中一个传感器出现故障，那么计算机可能会采取错误的行动，从而伤害人员或损坏财产。因此，确保为计算机提供测量的所有传感器正常工作是极其重要的。本项目旨在通过开发检查传感器提供的测量是否正确以及计算机是否可以安全使用的方法，提高这些系统的安全性。自适应延迟左逆（ADLI）构建了一个动态系统的因果延迟左逆，该动态系统表示两组传感器之间的关系，即输入传感器（怀疑）和输出传感器（假设为健康）。将考虑传感器的多种组合，以确定输出传感器是否确实健康。来自正常传感器的测量值用于驱动延迟左逆，其输出提供来自可疑输入传感器的预期测量值的估计。通过将这些估计值与实际测量值进行比较，可以检测和诊断传感器故障。ADLI将应用于离散非线性运动微分方程，这些方程涉及来自多个传感器的信号，从而为传感器故障检测和诊断提供手段。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。