New Techniques for Fault Detection and Diagnosis for Safety-Critical Applications

传感器是几乎所有技术系统的关键组件，尤其是那些依赖控制实现安全可靠操作的系统。控制系统在汽车、航空航天、机器人、建筑和过程控制应用中发挥着关键作用。安全关键型实施已大幅增加，并将随着系统变得越来越自主而继续增长。在自治系统中，传感器故障或故障可能导致性能严重下降，可能破坏控制系统的稳定，并可能产生破坏性的控制输入。所有这些事件都可能严重损害正在审议的系统的运作，并可能导致悲惨的后果，如人命损失。出于这些原因，迫切需要评估传感器完整性的技术，这些传感器对系统至关重要--S操作。对这类技术的研究是本项目的目标，预计将有助于提高安全关键自主系统的可靠性。本项目主要研究一种传感器故障检测技术，称为传感器对传感器识别(S2SID)。S2SID利用可自由获得和未知的外部或环境激励来识别称为伪传递函数(PTF)的传感器到传感器模型，该模型独立于激励。PTF是结构动力学中使用的传递率的时间域推广。在未知激励存在的情况下，在健康的传感器条件下执行S2SID。然后，使用同样由未知外部或环境激励引起的验证数据集，可以使用所识别的PTF来计算传感器到传感器的残差。该残差是根据识别的PTF预测的传感器输出与实际测量之间的偏差，为检测和诊断传感器故障提供了一个度量。与基于传递性算子的频域技术不同，该项目专注于能够利用瞬时信号特征的时域识别方法，这可能会降低频域估计。S2SID将在灵活的飞机应用上进行演示。

Sensors are critical components of virtually all technological systems, especially those that depend on control for safe and reliable operation. Control systems play a key role in automotive, aerospace, robotic, building, and process control applications. Safety-critical implementations have increased dramatically and will continue to grow as systems become increasingly autonomous. In the context of autonomous systems, sensors malfunctioning or failure can lead to severe degradation of performance, can destabilize the control system and may produce destructive control inputs. All of these events can severely compromise the operation of the system under consideration and may lead to tragic consequences, such as loss of human life. For these reasons, there is a compelling need for techniques that assess the integrity of sensors that are critical to a system?s operation. The investigation of such techniques is the objective of this project, which is expected to contribute to improvements to the reliability of safety-critical autonomous systems. This project focuses on a technique for sensor fault detection called sensor-to-sensor identification (S2SID). S2SID takes advantage of freely available and unknown external or ambient excitation to identify a sensor-to-sensor model called a pseudo transfer function (PTF), which is independent of the excitation. A PTF is a time-domain generalization of a transmissibility used in structural dynamics. S2SID is performed under healthy sensor conditions in the presence of unknown excitation. Then using a validation data set, again induced by unknown external or ambient excitation, the identified PTF can be used to compute a sensor-to-sensor residual. This residual, which is the discrepancy between the predicted sensor output based on the identified PTF and the actual measurements, provides a metric for detecting and diagnosing sensor faults. Unlike frequency-domain techniques based on transmissibility operators, this project focuses on time-domain identification methods that can exploit transient signal characteristics, which can degrade frequency-domain estimates. S2SID will be demonstrated on flexible aircraft applications.