This paper investigates fault detection, localization, and mitigation of autonomous vehicles platoons. The platoon is a network of autonomous vehicles that communicate together to move in a desired way. A fault in an autonomous vehicles platoon is a failure in either a physical component of a vehicle or a communication link between two vehicles in the platoon. This failure may lead to damage in one or more of the autonomous vehicles. Model-based health monitoring of a network of vehicles requires knowledge of a model of the system and the excitation signal, and thus may not be applicable. In this paper, we use measurements from available sensors in the platoon to identify sensor-to-sensor models that can be used for health monitoring, fault localization, and fault mitigation in the platoon. The dynamics of the network and the vehicles and the excitation signal that acts on the platoon are assumed to be unknown. We apply the proposed approach to a model of a platoon of autonomous vehicles and an experimental setup consisting of a platoon of three autonomous robots.

Fault Detection, Localization, and Mitigation of a Network of Connected Autonomous Vehicles Using Transmissibility Identification

The beam-like structures commonly exist in engineering systems and there are many critical issues relevant to fault diagnosis of those structures. Noticeably, among all other existing methods, transmissibility function-based diagnostic methods, providing much more sensitive damage indexes, are frequently applied and extensively studied, and it is thus meaningful to have a thorough review on most recent advance and related technical difficulties. This paper is, therefore, to present a comprehensive review on this topic for those engineering-oriented beam-like structures, focusing on fundamental theory, critical fault properties and potential applications, respectively. According to different transmissibility function-based features, existing methods are classified into several categories, i.e. general linear transmissibility function, general nonlinear transmissibility function, generalized frequency response function and the second-order output spectrum. With a chain-type multi-degree-of-freedom model with additional connections used for analysing dynamics of beam-like engineering structures, essential topics of these methods including types of transmissibility functions, damage indexes and procedures are discussed in detail. Moreover, the effectiveness, merits and demerits are illustrated by the numerical and experimental results, respectively. It should be noted that some methods discussed here can be readily extended and applied to diagnosis of other plate-like and rotor-like, etc., engineering structures. Apart from fault detection and localization, more sensitive transmissibility function-based damage indicators providing severity and residual life of damaged structures would be further development topics in this area.

Transmissibility function-based fault diagnosis methods for beam-like engineering structures: a review of theory and properties

An autonomous vehicle platoon is a network of autonomous vehicles that communicate together to move in a desired way. One of the greatest threats to the operation of an autonomous vehicle platoon is the failure of either a physical component of a vehicle or a communication link between two vehicles. This failure affects the safety and stability of the autonomous vehicle platoon. Transmissibility-based health monitoring uses available sensor measurements for fault detection under unknown excitation and unknown dynamics of the network. After a fault is detected, a sliding mode controller is used to mitigate the fault. Different fault scenarios are considered including vehicle internal disturbances, cyber attacks, and communication delays. We apply the proposed approach to a bond graph model of the platoon and an experimental setup consisting of three autonomous robots.

Output-Only Fault Detection and Mitigation of Networks of Autonomous Vehicles

This paper investigates actuator fault detection in linear systems with a number of actuators. We consider some of the most common actuator faults such as actuator loss of effectiveness and fatigue crack in the connection hinges. We use transmissibility operators, which are mathematical models that characterize the relationship between sensors in an underlying system, for fault detection of actuators. Transmissibilities identified under healthy conditions can be used along with measurements of healthy sensors to predict other sensors’ measurements without knowledge of the dynamics of the system or the excitation that acts on the system. We apply the proposed approach to an analytical model with twelve actuators, and an experimental setup consisting of twelve electromechanical actuators and two accelerometers.

Output-Only Measurements for Fault Detection of Multi-Actuator Systems in Motion Control Applications

Transmissibility is a mathematical model that relates a subset of a system’s outputs to another subset of outputs of the same system without knowledge of the external excitation or the dynamics of the system. This study investigates fault detection, localization, and mitigation of connected autonomous vehicles (CAV) platoons using transmissibility operators. A CAV platoon is a network of connected autonomous vehicles that communicate together to move in a specific path with the desired velocity. Failure in a physical component of a vehicle, or failure in the form of an internal delay, a cyber-attack, or a communication time-delay affects the safety and security of the CAV platoons. In this paper, we use measurements from sensors available in CAV platoons to identify transmissibility operators, which are used for health monitoring, fault localization, and fault mitigation in the platoon. We first consider the case of vehicle-to-cloud communication (V2C) to monitor the platoon’s health. Then, we assume that the platoon loses communication with the cloud, and we monitor the health of the platoon based on vehicle-to-vehicle (V2V) communication. We apply the proposed technique to a model of the platoon obtained using the bond graph approach, and an experimental setup consisting of three connected autonomous robots.

Transmissibility-Based Health Monitoring of the Future Connected Autonomous Vehicles Networks

Aircraft Structural Health Monitoring Using Transmissibility Identification

This study investigates fault detection and mitigation of connected autonomous vehicle platoons with a human-driven vehicle using transmissibility operators. Transmissibility-based health monitoring uses available sensor measurements only and does not require knowledge of the excitation signal or the dynamics of the platoon. The human-driver behaviour can be considered as an independent excitation that acts on the platoon along with the desired velocity of the platoon. Therefore, transmissibility-based health monitoring is independent of the desired velocity of the platoon, the human-driver behaviour, and the underlying dynamics of the platoon. The perception sensors in the vehicle that follows the human-driven vehicle play a crucial role in the safety of the platoon. Thus, we consider failure in these sensors in addition to failures in the communication links such as a cyber-attacks and communication time delay. Next, we use a transmissibility-based sliding-mode control to mitigate the proposed faults. The proposed approach is validated numerically using simulation models.

Health Monitoring of Mixed Autonomous and Human-driven Vehicle Platoon using Transmissibility Identification

Transmissibilities are mathematical models that describe the relationship between outputs of an underlying system. This paper uses transmissibilities for fault detection and localization in a class of flexible structures. We assume that the dynamics of the beam and the excitation signal acting on it are unknown. We consider an experimental setup consisting of a flexible cantilever beam with multiple accelerometers attached to it. The dynamics of the beam and the excitation signal that acts on it are assumed to be unknown. Transmissibility operators between the accelerometers are identified under healthy conditions of the beam, and then used for online fault detection and localization in the flexible beam. We consider a change-of-stiffness fault in the flexible beam to demonstrate the proposed algorithm. In order to test the algorithm under different faults that can be difficult to implement experimentally, we develop a bond graph model of the beam. A simulation of the bond graph model is used to obtain the acceleration measurements at different locations on the beam, and the proposed algorithm is tested under fatigue faults.

Khaled F. Aljanaideh

Papers

Fault Detection, Localization, and Mitigation of a Network of Connected Autonomous Vehicles Using Transmissibility Identification

Aircraft Structural Health Monitoring Using Transmissibility Identification

Output-Only Fault Detection and Mitigation of Networks of Autonomous Vehicles

Health Monitoring of Mixed Autonomous and Human-driven Vehicle Platoon using Transmissibility Identification

Fault Detection in Flexible Beams Based on Output Only Measurements