Multi-Sensor Fault Detection, Identification, Isolation and Health Forecasting for Autonomous Vehicles

Sensors

Hossein Hamid¹  Saber Fallah¹  Saeid Safavi¹  MohammadAmin Safavi² 

[1] Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab (CAV-Lab), University of Surrey, Guildford GU2 7XH, UK;Department of Mechanical Engineering, Isfahan University of Technology, Iran 84156-83111, Iran;

关键词: health forecasting;    fault prediction;    fault detection;    fault isolation;    machine learning;   

DOI  :  10.3390/s21072547

来源: DOAJ

【 摘 要 】

The primary focus of autonomous driving research is to improve driving accuracy and reliability. While great progress has been made, state-of-the-art algorithms still fail at times and some of these failures are due to the faults in sensors. Such failures may have fatal consequences. It therefore is important that automated cars foresee problems ahead as early as possible. By using real-world data and artificial injection of different types of sensor faults to the healthy signals, data models can be trained using machine learning techniques. This paper proposes a novel fault detection, isolation, identification and prediction (based on detection) architecture for multi-fault in multi-sensor systems, such as autonomous vehicles.Our detection, identification and isolation platform uses two distinct and efficient deep neural network architectures and obtained very impressive performance. Utilizing the sensor fault detection system’s output, we then introduce our health index measure and use it to train the health index forecasting network.

【 授权许可】

Unknown