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Related Experiment Video

Updated: Nov 7, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

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

Saeid Safavi1, Mohammad Amin Safavi2, Hossein Hamid1

  • 1Department of Mechanical Engineering Sciences, Connected Autonomous Vehicle Lab (CAV-Lab), University of Surrey, Guildford GU2 7XH, UK.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

Fault Types01:18

Fault Types

186
When analyzing a single line-to-ground fault from phase A to ground at a three-phase bus, it is important to consider the fault impedance. This impedance is zero for a bolted fault, equal to the arc impedance for an arcing fault, and represents the total fault impedance for a transmission-line insulator flashover. To derive sequence and phase currents, fault conditions are translated from the phase domain to the sequence domain.
For line-to-line faults occurring between phases B and C, the...
186

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This study introduces a new system for autonomous vehicles to detect, isolate, and predict sensor faults. This enhances safety by identifying potential issues before they cause failures in self-driving cars.

Area of Science:

  • Engineering
  • Computer Science
  • Artificial Intelligence

Background:

  • Autonomous driving systems rely on sensor data for navigation and decision-making.
  • Sensor failures in autonomous vehicles can lead to critical safety issues and accidents.
  • Current systems require improved methods for early fault detection and prediction.

Purpose of the Study:

  • To develop a novel architecture for fault detection, isolation, identification, and prediction (FDIDP) in multi-sensor systems for autonomous vehicles.
  • To enhance the reliability and safety of autonomous driving by addressing sensor fault vulnerabilities.
  • To create a system capable of forecasting potential sensor failures.

Main Methods:

  • Utilized real-world autonomous vehicle data combined with artificially injected sensor faults.
Keywords:
fault detectionfault isolationfault predictionhealth forecastingmachine learning

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  • Developed and implemented two distinct deep neural network architectures for fault detection, identification, and isolation.
  • Introduced a health index measure derived from the fault detection system's output.
  • Trained a health index forecasting network using the developed health index.

    • Main Results:

    • Achieved very impressive performance in detecting, identifying, and isolating multi-sensor faults.
    • The proposed architecture demonstrated high accuracy in identifying various types of sensor faults.
    • Successfully developed a quantifiable health index for sensor systems.
    • The health index forecasting network showed promise in predicting future sensor health.

    Conclusions:

    • The novel FDIDP architecture significantly improves the ability to manage sensor faults in autonomous vehicles.
    • The system offers a robust approach to enhance the safety and reliability of self-driving technology.
    • Forecasting sensor health provides a proactive maintenance strategy, reducing the risk of critical failures.