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An Innovative Technique for Fault Analysis of Electric Automated Vehicles
by
Gauterin, Frank
, Li, Shiqing
, Frey, Michael
in
Automatic vehicle identification systems
/ Automation
/ Automobile industry
/ electric automated vehicle
/ Failure analysis
/ Failure modes
/ fault analysis
/ Fault detection
/ fault diagnosis
/ Fault tree analysis
/ Faults
/ FMEA
/ FTA
/ Identification
/ Methods
/ Product development
/ Product life cycle
/ Simulation
/ Vehicles
2024
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An Innovative Technique for Fault Analysis of Electric Automated Vehicles
by
Gauterin, Frank
, Li, Shiqing
, Frey, Michael
in
Automatic vehicle identification systems
/ Automation
/ Automobile industry
/ electric automated vehicle
/ Failure analysis
/ Failure modes
/ fault analysis
/ Fault detection
/ fault diagnosis
/ Fault tree analysis
/ Faults
/ FMEA
/ FTA
/ Identification
/ Methods
/ Product development
/ Product life cycle
/ Simulation
/ Vehicles
2024
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Do you wish to request the book?
An Innovative Technique for Fault Analysis of Electric Automated Vehicles
by
Gauterin, Frank
, Li, Shiqing
, Frey, Michael
in
Automatic vehicle identification systems
/ Automation
/ Automobile industry
/ electric automated vehicle
/ Failure analysis
/ Failure modes
/ fault analysis
/ Fault detection
/ fault diagnosis
/ Fault tree analysis
/ Faults
/ FMEA
/ FTA
/ Identification
/ Methods
/ Product development
/ Product life cycle
/ Simulation
/ Vehicles
2024
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An Innovative Technique for Fault Analysis of Electric Automated Vehicles
Journal Article
An Innovative Technique for Fault Analysis of Electric Automated Vehicles
2024
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Overview
A serious accident might occur as a direct result of a defective component or system, especially at an automated vehicle. The objective of this study is to establish a fault analysis methodology that can effectively improve the reliability of electric and automated vehicles. To achieve this, We proposed a method for identifying potential faults by combining Failure Modes and Effects Analysis (FMEA) with Fault Tree Analysis (FTA), creating a database of possible (example) faults that maps the causal relationship between causes, symptoms and faults, which enables more thorough fault analysis and serves as the foundation for further study. Using the fault database, we demonstrate a practical application involving fault injection and simulation, which can provide a more intuitive and practical representation of the effects of faults. The methodology is validated with the demonstrator vehicle from the joint project. This approach is scalable and can also be well applied to other electric automated vehicles with similar structure, providing a reliable tool to the system fault analysis for future work.
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