The automotive industry faces significant challenges regarding vehicle maintenance and component longevity. Conventional fault diagnosis systems often fall short, leading to unplanned downtime and unexpected breakdowns. AI for Vehicle Fault Diagnosis caters the pressing need for advanced fault diagnosis technologies that can effectively address these issues.
Importance of Artificial Intelligence
As vehicles become more complex and autonomous, integrating new methodologies becomes crucial. Artificial Intelligence (AI) stands out as a promising solution. While its potential is recognized, a systematic review of its application in Vehicle Fault Diagnosis (VFD) systems has been lacking. Recent studies have begun to fill this gap, showcasing how AI can transform diagnostic processes.
Machine Learning and Deep Learning Techniques
This research focuses on two main areas: machine learning and deep learning algorithms. These techniques can significantly enhance vehicle fault diagnosis across various vehicle subsystems such as engines, gearboxes, suspensions, and brakes. By applying these methods, manufacturers can improve safety and reliability while reducing the costs associated with maintenance.
Applications of AI in Vehicle Fault Diagnosis
The impact of AI is evident in both electric vehicles and autonomous cars. Companies are increasingly using these technologies to streamline diagnostic processes. For instance, diagnostics powered by AI lead to increased accuracy in detecting faults early, thus preventing costly repairs down the line.
Combining Techniques for Better Results
Relying solely on one AI technique may limit performance; however, combining methods shows promise for more reliable diagnostics. This synthesis of information helps reveal trends that can guide the future development of smarter automotive innovations, aligning with the principles of Industry 4.0.
In summary, exploring the potential of AI in VFD addresses current challenges and paves the way for improved vehicle safety and efficiency. Such advancements are vital for the transformation of modern transportation systems.
Source
https://doi.org/10.32604/cmes.2024.056022
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