Advances in Artificial Intelligence (AI) have paved the way for a remarkable transformation in the automotive industry. AI is having a big impact on Vehicle Fault Diagnosis (VFD). VFD is also called vehicle diagnostics. It is important but can be difficult. VFD is part of taking care of cars. It involves capturing and studying signals from different parts of the car. These signals help find out why a car has problems or stops working right.
Problems Associated with Automotive Maintenance
Conventional fault diagnosis systems have constrained the automotive industry, leading to critical issues with vehicle maintenance and component longevity. Modern vehicles are becoming more complex and able to function on their own. This has created an urgent need for new ways to diagnose issues. These new methods are necessary to improve safety, reliability, and maintainability (ease of maintenance). Safety means protecting people from harm. Reliability means the vehicles work well over time. Maintainability refers to how easy it is to keep vehicles in good condition. Recent research has shown that AI, particularly machine learning (ML) and deep learning (DL), offers promising solutions to address these challenges.
Transforming Vehicle Fault Diagnosis with AI
AI-based VFD systems have the potential to enhance diagnostic accuracy, enable predictive maintenance, and adapt to evolving vehicle complexities. These advanced techniques can lower maintenance expenses, improve vehicle performance and longevity, increase vehicle safety and reliability, and reduce environmental impact by optimizing the preventive maintenance schedule.
Automotive Maintenance Keeping Pace with Evolving Technologies
Researchers have looked into different machine learning (ML) algorithms. They use these algorithms to predict and warn about problems in vehicle systems. These systems include the transmission, engine, and tires. However, these methods are not very effective. They need real-world datasets to work well. These datasets are often only partially labeled. Labeling means providing clear information about the data. It requires great effort to fully label these datasets. That’s why the effectiveness of these methods is limited. To address this, future research could explore using meta-learning and combining different AI models to optimize existing technologies.
The newest deep learning (DL) algorithms and other artificial intelligence (AI) methods are constantly improving. Scientists work hard to overcome the natural limits of machine learning (ML). Machine learning is a type of computer system that learns from data. They face these challenges when using ML in vector field-driven (VFD) systems. A vector field-driven system uses mathematical directions, like arrows on a map, to describe flows or forces. VFD systems are technologies used to control the speed and torque of machines. Recent studies look at how deep learning can help find problems in electric vehicles. These techniques are especially useful for detecting, diagnosing, and predicting faults in car batteries.
Closing Remarks
This thorough review demonstrates how various ML and DL algorithms significantly outperform model- and signal-oriented approaches in the fault analysis of engine, suspension, tire, gearbox, and brake systems. By providing a nuanced understanding of AI’s role in revolutionizing vehicle maintenance, this article seeks to offer valuable insights for researchers, industry professionals, and policymakers, paving the way for future advancements in automotive diagnostics and maintenance strategies.
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