Breakdowns can cause significant disruptions in automotive and fleet operations, potentially leading to costly maintenance and safety risks. While traditional maintenance methods fall short, AI-powered predictive maintenance offers a more innovative solution. By proactively identifying and addressing potential issues before they affect performance, AI-driven predictive maintenance helps reduce costs for automotive manufacturing plants and enhances driver safety.
The high cost of traditional maintenance
Traditional maintenance methods, such as reactive and preventive maintenance, come with significant drawbacks. Reactive maintenance addresses equipment issues only after they occur, leading to unexpected downtime that disrupts production and results in higher repair costs.
Preventive maintenance is a strategy intended to prevent failures through scheduled tasks. However, preventive maintenance schedules may not account for real-time changes in equipment conditions, usage patterns, or operating environments. Fixed schedules can lead to over-maintenance, where components are replaced or serviced more frequently than necessary, resulting in unnecessary costs and downtime. Conversely, if the schedules are too infrequent, there is a risk of under-maintenance, where components may fail before the next scheduled maintenance. Research reveals that over two-thirds of industrial businesses experience unplanned outages at least once a month, costing approximately 125,000 USD per hour. Despite these high costs, 21% of businesses rely on reactive maintenance, resulting in lost customer trust and reduced productivity.
The next-level maintenance powered by AI
AI-powered predictive maintenance represents an advancement in maintenance strategies. By utilizing machine learning algorithms, this approach predicts when a vehicle is likely to experience a failure. Unlike traditional reactive practices and scheduled check-ups, AI predictive maintenance leverages historical data to accurately forecast future outcomes, allowing for remote diagnosis and intervention before major breakdowns occur. As pointed out by Deloitte, predictive maintenance can reduce breakdowns by up to 70%.
The integration of AI has elevated predictive maintenance to new heights. Modern vehicles with numerous sensors continuously monitor their performance and health. AI systems analyze this data in real time to identify issues that traditional diagnostics might overlook. These systems provide timely maintenance alerts and predict optimal times for specific tasks to prevent costly breakdowns. According to McKinsey, AI can detect problems such as faulty sensors, worn brake pads, or engine issues before they escalate, potentially reducing downtime by 30-50%.
Improving defect detection and preventing breakdowns
In automotive manufacturing, improving fault detection ensures that only high-quality products can reach the market to maintain the brand’s reputation. BMW Group Plant Regensburg—part of the renowned German luxury vehicle and motorcycle manufacturer—utilized an advanced analytical system in its vehicle assembly process. This system can proactively identify potential issues by analyzing data from component performance, conveyor operations, and barcode scanning. When an issue is identified, the system promptly triggers an alarm and sends a warning to the 24/7 maintenance control center. The system also incorporates machine learning models developed by BMW, which analyze data and generate visual heatmaps to highlight different types of faults. These heatmaps enable technicians to remove and repair the affected conveyor element more efficiently, which saves 500 minutes of assembly disruption annually.
One of the benefits of defect detection with AI is how this innovative technology can reduce unplanned breakdowns. Volvo Trucks and Mack Trucks (subsidiaries of Volvo) developed a system that collects detailed breakdown data, such as location, timing, altitude, temperature, gear, RPM, and torque load. These advancements have led to a 70% reduction in diagnostic time for breakdown detection and a 25% decrease in repair time through real-time data analysis. Furthermore, by analyzing data from sensors, telematics, and maintenance records, the system can predict and prevent component failures, reducing unplanned stops by 25% and enhancing overall operational efficiency.
Driving towards a sustainable future
The global shift to electric road transport is predicted to reduce emissions over the next few decades. With the increased fuel prices and the appeal of a greener alternative to traditional combustion engines, electric vehicles (EVs) have rapidly gained popularity. However, without effective maintenance strategies, EV components can be expensive to replace and manufacture, which can lead to significant amount of waste. A study comparing emissions from EVs and internal combustion engine (ICE) vehicles revealed that 46% of the carbon emissions from EVs come from the production process, compared to 26% for ICE vehicles.
Hence, implementing AI-powered predictive maintenance systems can enhance battery lifecycle and reduce potential EV waste. A study by Rao et al. (2023) introduced a pioneering approach to predictive maintenance that integrates optical and quantum-enhanced AI techniques. This innovative AI model is designed to detect early signs of component degradation to enable proactive maintenance and reduce downtime. As a result, this system is estimated to reduce resource consumption with component replacements. According to the study, the use of AI-based predictive maintenance results in a 30% reduction in emissions, a 25% decrease in energy consumption, and a 20% reduction in waste. These benefits can contribute to significant environmental and economic advantages for EV operators and the society.
FPT Software’s vision for the future of AI in automotive
With an ambition to becoming one of the top 50 digital transformation service providers by 2030, FPT Software strategically partnered with major AI disruptors, including NVIDIA, AITOMATIC, and Mila Institute, to position Vietnam as a leading AI nation. In line with these advancements, the company has launched its subsidiary FPT Automotive to meet the growing global demand for software-defined vehicles (SDVs). FPT Software aims to drive businesses toward a future of smarter, more sustainable automotive solutions by harnessing AI and integrating it with the automotive industry.
Discover how FPT Software can drive your business forward with cutting-edge AI solutions and empower you to excel in the evolving landscape of Software-Defined Vehicles.
