Advancing Automotive Innovation Through Predictive Maintenance and Generative AI with AWS

A New Era in Automotive Engineering

The automotive landscape is rapidly transforming. With software-defined vehicles (SDVs), advanced driver-assistance systems (ADAS), and electrification at its core, traditional approaches to vehicle diagnostics and maintenance are reaching their limitations. To stay competitive, leading industries are integrating predictive technologies and generative AI, heralding a new era of proactive optimization.

KPIT, leveraging its deep domain expertise and advanced technologies in collaboration with Amazon Web Services (AWS), is at the forefront of driving these innovations.

Challenges in Vehicle Diagnostics and Maintenance

For decades, diagnostics methodologies have relied heavily on reactive processes. When critical components such as brake pads or clutch systems fail unexpectedly, downtime increases, customer satisfaction drops, and maintenance costs escalate.

Across the industry, OEMs and workshops face the following common challenges:

• Component Wear and Failure: Frequent degradation of critical parts leading to unplanned downtime and costly repairs.
• Noise in Data: The abundance of telemetry data often overwhelms teams, requiring advanced filtration and signal extraction.
• Operational Inefficiency: Limited use of proactive insights for streamlining service and reducing costs.

Going beyond the traditional approach demands models and solutions tailored to domain expertise, real-world testing, and actionable insights derived from field data.

KPIT’s Game-Changing Approach: Predictive Maintenance with AI and RUL Models

One of the key challenges we’ve observed in the automotive industry is that while OEMs possess vast amounts of vehicle and operational data, they often lack a clear, actionable strategy to harness its full potential for predictive maintenance. At the same time, while many IT service providers bring strong AI/ML capabilities to the table, they typically lack the deep domain knowledge required to translate raw data into meaningful automotive insights. This gap between data availability and value realization slows down innovation.

At KPIT, we are uniquely positioned to bridge this gap. With our deep automotive domain expertise and proven AI/ML capabilities, we partner with OEMs to unlock the value of their data and accelerate the journey towards predictive, data-driven maintenance solutions.

Key Features of Our Approach:

1. AI-Driven Predictive Models: Advanced algorithms capable of predicting wear and tear with over 90% accuracy.
2. Health Indicators and Degradation Patterns: Analyzing telemetry data for actionable insights into vehicle longevity.
3. Data Augmentation: Utilizing KPIT’s augmentation technology, models achieve robustness even with incomplete lifecycle data.
4. Cloud-Based Scalability: AWS SageMaker allows scalable, on-demand predictive maintenance powered by pre-built RUL models.
5. End-to-End Integration: Models seamlessly plug into OEM systems, reducing development time by 60%.
6. Collaborative Innovation: We work closely with our customers to co-explore and co-develop new predictive maintenance use cases, ensuring solutions are aligned with real-world needs and deliver tangible business value.

Generative AI: Unlocking the Future of Autonomous Mobility

Generative AI brings further innovation to autonomous mobility and diagnostics by:

• Enhancing diagnostics with guided advisories powered by machine learning.
• Facilitating advanced data labeling processes for autonomous vehicle training.
• Driving solutions in autonomous mobility through neural networks and HD maps integration.

For instance, guided diagnostics solutions provide technicians with structured workflows and predictive fixes, based on telemetry data analysis. This approach reduces complexity, filters noise, and establishes true root cause identification.

KPIT and AWS Collaboration: The Power of Two

By collaborating with AWS, KPIT ensures reliability, scalability, and time-to-market advantages.

1. Faster Time to Market – AWS SageMaker’s ready-to-use AI/ML capabilities, combined with KPIT’s Integration-Ready RUL Models, eliminate the need for extensive development. This enables companies to seamlessly integrate predictive maintenance solutions, drastically reducing time to market.
2. Scalability: AWS SageMaker provided flexibility to scale the solution as needed, accommodating future growth and additional use cases.
3. Accuracy: Leveraging AWS’s  Built-in algorithms and pretrained models and KPIT’s domain expertise ensured high accuracy in RUL predictions.
4. Cost Efficiency: The cloud-based solution reduced the need for local infrastructure, resulting in cost savings for Mahindra.
5. Architecture: AWS and KPIT teams jointly developed Architecture for customer to optimize the Total Cost of Ownership (TCO).

Reference: Here

Conclusion: Building the Future Together

KPIT’s expertise in AI, generative technologies, and automotive domain knowledge, paired with AWS’s cloud capabilities, continues to shape smarter, predictive solutions for the automotive landscape. By embracing these innovations, OEMs can reduce downtime, optimize component usage, and elevate customer experiences.

Whether it’s predictive diagnostics, guided advisories, or autonomous vehicle development, KPIT delivers the solutions needed for a smarter and more proactive automotive ecosystem