AI-Powered Engine Bearing Wear Inspection with 85% Accuracy for a Global Automotive Leader

Our Client

Our client is a global Japanese automotive manufacturer with a legacy of 60+ years, operates across automobile, motorcycle, power products, and engines, as well as mobility solutions. Some of their key strengths are a huge aftermarket service network and huge installed base. Quick and precise service is the essential part of their business ecosystem which helps them keep their customers and brand reputation intact while maintaining an impressive operational efficiency.

Market Trends: AI‑Driven Service Operations in Manufacturing

With the growing complexity of automotive operations and the evolution in customer demand for fast service, manufacturers are embracing AI and computer vision solutions to streamline service processes through cutting-edge AI-driven manufacturing operations solutions. Manual inspection systems have difficulty in keeping pace with increasing volumes, varied image quality and the growing necessity of a comprehensive defect detection.

Modern manufacturers are implementing AI-based inspection and decision systems to enhance their speed, consistency, and service productivity. However, to achieve value, this will need a shift from isolated AI pilots to integrated workflow solutions that bring people, processes, and intelligent systems into synchronized alignment.

Key Challenges

With the rise of the number of service volumes, the client experienced a rising number of limitations in their inspection process:

• Inconsistent image quality impacting defect detection

The poor quality or inconsistent quality of the image during the inspection caused delays in the inspection and poor classification.

• Heavy reliance on manual inspection and expert judgment

The manual detection of the type of wear created inefficiencies, inconsistencies and an increase in the operational cost.

• Rigid detection systems with limited learning capability

Conventional methods were not flexible enough to undergo continued improvement as manual re-calibration and supervision had to be done.

Our client required a solution that would be able to expand to multiple service centers and enhance speed, accuracy and consistency, without any increase in operational load.

LTM’s Solution

LTM implemented a business creativity-led, AI-powered transformation strategy, which melded service domain expertise and a smart vision system to transform and automate bearing inspection. Here’s a look at the workflow and key components of the solution:

                                                                          Figure 1: AI‑Driven Bearing Wear Inspection and Classification Workflow

The key aspects of the solution included:

• Automated Pre-screening: AI models inspect images to automatically classify them as ‘good’ or ‘bad’ quality
• Auto Defect Detection: AI models detect defects and classify the type of bearing wear
• Continuous Improvement: The model learns from defect patterns, improving detection over time and reducing reliance on manual intervention
• End‑to‑End Workflow Reimagination: Redesigned bearing inspection as an AI-assisted decision based on image capture leading to automated classification and repair suggestions.
• AI‑powered Image Intelligence: Implemented computer vision and deep learning models to:
   1. Automatically measure image quality
   2. Detect bearing defects
   3. Familiarize oneself with the types of wear high accuracy

• Persona‑driven Application Design: Developed mobile and web-based applications based on the needs of service technicians and other operational stakeholders:
  1. Guided image capture and instant analysis mobile apps
  2. Web dashboards to review, recommend, and report on inspections

Figure 2: Future‑State Bearing Recall and Service Workflow Enabled by AI/ML

• Continuous learning and governance: On-going learning and management: Incorporated ML/Ops and statistical controls to track performance, enhance model accuracy and be transparent over time.

Business Benefits

The transformation delivered quantifiable increase in service quality, speed, and scalability:

• AI‑driven Inspection Accuracy at Scale : The accuracy of AI-driven inspection at scale was up to 85% in real-world bearing wear classification, and image quality checking has an accuracy of over 95%.

• Faster Service Decision‑making : 1.5 seconds per image on average of inspection response time, quickening the repair processes and customer resolutions.

• Reduced Manual Intervention and Cost : Automated pre-screening and defect classification greatly decreased reliance on manual inspection and expert survey.

• Future‑ready Service Foundation : A self-educating inspection platform that can adapt to novel patterns of wear, products, and service needs.

Conclusion

By partnering with LTM, the client transformed bearing inspection into a scalable, AI-enabled service capability that improved operational agility, consistency, and customer responsiveness across its service network. Our approach of integration of human service knowledge and AI-based vision systems enabled them to work faster, more accurately and confidently across their service network. Beyond automating inspections, the initiative established a future-ready foundation for data-driven service operations—enabling faster decision-making, reducing operational dependencies, and strengthening the organization’s ability to deliver reliable, high-quality customer experiences at scale.

LTM Leader Quote

“Service excellence today demands more than speed—it demands intelligence at scale. By combining deep service-domain expertise with AI-powered vision, we helped reimagine bearing inspection as a real-time decision workflow that delivers accuracy, consistency, and confidence across the service network.”

Senior Leader, Manufacturing - Americas, LTM