AI-Core Automates Railway Brake Pad Inspection with Computer Vision

AI-powered railway brake pad inspection

Akkodis AI-Core uses computer vision and machine learning to automate railway brake pad inspection. The solution analyzes captured images to measure brake pad thickness, detect visible anomalies, and compare results with predefined maintenance criteria.

By reducing repetitive manual measurements, AI-Core enables faster, more consistent brake assessments. It also helps maintenance teams determine whether a component can remain in service or requires further inspection, maintenance, or replacement.

Automated anomaly detection

Brake pad thickness is not the only indicator of component condition. Unusual wear patterns, visible damage, and other irregularities may also require attention.

AI-Core analyzes inspection images and flags potential anomalies for review by qualified technicians. This allows maintenance specialists to focus on higher-priority assets instead of manually reviewing every image.

Consistent, traceable assessments

Inspection results can vary depending on the technician, measurement method, and operating environment. AI-Core applies a consistent analytical process to each image, supporting standardized assessments across assets and maintenance locations.

Measurements and anomaly indicators can be stored as part of a digital condition record. Maintenance teams can use this information to monitor brake pad wear, plan interventions, and improve asset lifecycle management.

How automated brake inspection works

AI-Core supports a six-step inspection workflow:

1. Capture images: Images of the brake pads are collected using a compatible imaging system.
2. Identify the brake pad: Computer vision locates the brake pad and relevant inspection area.
3. Measure thickness: AI-Core calculates and records the visible brake pad thickness.
4. Detect anomalies: Machine learning models identify unusual wear, visible damage, and other irregularities.
5. Apply maintenance criteria: Measurements and anomaly indicators are evaluated against predefined thresholds and business rules.
6. Prioritize action: Assets requiring attention are flagged for technician review, servicing, or component replacement

Extending AI-Core across railway maintenance

AI-Core’s computer vision capabilities can also be adapted to other inspection processes. Potential applications include:

• Automated component inspection
• Railway asset monitoring
• Surface and component anomaly detection
• Maintenance trend analysis
• Inspection reporting and prioritization

Railway operators can begin with a focused brake inspection use case and progressively extend AI-powered inspection to other assets and maintenance processes.

Reduce manual inspection effort by up to 90%

By automating brake pad measurement and anomaly detection, AI-Core can help railway operators achieve:

• Up to 90% potential reduction in manual inspection effort
• Faster brake pad assessments
• More consistent inspection results
• Earlier identification of visible anomalies
• Better use of specialist maintenance resources
• Improved maintenance planning
• Lower asset lifecycle costs
• Increased fleet availability

Accelerate automation and decision with AI-Core

AI-Core combines advanced AI, automation, and analytics to help organizations improve efficiency and make faster, data-driven decisions.

Learn more about AI-Core