BaSys4Maintain Insights

Predictive maintenance is becoming essential for keeping modern production systems efficient, reliable, and competitive. Yet many machine components, such as precision ball screw drives, remain difficult to monitor without deep integration into machine controls. The BaSys4Maintain research project tackles this challenge by bringing together AI, intelligent sensing, and digital twins to enable predictive maintenance as a local, real-time service.

BaSys4Maintain is a German research initiative focused on enabling predictive maintenance for ball screw drives through AI-based diagnostics and digital asset modeling. The project builds on the BaSys4.0 infrastructure, which provides a standardized framework for representing and managing distributed industrial assets through Asset Administration Shells (AAS). By embedding predictive maintenance capabilities into this digital ecosystem, plant operators gain actionable insights, reduce downtime, and unlock new after-sales service models, without requiring intrusive modifications to existing machines.

Our Contribution

In this project, we contributed several key components, bringing together sensor development, machine learning, and edge-focused software engineering:

• AI-Powered Condition Monitoring – A custom machine learning model evaluates sensor data to detect preload loss, identify anomalies, and support early fault diagnosis.
• Edge Integration – The AI module was deployed locally on an embedded evaluation unit, enabling fast, secure, and cloud-independent predictive maintenance.
• Aging & Wear Detection – Algorithms capable of detecting the aging and mechanical degradation of ball screw drives, including early signs of backlash and performance loss, even under disturbed or noisy conditions.
• Operation Without Reference Runs – The developed algorithms are designed to perform during the machine’s normal operation, requiring no additional procedures for predictive maintenance.

Real-world Applications

By combining intelligent sensor systems, AI-based anomaly detection, and standardized digital asset models, BaSys4Maintain enables:

• Local, cloud-independent predictive maintenance
• Early detection of preload loss and mechanical wear
• Easy integration into existing industrial environments
• Improved uptime and reduced maintenance effort
• New service models through digital twins and distributed asset management

Validation & Testing

To evaluate the system in realistic conditions, a physical demonstrator was employed to replicate the movement, wear, and preload changes of ball screw drives. This setup allows the end-to-end workflow, from sensing to AI inference to digital twin visualization, to be validated under controlled but realistic scenarios, ensuring reliable performance before industrial deployment.

Outlook

BaSys4Maintain demonstrates how AI, edge intelligence, and digital twins can transform industrial maintenance. The results lay the groundwork for scalable, cost-efficient monitoring solutions that make production systems smarter, safer, and more resilient. The project also contributes to the ongoing advancement of the BaSys ecosystem, strengthening the foundation for future autonomous and data-driven industrial services.