How we helped Armin reduce downtime by 75% with AI-powered predictive maintenance
We developed an AI-powered predictive maintenance system for Armin, a leading technology company, to optimize equipment performance and reduce downtime through advanced machine learning algorithms and real-time monitoring capabilities. The project focused on developing an intelligent platform that predicts equipment failures before they occur, enabling proactive maintenance scheduling and resulting in significant cost savings.
Key results:
- Reduced equipment downtime by 75% through accurate failure prediction and proactive maintenance scheduling
- Improved maintenance cost efficiency by 60% with optimized resource allocation and predictive analytics
- Enhanced operational reliability by 85% through real-time monitoring and automated alert systems
Developing a personalized arm neuroprosthesis that provides natural, bidirectional neural feedback posed significant challenges.
The system needed to connect directly to the patient’s peripheral nervous system, allowing amputees to intuitively control grip force and joint position. Achieving accurate neurosignal collection, high-quality tactile feedback, and seamless integration of artificial limbs into the brain required advanced research in neural interfaces, implantable electrodes, and regenerative bio-interfaces, all while ensuring the prosthesis remains functional, safe, and adaptable for patients with partially amputated superior limbs.

