Next-Gen Predictive Maintenance with Edge AI for Industrial Equipment
Overview
The client wanted to implement an AI-driven predictive maintenance solution for critical industrial equipment, including drilling and milling machines, presses, air conditioning units, and conveyors. The need was to minimize unplanned downtimes, extend consumable lifespans, reduce non-quality costs, and enable scalable, real-time monitoring through smart sensors with embedded intelligence
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Challenges
Frequent unexpected downtimes due to lack of early fault detection
Shortened lifespan of machine components and consumables
High cost of poor quality and reactive maintenance practices
Solution
- Developed a smart sensor Proof of Concept using STM32 microcontroller-based sensors integrated with NanoEdge AI Studio for embedded intelligence
- Enabled real-time vibration and condition monitoring with STM sensor cards to capture early anomaly indicators
- Implemented on-device AI algorithms for anomaly detection, reducing dependency on cloud latency
- Integrated processed insights & raw data into a central SNC database to support fleet learning, trend analysis, & AI model
- Designed a predictive maintenance dashboard to visualize machine health, component lifespan, and generate early maintenance alerts
- Integrated the solution with automation platform, enabling system-wide monitoring, proactive interventions, and scalable deployment
Outcomes
- Reduced unplanned downtime by enabling early fault detection, improving production continuity and preventing costly delays
- Extended equipment lifespan and lowered non-quality costs by optimizing consumable usage and preventing breakdown-driven defects, ensuring higher operational efficiency and ROI
