NeoAutomate for Embedded Predictive Maintenance

Research & innovation

NeoAutomate

This demonstrator illustrates an embedded predictive maintenance approach, integrated as closely as possible with the hardware to enable rapid, reliable, and non-intrusive fault detection. 

Illustration of our demo, NeoAutomate

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Embedded Predictive Maintenance

The solution integrates directly into the existing environment and remains hardware-agnostic thanks to EcoStruxture Automation Expert (subject to compliance with the IEC 61499 standard), which facilitates its deployment across various industrial settings.

Based on a low-impact STMicroelectronics board, it enables predictive maintenance algorithms to be executed directly on the embedded board, as close as possible to the hardware.   

Diagram illustrating a connected smart factory architecture (Industry 4.0), showing how data is collected from industrial equipment (fans, conveyors, machine tools) using sensors and programmable logic controllers (PLCs). This data is then transmitted via industrial protocols (STM32, IoT, Wi-Fi) to gateways (iPC) and centralized on a monitoring platform. This visualization highlights the integration of IoT, artificial intelligence, and cloud technologies to optimize predictive maintenance, real-time monitoring, and the operational efficiency of industrial processes.

This plug-and-play approach requires no intrusive modifications to existing equipment and ensures a high refresh rate, enabling more accurate anomaly detection. This will allow for more detailed data sampling and thus the detection of minor deviations.

Error and alert management is handled directly at the STM32 board level, allowing failures to be reported without relying on complex monitoring systems. By leveraging modern automation standards, notably IEC 61499 via EcoStruxure Automation Expert, NeoAutomate offers a modular, distributed, and replicable solution that can be rapidly deployed on new machines or production lines.

This edge-based and embedded approach enhances equipment reliability while reducing maintenance costs and downtime. 

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