Code free machine learning tool for edge IoT equipment

December 01, 2021 // By Nick Flaherty
Code free machine learning tool for edge IoT  equipment
ST updates machine learning algorithms and tools to better predict equipment anomalies and future behaviour with NanoEdge AI Studio V3

STMicroelectronics has updated its machine learning algorithms and development tools for STM32 microcontrollers following its acquisition of Frenech startup Cartesiam earlier this year.

Version 3 of Cartesiam's NanoEdge AI Studio is the first major upgrade of the software tool for machine-learning applications following the Cartesiam deal and comes as the shift of AI capabilities from the cloud to the edge offers manufacturers potential to fundamentally improve industrial processes, optimize maintenance costs, and deliver innovative functions in equipment that can sense, process data, and act locally to improve latency and information security. Applications include connected devices, household appliances, and industrial automation.

NanoEdge AI Studio simplifies the creation of machine learning, anomaly learning, detection and classification on any STM32 microcontroller. The V3 release also includes prediction capabilities such as regression and outliers libraries. The tool makes it easier for users to integrate such cutting-edge machine-learning capabilities quickly, easily, and cost-effectively into their equipment.

ST has also eliminated the need to write code for its industrial-grade sensors with new high-speed data acquisition and management capabilities using the STWIN development board. NanoEdge AI Studio software enhances security by using local data storage and processing, instead of transferring to, and processing data in, the cloud.

A completely redesigned user interface makes it even easier for non-experts to develop state-of-the-art machine-learning libraries with improved support for anomaly detection, particularly for predictive maintenance to anticipate wear-and-tear phenomena or to better deal with equipment obsolescence. 

ST has also added regression algorithms to extrapolate data and predict future data patterns for energy management or forecasting remaining life of equipment.

“We had the opportunity to use NanoEdge AI Studio with one of our major aerospace customers,” said Steve Peguet, Scientific Director, Innovation Department of engineering consultancy Alten Group. “For machine drilling during the manufacture of expensive parts, where a worn drill-bit or the slightest anomaly can have significant consequences, Alten used NanoEdge AI Studio to integrate Machine-Learning algorithms into the drilling equipment. The solution tested on a production line was so effective that Alten has launched a practice around this technology to support its customers and to industrialize these first results to deploy a disruptive solution of drilling tools prescriptive maintenance in their factories.”

“Our major railway customers are asking us to provide them with autonomous low-power wireless based predictive maintenance solutions to increase uptime, optimize costs and avoid costly downtime,” said David Dorval, CEO and founder of Stimio, a company specialized in development of industrial IoT solutions for the railway and other industries (IIoT). “The contribution of edge low-power AI is at the heart of our strategy and after benchmarking several Edge AI software solutions, we chose NanoEdge AI Studio from STMicroelectronics to enrich our Oxygen Edge offering with powerful low-power AI algorithms."

blog.st.com/nanoedge-ai-studio/

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