Semiconductor maker STMicroelectronics (ST) has released an updated version of its NanoEdge AI Studio, a machine learning system designed for industrial IoT and embedded industrial systems. The new release is the first since ST acquired the suite following its take-over of Cartesiam earlier this year.

The latest release of NanoEdge AI Studio provides new algorithms to better predict equipment anomalies and future behaviour; new capabilities to ease use of industrial sensor data acquisition and management using an ST development board; and enhanced user interface to make machine-learning implementation easier for embedded developers with no data-science skills.

According to ST, the new version of NanoEdge AI Studio comes as the shift of AI capabilities from the cloud to the edge offers manufacturers phenomenal potential to fundamentally improve industrial processes, optimise 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 is designed to simplify the creation of machine learning, anomaly learning, detection and classification on any STM32 microcontroller. The new release includes prediction capabilities such as regression and outliers libraries. The tool aims to make it easier for users to integrate machine-learning capabilities quickly, easily, and cost-effectively into their equipment. According to ST, no data-science expertise is needed.

Steve Peguet, Scientific Director, Innovation Department of Alten Group, an international technology consulting and engineering company, said: “We had the opportunity to use NanoEdge AI Studio with one of our major aerospace customers. 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 industrialise these first results to deploy a disruptive solution of drilling tools prescriptive maintenance in their factories.”

David Dorval, CEO and founder of Stimio, a company specialised in development of industrial IoT solutions for the railway and other industries (IIoT), said: “Our major railway customers are asking us to provide them with autonomous low-power wireless based predictive maintenance solutions to increase uptime, optimise costs and avoid costly downtime. 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.”