NXP Semiconductors has agreed a collaboration with Microsoft to bring artificial intelligence and machine learning capabilities for anomaly detection to Azure internet of things users.
By combining complementary strengths – NXP’s offline machine learning capability and embedded processing with Microsoft’s cloud expertise – the two companies jointly demonstrates a new Anomaly Detection Solution for Azure IoT at Microsoft Build in Seattle recently.
The solution consists of a small form factor, low power system-on-module powered by NXP’s i.MX RT106C Crossover Processors, a full suite of sensors, and an associated Anomaly Detection Toolbox.
The toolbox utilizes various machine learning algorithms such as Random Forest and Simple Vector Machine, to model normal behavior of devices, detect anomalous behavior through a combined local and cloud mechanisms.
This allows much lower cloud bandwidth requirements while maintaining full online logging and processing capabilities at a fraction of the cost.
Applications include predictive maintenance for rotating components, presence detection and intrusion detection.
Denis Cabrol, executive director and general manager of IoT and Security Solutions at NXP, says: “Preventing failures and reducing downtime are key to enhance productivity and system safety.
“We partnered with Microsoft to combine the power of Azure IoT with local intelligence running on NXP’s embedded technology to unlock innovation for the IoT – as part of our continued efforts to bring cognitive services down to the silicon.”
Rodney Clark, vice president, IoT sales, Microsoft, says: “We are proud to expand our collaboration with NXP to include the new Azure IoT and i.MX RT106C Anomaly Detection Solution.
“Solutions like this from NXP empower developers with products, tools and services to accelerate development of complete edge to cloud solutions.”
NXP’s cost-effective anomaly detection solution is designed with a robust set of sensors and high-performance i.MX RT106C crossover microcontroller running up to 600MHz, capable of collecting and analyzing sensor data in real time locally at the edge.
The solution seamlessly connects to the Azure IoT Cloud, providing customers an easy way to transfer data to the cloud, where they can visualize the data and utilize powerful data analytical tools to train behavior prediction models for deploying on edge devices.