NXP and Microsoft Demonstrates Edge-to-Cloud Machine Learning Solution for Predictive Maintenance
NXP Semiconductors announced collaboration with Microsoft to bring Artificial Intelligence (AI) and Machine Learning (ML) capabilities for anomaly detection to Azure IoT users.
By combining complementary strengths, NXP’s offline machine learning capability and embedded processing with Microsoft’s cloud expertise, the two companies jointly demonstrate a new Anomaly Detection Solution for Azure IoT at Microsoft Build.
The solution consists of a small form factor, low power System-on-Module (SOM) powered by NXP’s i.MX RT106C Crossover Processors, a full suite of sensors, and an associated Anomaly Detection Toolbox. The toolbox utilizes various ML algorithms such as Random Forestand Simple Vector Machine (SVM), 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.
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.
For more information visit, https://iotedge.nxp.com/anomaly-detection/.
More eeNews articles on predictive maintenance: https://www.eenewsembedded.com/search/node/predictive%20maintenance