For companies with connected assets distributed across a country or around the world, edge analytics makes remote asset management easier by putting application logic onsite. However, some businesses may have remote assets without easy access to the cloud, and they may be reluctant to send data outside their own networks. Schneider Electric is out to solve that challenge for the oil and gas industry. The company has pushed edge solutions into new, predictive realms with the help of Azure Machine Learning and Azure IoT Edge.
The Internet of Things (IoT) has brought remote monitoring and control to a vast array of devices, from building thermostats to retention pond spillways to oil and gas artificial lift pumps. IoT connectivity can facilitate routine adjustments to settings or performance, or it can enable shutdown of a device that is failing. However, once the device reaches an abnormal operating condition, it may be too late to maintain efficiency, prevent damage, or avoid a dangerous situation.
But what if it were possible to embed intelligence at the network edge—where IoT devices reside—that could predict abnormal operating conditions before they happen and take preventive action?
IOT Frames enable IoT LifeCycle Management resulting in dynamic updates and proactive replacement of critical connected devices over time. We understand that things change, and cities and buildings need to capture and secure the proper data for maintenance and updates.