IMPLEMENTING SENSEYE
Senseye is designed to connect with existing factory historians (such as Schneider Electric Wonderware™) as well as Internet of Things (IoT) middleware platforms such as SIEMENS MindSphere™, GE Predix™ and PTC ThingWorx™. It can even take data from regularly exported CSV files.
CONNECTION TO DATA
Based in the cloud, there is no infrastructure for you to set up, and nothing to install. Senseye has a number of different data adaptors that connect with popular historian and maintenance management platforms, with more being developed all the time.
However your machinery data lake looks and evolves, Senseye will work with it.
DATA-DRIVEN PROGNOSTICS
Senseye uses a ‘data-driven prognostics’ approach. Once a data connection is made, Senseye enters an unsupervised learning phase. It uses either historical data (if available) or takes 14 days to learn normal machine behaviour, automatically building models of this.
At the same time, it can connect with a Computerised Maintenance Management System (CMMS) in order to learn what actions the maintenance team are taking and build prognostic (Remaining Useful Life) models.
Data Connection Made
Learning Phase Commenced
Connection with CMMS
Automated Prognostics
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AUTOMATIC WORK ORDER INTERFACE
Senseye is able to use this interface to automatically raise work orders when machines become unhealthy and learn from the actions of the maintenance team so that it can provide better prognostics and diagnostics for similar issues in the future.
Senseye has patents-pending on its technology, including the use of a user-feedback mechanism. This will allow the product to learn from user interaction, thus improving the quality of its detection and diagnostics.
