Senseye predictive maintenance system launched on MindSphere operating system

Senseye, a leader provider of predictive maintenance analytics, has announced that its award-winning condition monitoring and prognostic software is now available as a native application on MindSphere, the open cloud-based IoT operating system from Siemens.

Senseye, which was made a MindSphere Gold partner in August 2018, first provided its software to MindSphere users in June 2018, when it was made available as a complimentary service that could be connected to the operating system.

This new version of Senseye’s application was developed specifically for MindSphere and is hosted within the operating environment itself.

This development is intended to make it quicker and easier for MindSphere users to adopt Senseye’s automated predictive maintenance product in their plants, and also to improve the immediate accuracy of its analysis by providing access to a wider range of historic data.

Simon Kampa, CEO of Senseye, said: “Our partnership with Siemens is fantastic for MindSphere users, helping them enable them to improve the effectiveness and efficiency of their maintenance activities by analysing data already captured by all modern Industry 4.0 assets.”

“By providing users with the opportunity to access Senseye as a native app in the MindSphere operating environment, we have reduced the manual processes involved in connecting data to the Senseye product.”

“We are making it even quicker and easier for engineers to understand the health of their industrial assets and see precisely when those assets are most likely to fail, without any additional sensor or application investment.”

Paul Kaeley, Senior Vice President for Global Partner Ecosystem at Siemens PLM Software, said: “Senseye of delivering tremendous value to manufacturers and other industrial organisations through its predictive maintenance application.”

“Senseye provides a compelling solution to MindSphere users who want to take full advantage of the industrial IoT data at their disposal and boost the insight available to their maintenance operations.”