Operational data
Driven by Industry 4.0 / the Industrial Internet of Things, industrial operations are increasingly autonomous. Sensors all over the factory provide real-time data on the status of production and machinery to optimize operations. Welcome to the fourth Industrial Revolution.
Senseye is the only product to offer simple predictive maintenance; using machine-learning to perform condition monitoring and prognostics analysis at scale and without requiring deep pockets or a team of expert data analysts. Senseye is your key to reducing unplanned machine downtime and increasing Overall Equipment Effectiveness (OEE).
Instant deployment and actionable results within two weeks.
Senseye isn’t restricted to any machine type as it learns about your machinery and builds health models automatically.
Over 40 years of predictive maintenance, condition monitoring and prognostics expertise, combined with the latest advances in machine learning and semantic technology.
From one machine to thousands, Senseye delivers Uptime as a Service as a scalable product to drive your maintenance strategy.
Be a maintenance hero
Senseye eliminates the chaos and uncertainty from conventional predictive maintenance, helping you to become a maintenance hero. Its advanced machine-learning works like an experienced team of condition-monitoring engineers in the cloud, getting the right information to you ahead of time, multiplying your efforts.
Fact: 45-55% increased productivity of maintenance staff
Scalable predictive maintenance
Senseye enables simple predictive maintenance, reducing unplanned downtime and maximizing OEE; it’s the ultimate cloud-based machine health application for time-starved production and operations managers. Our proprietary machine learning technology infused with over 40 years of machine failure expertise gives you fast, accurate Remaining Useful Life (RUL) information without upfront costs.
Fact: 30-50% reduction in machine downtime
Minimize unplanned downtime
Senseye helps you to reduce the Total Cost of Ownership of your assets, increasing throughput, saving money and improving profitability.
It’s the ultimate cloud-based uptime application to let you know what’s happening now and in the future, in a really simple way and at any scale.
Fact: 10-40% reduction in maintenance costs
Transformational
Leveraging industrial trends like the Industrial Internet of Things and condition monitoring analytics at scale with our unique prognostics engine, Senseye delivers a transformational predictive maintenance capability. As the only product to offer scalable condition monitoring, Senseye is leading predictive maintenance.
Fact: 85% increase in downtime forecasting accuracy
Senseye connects to your sensor, maintenance and operational data stored in a factory historian, database or IoT middleware solution. There is no need to add any hardware or install any software on-site.
Vibration
Temperature
Acoustics
Current
Operational data
Machine specs
Historic performance
Any other data
Don't worry about these,
Senseye uses them
all automatically!
Senseye clever stuff
Easy and simple to use,
Senseye automatically calculates machine health.
Bearing failure in 200 hours
Pressing machine is binding
Belt will fail in 10 days
Check driveshaft for excessive play
Get in touch and start saving money
Are you ready to take control? Start getting ahead of downtime by using Senseye to guide your decisions.
Get in contact to share your requirements and discover how much Senseye could improve your operations.
Senseye, the scalable predictive maintenance leader is inviting delegates interested in learning about how Industry 4.0 technology can reduce unplanned machine downtime to visit for a live demonstration at stand K15a at the upcoming Sensors & Instrumentation show to be held at the Birmingham NEC over the 26th and 27th of September.
Read MoreTime and again we encounter excited maintenance managers who possess terabytes of data; eager to kick-off their predictive maintenance project and avoid machine downtime. It never stops being painful when we take a look at the data and find that much of it us unusable for predictive maintenance purposes.
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