Trusted by Fortune 500 industrial companies worldwide, Senseye PdM is the only leading cloud-based Predictive Maintenance solution to achieve true Industry 4.0 best practices. Maintenance teams can halve unplanned downtime and increase maintenance efficiencies, saving money and time by using this unique technology powered by proprietary machine-learning algorithms and machine learning to automatically forecast machine failure and remaining useful life, achieving a typical ROI of less than 3 months.
Driven by Industry 4.0 / the Industrial Internet of Things (IIoT), industrial operations are increasingly autonomous. Factories contain numerous sensors that provide real-time data on the status of production and machinery to optimize operations.
SENSEYE PdM HELPS YOU
- REDUCE the need for manual inspections and spares inventory
- DETECT abnormalities and understand machine Remaining Useful Life
- OPERATE a successful predictive maintenance program
No need for expert consultants or expensive customization
- Proven Expertise & Pedigree
- Enterprise-scale, yet easy to use
- Category leading Return on Investment
- System automatically learns
BOOK A DEMO TO SEE SENSEYE PdM IN ACTION!
See Senseye PdM in action and discuss how its approach to diagnostics, prognostics and condition monitoring can help with your predictive maintenance needs.
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SENSEYE PdM FEATURES
Senseye PdM integrates seamlessly with your existing infrastructure investments, using data captured by your factory historian or IoT middleware to better understand your machinery. The process is entirely automated, so there is no need for extensive condition monitoring or knowledge to get it up and running. By collecting key data such as abnormal vibrations, current or pressure and temperature fluctuations, the system generates machine behaviour models automatically, which can then be used in your on-going predictive maintenance efforts.
- Condition Indicators
- Completely automated
- Seamlessly integrated
- Additional encrypted data sources
- Machine Attention Calculation
- Insights and alert notifications
SKF, Siemens, GE, Schneider Electric, Nissan, Tata Steel, Smufit Kappa and many more…
- 85% improvement in downtime forecasting accuracy
- 50% reduction in unplanned machine downtime
- 55% increase in maintenance staff productivity
- 40% reduction in maintenance costs