Senseye is the only solution that offers scalable predictive maintenance. It uses machine learning to perform condition monitoring and prognostics analysis, without requiring deep pockets or a team of expert data analysts.

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 optimise operations. Welcome to the fourth Industrial Revolution.

Senseye is your operations tool for reducing unplanned machine downtime and increasing Overall Equipment Effectiveness (OEE).


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the need for manual inspections and spares inventory

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abnormalities and understand machine Remaining Useful Life

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a successful predictive maintenance program

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From one machine to thousands, Senseye delivers truly scalable predictive maintenance to ensure that your maintenance strategy can cover all of the assets that you depend upon. Senseye is machine agnostic, covering all of your machines, not just those that are critical or of high value.

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Cost Effective

Senseye is sold as Software as a Service (SaaS), with a scalable pricing model and a Return On Investment (ROI) of typically less than 4 months.

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Time to value

Senseye requires no customisation or extensive setup by expensive consultants. With a supported data connection, deployment is near-instant and actionable results start to be delivered within 14 days.

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Specialists not required

Senseye is Operations Technology (OT), designed to automate the analysis of detection, diagnostics and prognostics to give accurate and useful machine health insights for the team on the shop floor.

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Senseye integrates seamlessly with your existing infrastructure investments, using data captured by your factory historian or IoT middleware to understand the future health of 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

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Senseye uses Condition Indicators collected from your machinery and stored in your factory historian or IoT middleware platform. The condition indicators used can be from vibration, temperature, current, acoustic, pressure and other sensor systems, either captured by the machine PLC or retrofitted.

Other Data Sources

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Current (and if available) past data is utilised by the system and combined with asset maintenance information to automatically build prognostics models, allowing you to monitor the Remaining Useful Life of your machinery. Senseye uses the same levels of encryption as modern banks and needs only one-way communication.

Senseye Automation

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There’s no need to have a technical background in condition monitoring; Senseye is completely automated and can integrate with your existing maintenance management systems. This means that you’re able to focus your maintenance efforts on the areas that need the most attention.

Machine Health Calculation

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Using condition monitoring detection and diagnostics techniques as well as prognostics models, Senseye calculates the health and Remaining Useful Life of your machinery. This calculation is based on a series of algorithms, which are constantly in operation to watch over your assets.

Insights and Alerts

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Insights and alerts are generated and delivered to relevant organisation members, with information about matching failure modes and likely Remaining Useful Life. With this information, Predictive Maintenance can be effectively performed.


Get downtime down, using Senseye to power your predictive maintenance programme today.

Get in contact to share your requirements and discover how much Senseye could improve your operations.


Senseye runs a regular Predictive Maintenance webinar, Q&A and live demo, on the last Thursday of every month. Register to be sent information on joining the upcoming session.

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