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Truly Scalable Predictive Maintenance with Senseye, Limited Live Demonstrations Available

Southampton, UK - October 25, 2017

Senseye, the scalable predictive maintenance leader, is excited to announce that it will be giving live demonstrations of its ground-breaking automated condition monitoring and prognostics software, featuring Remaining Useful Life calculations at the upcoming ‘Connected Manufacturing 2017’ conference in Birmingham over the 1st and 2nd of November.

Senseye Sales Manager, Kamran Farooq will present a special talk on “Prognostics, the key to Predictive Maintenance”, discussing how Senseye’s use of prognostics enables manufacturers and industrial companies to easily get an understanding of the Remaining Useful Life of their machinery and reduce unplanned downtime by up to 50%.

“Senseye is ready to automatically analyse data collected from Industry 4.0 machinery, helping companies to save money and improve their maintenance accuracy by up to 85%!”, says Kamran Farooq, Senseye Sales Manager.

Senseye will be exhibiting its software with live demonstrations on stand CM21a throughout the event dates.

Connected Manufacturing is the UK’s only event dedicated to ‘Industry 4.0’, which provides opportunities for suppliers to meet and do business with a large range of attendees from sectors as diverse as aerospace, automotive, motorspot, rail, marine and many more.

Trusted by a number of Fortune 100 companies, Senseye is the leading automated cloud-based condition monitoring and prognostics product. The award-winning software requires no technical expertise and is available as a simple subscription service, enabling customers to rapidly start and expand their predictive maintenance programs.

To learn more and stop downtime getting you down, visit

About Senseye Ltd. The scalable Predictive Maintenance product is a cloud-based tool to help manufacturers avoid downtime and save money by automatically forecasting machine failure without the need for expert manual analysis. It’s a revolution in predictive maintenance that can be used on any machine from any manufacturer, taking information from existing Industrial IoT sensors and platforms to automatically diagnose failures and provide the remaining useful life of machinery.

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