Senseye, the Uptime as a Service company, will be revealing how it’s making diagnostic engineers a thing of the past at the NMI Future World 2016 “Deep Tech” conference to be held in London, UK over the 15th-16th of September.
Senseye’s product PROGNOSYS is revolutionising predictive maintenance by automating analysis of condition monitoring and enabling prognostics – diagnosing current and future failures, automatically. The software is cloud-based and easy to use, already trusted by Fortune 100 companies to help forecast failure, lower unplanned downtime and save operational costs.
The Industrial Internet of Things generates huge amounts of machine condition information but this has been largely untapped due to human and technical limitations. Senseye will discuss where the disciplines of Condition Monitoring and Prognostics have come from and how they are being shaped by advances in cloud-computing, machine learning and artificial intelligence, making the traditional role of the human diagnostic engineer a thing of the past.
Robert Russell, CTO of Senseye says “We’re excited to discuss with our peers how advances in AI and Machine Learning are helping manufacturers in the real world; the IIoT we’ve been promised for some time is finally arriving!”
“Senseye are a great example of a growing UK tech company that can exploit know-how in machine learning to deliver high-value solutions - in this case, helping to take manufacturing technology and capability to a new level.” Says, Alastair McGibbon, CTO at NMI.
Future World is a conference for entrepreneurs and businesses engaging in “Deep Tech”. These developments are consistently introducing disruptions across domains from agriculture to medical, industrial to fashion.
Senseye is offering a free assessment of how much manufacturers could save with their cloud-based, easy to use diagnostics, prognostics and condition monitoring product.
If you’d like to stop downtime getting you down, download our Using the IIoT to predict the Remaining Useful Life of your assets white paper here: