New software can help manufacturers prevent downtime before it happens

A UK-based software developer has created a programme it says delivers a new approach to prognostics and condition monitoring and could help manufacturers prevent machinery failures “months” in advance.

The ‘PROGNOSYS’ software has been developed by Senseye, a company based in Southampton that specialises in machine learning and data science to improve Overall Equipment Effectiveness. 

Senseye says that by using the principles of condition monitoring enabled by advanced machine learning and the Internet of Things, PROGNOSYS can predict costly failures in machinery months in advance, helping businesses to save money by avoiding downtime. 

The programme works by taking in measurements like vibration, humidity, acoustic emissions and power usage and uses technology like artificial intelligence and machine learning to predict when and how machinery is likely to fail. It’s designed to be affordable for all manufacturers and used with any type of machinery, says Senseye. It’s also entirely cloud-based so there’s nothing to maintain. 

The software can be used for plastics processing machinery and Senseye already has a pilot running with a long-established plastics and rubber extrusion and moulding company in Southampton.

To help manufacturers prove the value, Senseye has introduced a limited-time on-boarding programme to help manufacturers learn about the value of PROGNOSYS for zero cost. This programme, it says, provides a no-cost installation and use of the cloud-solution for a period of three months. 

“We’ve been working hard on developing this complex technology from its origins in Aerospace and Defence to be easy to use and accessible to the manufacturing industry,” explained Simon Kampa, CEO of Senseye. “We’re thrilled to be running this pilot to show off PROGNOSYS and demonstrate the power of the Industrial Internet of Things”. 

Senseye is accepting applications to join the web-based pilot programme at no cost, on a first-come-first served basis. Interested companies can sign up using the weblink below.