SOUTHAMPTON, UK, 24/02/2017
Senseye, the Uptime-as-a-Service leader, today announced the launch of version 2.3 of its automatic condition monitoring and prognostics software, bringing Remaining Useful Life calculations to all customers – whether they operate 10 or 10,000 assets. Senseye is the only product in the world to offer automated condition monitoring combined with Remaining Useful Life analysis.
Knowing the Remaining Useful Life of machinery helps industrial companies to implement cost-effective predictive maintenance, typically allowing for a 10-40% reduction in maintenance costs and downtime reduction of 30-50%. The software is already trusted by a major automotive OEM, helping them to avoid downtime costs of over $2m per hour.
Remaining Useful Life has been an academic focus until now, accessible only to those with extensive data engineering skills. Senseye’s patent-pending technology makes it accessible to all. Its automated analysis is designed to be easy to use by maintenance teams and managers and is backed by Senseye’s extensive background in condition monitoring from the highly competitive aerospace & defence industry.
Robert Russell, Senseye CTO says, “Being able to see the Remaining Useful Life of machinery – without requiring expert input – empowers the hero maintainers to get maximum value from their condition monitoring solutions.”
Trusted by a number of Fortune 100 companies, Senseye is the leading cloud-based condition monitoring and prognostics product. The award-winning solution is usable from day one and available as a simple subscription service, enabling customers to rapidly expand their predictive maintenance programs.
About Senseye Ltd. Leading Uptime-as-a-Service company Senseye develops cloud-based software that automates condition monitoring and prognostics, enabling subscribers to predict failures in machinery months in advance. Senseye harnesses data science, deep expertise in machine learning and real-world industry know-how to provide a robust and scalable approach to reducing downtime and operational costs.