Southampton, UK, 05/03/2016
Senseye, provider of the PROGNOSYS, cloud-based prognostics and condition monitoring solution to the manufacturing industry, is proud to announce that Steve McEvoy, a leader in machinery health monitoring, prognostics and condition monitoring, formerly with GE Aviation Systems and Condition Monitoring Group, has joined Senseye’s advisory board.
Simon Kampa, CEO of Senseye says “We’re really pleased that Steve has agreed to join our team and help us to further our ground-breaking prognostics, diagnostics and condition monitoring product PROGNOSYS. Steve’s knowledge and expertise is a valuable addition to our team and compliments the 45 years of combined experience we already have in this challenging area.”
Steve brings with him a wealth of expertise from a deep background of over 25 years in cutting-edge Prognostics and Condition Monitoring techniques from the aerospace industry, along with extensive experience in strategic planning and product development.
Steve commented “I’m excited about Senseye’s approach and what they are able to do with automatically forecasting machine failure. I’m confident that the techniques PROGNOSYS uses will save manufacturers a lot of money and I’m looking forward to sharing my knowledge and expertise to further enhance this leading product”.
Using his experience from the demanding aerospace and defence industries, Steve will be working with Senseye in an advisory capacity, helping to steer technology and implementation decisions that will scale with the Industrial Internet of Things in large and small manufacturing applications.
PROGNOSYS is available right now, with Senseye 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, get in touch at http://www.senseye.io
About Senseye Ltd. Senseye develops PROGNOSYS, an infrastructure free software solution that delivers a new approach to prognostics and condition monitoring to predict failures in machinery months in advance. Senseye brings over 40 years of real world industry know-how, combined with deep expertise in machine learning and data science to a robust and scalable approach to prognostics and condition monitoring to improve Overall Equipment Effectiveness. www.senseye.io