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.
How do you know when you’re ready to take advantage of software like Senseye to automate the analysis of condition monitoring data from your machinery?
Change is uncomfortable, implementing new technology, and the associated process change and cultural changes bring significant challenges as well as benefits, even if all goes smoothly. Whilst manually analysing Condition Monitoring is useful, moving to fully automate analysis allows factory and organisation-level scalability. It’s important to be sure that the correct systems and assets are in place to support this new way of operating.
“You can’t fix something that isn’t broken” used to be the philosophy of traditional Reactive Maintenance, many years ago. Help was only called for once a machine had completely failed.
Condition monitoring (CM) and prognostics solutions can be costly, it’s easy to spend than the cost of the machinery – but how much should you be spending and how do you know that the solution represents good value? When considering investing in a predictive maintenance solution, it’s important to understand the potential savings for your business before looking at how much to budget. If the numbers don’t add up, then you may need to rethink your CM ambitions.
You’ve heard the hype and it all sounds very impressive. But what is predictive maintenance and what is it best used for?
For optimal performance, all factory machinery needs to be maintained and companies often have maintenance agreements in place, usually with the original machine manufacturers or their approved service network. Whilst useful, these agreements are largely service-interval schedules, which don’t take into account actual usage and so do little to prevent unplanned downtime. This is the widely-used preventative and reactive maintenance model:
Senseye, the scalable predictive maintenance leader is inviting delegates interested in learning about how Industry 4.0 technology can reduce unplanned machine downtime to visit for a live demonstration at stand K15a at the upcoming Sensors & Instrumentation show to be held at the Birmingham NEC over the 26th and 27th of September.
Time and again we encounter excited maintenance managers who possess terabytes of data; eager to kick-off their predictive maintenance project and avoid machine downtime. It never stops being painful when we take a look at the data and find that much of it us unusable for predictive maintenance purposes.
Senseye, the Uptime as a Service leader today announced that it has reached a significant prognostics-at-scale milestone of automatically monitoring over 1,000 machines at a single customer site for early signs of mechanical damage and failure; using machine learning to help the client to avoid unplanned downtime, without relying on expensive consultants.
It’s an unfortunate fact of reality that no-matter how advanced the equipment, there will come a time when the machine unexpectedly fails and your factory experiences losses caused by unplanned machine downtime. The costs of this can vary dramatically; from over $1.5M per hour in automotive manufacturing to thousands per hour in the Fast Moving Consumer Goods (FMCG) industry; no-matter the cost it’s still extremely undesirable.
Senseye, the Uptime as a Service company today announced that it is celebrating its global recognition as a predictive maintenance leader with its automated condition monitoring and prognostics product by offering a limited discount to new customers in June.