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.
Servitization is a relatively new term in the industrial sector but it’s quickly picking up a following; with The Manufacturer magazine even dedicating a whole series of conferences to it. In a sentence, it’s the concept that any product / offering, can be delivered ‘as a service’ (e.g. by the hour or unit) rather than as a discrete product + maintenance combination. A classic example of this is Rolls-Royce in the 80s shifting its business to a ‘Power by the Hour’ model where no longer would they simply sell jet engines and warranties, they would instead offer the operator flight-hours – the responsibility would then be entirely on Rolls Royce to deliver the contracted number of hours whilst achieving certain availability metrics.
Prognostics, the science of being able to accurately forecast the Remaining Useful Life (RUL) of machinery is a key technique in forecasting machine failure. We believe that prognostics is one of the most important enablers of predictive maintenance yet a number of reasons have blocked it from widespread adoption:
Press release: For immediate release
Southampton, UK, 16/03/2017
Predictive analytics is the catch-all term for any technologies which can help you to predict the future by analysing past historical events as well as real-time data feeds. It’s had some particularly successful case studies (‘Other customers bought XYZ’ on Amazon) as well as some potentially unnerving ones (Target predicting a girl’s pregnancy before her family knew).
Press release: For immediate release
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.
Predictive maintenance sounds great – maintain your assets, before they show outward signs of failure and cause unplanned downtime (loss of revenue), whilst spending less money than you would for a preventative maintenance program – boosting profitability and throughput. The benefits are quite clear. Why then do relatively few companies have an active predictive maintenance program?