Opening up the world of predictive maintenance to SMEs

Today, maintenance of factory machinery is performed mainly at fixed intervals, or on a run-to-failure basis. Real-time data from machines, that could help predict when it’s time for a check-up, is not usually available, as heavy sensorisation of the equipment is too costly for most SMEs.

The creation of Add-on, Low cost, Multi-purpose (ALM) modules for measuring real-time parameters such as vibrations, energy consumption and temperature will allow factory managers to quickly and simply optimise machinery performance and reduce costs –switching from established patterns to the more effective method of predictive maintenance.

Importantly, the change won’t require big investment: ALMeS innovative sensors are based on standard fibre optics, low-cost microcontrollers and machine-learning software. By introducing predictive maintenance methods in their plants, manufacturers could reduce maintenance costs by 25%-35%, eliminate more than 70% of breakdowns and boost a 25% increase in productivity, according to figures provided by EIT Digital.

 

Customers of the new solution – which is scheduled for market launch by the end of 2017 – will be able to recoup the one-time fee for system implementation, plus the yearly evolutionary maintenance fee to be paid to ALMeS partners, with the savings made during the first year of usage.