Asynchronous electric motors are critical components that play a major role in most industrial applications. Not only do
they consume a large part of the energy required by your application, and
their failure or that of the load they drive, can have a
significant impact on the application itself.
To
control, maintain and
optimize these motors and their loads, you need precise and actual data to:
- Optimize your application outcome of the production line.
- Save energy to make processes more efficient and reduce your carbon footprint.
- Implement an effective predictive maintenance strategy.
Indeed,
reducing unplanned
downtime is a major objective in most production environments and eliminating it is a must in critical processes. To achieve this goal,
different maintenance principles are used:
- The preventive maintenance strategy is widely used, but there is always a risk that the load will require unplanned maintenance (reactive maintenance) due to an unpredicted failure.
- Predictive maintenance is the optimal solution to reduce downtime, but it is still considered costly and mainly dedicated to critical parts.
Today, motors controlled by electromechanical components have no native measurements, so it is necessary to add sensors to collect the data needed for predictive maintenance.
