Make maintenance smarter Increase productivity by 25%
Reduce breakdowns by 70% And, lower costs by 25%
Predictive maintenance aims to find the right moment to perform maintenance so that a system's
components are not prematurely replaced while ensuring the reliability of the whole system. From
espresso machines to jet engines, the technique is ubiquitous and fairly straight forward.
First, original equipment manufacturers (or OEMs)
design and install a series of sensors in the original product.
Second, the equipment owners and operators rigorously record and inspect the sensor data.
Humans do not have the capability to identify non-linear interactions in these trends.
So, for step three, we introduce algorithms. Interpretable predictive analytics techniques
provide simple to use flow charts from data to not only predict likelihood of component failure,
but also likelihood of survival of the part over time
accurately. Interpretability allows engineers to understand and sense-check failure modes.
Fourth, based on real time knowledge the asset owners (whether it's Starbucks'
espresso machines or Delta Airlines' engines) can dispatch maintenance personnel right before
the moment of failure. Leading to increased productivity, reduced breakdowns, and lower costs.