Database Reference
In-Depth Information
predictive maintenance may be. But part of the question to ask yourself is: what happens if
you only save a month of sensor data at a time, but the critical events leading up to a cata-
strophic part failure happened six weeks or more before the event? Maybe temperatures ex-
ceeded a safe range or an outside situation caused an unusual level of vibration in the com-
ponent for a short time two months earlier. When you try to reconstruct events before the
failure or accident, you may not have the relevant data available any more. This situation is
especially true if you need to look back over years of performance records to understand
what happened in similar situations in the past.
The better alternative is to make use of the tools described in this report so that it is practical
to keep much longer time spans for your sensor data along with careful maintenance histor-
ies. In the case of equipment used in jet aircraft, for instance, it is not only the airline that
cares about a how equipment performs at different points in time and what the signs of wear
or damage are. Some manufacturers of important equipment also monitor ongoing life histor-
ies of the parts they produce in order to improve their own design choices and to maintain
quality.
Manufacturers are not only concerned with collecting sensor data to monitor how their
equipment performs in factories during production; they also want to manufacture smart
equipment that reports on its own condition as it is being used by the customer. The manu-
facturer can include a service to monitor and report on the status of a component in order to
help the customer optimize function through tuning. This might involve better fuel consump-
tion, for example. These “smart parts” are of more value than mute equipment, so they may
give the manufacturer a competitive edge in the marketplace, not to mention the benefits they
provide the customer who purchases them.
The benefits of this powerful combination of detailed maintenance histories plus long-term
time series databases of sensor data for machine learning models can, in certain, industries,
be enormous.
