Database Reference
In-Depth Information
Figure 6-1. Predictive maintenance scheduling—replacing parts before a serious problem oc-
curs—is a huge benefit in systems with expensive and highly critical equipment such as this turbine
inside a jet engine.
If you keep good, detailed long-term histories of maintenance on essential components of
equipment down to the level of the part number, location, dates it went into use, notes on
wear, and the dates of any failures, you may be able to reconstruct the events or conditions
that led up to failures and thus build a model for how products wear out, or you may find
predictive signs or even the cause of impending trouble. This type of precise, long-term
maintenance history is not a time series, but coupled with a time series database of sensor
data that records operating conditions, you have a powerful combination to unlock the in-
sights you need. You can correlate the observations your sensors have made for a variety of
parameters during the days, weeks, or months leading up to the part failure or up to an ob-
served level of wear that is disturbing. This pattern of retrospective machine learning analys-
is on the combination of a detailed maintenance history and a long-term time series database
has widespread applicability in transportation, manufacturing, health care, and more.
Why do you need to go to the trouble of saving the huge amount of time series sensor data
for long time ranges, such as years, rather than perhaps just a month? It depends of course on
your particular situation and what the opportunity cost of not being able to do this style of
