in this video we're going to demonstrate
how you can use the agnostic feature
designer app to extract features for
developing a predictive maintenance
algorithm we start by importing our
dataset into the app the data has been
collected from a trip Lex pump on their
different fault conditions it is stored
in an ensemble which is a specialized
data store for developing predictive
maintenance algorithms the in-sample
data store contains 1.2 seconds long
measurements of flow and pressure and
also the fault codes for each of these
measurements after we import the data
set it shows up in the data browser to
visualize the flow signal we selected
and click signal trace the spots all the
measurements with different fault
conditions we can now group the
measurements by the fault codes by
selecting this option if we zoom in
using the pan or strip below we can
better see how measurements are
highlighted with different colors based
on different fault types
next we'll extract time-domain and
spectral features from this data we go
back to the feature designer tab and
under this menu we select signal
features to generate statistics features
will first use the flow data and later
extract features from the pressure
signal here we have commonly used time
domain features such as the mean
standard deviation kurtosis and skewness
now that we computed the time domain
features will continue with extracting
spectral features the app can use the
time domain data to estimate the signal
spectra of these signals which can then
be used to extract spectral features we
select spectral estimation and click
power spectrum here you can try out
nonparametric or
metric methods to compute the spectrum
and compare their results we choose the
auto regressive model with a model order
of 20 next to compute spectral features
we click here we select the frequency
band such that it includes the first
four peaks the reason is that due to
noisy data at higher frequencies it's
harder to distinguish the spectral Peaks
therefore any features extracted from
higher frequencies won't contribute to
the performance of machine learning
models so far we identify the time and
spectral features from the flow data you
can repeat the same process with the
pressure data and extract some
additional features
now all the extracted features from flow
and pressure data are stored in the
feature table one after selecting this
table we can click on the feature table
view that shows all the computed feature
values in a tabular form different
features are displayed on different
columns we can also use histograms which
show distributions of the computed
features on these plots different colors
indicate different faults due to
overlapping distributions of different
flow types and high number of features
it's hard to decide on which features
are more separable and distinctive the
app lets us rank all the features to
identify the ones that effectively
separate different types of thoughts on
the feature designer tab when we click
rank features the app uses one-way ANOVA
to calculate ranking scores for all the
features the results of the ANOVA test
are displayed on the right hand side
whereas the bars on the left shows the
normalized scores for different features
we can view the feature names by
hovering over the bars
the features with a higher score are
good candidates for a training a machine
learning model for further evaluation of
the extracted features we can now export
them to the classification learner where
we can train machine learning models for
fault classification