  In this work Time Series Classification techniques are investigated, and
especially their applicability in applications where there are significant
differences between the individuals where data is collected, and the
individuals where the classification is evaluated. Classification methods are
applied to a fault classification case, where a key assumption is that data
from a fault free reference case for each specific individual is available. For
the investigated application, wave propagation cause almost chaotic changes of
a measured pressure signal, and physical modeling is difficult. Direct
application of One-Nearest-Neighbor Dynamic Time Warping, a common technique
for this kind of problem, and other machine learning techniques are shown to
fail for this case and new methods to improve the situation are presented. By
using relative features describing the difference from the reference case
rather than the absolute time series, improvements are made compared to
state-of-the-art time series classification algorithms.
