  The search for patterns or motifs in data represents an area of key interest
to many researchers. In this paper we present the Motif Tracking Algorithm, a
novel immune inspired pattern identification tool that is able to identify
variable length unknown motifs which repeat within time series data. The
algorithm searches from a completely neutral perspective that is independent of
the data being analysed and the underlying motifs. In this paper we test the
flexibility of the motif tracking algorithm by applying it to the search for
patterns in two industrial data sets. The algorithm is able to identify a
population of motifs successfully in both cases, and the value of these motifs
is discussed.
