Affiliations: Department of Mechanical Engineering, College of
Engineering, UAE University, PO Box 17555, Al-Ain, UAE. Tel.: +97 1507236201;
Fax: +97 137623158, E-mail: [email protected]
Note: [] Corresponding author
Abstract: Knowledge extracted from time series data influences decision-making
in business, medicine, manufacturing, science and in other fields. Various
knowledge extraction methods have so far been proposed wherein it is typically
assumed that a piece of time series data possesses a set of trends that
deterministically or stochastically repeat in time. However, for noisy time
series data (data having no trend) the delay maps (return maps)
x(t),x(t+δ)), t=0,1, �, δ=1, 2, �,
N(N is a small integer), are more informative
than the time series itself. This paper shows a knowledge extraction method
that extracts a small set of "if {�} then
{�}" rules from the return maps of a given set of time
series data. A JAVA™ based tool is developed to
automate the rule extraction process. This tool is also able to use the
extracted rules recursively to simulate the qualitatively similar time series.
The performance of the proposed knowledge extraction method (as well as the
tool) is demonstrated by using an example time series (surface roughness
profile of a machined surface). This exemplification demonstrates that the
proposed knowledge extraction method can be used to enhance the performance of
computer integrated manufacturing systems by giving those systems a means to
exchange the information of nonlinear behaviors among the subsystems (process
planning, quality control, and so on).
