Edwin Lughofer, Christian Eitzinger, Carlos Guardiola,
"On-line Quality Control with Flexible Evolving Fuzzy Systems"
, in Moamar Sayed-Mouchaweh and Edwin Lughofer: Learning in Non-Stationary Environments: Methods and Applications, Springer, New York, Seite(n) 375-406, 2012
On-line Quality Control with Flexible Evolving Fuzzy Systems
Sprache des Titels:
Learning in Non-Stationary Environments: Methods and Applications
This chapter is dealing with the application of flexible evolving fuzzy
systems (as they are described in Chapter 10) in on-line quality control systems and
therefore also provide a complete evaluation of these on (noisy) real-world data sets.
Hereby, we are tackling with two different types of quality control applications:
The first one is based on visual inspection of production items and therefore can
be seen as a post-supervision step whether items or parts of items are ok or not,
laying the basis for sorting out of bad products and decreasing customers? claims.
The second one is conducted directly during the production process as dealing
with a plausibility analysis of process measurements (such as temperatures, pressures
etc.) and therefore opens the possibility of an early intervention for product
improvement (internal correction or external reaction).
In both scenarios, permanent update of non-linear fuzzy models/classifiers during
on-line operation based on data streams is an essential issue in order to cope with
changing system dynamics, range extensions of measurements and features and the inclusion
of new operating conditions (e.g. fault classes) on demand without requiring
time-intensive re-training phases. In the result section of this chapter, we will
explicitly highlight the performance gains achieved when using flexible evolving
fuzzy systems in both quality control paths.