Online quality control with flexible evolving fuzzy systems

Abstract

This chapter is dealing with the application of flexible evolving fuzzy systems (described in Chap. 9) in online quality-control systems and therefore also provides 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 postsupervision 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 nonlinear fuzzy models/classifiers during online 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 retraining phases. In the result section of this chapter, we will explicitly highlight the performance gains achieved when using flexible evolving fuzzy systems (EFS) in both quality-control paths.