Data-reduction method for spatial data using a structured wavelet model

Abstract

Recent advances in sensor instrumentation have provided opportunities for process engineers to collect data at various process steps in order to detect process problems and develop remedial procedures. This article presents a structured wavelet model for the reduction of two-dimensional data having distinct structures. The wavelet component of our model can handle irregular data patterns exhibiting many peaks and valleys, while the existence of a distinct data structure prompts the use of polynomial functions on wavelet coefficients. The two-dimensional antenna data is reduced with a structured wavelet model followed by some procedures for the detection of process defects based on the reduced-size data. A real-life example is presented to illustrate the usefulness of the proposed tools in detecting process problems from a potentially large volume of data exhibiting many peaks and valleys.

Keywords:

• Fault detection
• Intelligent manufacturing
• Process control
• Structured model
• Wavelets

Acknowledgements

The authors thank the anonymous referees and Associate Editor for their valuable comments and suggestions. This research was supported by National Science Foundation grant DMI-0400071.