Functional data characterize quality or reliability performance of many
manufacturing processes. They are very informative in process monitoring
and controlling for nano-machining, ultra-thin semiconductor fabrication,
and antenna, steel-stamping or chemical manufacturing processes as seen in
many literature. In this talk, we present wavelet-based statistical
process control (SPC) procedures and evaluates their performance using
simulation studies. Unlike the recent SPC research on linear profile data
for monitoring global changes of data patterns, our methods focus on local
changes in data segments. In contrast to most of the SPC procedures
developed for detecting a known type of process change, our idea of
updating the selected parameters enables the handling all types of process
changes whether known or unknown. (Joint work with Jye-Chyi Lu).