Data mining for degradation modelling

Accelerated degradation testing is widely accepted in
competitive industries. As there is no longer the
need to test till failures, there are tremendous cost
and time benefits on fully capitalizing on such a
testing regime. Consequently, this research has aimed
for better understanding of the relationship between
design and degradation using the degradation data.
Artificial neural network is widely used for complex
problems in the literature. This book proposes and
demonstrates the neural network modelling methodology
into capturing the non parametric relationship
between design and degradation, specific to the
particular problem domain. In particular, two models
of different practical significance are developed and
compiled as Windows executables for predicting
material performances.

Autorentext
Hungyen Lin received B.Comp Sys. Eng. (Honors) from The University of Adelaide in 2003 and the M.S. degree from UniSA in 2005. He is currently working towards a Ph.D. degree in Electrical & Electronic Engineering at The University of Adelaide on an APA scholarship. His current research involves THz near-field imaging and signal processing.

Klappentext
Accelerated degradation testing is widely accepted in competitive industries. As there is no longer the need to test till failures, there are tremendous cost and time benefits on fully capitalizing on such a testing regime. Consequently, this research has aimed for better understanding of the relationship between design and degradation using the degradation data. Artificial neural network is widely used for complex problems in the literature. This book proposes and demonstrates the neural network modelling methodology into capturing the non parametric relationship between design and degradation, specific to the particular problem domain. In particular, two models of different practical significance are developed and compiled as Windows executables for predicting material performances.