Multi-scale statistical process monitoring in machining
Xiaoli Li
Xin Yao
Centre of Excellence for Res. in Comput. Intelligence & Applications, Univ. of Birmingham, UK;
This paper appears in: Industrial Electronics, IEEE Transactions on
Publication Date: June 2005
Volume: 52,
Issue: 3
On page(s): 924- 927
ISSN: 0278-0046
INSPEC Accession Number: 8464617
Digital Object Identifier: 10.1109/TIE.2005.847580
Current Version Published: 2005-05-31
Abstract
Most practical industrial process data contain contributions at multiple scales in time and frequency. Unfortunately, conventional statistical process control approaches often detect events at only one scale. This paper addresses a new method, called multiscale statistical process monitoring, for tool condition monitoring in a machining process, which integrates discrete wavelet transform (WT) and statistical process control. Firstly, discrete WT is applied to decompose the collected data from the manufacturing system into uncorrelated components. Next, the detection limits are formed for each decomposed component by using Shewhart control charts. A case study, i.e., tool condition monitoring in turning using an acoustic emission signal, demonstrates that the new method is able to detect abnormal events (serious tool wear or breakage) in the machining process.
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