Multi-scale statistical process monitoring in machining

Xiaoli LI, Xin YAO

Research output: Journal PublicationsJournal Article (refereed)peer-review

48 Citations (Scopus)

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. © 2005 IEEE.
Original languageEnglish
Pages (from-to)924-927
Number of pages4
JournalIEEE Transactions on Industrial Electronics
Volume52
Issue number3
DOIs
Publication statusPublished - Jun 2005
Externally publishedYes

Keywords

  • Condition monitoring
  • Machining processes
  • Statistical process control (SPC)
  • Wavelet transform (WT)

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