A virtual sensing based augmented particle filter for tool condition prognosis

Jinjiang Wang, Yinghao Zheng, Peng Wang, Robert X. Gao

Research output: Contribution to journalArticlepeer-review

34 Scopus citations

Abstract

Timely evaluation and prediction of tool condition is critical to establish optimized maintenance plans in order to enhance production, minimize costly downtime. This paper presents an augmented particle filter based on virtual sensing technique with support vector regression (SVR) model to account for uncertainties in the tool condition degradation process. Tool condition is predicted by recursively updating a physics-based tool condition degradation model with virtual measurement approximately estimating tool degradation condition through virtual sensing technique, following a Bayesian inference scheme. Additionally, in order to improve estimation accuracy of virtual sensing model, different state-of-the-art dimension reduction techniques including principal component analysis (PCA) and its kernel version (KPCA), locality preserving projection (LPP) method have been investigated for feature fusion in a virtual sensing model, and the KPCA method performs best in terms of sensing accuracy. Afterwards, virtual measurement is then incorporated into particle filter. The effectiveness of the developed method is experimentally validated in a set of machining tool run-to-failure tests on a computer numerical control (CNC) milling machine.

Original languageEnglish
Pages (from-to)472-478
Number of pages7
JournalJournal of Manufacturing Processes
Volume28
DOIs
StatePublished - Aug 2017

Bibliographical note

Publisher Copyright:
© 2017 The Society of Manufacturing Engineers

Keywords

  • Augmented particle filter
  • Feature fusion
  • Tool condition prognosis
  • Virtual sensing technique

ASJC Scopus subject areas

  • Strategy and Management
  • Management Science and Operations Research
  • Industrial and Manufacturing Engineering

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