Functional Series TARMA Models for Non-stationary Tool Vibration Signals Representation and Wear Estimation

Aghdam B. H., CİĞEROĞLU E.

34th IMAC Conference and Exposition on Structural Dynamics, Florida, United States Of America, 25 - 28 January 2016, pp.141-151 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1007/978-3-319-29956-3_16
  • City: Florida
  • Country: United States Of America
  • Page Numbers: pp.141-151
  • Middle East Technical University Affiliated: Yes


This article addresses the problem of tool wear estimation using vibration signals. Time dependent time series models are suitable for extraction of time varying dynamics embedded in the non-stationary signals. A version of non-stationary time series known as Functional Series Time dependent AutoRegressive Moving Average (FS-TARMA) is employed for estimation of tool vibration signals and identification of the dynamics of tool/holder system. The obtained models associated with different levels of tool wear are compared by using characteristic quantities calculated based on model parameters. In this method, called model parameter-based method wear is estimated using a feature that is a function of model parameter vector obtained from FS-TARMA models. The advantage of this method over the ARMA metric employed in a previous study is that it does not violate the non-stationarity assumption of signals. The results of this study demonstrate that the FS-TARMA models with model parameter-based method provides higher accuracy in wear estimation compared with ARMA counterpart and also FS-TARMA with ARMA metric.