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

Date

2016-01-25

Author

Aghdam, Behrang Hosseini
Ciğeroğlu, Ender

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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.

Subject Keywords

Turning, Vibration, Non-stationary, FS-TARMA, Tool wear

URI

https://hdl.handle.net/11511/81730
https://link.springer.com/chapter/10.1007/978-3-319-29956-3_16

Collections

Department of Mechanical Engineering, Conference / Seminar

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Citation Formats

B. H. Aghdam and E. Ciğeroğlu, “Functional Series TARMA Models for Non stationary Tool Vibration Signals Representation and Wear Etimation,” 2016, Accessed: 00, 2021. [Online]. Available: https://hdl.handle.net/11511/81730.