Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

Raúl Mario del Toro Matamoros; Rodolfo Haber

doi:10.20944/preprints201709.0104.v3

Preprint Article Version 3 This version is not peer-reviewed

Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model

Raúl Mario del Toro Matamoros and Rodolfo Haber ^*

Version 1 : Received: 21 September 2017 / Approved: 21 September 2017 / Online: 21 September 2017 (16:51:50 CEST)
Version 3 : Received: 28 September 2017 / Approved: 28 September 2017 / Online: 28 September 2017 (15:02:05 CEST)

How to cite: del Toro Matamoros, R.M.; Haber, R. Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model. Preprints 2017, 2017090104 (doi: 10.20944/preprints201709.0104.v3). del Toro Matamoros, R.M.; Haber, R. Eccentricity Estimation in Ultra-Precision Rotating Devices Based on a Neuro-Fuzzy Model. Preprints 2017, 2017090104 (doi: 10.20944/preprints201709.0104.v3).

Abstract

Monitoring complex electro-mechanical processes is not straightforward despite the arsenal of techniques nowadays availanle. This paper presents a method based on Adaptive-Network-based Fuzzy Inference System (ANFIS) to estimate eccentricity of its spinning axis. The method is experimentally tested on an ultra-precision rotating device commonly used for micro-scale turning. The developed model has three inputs, two obtained from a frequency domain analysis of a vibration signal and the third, which is the device rotation frequency. A comparative study demonstrates that an adaptive neural-fuzzy inference system model provides better error-based performance indices for detecting imbalance than a non-linear regression model. This simple, fast, and non-intrusive imbalance detection strategy is proposed to counteract eventual deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Subject Areas

neuro-fuzzy modelling; intelligent monitoring; manufacturing processes

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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