Preprint Article Version 1 This version is not peer-reviewed

Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices

Version 1 : Received: 3 October 2017 / Approved: 3 October 2017 / Online: 3 October 2017 (13:39:35 CEST)

How to cite: Haber, R.; del Toro Matamoros, R.M. Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices. Preprints 2017, 2017100015 (doi: 10.20944/preprints201710.0015.v1). Haber, R.; del Toro Matamoros, R.M. Hybrid Incremental Model for Imbalance Detection in Ultra-high Precision Rotating Devices. Preprints 2017, 2017100015 (doi: 10.20944/preprints201710.0015.v1).

Abstract

A novel method based on a hybrid incremental modeling approach has been designed and applied to imbalance detection in ultra-high precision rotating machines. The model is obtained by a two-step iterative process that combines an overall model (least-squares fitting) with a local model (fuzzy k-nearest-neighbour) to take advantage of their complementary capacities.  Three normalization strategies of evaluating the effect on accuracy are analyzed.  A comparative study demonstrates that the hybrid incremental model provides better error-based performance indices for detecting imbalance than a nonlinear regression model and an adaptive neural-fuzzy inference system model. The suitability of Mahanolobis normalization for hybrid incremental modeling is also demonstrated in this case study. The proposed strategy for imbalance detection is simple, fast, and non-intrusive, reducing the deterioration in the performance of ultra-high precision rotating machines due to vibrations.

Subject Areas

modeling; intelligent systems; imbalance detection; ultra-high precision rotating machine

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