Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder

Version 1 : Received: 9 August 2022 / Approved: 16 August 2022 / Online: 16 August 2022 (09:44:46 CEST)

A peer-reviewed article of this Preprint also exists.

Ahmer, M.; Sandin, F.; Marklund, P.; Gustafsson, M.; Berglund, K. Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder. Machines 2022, 10, 794. Ahmer, M.; Sandin, F.; Marklund, P.; Gustafsson, M.; Berglund, K. Using Multivariate Quality Statistic for Maintenance Decision Support in a Bearing Ring Grinder. Machines 2022, 10, 794.

Journal reference: Machines 2022, 10, 794
DOI: 10.3390/machines10090794

Abstract

Grinding processes’ stochastic nature poses a challenge in predicting the quality of the resulting surfaces. Post-production measurements for form, surface roughness, and circumferential waviness are commonly performed due to infeasibility in measuring all quality parameters during the grinding operation. Therefore, it is challenging to diagnose the root cause of quality deviations in real-time resulting from variations in the machine’s operating condition. This paper introduces a novel approach to predicting the overall quality of the individual parts. The grinder is equipped with sensors to implement condition-based maintenance and is induced with five frequently occurring failure conditions for the experimental test runs. The crucial quality parameters are measured for the produced parts. Fuzzy c-means (FCM) and Hotelling’s T-squared (T2) have been evaluated to generate quality labels from the multi-variate quality data. Benchmarked random forest regression models are trained using fault diagnosis feature set and quality labels. Quality labels from the T2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T2 labels achieves more than 94% accuracy when compared to the measured ring disposition. The predicted overall quality using the sensors’ feature set is compared against the threshold to reach a trustworthy maintenance decision.

Keywords

grinding; multivariate statistics; maintenance decision; condition-based maintenance; condition monitoring; health management; prognostics; fault diagnosis

Subject

ENGINEERING, Industrial & Manufacturing Engineering

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