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

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 predict 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 (T-2) 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 T-2 statistic of quality parameters are preferred over FCM approach for their repeatability. The model, trained from T-2 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.

Automated summary

The stochastic nature of grinding processes presents a challenge in predicting surface quality. This paper introduces a novel approach to predict the overall quality of individual parts and implement condition-based maintenance using sensors. The model trained with T-2 labels achieves over 94% accuracy in comparison to measured results, enabling reliable maintenance decision-making.