Grinding wheel condition monitoring with boosted minimum distance classifiers

Grinding wheels get dull as more material is removed. This paper presents a methodology to detect a ‘dull’ wheel online based on acoustic emission (AE) signals. The methodology has three major steps: preprocessing, signal analysis and feature extraction, and constructing boosted classifiers using the minimum distance classifier (MDC) as the weak learner. Two booting algorithms, i.e., AdaBoost and A-Boost, were implemented. The methodology was tested with signals obtained in grinding of two ceramic materials with a diamond wheel under different grinding conditions. The results of cross-validation tests indicate that: (i) boosting greatly improves the effectiveness of the basic MDC; (ii) over all A-Boost does not outperform AdaBoost in terms of classification accuracy; and (iii) the performance of the boosted classifiers improves as the ensemble size increases.