Online unsupervised monitoring for hob wear based on multi-domain vibration features extraction and improved Q-statistic control chart

Abstract

Current mainstream wear diagnosis approaches rely on the entire lifecycle wear-labeled datasets, which is costly, delayed, and low-universality. To achieve the monitoring goal of online, real-time, and strong generalization for hob wear and get rid of the dependence on the wear datasets, an online unsupervised monitoring method is explored based on the multi-domain features extraction and improved Q-statistic control chart, which is progressive. First, the monitoring feature matrix of each hob shifting period is transformed into the principal component subspace, the wear subspace, and the interference subspace based on the principal component analysis (PCA). Then, features in the wear subspace of the previous shifting period are used to get the control limit and those of the next shifting period are used to calculate the Q-statistic. The hob wear status is determined by comparing the control limit and the Q-statistic. The gear hobbing experiment verifies the feasibility of the proposed method.