A Novel LDA-based Approach for Motor Bearing Fault Detection

Early detection of abnormalities for electrical motors is a key point to reduce economic losses caused by unscheduled maintenance and shutdown time. In this context, health monitoring and fault diagnosis are crucial tasks to be performed. We introduce a novel Linear Discriminant Analysis (LDA) based algorithm to deal with fault data dimension reduction and fault detection issues. In particular the algorithm, namely Δ-LDA, is designed to overcome the problem of a between-class scatter matrix trace very close to zero. Indeed, if the information of the expected value is not sufficient to discriminate the classes, we propose the use of the difference of covariance matrices. A performance comparison with other conventional methods, e.g. principal component analysis and classical LDA, is proposed. In particular experimental results show that the proposed algorithm improves the classification accuracy if the classes are overlapped, and gives comparable results in the remaining scenarios.