Feature Recognition and Selection Method of the Equipment State Based on Improved Mahalanobis-Taguchi System

Abstract

Mahalanobis-Taguchi system (MTS) is a kind of data mining and pattern recognition method which can identify the attribute characteristics of multidimensional data by constructing Mahalanobis distance (MD) measurement scale. In this paper, considering the influence of irregular distribution of the sample data and abnormal variation of the normal data on accuracy of MTS, a feature recognition and selection model of the equipment state based on the improved MTS is proposed, and two aspects of the model namely construction of the original Mahalanobis space (MS) and determination of the threshold are studied. Firstly, the original training sample space is statistically controlled by the X-bar-S control chart, and extreme data of the single characteristic attribute is filtered to reduce the impact of extreme condition on the accuracy of the model, so as to construct a more robust MS. Furthermore, the box plot method is used to determine the threshold of the model. And the stability of the model and the tolerance to the extreme condition are improved by leaving sufficient range of the variation for the extreme condition which is identified as in the normal range. Finally, the improved model is compared with the traditional one based on the unimproved MTS by using the data from the literature. The result shows that compared with the traditional model, the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.