Feature Recognition and Selection Method of the Equipment State Based on Improved Mahalanobis-Taguchi System
作者机构：School of AutomobileChang'an UniversityXi'an 710064China School of Mechanical EngineeringNorthwestern Polytechnical UniversityXi'an 710072China
出 版 物：《Journal of Shanghai Jiaotong university(Science)》 (上海交通大学学报（英文版）)
年 卷 期：2020年第25卷第2期
页 面：214-222页
核心收录：
学科分类：0711[理学-系统科学] 07[理学] 071101[理学-系统理论]
基 金：the National Natural Science Foundation of China(No.71401016) the Shaanxi Provincial Natural Science Foundation of China(No.2019JM-495) the Fundamental Research Funds for Central Universities of Chang'an University(Nos.300102228110 and 300102228402)
主 题：Mahalanobis-Taguchi system(MTS) extreme condition X-bar-S control chart box plot method Mahalanobis space(MS) Mahalanobis distance(MD) threshold feature recognition equipment state
摘 要：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 *** 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 ***,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 ***,the box plot method is used to determine the threshold of the *** 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 ***,the improved model is compared with the traditional one based on the unimproved MTS by using the data from the *** result shows that compared with the traditional model,the accuracy and sensitivity of the improved model for state identification can be greatly enhanced.
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