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Main positions:企业技术顾问
Degree:Doctoral Degree in Engineering
School/Department:College of Automation Engineering

周翟和

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Education Level:南京航空航天大学

Paper Publications

An improved principal component analysis in the fault detection of multi-sensor system of mobile robot
Date of Publication:2018-01-01 Hits:

Affiliation of Author(s):自动化学院
Journal:Int. J. Online Eng.
Abstract:To cope with the fault detection in dynamic conditions of inertial components in the mobile robots, an improved principal component analysis (PCA) method was proposed. This work took a five gyroscopes redundancy allocation model to realize the measurement of the attitude. It is hard to distinguish the fault message from dynamic message in dynamic system that results in false alarm and missing inspection, so we firstly used the parity vector to preprocess the measurement data from the sensors. A fault was detected when the preprocessed data was dealt with PCA method. The effectiveness of the improved PCA method introduced in this paper was verified by comparing fault detection capabilities of conventional PCA method under the dynamic conditions of the step fault. The results of the simulation and experimental verification of the method was expected to contribute to the fault detection and improve the accuracy and reliability of the multi-sensors system in dynamic conditions. © 2018, Kassel University Press GmbH.
ISSN No.:1868-1646
Translation or Not:no
Date of Publication:2018-01-01
Co-author:Zhang, Qianyun,Zhao, Qingtao,Chen, Ruyi,zeng qingxi
Correspondence Author:zdh
Date of Publication:2018-01-01