Fault diagnosis method for MIMU sensors based on fuzzy AGA-KPCA

被引：0
|
作者：
Gao Y. [1 ]
Cai Y. [2 ]
Sheng A. [1 ]
机构：
[1] Engineering Machinery College, Hunan Sany Polytechnic College, Changsha
[2] Combat Support College, Rocket Force University of Engineering, Xi'an
来源：
Zhongguo Guanxing Jishu Xuebao/Journal of Chinese Inertial Technology | 2022年 / 30卷 / 06期
关键词：
Adaptive genetic algorithm; Fault diagnosis; Kernel principal component analysis; Micro inertial measurement unit; Sensor;
D O I：
10.13695/j.cnki.12-1222/o3.2022.06.019
中图分类号：
学科分类号：
摘要：
The reliability of sensor output data is the premise to ensure its function. In view of the weakness of principle component analysis (PCA) in dealing with nonlinear problems, a fault diagnosis method for nonlinear micro inertial measurement unit (MIMU) sensors based on kernel principal component analysis (KPCA) is proposed. By constructing the KPCA model, fault monitoring and location can be realized by predicting errors and variable values of sensor contribution. In order to reduce the blindness of parameter selection and modeling workload, the adaptive genetic algorithm (AGA) improved by fuzzy inference is applied to optimize the KPCA kernel function parameters automatically. The simulation results show that the proposed method has good fault detection and identification ability for MIMU sensors. Compared to conventional KPCA, the average accuracy of fault detection is increased by 18.44%, which proves the effectiveness and advantages of the method. © 2022, Editorial Department of Journal of Chinese Inertial Technology. All right reserved.
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页码：835 / 840
页数：5
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