Earticle

초록

영어

Because of the nonlinear characteristics of monitoring system in military barracks, the traditional KPCA method either have low sensitivity or unable to detect the fault quickly and accurately. In order to make use of higher-order statistics to get more useful information and meet the requirements of real-time fault diagnosis and sensitivity, a new method of fault detection and diagnosis is proposed based on multivariate statistical kernel principal component analysis (MSKPCA), which combines statistic pattern analysis framework (SPA) and kernel principal component analysis (KPCA). First, the transformation of multivariate statistics and kernel function are conducted in which technology of moving time window is used. Then, PCA is executed to analysis the kernel function obtained from the first step. Moreover, the statistics of T^2 and SPE and the control limits of them are calculated. Finally, simulations on a typical nonlinear numerical example show that the proposed MSKPCA method is more effective than PCA and KPCA in terms of fault detection and diagnosis.

목차

Abstract
 1. Introduction
 2. Multivariate Statistical Sample Space Constructing
 3. Improved Kernel Principal Component Analysis
 4. Simulation Experiment
 5. Conclusions
 References

키워드

저자

• Kaiwen Luo [ Department of Information Engineering, Logistical Engineering University, Chongqing 400016, China ]
• Shenglin Li [ Department of Information Engineering, Logistical Engineering University, Chongqing 400016, China ]
• Ren Deng [ Department of Information Engineering, Logistical Engineering University, Chongqing 400016, China ]
• Wei Zhong [ Department of Information Engineering, Logistical Engineering University, Chongqing 400016, China ]
• Hui Cai [ Department of Information Engineering, Logistical Engineering University, Chongqing 400016, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605