Fault Detection of Artillery Automatic Loading System Based on PCA

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The motion process of the automatic loading system is a high overloading and intermittent motion environment will bring about motor windings loosening, transmission system wear and tear, fracture, sensor failure and other security risks or system failures. In the paper no-stationary signal analysis by wavelet transform through wavelet decomposition and non-linear threshold de-noising. And use PCA established system model for on-line monitor. By calculate and analysis four kind of result to find fault source. Finally through the experimental prove the reliability of the method.

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Periodical:

Edited by:

Liangzhong Jiang

Pages:

459-464

Citation:

P. J. Zhang et al., "Fault Detection of Artillery Automatic Loading System Based on PCA", Advanced Materials Research, Vol. 590, pp. 459-464, 2012

Online since:

November 2012

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$38.00

[1] Zhang Chuangmin. A large caliber artillery self-loading machine control technology [D], Nanjing: Nanjing University of Science and Technology, 2003: 2-4.

[2] Cheng Huitao, Huang Wenhu, Jiang Xingwei. Gray model based fault forecasting technique and its application in propulsion system of space[J]. Journal of Propulsion Technology, 1998(6): 24-27.

[3] Hu Guangshu. Digital Signal Processing - theory, algorithms and practice [M] . Beijing, Tsinghua University Press, 2001: 3 -4.

[4] Wang Xiaobin, Niu Zheng, Niu Yuguang, et al. Behavior analysis of sensor fault detection using PCA approach[J]. Chinese Journal of Sensors And Actuators, 2003, 12 (4): 419-421.

[5] Zhang Jie, Yang Xianhui. Multivariate Statistical Process Control [M]. Beijing: Chemical Industry Press, 2000: 24 -28, 70 -77.

[6] Russel E L, Chiang L H, Braatz R D. Fault detection in industrial process using canonical variate analysis and dynamic principal component analysis[J]. Chemometrics and Intelligent Laboratory System, 2000, 51: 80-94.

DOI: https://doi.org/10.1016/s0169-7439(00)00058-7

[7] RAICH A, CINAR A.Diagnosis of process disturbance by statistical distance and angle measures[J].Computers and Chemical Engineering, 1997, 21(6):661-673.

DOI: https://doi.org/10.1016/s0098-1354(96)00299-2

[8] Tipping M E, Bishop C M. Probbailistic principal component analysis[J]. Journal of the Royal Statistical Society: B. 1999, 61(3): 611-622.