Fault Diagnosis Method for Analog Circuits Based on Matrix Perturbation Analysis

被引：0
|
作者：
Zhou Q. [1 ,2 ]
Xie Y. [2 ]
机构：
[1] School of Physics and Electronic Engineering, Yibin University, Yibin
[2] School of Automation Engineering, University of Electronic Science and Technology of China, Chengdu
来源：
| 1600年 / Science Press卷 / 52期
关键词：
Analog circuit; Fault diagnosis; Fault model; Matrix perturbation; Parameter identification;
D O I：
10.3969/j.issn.0258-2724.2017.02.022
中图分类号：
学科分类号：
摘要：
To integrate the fault detection, fault localization and parameter identification of analog circuits in one system and reduce the cost and facilitate the engineering implementation of fault diagnosis, an fault diagnosis and parameter identification method for analog circuits based on matrix perturbation theory was proposed. First, curve fitting for the sampled time series of the faulty circuit was conducted, and the phase deviation of the circuit was treated as one fault signature. Then, a matrix was built using the sampled time series, and the trace of this matrix was used as the other fault signature. Finally, the phase deviation and trace were used as joint fault signatures, and the fault diagnosis model was established according to the correspondence between the changes of the fault signatures and the fault device parameter variations. The experimental results of two international standard circuits show that the accuracy of fault location ranges from 98.5% to 100% and the error of fault parameter identification is in the range of -1.2% to 1.72%. © 2017, Editorial Department of Journal of Southwest Jiaotong University. All right reserved.
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页码：369 / 378
页数：9
相关论文
共 19 条
  • [1] Li F., Woo P.Y., Fault detection for linear analog IC-the method of short-circuits admittance parameters, IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications, 49, 1, pp. 105-108, (2002)
  • [2] Jeongjin R., Abraham J.A., Subband filtering for time and frequency analysis of mixed-signal circuit testing, IEEE Transactions on Instrumentation and Measurement, 53, 2, pp. 602-611, (2004)
  • [3] Tadeusiewicz M., Halgas S., Korzybski M., An algorithm for soft fault diagnosis of linear and nonlinear circuits, IEEE Transactions on Circuits and Systems I Fundamental Theory and Applications, 49, 11, pp. 1648-1653, (2002)
  • [4] Aminian F., Aminian M., Collins H.W., Analog fault diagnosis of actual circuits using neural networks, IEEE Transactions on Instrumentation and Measurement, 51, 3, pp. 544-550, (2002)
  • [5] Zhao L., Mu J., Fault diagnosis method for jointless track circuit based on AOK-TFR, Journal of Southwest Jiaotong University, 46, 1, pp. 84-91, (2011)
  • [6] Xiao L., Wang T., Qin X., Remote virtual fault diagnosis system of equipment based on COM, Journal of Southwest Jiaotong University, 38, 5, pp. 566-569, (2003)
  • [7] Yang C., Tian S., Long B., Et al., Methods of handling the tolerance and test-point selection problem for analog-circuit fault diagnosis, IEEE Transactions on Instrumentation and Measurement, 60, 1, pp. 176-185, (2011)
  • [8] Aminian M., Aminian F., Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor, IEEE Transactions on Circuits and Systems II Analog and Digital Signal Processing, 47, 2, pp. 151-155, (2000)
  • [9] Yang C., Tian S., Liu Z., Et al., Fault modeling on complex plane and tolerance handling methods for analog circuits, IEEE Transactions on Instrumentation and Measurement, 62, 11, pp. 2730-2738, (2013)
  • [10] Dai X., Zhong G., Wu H., Application of the wavelet transform to the failure diagnosis of tilting trains, Journal of Southwest Jiaotong University, 35, 6, pp. 651-655, (2000)
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