The soft fault diagnosis of nonlinear analog circuits is an important guarantee of the stable and reliable operation of electronic products. In view of the low accuracy and heavy computation load of current soft fault diagnosis methods for nonlinear analog circuits, this paper presents a soft fault diagnosis method for nonlinear analog circuits based on fractal theory. Analyzing the single-fractal and generalized multi-fractal diagnosis mechanisms, and taking the fault signal as an example, the proposed method calculate the fractal dimension of the fault signal by the single-fractal box dimension and generalized multi-fractal dimension calculation method, and analyzes the influence of different frequency input signals on the features of the fault state signal through experimental simulation. It is concluded that the increasing frequency of the input signal has little effect on the fractal characteristics of the fault signal. Comparing the single-fractal and the generalized multi-fractal diagnosis method, the author discovers that the effect is better when generalized multi-fractal dimension sequence is used to diagnose the circuit fault.
Single-fractal, generalized multi-fractal, feature extraction, fault diagnosis, nonlinearity
1. S. Pavan, Efficient simulation of weak nonlinearities in continuous-time oversampling converters, 2010, IEEE Trans. Circuits Syst. I, vol. 57, no. 8, pp. 1925-1934.
2. A. Borys, On Definition of Operator o for Weakly Nonlinear Circuits, 2016, International Journal of Electronics and Telecommunications, vol. 62, no. 3, pp. 253-259.
3. W.Z Wang, X.Q. Tang, W. Ou, et al, Volterra Series Identifitication: Overview and a Simplified Method, 1999, Journal of Nonlinear Dynamics in Science and Technology, vol. 6, no. 1, pp. 30-36.
4. H.Y. Yuan, T.L. Wang, G. Chen, et al, Fault diagnosis method in nonlinear analog circuit based on Volterra frequency-domain kernel and neural network, 2007, Chinese Journal of Scientific Instrument, vol. 28, no. 5, pp. 807-811.
5. H.J. Lin, L.Y. Zhang, D.Y. Ren, et al, Fault diagnosis in nonlinear analog circuit based on Wiener kernel and BP neural network, 2009, Chinese Journal of Scientific Instrument, vol. 30, no. 9, pp. 1946-1949.
6. A.D. Asfani, Syafaruddin, H.M. Purnomo, et al, Neural network based real time detection of temporary short circuit fault on induction motor winding through wavelet transformation, 2014, International Journal of Innovative Computing, Information and Control, vol. 10, no. 6, pp. 2277-2293.
7. J. Seshadrinath, B. Singh, B.K. Panigrahi, Single-turn fault detection in induction machine using complex-wavelet-based method, 2012, IEEE Transactions on Industry Applications, vol. 48, no. 6, pp. 1846-1854.
8. S. J. Giaccone, R. G. Bossio, G. O. García, et al, Wavelet analysis for stator fault detection in induction machines, 2011, International Journal of Wavelets, Multiresolution and Information Processing, vol. 9, no. 3, pp. 361-374.
9. X.B. Mao L.H. Wang, C.X. Li, SVM classifier for analog fault diagnosis using fractal features, 2008, Second International Symposium on Intelligent Information Technology Application, 2008, Shanghai, China, pp. 553-557.
10. S.L. Zhou, J. Liao, X.J. Shi, New Method to Extract Analog Circuit Fault Features Based on FrFT-FD and KPCA, 2014, Journal of Vibration, Measurement and Diagnosis, Vol. 34, No. 2, pp. 337-344.
11. V. Uritsky, N. Smirnova, V. Troyan, et al, Critical dynamics of fractal fault systems and its role in the generation of pre-seismic electromagnetic emissions, 2004, Physics and Chemistry of the Earth, vol. 29, no. 4-9, pp. 473-480.
12. P. Purkait, S. Chakravorti, Impulse fault classification in transformers by fractal analysis, 2003, IEEE Transactions on Dielectrics and Electrical Insulation, vol. 10, no. 1, pp. 109-116.
13. Z.Z. Wang, M. Han, P. Liu, et al, Fault detection of motor's inter-turn short circuit based on BP neural network, 2012, International Journal of Modeling, Identification and Control, vol. 16, no. 3, pp. 234-240.
14. X.J. Xiang, Z. Lin, the design of arc fault circuit interrupter based on wavelet transformation, 2013, Applied Mechanics and Materials, vol. 261-262, pp. 476-481.
15. N. Huang, H. Chen, G. Cai, et al, Mechanical Fault Diagnosis of High Voltage Circuit Breakers Based on Variational Mode Decomposition and Multi-Layer Classifier, 2016, Sensors, vol. 16, no. 11, pp. 1887.