Abstract
Feature extraction plays an important role in the field of fault diagnosis of analog circuits. How to effectively extract fault features is crucial to diagnostic accuracy. The components tolerance and circuit nonlinearities of analog circuits can cause some part overlapping of primal signal among different component faults in time domain and frequency domain. Currently, the existing method aims at wavelet features, statistical property features, conventional frequency features and conventional time-domain features. There is no decoupling ability for the feature extraction methods mentioned above. To solve the problem, a new fault features extraction method is proposed. The diagnostic results are compared with those from other methods. Firstly, it is proposed to use the statistical property features of transformed signals by the fractional Fourier transform in the optimal fractional order domain as fault features, such as range, mean, standard deviation, skewness, kurtosis, entropy, median, the third central moment, and centroid. And then, KPCA is used to reduce the dimensionality of candidate features so as to obtain the optimal features. Next, normalization is applied to rescale input features. Finally, extracted features are trained by SVM to diagnose faulty components in analog circuits. The simulation results show that compared with traditional methods, the proposed method is quite efficient to improve diagnostic accuracy.
Similar content being viewed by others
References
Almeida, L. B. (1994). The fractional Fourier transform and time-frequency representations. IEEE Transactions on Signal Processing, 42(11), 3084–3091.
Aminian, M., & Aminian, F. (2000). Neural-network based analog-circuit fault diagnosis using wavelet transform as preprocessor. IEEE Transactions on Circuits and Systems Part II: Express Briefs, 47(2), 151–156.
Aminian, M., & Aminian, F. (2001). Fault diagnosis of nonlinear analog circuits using neural networks with wavelet and Fourier transforms as preprocessors. Journal of Electronic Testing, 17(6), 471–481.
Aminian, M., Aminian, F., & Collins, H. W. (2002). Analog fault diagnosis of actual circuits using neural networks. IEEE Transactions on Instrumentation and Measurement, 51(3), 544–550.
Aminian, M., & Aminian, F. (2007). A modular fault-diagnostic system for analog electronic circuits using neural networks with wavelet transform as a preprocessor. IEEE Transactions on Instrumentation and Measurement, 56(5), 1546–1554.
Bandler, J. W., & Salama, A. E. (1985). Fault diagnosis of analog circuits. Proceedings of the IEEE, 73(8), 1279–1325.
Billur, B., & Birsel, A. (2002). Fractional Fourier transform pre-processing for neural networks and its application to object recognition. Neural Networks, 15(1), 131–140.
Cui, J., & Wang, Y. R. (2011). A novel approach of analog circuit fault diagnosis using support vector machines classifier. Measurement, 44(1), 281–289.
Fedi, G., Giomi, R., Luchetta, A., Manetti, S., & Piccirilli, M. C. (1998). On the application of symbolic techniques to the multiple fault location in low testability analog circuits. IEEE Transactions on Circuits and Systems Part II: Analog and Digital Signal Processing, 45(10), 1383–1388.
Kumar, A., & Singh, A. P. (2013). Fuzzy classifier for fault diagnosis in analog electronic circuits. ISA Transactions, 52(6), 816–824.
Li, F., & Woo, P. Y. (2002). Fault detection for linear analog IC-the method of short-circuit admittance parameters. IEEE Transactions on Automatic Control, 49(1), 105–108.
Mendlovic, D., & Ozaktas, H. M. (1993). Fractional Fourier transforms and their optical implementation: Part I. Journal of the Optical Society of America, 10(9), 1875–1881.
Mendlovic, D., & Ozaktas, H. M. (1993). Fractional Fourier transforms and their optical implementation: part II. Journal of the Optical Society of America, 10(12), 2522–2531.
Namias, V. (1980). The fractional order Fourier transform and its application to quantum mechanics. IMA Journal of applied mathematics: Institute of Mathematics and Its Application, 25(3), 241–265.
Spina, R., & Upadhyaya, S. (1997). Linear circuit fault diagnosis using neuromorphic analyzers. IEEE Transactions on Circuits and Systems II: Express Briefs, 44(3), 188–196.
Stopjakova, V., Malosek, P., & Nagy, V. (2006). Neural network-based defect detection in analog and mixed IC using digital signal preprocessing. Journal of Electrical Engineering, 57(5), 249–257.
Sun, J. J., Zhao, J. J., & Sun, W. M. (2011). Research on analog circuit fault feature extraction based on FRFT-KPCA Method. In 10th International conference on electronic measurement & instruments (pp. 170–174).
Tang, J., Hu, Y. A., Lin, T., & Chen, Y. (2010). Analog circuit fault diagnosis based on fuzzy support vector machine and kernel density estimation. In 3th International conference on advanced computer theory and engineering (pp. 544–548).
Yuan, L. F., He, Y. G., Huang, J. Y., & Sun, Y. C. (2010). A new neural-network-based fault diagnosis approach for analog circuits by using kurtosis and entropy as a preprocessor. IEEE Transactions on Instrumentation and Measurement, 59(3), 586–595.
Zuo, L., Hou, L. G., Zhang, W., & Wu, W. C. (2010). Applying wavelet support vector machine to analog circuit fault diagnosis. In 2nd International workshop on education technology and computer science (pp. 75–78).
Zheng, H. Y., Li, H. B., & Zeng, F. J. (2011). Improvement and application of a real-timing analog circuit fault diagnosis method. In 4th international conference on intelligent computation technology and automation, Shenzhen (pp. 155–158).
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Song, P., He, Y. & Cui, W. Statistical property feature extraction based on FRFT for fault diagnosis of analog circuits. Analog Integr Circ Sig Process 87, 427–436 (2016). https://doi.org/10.1007/s10470-016-0721-5
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10470-016-0721-5