Switched circuit fault diagnosis method based on wavelet transformation and ICA (independent component analysis) feature extraction

Abstract

The invention discloses a switched circuit fault diagnosis method based on wavelet transformation and ICA (independent component analysis) feature extraction. The switched circuit fault diagnosis method includes the steps of firstly, performing classifier training and fault dictionary construction, namely, based on circuit simulation, acquiring feature parameters by a method based on wavelet transformation and ICA feature extraction, and constructing a fault dictionary and a training classifier based on the feature parameters; secondly, performing fault diagnosis, namely, acquiring the feature parameters aiming at a switched current circuit to be diagnosed by referring to the fault dictionary and by the method based on wavelet transformation and ICA feature extraction, inputting the feature parameters into a trained classifier, and subjecting the switched current circuit to be diagnosed to fault diagnosis to obtain output signals of the classifier, namely, fault diagnosis results. The switched circuit fault diagnosis method based on wavelet transformation and ICA feature extraction has the advantages of ingenious concept, easiness in implementation, simulation proving and capability of distinguishing various fault types more accurately as compared with an existing method.

Description

technical field [0001] The invention relates to a switching circuit fault diagnosis method based on wavelet transform and ICA feature extraction. Background technique [0002] Switched Current (SI) technology is an analog sampling data signal processing technology proposed in the late 1980s. As an alternative technology of switched capacitor technology, it processes continuous-time analog signals with discrete-time sampling data, and has the advantages of low voltage, high speed, low power consumption, small chip area and good high-frequency characteristics, and has gained rapid development in the past ten years. development of. SI technology does not contain linear capacitors and high-performance operational amplifiers, is fully compatible with digital CMOS process technology, and is easy to realize monolithic integration of large-scale digital-analog hybrid circuits. However, testing and diagnostics of the analog portion of mixed-signal circuits has been slow. Although ...

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Problems solved by technology

Due to the measurement of current parameters, the amount of relevant fault information used for testing and diagnosis is incomplete, so that fault location cannot be accurately performed

[0005] In the literature [Guo, J., R., He, Y.G., Liu M.R..Wavelet neural network approach for testing of switched-current circuits.J Electron Test, 27: 611-625, 2011.] [recorded as literature [3 ]], the author uses the wavelet neural network to diagnose SI circuits and can correctly diagnose all hard faults, but the diagnosis rate for soft faults, especially for low-sensitivity transistors, is very low, only about 80%

In addition, in the literature [Long, Y., He, Y.G., & Yuan, L.F.Fault dictionary based switched current circuit fault diagnosis using entropy as a preprocessor.Analog Integrated Circuits and Signal Processing, 66(1), 2011:93-102.] [Recorded as document [1]], the author introduced the concept of fault feature preprocessing in SI circuit testing and diagnosis for the first time, through the information entropy preprocessing feature extraction of the collected fault response signals, and the calculation of information entropy fuzzy set construction Fault dictionary, for fault classification, reduces calculation and fault diagnosis time, and the diagnosis accuracy rate reaches about 95%, but this method is only suitable for fault diagnosis of small and medium-scale switching current circuits

In the literature [Zhang, Z., Duan, Z., Long, Y., & Yuan, L.F.A new swarm-SVM-based fault diagnosis approach for switched current circuit by using kurtosis and entropy as a preprocessor.Analog Integrated Circuits and Signal Processing, vol.81, no.1.2014.] [recorded as literature [2]], the author added a characteristic parameter - kurtosis, proposed the SI circuit test and Diagnosis method, the correct rate of soft fault diagnosis has been further improved, reaching about 99%, but it still cannot reach 100% fault diagnosis and identification

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