Fault Diagnosis Method of Switching Circuit Based on Wavelet Transform and ICA Feature Extraction

Abstract

The invention discloses a switching circuit fault diagnosis method based on wavelet transform and ICA feature extraction, step 1: classifier training and constructing a fault dictionary, based on circuit simulation, adopting a method based on wavelet transform and ICA feature extraction to obtain characteristic parameters, based on The characteristic parameters construct the fault dictionary and train the classifier; Step 2: Fault diagnosis: refer to the fault dictionary, use the method based on wavelet transform and ICA feature extraction to obtain the characteristic parameters for the switching current circuit to be diagnosed, and input the characteristic parameters into the training Fault diagnosis is carried out in the classifier of the switch current circuit to be tested, and the output signal of the classifier is the fault diagnosis result. The invention has an ingenious concept and is easy to implement. The simulation proves that compared with the existing method, various fault types can be more accurately distinguished .

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] In ], the author used 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.]【Note For the literature [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 signal, and the calculation of information entropy fuzzy set to construct the fault dictionary , carry out fault classification, reduce 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 entropyas a preprocessor. Analog Integrated Circuits and Signal Processing, vol. 81, no.1.2014.] [recorded as literature [2]], the author added a characteristic parameter - kurtosis, and proposed a SI circuit testing and diagnosis method based on information entropy and kurtosis preprocessing of particle swarm support vector machine , 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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