Switching circuit fault classifying method based on wavelet transform and ICA feature extraction
Switching circuit fault classifying method based on wavelet transform and ICA feature extraction
 CN 104,714,171 A
 Filed: 04/06/2015
 Published: 06/17/2015
 Est. Priority Date: 04/06/2015
 Status: Active Application
First Claim
1. , based on an onoff circuit Fault Classification for wavelet transformation and ICA feature extraction, the method is used for the failure modes of SwitchedCurrent Circuit, it is characterized in that, comprises the following steps:
 Step 1;
produce pseudo random signal as test and excitation signal;
Pseudo random signal is pseudorandom pulse sequence;
Step 2;
failure definition pattern;
Based on circuit simulation, carry out sensitivity analysis to SwitchedCurrent Circuit to be measured, the change obtaining component parameters changes the single order of electric network system features, with the fault element most possibly broken down in positioning circuit;
And divide fault mode based on fault element location;
The quantity of fault element is N, then the kind of fault mode is 2*N;
N is natural number;
Step 3;
the original response data of Acquisition Circuit;
Encourage tested SwitchedCurrent Circuit by pseudo random signal, with ASIZ software, the various malfunction of tested SwitchedCurrent Circuit and normal condition are emulated, collect original response data from the output terminal of SwitchedCurrent Circuit;
These original response data are curtage data;
Step 4;
adopt Haar small echo orthogonal filter to carry out preservice to original response data;
Utilize Haar small echo orthogonal filter as the pretreatment system of acquisition sequence, obtain lowfrequency approximation information and the detail of the high frequency of observation signal;
Step 5;
Fault characteristic parameters extracts;
Entropy and the kurtosis of lowfrequency approximation information and detail of the high frequency is calculated respectively for pretreated signal;
Obtain following Fault characteristic parameters;
lowfrequency approximation entropy, lowfrequency approximation kurtosis, lowfrequency approximation entropy fuzzy set, lowfrequency approximation kurtosis fuzzy set, high frequency detail entropy, high frequency detail kurtosis, high frequency detail entropy fuzzy set and high frequency detail kurtosis fuzzy set;
Step 6;
based on the Fault characteristic parameters structure fault dictionary extracted, thus realize onoff circuit failure modes.
Chinese PRB Reexamination
Abstract
The invention discloses a switching circuit fault classifying method based on wavelet transform and ICA feature extraction. The method comprises the following steps: (1) generating a pseudo random signal as a test stimulation signal; (2) defining a fault mode; (3) acquiring the original response data of the circuit; (4) pretreating the original response data by a Haar wavelet orthogonal filter; (5) extracting the fault feature parameters, and calculating the entropy and kurtosis as well as fuzzy sets thereof of lowfrequency approximate information and highfrequency detail information for the pretreated signal respectively; and (6) constructing a fault dictionary based on the extracted fault feature parameters so as to realize fault classification of the switching circuit. The method disclosed by the invention has the advantages of skillful concept, easiness in implementation and simulation proof and can distinguish the fault types more accurately than the existing method.

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7 Claims

1. , based on an onoff circuit Fault Classification for wavelet transformation and ICA feature extraction, the method is used for the failure modes of SwitchedCurrent Circuit, it is characterized in that, comprises the following steps:

Step 1;
produce pseudo random signal as test and excitation signal;Pseudo random signal is pseudorandom pulse sequence; Step 2;
failure definition pattern;Based on circuit simulation, carry out sensitivity analysis to SwitchedCurrent Circuit to be measured, the change obtaining component parameters changes the single order of electric network system features, with the fault element most possibly broken down in positioning circuit;
And divide fault mode based on fault element location;
The quantity of fault element is N, then the kind of fault mode is 2*N;
N is natural number;Step 3;
the original response data of Acquisition Circuit;Encourage tested SwitchedCurrent Circuit by pseudo random signal, with ASIZ software, the various malfunction of tested SwitchedCurrent Circuit and normal condition are emulated, collect original response data from the output terminal of SwitchedCurrent Circuit;
These original response data are curtage data;Step 4;
adopt Haar small echo orthogonal filter to carry out preservice to original response data;Utilize Haar small echo orthogonal filter as the pretreatment system of acquisition sequence, obtain lowfrequency approximation information and the detail of the high frequency of observation signal; Step 5;
Fault characteristic parameters extracts;Entropy and the kurtosis of lowfrequency approximation information and detail of the high frequency is calculated respectively for pretreated signal;
Obtain following Fault characteristic parameters;
lowfrequency approximation entropy, lowfrequency approximation kurtosis, lowfrequency approximation entropy fuzzy set, lowfrequency approximation kurtosis fuzzy set, high frequency detail entropy, high frequency detail kurtosis, high frequency detail entropy fuzzy set and high frequency detail kurtosis fuzzy set;Step 6;
based on the Fault characteristic parameters structure fault dictionary extracted, thus realize onoff circuit failure modes.


2. the onoff circuit Fault Classification based on wavelet transformation and ICA feature extraction according to claim 1, it is characterized in that, the computing method of described information entropy are:

Information entropy $J\left(x\right)={k}_{1}{\left(E\right\{\mathrm{xexp}({x}^{2}/2)\left\}\right)}^{2}+{k}_{2}{\left(E\right\{\leftx\right\}\sqrt{2/\mathrm{\π)2;}}}^{}$ In formula,${k}_{1}=36/(8\sqrt{3}9)$ And k _{2}=1/ (26/ π
), x are the data that the primary current response data of the circuitundertest output terminal extracted obtains through wavelet transformation;
E represents expectation value;
The computing method of described kurtosis are; Kurtosis kurt (x)=E{x ^{4}3 [E{x ^{2}] ^{2}, x is the data that the primary current response data of the circuitundertest output terminal extracted obtains through wavelet transformation, and E represents expectation value.


3. the onoff circuit Fault Classification based on wavelet transformation and ICA feature extraction according to claim 2, it is characterized in that, fuzzy set is transistor transconductance value g _{m}the information entropy obtained when range of tolerable variance changes for 5% or 10% or the constant interval of kurtosis;

Lowfrequency approximation information entropy fuzzy set and detail of the high frequency entropy fuzzy set are a numerical intervals;
Normal mode refers to the pattern that circuit does not break down;
And set a failure code to each fault mode and normal mode;Fault mode, normal mode, failure code and fault eigenvalue and fault signature fuzzy set are become a table as one group of data rows, if fault signature fuzzy set is enough to isolate all faults, namely set up the fault dictionary being used for SwitchedCurrent Circuit failure modes by existing information.


4. the onoff circuit Fault Classification based on wavelet transformation and ICA feature extraction according to claim 2, is characterized in that, in step 1, pseudo random signal is 255 pseudorandom sequences that employing 8 rank linear feedback shift register produces.

5. the onoff circuit Fault Classification based on wavelet transformation and ICA feature extraction according to claim 4, it is characterized in that, in step 2, SwitchedCurrent Circuit specialty simulation software A S I Z emulation is adopted to carry out sensitivity analysis with localizing faults element to circuit.

6. the onoff circuit Fault Classification based on wavelet transformation and ICA feature extraction according to claim 5, it is characterized in that, in step 3, timedomain analysis and 30 Monte Carlo Analysis are carried out to various fault mode and normal condition, sample to failure response signal with the sample frequency of 250KHZ at the output terminal of circuit, the sampled signal obtained is original response data simultaneously.

7. the onoff circuit Fault Classification based on wavelet transformation and ICA feature extraction according to any one of claim 16, it is characterized in that, described SwitchedCurrent Circuit is the oval band pass filter circuit in six rank chebyshev lowpass filter circuit or six rank.
Specification(s)