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
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1. , based on an on-off circuit Fault Classification for wavelet transformation and ICA feature extraction, the method is used for the failure modes of Switched-Current 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 pseudo-random pulse sequence;

    Step 2;

    failure definition pattern;

    Based on circuit simulation, carry out sensitivity analysis to Switched-Current 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 Switched-Current Circuit by pseudo random signal, with ASIZ software, the various malfunction of tested Switched-Current Circuit and normal condition are emulated, collect original response data from the output terminal of Switched-Current Circuit;

    These original response data are curtage data;

    Step 4;

    adopt Haar small echo orthogonal filter to carry out pre-service to original response data;

    Utilize Haar small echo orthogonal filter as the pretreatment system of acquisition sequence, obtain low-frequency approximation information and the detail of the high frequency of observation signal;

    Step 5;

    Fault characteristic parameters extracts;

    Entropy and the kurtosis of low-frequency approximation information and detail of the high frequency is calculated respectively for pretreated signal;

    Obtain following Fault characteristic parameters;

    low-frequency approximation entropy, low-frequency approximation kurtosis, low-frequency approximation entropy fuzzy set, low-frequency 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 on-off circuit failure modes.

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