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Method and system for ascertaining anomalies in electric motors

  • US 6,006,170 A
  • Filed: 09/25/1997
  • Issued: 12/21/1999
  • Est. Priority Date: 06/28/1996
  • Status: Expired due to Fees
First Claim
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1. A computer-based method for ascertaining anomalies in an electric motor, comprising the steps of:

  • (a) obtaining a set of samples of supply current for a motor known to be in a normal condition;

    (b) computing and processing a set of Fast Fourier Transforms (FFT'"'"'s) based on waveforms of said supply current for said motor known to be in a normal condition so as to create a feature set of input vectors;

    (c) calculating all possible input vector assignments for clusters 1 through n using Ward'"'"'s method, where n is the number of p-dimensional input vectors, in accordance with the following steps;

    Step 1;

    initializing,Step 2;

    setting k, the number of groups considered, equal to n, the number of input vectors,Step 3;

    setting the "best value" Z[pk13 1, qk-- 1, k-1] equal to some initial large value and putting i equal to the smallest active identification number,Step 4;

    setting j equal to the first active identification number greater than i,Step 5;

    computing Z[i,j,k-1] associated with the hypothesized union of sets i and j,Step 6;

    determining whether Z[i,j,k-1] is better than the best value Z[pk-- 1, qk-- 1, k-1] up to this comparison;

    if the answer yes, this is followed byStep 7, replacing the old value of Z[pk-- 1, qk-- 1, k-1] by Z[i,j,k-1] and making pk-- 1=i and qk-- 1=j and proceeding to Step 8;

    if the answer is no, this is followed byStep 8, ascertaining whether j is equal to the last active identification number;

    if the answer is no, thenStep 9, set j equal to the next higher active identification number, and return to Step 5;

    if the answer is yes, proceed toStep 10, determine whether i is egual to the next higher active identification number;

    if not proceed to Step 11;

    if yes, proceed to Step 12,Step 11, set i equal to the next higher active identification number (Step

         11) and return to Step 4;

    Step 12, find a best union of 2 sets and identify it by the identification numbers pk-- 1 and qk-- 1, the value associated with their union being Z[pk-- 1, qk-- 1, k-i], then toStep 13, identify the new union by the number pk-- 1 and make the identification number qk-- 1 inactive, Step 14, calculate trace values for 2 clusters that were merged and for the new cluster which was formed by the merge,Step 15, compute lambda-- r and E-- r for this step in the clustering procedure,Step 16, determine whether k, the number of groups under consideration, is equal to 2;

    if not, proceed to Step 17;

    if the answer is yes, proceed to Step 18,Step 17, set k equal to k-1 (Step

         17) and return to step 2;

    Step 18, calculate the approximate weight of evidence for all steps from 1 to n-1;

    (d) computing the Approximate Weight of Evidence (AWE) for said cluster counts 1 through n;

    (e) selecting that count associated with a maximum AWE and designating this count as s;

    (f) for each cluster 1 through s, finding the member vector farthest from the cluster'"'"'s centroid and defining this as the cluster'"'"'s radius;

    (g) reading in a single input sample for a motor under supervision;

    (h) computing and processing an FFT based on said input sample for newly generating a feature vector; and

    (i) checking whether said newly generated feature vector is inside any of said clusters 1 through s as defined for each cluster by said radius; and

    (j) if not, outputting a warning signal.

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