Oil pumping well semi-supervised fault diagnosis method based on curvelet transformation and kernel sparsity
A curvelet transform and fault diagnosis technology, applied in character and pattern recognition, instruments, computer parts, etc., can solve the problems of unused and unmarked data, can not be well combined with the actual production situation, etc., to save manpower Cost, effect of strong generalization ability
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[0033] The present invention will be further described below with reference to the accompanying drawings.
[0034] like figure 1 As shown, a semi-supervised fault diagnosis method for pumping wells based on curvelet transform and kernel sparseness of the present invention includes the following steps:
[0035] 1) Obtain n (n=l+u) dynamometer data as training samples through an on-site dynamometer, wherein l dynamometers are known label data, and u dynamometers are unlabeled data;
[0036] 2) According to the classical wave equation, the finite difference method is used to convert n dynamometer diagrams into downhole pump diagrams, and then each pump diagram is converted into a grayscale image with a size of 256×256 pixels;
[0037] 3) For each pump work diagram X i Perform curvelet transformation to obtain the coefficient matrix C of s scales of the ith pump diagram i :
[0038] C i ={c ij}, i=1,...,n, j=1,...,s, where n is the total number of pump diagrams, s=log 2 256-3=
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