13-05-2021 дата публикации
Номер: US20210140431A1
A load identification method for reciprocating machinery based on information entropy and envelope features of an axis trajectory of a piston rod. According to the present disclosure, firstly, the position of an axial center is calculated according to a triangle similarity theorem to obtain an axial center distribution; secondly, features are extracted from the axial center distribution of the piston rod by means of an improved envelope method for discrete points as well as an information entropy evaluation method; thirdly, a dimensionality reduction is carried out on the features by means of manifold learning to form a set of sensitive features of the load; and finally, a neural network is trained to obtain a load identification classifier to fulfill automatic identification on the operating load of the reciprocating machinery. The advantages of the present disclosure are verified by means of actual data of a piston rod of a reciprocating compressor. 1{'sub': m', '1', '2', '3', 'm', 'm', '1', '2', '3', 'm', 'n', 'm', 'm', '1', 'm', 'm', '2', 'm', 'm', 'n, 'sup': T', 'T', 'T', 'T, 'step 1. setting different load conditions Load={0, d, 2d, 3d, . . . , wd}, w=0, 1, 2, . . . wherein d represents a load gradient, and the number of the load conditions is (w+1) in total; respectively acquiring, by an on-line monitoring system of reciprocating machinery, an original deflection displacement X={x, x, x, . . . , x} and original settlement displacement Y={y, y, y, . . . , y} of a piston rod in a corresponding load condition through an eddy current displacement sensor in a horizontal direction and an eddy current displacement sensor in a vertical direction to obtain an original data set XY={X,Y), (X,Y), . . . , (X,Y)}, wherein m represents the number of sampling points, and n represents the number of data groups;'}{'sub': m', 'm', 'm', '1', '2', '3', 'm', 'm', '1', '2', '3', 'm', 'n', 'm', 'm', 'm', '2', 'm', 'm', 'n, 'sup': T', 'm', 'T', 'T', 'T, 'step 2. removing average ...
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