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- W2375670490 abstract "Aims at the scarce use the vibration multi-dimensions image information of the rotating machinery in the fault diagnosis that affects the promotion and utilization of the diagnosis technology to some extent,a diagnosis method that is based on gray level-gradient co-occurrence matrix is studied.This method directly extracts the dimensional relationship characteristics information between pixel points and their adjacent ones points in time-frequency gray level figures of the vibration signal for rotating machinery by using gray level-gradient co-occurrence matrix that can show the distribution law of gray level and gradient of the pixel points.Rotating machinery fault diagnosis can be directly conducted by using BP artificial neural networks after extracting the information of image texture characteristic.This method is validated to get high diagnosis accuracy by conducting the tests for normal rotor,unbalanced rotor,misaligned rotor and loose bearing pedestal on the modeling of 600 MW turbine experimental bench,and compared with the method based on gray co-occurrence matrix.The presented method is feasible and can improve the diagnosis accuracy." @default.
- W2375670490 created "2016-06-24" @default.
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- W2375670490 date "2009-01-01" @default.
- W2375670490 modified "2023-09-26" @default.
- W2375670490 title "A fault diagnosis method based on gray level-gradient co-occurrence matrix of time-frequency image for rotating machinery" @default.
- W2375670490 hasPublicationYear "2009" @default.
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