Rolling bearing fault characteristic extraction method based on dictionary learning
The invention discloses a rolling bearing fault characteristic extraction method based on dictionary learning, and belongs to the field of signal processing. The method comprises the following steps that: obtaining a vibration signal from a sensor installed on a bearing to be shown as s, and marking...
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Main Authors | , , , , |
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Format | Patent |
Language | Chinese English |
Published |
21.12.2018
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Subjects | |
Online Access | Get full text |
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Summary: | The invention discloses a rolling bearing fault characteristic extraction method based on dictionary learning, and belongs to the field of signal processing. The method comprises the following steps that: obtaining a vibration signal from a sensor installed on a bearing to be shown as s, and marking sampling frequency as fs; adopting a harmonic separation filter, removing harmonic components whichare contained in the s and are generated by the vibration of other mechanisms to obtain a processing signal y; carrying out Shift-Invariant K-SVD (Singular Value Decomposition) dictionary learning onthe signal y to obtain sparse representation and an optimal dictionary, and according to the sparse representation and the dictionary, reconstructing and recovering a pure fault signal x; and carrying out envelope spectrum transformation on the signal x to obtain the frequency characteristics of a bearing fault vibration signal. By use of the method, a sparse representation method is adopted, andearly weak fault character |
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Bibliography: | Application Number: CN201811027613 |