A rotary machine fault feature extraction method based on time-frequency spectrum correlation analysis
A correlation analysis and rotating machinery technology, applied in the testing of mechanical components, computer components, machine/structural components, etc., can solve the problem of inability to reduce noise, difficulty in effectively extracting fault characteristic frequencies, and unintuitive regularity of impact characteristics Display and other problems to achieve the effect of removing noise and other interference signals and removing influence
Active Publication Date: 2019-06-28
ZHEJIANG COLLEGE OF ZHEJIANG UNIV OF TECHOLOGY
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The rotary machine fault feature extraction method based on time-frequency spectrum correlation analysis comprises the following steps: firstly, collecting a group of vibration signals on rotary machine equipment, and carrying out time-frequency transformation on the vibration signals to obtain a time-frequency spectrum; secondly, selecting an impact feature in the time-frequency spectrum, enabling the impact feature to perform point-by-point translation along a time axis from the initial time of the time-frequency spectrum, continuously masking a time-frequency block in the time-frequency spectrum, calculating a correlation coefficient between the impact feature and the masked time-frequency block, and obtaining a correlation coefficient sequence after translation is finished; finally, performing Fourier transform on the correlation coefficient sequence, and extracting the fault characteristic frequency from the frequency spectrum. The obtained correlation coefficient sequence curvecan visually display the change rule of the impact characteristic in the vibration signal of the rotary mechanical equipment, and the influence of noise and other interference signals is greatly removed; and the frequency spectrum of the correlation coefficient sequence can effectively and conveniently extract the fault characteristic frequency for fault diagnosis of the rotary mechanical equipment.
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