Combined slicing bearing fault diagnosis method on basis of MID (modulation intensity distribution) algorithm

Abstract

The invention disclose a combined slicing bearing fault diagnosis method on the basis of the MID (modulation intensity distribution) algorithm and relates to the technical field of bearing testing methods. The combined slicing bearing fault diagnosis method includes steps of determining axial frequency of a bearing and revolving frequency and theoretical characteristic frequency values of typical faults of a retainer according to measured revolving speed of the bearing; determining an axial frequency fluctuation range of the bearing and a fluctuation range of the axial frequency of the retainer according to revolving fluctuation of the bearing and calculating the axial frequency of the bearing and a modulation intensity slicing zone within the fluctuation range of the revolving frequency of the retainer respectively; finding out the maximum energy slices of a local part of the frequency fluctuation in the modulation intensity slicing zone to obtain corresponding actual axial frequency of the bearing and actual revolving frequency of the retainer; directly determining actual characteristic frequency of the typical faults; calculating a single slice of modulation intensity of the actual characteristic frequency of the typical faults; judging whether bearing signals are faulted, and if yes, outputting types and positions of the faults. The combined slicing bearing fault diagnosis method is high in calculating efficiency and insensitive to noise and has high advantages for extraction of weak fault characteristics.

Description

technical field [0001] The invention relates to the technical field of bearing testing methods, in particular to a combined slice bearing fault diagnosis method based on MID algorithm. Background technique [0002] Most of the motion of rotating machinery is periodic rotation and reciprocation, so there are a large number of random and periodic components in the vibration signal, and its second-order statistical characteristic parameters show non-stationary characteristics due to time-varying. When complex components such as bearings and gears fail, their carrier (modulation source) is often discrete or random, and the modulated signal is often submerged in strong background noise, which is difficult to separate and extract. The envelope demodulation analysis method based on the classic Fourier (Fourier) transform assumes that the analyzed signal has the characteristics of linearity, stationarity and minimum phase, and it is a global transformation of the signal. Although t...

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Problems solved by technology

ICC and IMID can extract fault features very well, but before determining the fault type, a large number of calculations are required to estimate the description matrix of the modulation density factor, which cannot meet the real-time requirements of industrial production

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