Rolling bearing fault diagnosis method based on integral inherent time scale decomposition algorithm

Abstract

A rolling bearing fault diagnosis method based on integral inherent time scale decomposition algorithm comprises the following steps of collecting a vibration signal of a rolling bearing via a displacement sensor, decomposing the collected vibration signal via the integral inherent time scale decomposition algorithm to generate a plurality of rotational components and residual error signals, selecting sensitive rotational components capable of reflecting fault information from all the rotational components, conducting an envelope spectrum analysis on the sensitive rotational components and determining fault types according to envelope spectrum amplitude values corresponding to the fault feature frequency. A modal mixing problem of inherent time scale decomposition algorithm can be solved and great foundation is provided for feature extraction; according to peakedness calculation, rotational components sensitive to the fault are selected; and the fault type is determined via the analysis of the sensitive component envelope spectrum amplitude values corresponding to the fault feature frequency. Rolling bearing faults can be accurately identified and the method is suitable for rolling bearing fault diagnosis.

Description

technical field [0001] The invention relates to a rolling bearing fault diagnosis method. In particular, it involves a fault diagnosis method for rolling bearings based on an ensemble intrinsic time scale decomposition algorithm. Background technique [0002] Vibration analysis is the simplest and most direct method for fault diagnosis of rolling bearings. Typical vibration analysis methods include: wavelet transform, Wigner distribution, empirical mode decomposition, etc., but they all have their own shortcomings. Wavelet transform is not adaptive and the choice of basis function depends too much on the user's experience. The Wegener distribution has high time-frequency resolution, but the appearance of cross terms limits its application. Empirical mode decomposition is an adaptive time-frequency decomposition method, which has been widely used in power machinery fault diagnosis, but this method has over-envelope, modal confusion, end-point effects, and problems caused by...

AI Technical Summary

Problems solved by technology

The first problem is that the added white noise cannot be completely offset. Although the final white noise residual can be reduced by selecting a larger number of added noises, the corresponding calculation time will also be greatly increased, which is contrary to the impact of fault diagnosis on the algorithm speed. requirements

Another problem is that the addition of different white noise may cause the number of rotation components generated by each decomposition to be unequal, which brings difficulties to the final aggregated average.

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