Method for extracting fault characteristics of rolling bearing based on Daubechies wavelet energy base

Abstract

The invention provides a novel method for extracting fault characteristics of a rolling bearing based on a Daubechies wavelet energy base. The method comprises a step of performing Daubechies waveletdecomposition reconstruction on a rolling bearing vibration signal, a step of determining a reconstructed wavelet layer number i according to a set error value, a step of extracting first ith layer ofDaubechies wavelet with maximum specific weight and carrying out orthogonal normalization, a step of calculating the first ith layer of Daubechies wavelet and establishing a fault mode classificationspace, a step of calculating the projection coordinates of a time domain signal in the fault mode classification space under different working conditions and calibrating fault characteristics, a stepof carrying out space division on different working condition signal characteristics by using a support vector machine and dividing a fault characteristic area in the fault mode classification space,and a step of carrying out Daubechies wavelet decomposition, reconstruction and orthogonal normalization on a newly obtained working condition signal, calculating a power spectrum, calculating the fault mode classification space coordinates, and judging the fault characteristic area. According to the method, the single-point fault characteristic signal of the rolling bearing can be effectively extracted, and a diagnosis result has high precision.

Description

technical field [0001] The invention relates to the field of fault diagnosis of rolling bearings, in particular to a method for extracting and diagnosing single-point fault characteristic signals of rolling bearings. Background technique [0002] As one of the important parts of industrial equipment, rolling bearings play a vital role in the safe operation of the equipment. Since the rolling bearing is a mechanical wearing part, one of its notable features is that the life span is large and the cause of the failure is complex and changeable. In practical applications, some bearings have multiple faults when they are used far short of their expected design life, and some bearings are still operating normally even though their service life is far beyond their design life. Therefore, in order to prevent bearing failure, the detection of the bearing running state is particularly important. [0003] Nowadays, most of the fault diagnosis and analysis of rolling bearings are base...

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Problems solved by technology

[0004] In view of the non-stationarity of the vibration signal of the faulty bearing and the shortcomings of low fault recognition rate, the purpose of the present invention is to provide

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