SVR antifriction bearing performance degradation prediction method based on krill-herd algorithm

Abstract

An SVR antifriction bearing performance degradation prediction method based on a krill-herd algorithm belongs to the field of functional approximation rotating machinery prediction methods. The method comprises the following steps: firstly analyzing time domain, frequency domain and time-frequency domain feature indexes, and proposing a feature extraction method based on combination of CEEMD and wavelet packet half-soft threshold noise reduction to perform fault diagnosis of an antifriction bearing; performing comprehensive evaluation of the fault degradation feature of the antifriction bearing for multiple feature parameters, and proposing a method of combining the LLE nonlinear feature dimension reduction method with the fuzzy C mean value; and finally, introducing the basic theory of the support vector regression machine, and proposing the prediction model of multivariable support vector regression machine based on the krill herd algorithm, optimizing parameters of the SVR, and selecting the optimal C, [sigma] parameters. The method is advantaged by high prediction precision, short calculation time, and good feature value prediction effect after clustering. The degradation process of the antifriction bearing can be precisely predicted through the abovementioned three steps.

Description

technical field [0001] The invention belongs to the field of rotating machinery prediction methods of functional approximation, and specifically relates to a method for feature extraction based on the combination of CEEMD and wavelet packet transform, and a method for dimensionality reduction processing of nonlinear signals by using multi-feature fusion technology. Background technique [0002] With the breakthroughs and leaps in science and technology, the diagnosis and prediction of rotating machinery equipment faults have attracted widespread attention, and large-scale rotating machinery equipment has become more and more automated, sophisticated, and complex with the development of the times. The requirements are also getting stricter. Equipment will gradually enter a stage of high incidence of wear and tear over time. The entire production line in the factory may be paralyzed due to the failure of individual components, which will not only cause economic crisis to the ...

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Problems solved by technology

However, multi-characteristic parameters have the disadvantages of large amount of information and poor sensitivity, and cannot comprehensively evaluate bearing degradation.

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