Bearing variable working condition fault diagnosis method based on Hessian locally linear embedding

Abstract

The invention discloses a bearing variable working condition fault diagnosis method based on Hessian locally linear embedding (HLLE). The method may improve the stability of a bearing fault characteristic and a fault diagnosis capability under a variable working condition. The method comprises: acquiring an inherent manifold characteristic of a manifold topological structure in a bearing original vibration signal by using a HLLE method; performing fast Fourier transform (FFT) on the inherent manifold characteristic to obtain a spectrogram, extracting, from the spectrogram, the amplitude at the bearing fault character frequency and the amplitudes at special frequency such as the second harmonic frequency, the third harmonic frequency or the like in order to form a bearing fault characteristic vector; and on the basis of the acquired fault characteristic, classifying the bearing fault states by using an information geometry-based support vector machine (IG-SVM) so as to achieve a variable working condition fault diagnosis capability. The invention provides a bearing with a fault characteristic extracting scheme capable of effectively resisting to working condition interference by using a fault characteristic extracting method based on the HLLE-FFT. The method guarantees the accuracy of bearing fault diagnosis and has good practical engineering application value.

Description

technical field [0001] The present invention relates to the technical field of bearing variable working condition fault diagnosis, in particular to a support vector machine based on Hessian local linear embedding (Hessian locally linear embedding, HLLE), fast Fourier transform (FFT) and information geometry-based support vector machine (information geometry-based support vector machine). , IG-SVM) fault diagnosis method. Background technique [0002] Bearing is an important part of the electromechanical system, and its performance has a very important impact on the safe and reliable operation of the system. As the electromechanical system becomes increasingly complex and the internal coupling is also enhanced, the nonlinear, non-stationary, and chaotic characteristics of the collected bearing vibration signals are increasingly enhanced, and the bearing fault diagnosis based on vibration signals is becoming more and more difficult. Sudden bearing failure will bring huge econ...

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Problems solved by technology

However, the proposals of these methods do not have a strong nonlinear theoretical basis, and problems such as over-envelope, under-en...

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