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Mechanical vibration fault characteristic time domain blind extraction method

A technology of fault characteristics and mechanical vibration, used in mechanical bearing testing, special data processing applications, instruments, etc., can solve problems such as sequence uncertainty, blind separation result sequence uncertainty, affecting the effect of blind separation, etc., to reduce the impact Effect

Active Publication Date: 2014-12-10
KUNMING UNIV OF SCI & TECH
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Problems solved by technology

[0003] However, the observation signal received by the sensor contains a large number of periodic components, which seriously affects the effect of blind separation in the process of blind extraction of the fault source signal; in addition, the separation result of blind extraction of the observation signal will produce sequence uncertainty problems
In view of the above existing problems, the present invention proposes a time-domain blind extraction method of mechanical vibration fault characteristics, which is suitable for dealing with long convolution data problems, can effectively reduce the influence of periodic components on blind separation results, and at the same time solve the problem of order uncertainty of blind separation results , and finally achieve bearing fault feature extraction

Method used

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  • Mechanical vibration fault characteristic time domain blind extraction method
  • Mechanical vibration fault characteristic time domain blind extraction method

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Embodiment 1

[0036] Embodiment 1: as Figure 1-8 As shown, a mechanical vibration fault feature time domain blind extraction method, the specific steps of the mechanical vibration fault feature time domain blind extraction method are as follows:

[0037] Step1, initialization delay parameter L, number of principal components n pc , the number of clusters n cluster ;

[0038] Step2. Centralized processing The signal x(t) is obtained from the vibration observation signal x(t) received by the acceleration sensor, and the high-dimensional subspace signal x'(t) is formed by zero padding, and the high-dimensional subspace signal x'(t) is processed PCA principal component analysis to obtain low-dimensional signal x(t) pc ;

[0039] Step3, for the low-dimensional signal x(t) obtained in step Step2 pc Execute the FastICA independent component analysis algorithm to obtain the independent component ic(t), and calculate the normalized kurtosis kurt of the independent component ic(t) ic and find ...

Embodiment 2

[0056] Embodiment 2: as Figure 1-8 As shown, a time-domain blind extraction method of mechanical vibration fault characteristics, this embodiment is the same as Embodiment 1, the difference is that this specific embodiment is based on the composite fault diagnosis experiment of the inner and outer rings of the bearing in a rotating test bench. Example:

[0057] figure 1 Indicates the installation positions of the two acceleration sensors on the test bench, figure 1 In the figure, a pair of PCB acceleration sensors are installed vertically on the bearing housing to pick up vibration signals. The relevant parameters of the faulty bearing are: pitch circle diameter D=39mm, rolling body diameter d=7.5mm, rolling body number Z=12, contact angle α=0°. The spindle in the inner ring of the bearing rotates and the outer ring is fixed. In embodiment 2, the rotating speed is 1200r / min, that is, the rotational frequency f r When it is 20Hz, the sampling frequency fs=8192Hz, the numb...

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Abstract

The invention relates to a mechanical vibration fault characteristic time domain blind extraction method, and belongs to the technical field of mechanical equipment status monitor and fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method includes: firstly, expanding a vibration observation signal into a high dimension signal subspace; then, obtaining a low dimension signal; afterwards, performing FastICA independent component analysis, calculating normalization kurtosis of all independent components, figuring out a component signal corresponding to the minimum normalization kurtosis, and using an orthogonal matching pursuit algorithm to reconstitute periodic signals; subsequently, removing the reconstituted periodic signal from each independent component, and then using an improved KL distance algorithm to calculate a distance matrix among the independent components after the periodic signals are removed from the independent components, and performing dynamic particle swarm clustering so as to obtain an estimation signal; finally, analyzing an envelope demodulation spectrum of the estimation signal, and performing fault diagnosis. The mechanical vibration fault characteristic time domain blind extraction method is suitable for processing a long convolution data problem, can effectively reduce influences from periodic ingredients on a blind separation result, and simultaneously can solve blind separation result order uncertainty problems, and finally achieves bearing fault characteristic extraction.

Description

technical field [0001] The invention relates to a time-domain blind extraction method of mechanical vibration fault characteristics, which belongs to the technical field of mechanical equipment state monitoring and fault diagnosis. Background technique [0002] Bearings are common components of rotating machinery, and their status has a great impact on the working conditions of the machine, directly affecting the safety and efficiency of the production system. In the actual working environment, the complex mechanical structure, multiple other interference sources and strong background noise make the observation signal received by the sensor often be a convolution mixture model formed by complex mixing of multiple signals, making the fault source to be identified The signal extraction is similar to a blind deconvolution process. [0003] However, the observation signal received by the sensor contains a large number of periodic components, which seriously affects the blind se...

Claims

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Application Information

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IPC IPC(8): G01M13/04G06F19/00
Inventor 伍星刘凤潘楠周俊刘畅柳小勤伞红军贺玮
Owner KUNMING UNIV OF SCI & TECH
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