Wind power gearbox fault diagnosis method based on self-adaptive resonance sparse decomposition theory

A technology of resonance sparse decomposition and wind power gearbox, which is applied in machine gear/transmission mechanism testing, mechanical component testing, machine/structural component testing, etc. It can solve problems such as strong signal background noise and weak fault characteristic information, and achieve The effect of improving optimization efficiency and reducing the amount of calculation

Inactive Publication Date: 2017-02-22
HARBIN INST OF TECH
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  • Abstract
  • Description
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Problems solved by technology

[0005] The purpose of the present invention is to solve the problem of strong background noise and weak fault feature information of the existing wind power gearb

Method used

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  • Wind power gearbox fault diagnosis method based on self-adaptive resonance sparse decomposition theory
  • Wind power gearbox fault diagnosis method based on self-adaptive resonance sparse decomposition theory
  • Wind power gearbox fault diagnosis method based on self-adaptive resonance sparse decomposition theory

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specific Embodiment approach 1

[0024] Specific implementation mode one: combine figure 1 Describe this embodiment, a wind power gearbox fault diagnosis method based on adaptive resonance sparse decomposition theory in this embodiment The specific process is as follows:

[0025] Step 1: Use the BBM noise and vibration detection system to collect the vibration data of the experimental gearbox, and obtain the fault vibration signals of the peeling off of the outer ring of the planet carrier bearing and the partial peeling of the planetary gear;

[0026] Step 2: According to the morphological characteristics of the fault vibration signal, determine the matching resonance component, and use the genetic algorithm to optimize the quality factor and proportional coefficient of the resonance sparse decomposition at the same time, and obtain the optimal parameter matrix X * ;

[0027] Step 3: The best parameter matrix X * Substitute into the adaptive resonance sparse decomposition method to realize the resonance sp...

specific Embodiment approach 2

[0030] Specific implementation mode two: combination image 3 This embodiment is described. The difference between this embodiment and the specific embodiment one is: in the second step, according to the morphological characteristics of the fault vibration signal, determine the matching resonance component, and use the genetic algorithm to decompose the quality factor and The proportional coefficients are optimized at the same time to obtain the best parameter matrix X * ; The specific steps are:

[0031] The fault impulse response of planetary carrier bearing outer ring peeling and planetary gear partial peeling is a continuous oscillation attenuation signal, and its morphological characteristics are different from normal gear meshing vibration, base vibration and background noise, so it should be decomposed into high-level components as much as possible. in the resonance component.

[0032] The morphological characteristics of the impulse response signals of the two faults...

specific Embodiment approach 3

[0043] Specific implementation mode three: the difference between this implementation mode and specific implementation mode one or two is: in the step two one, decompose the series j H and j L for:

[0044] OK S H ,S L The decomposition series j H and j L , to ensure that the frequency range of the basis function library can cover the frequency spectrum of the fault vibration signal; for a signal with a length of fault vibration signal N, j H and j L The maximum value of is determined by the following formula:

[0045]

[0046]

[0047] 1≤j H ≤j Hmax , 1≤j L ≤j Lmax ;

[0048] In the formula, α H is the high-resonance component low-pass scaling factor, α L is the low-pass scale factor of the low resonance component, β H is the high-pass scaling factor for high-resonance components, β L is the low-resonance component high-pass scaling factor, j Hmax Decompose the series for high resonance components, j Lmax is the low resonance component decomposition ser...

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Abstract

The invention provides a wind power gearbox fault diagnosis method based on the self-adaptive resonance sparse decomposition theory, relates to the wind power gearbox fault diagnosis method based on the self-adaptive resonance sparse decomposition theory, and aims at solving the problems that the early composite fault signals of the existing wind power gearbox have strong background noise and fault characteristic information is weak. The concrete process of the method comprises the steps that step one: the vibration data of the test gearbox are acquired by using a BBM noise vibration detection system so that fault vibration signals of planetary carrier bearing outer ring peeling and planetary gear local peeling are obtained; step two: the quality factor and the proportional coefficient of resonance sparse decomposition are simultaneously optimized so that X* is obtained; step three: X* is substituted in a self-adaptive resonance sparse decomposition method so that the high and low resonance components of the fault vibration signals are obtained; and step four: envelope analysis is performed on the high resonance component of the fault vibration signals so that fault information and the non-fault vibration signals are identified. The wind power gearbox fault diagnosis method based on the self-adaptive resonance sparse decomposition theory is suitable for the field of fault diagnosis.

Description

technical field [0001] The invention relates to a wind power gearbox fault diagnosis method based on adaptive resonance sparse decomposition theory. Background technique [0002] With the rapid development of the wind power industry, the reliability requirements of wind turbines are getting higher and higher, and its fault diagnosis technology has become the tertiary industry of this industry. As a key component of wind turbines, the wind power gearbox is of great significance to strengthen its condition monitoring and fault diagnosis. Due to the complex transmission structure and special working environment of the wind power gearbox, and its early fault information is very weak, the extraction process requires high frequency resolution, so more effective signal processing methods must be used to extract fault feature information. [0003] Common wind power gearbox fault diagnosis methods include short-time Fourier transform (STFT), wavelet transform, empirical mode decompo...

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Application Information

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IPC IPC(8): G01M13/02
CPCG01M13/021G01M13/028
Inventor 黄文涛孙宏健窦宏印王伟杰赵学增
Owner HARBIN INST OF TECH
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