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Local Fault Diagnosis Method of Rotating Machinery Based on Sparse Decomposition Optimization Algorithm

A technology of rotating machinery and optimization algorithms, which is applied in the testing of mechanical components, testing of machine/structural components, and instruments. The effects of time-consuming calculation, improved computing speed, and accelerated convergence

Active Publication Date: 2020-09-22
SOUTH CHINA UNIV OF TECH
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

The traditional learning dictionary is obtained by machine learning, which has a high degree of matching with the measured signal, but the calculation is heavy, and the dictionary lacks clear physical meaning; while the analytical dictionary has the advantages of mature theory, but it ignores the differences between different measured signals. difference, matching adaptability is not good enough

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  • Local Fault Diagnosis Method of Rotating Machinery Based on Sparse Decomposition Optimization Algorithm
  • Local Fault Diagnosis Method of Rotating Machinery Based on Sparse Decomposition Optimization Algorithm
  • Local Fault Diagnosis Method of Rotating Machinery Based on Sparse Decomposition Optimization Algorithm

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Embodiment

[0060] This embodiment provides a method for diagnosing local faults of rotating machinery based on sparse decomposition optimization algorithm, such as figure 1 As shown, it is the overall implementation process; the specific algorithm steps of the method are as follows figure 2 shown. combine figure 1 , figure 2 , the present invention will be further described by taking the bearing with partial fault in the rotating machine as the research object. The bearing parameters are shown in Table 1:

[0061] model Pitch diameter Rolling element diameter Number of rolling elements Contact angle NUP311EN 85mm 18mm 13 0°

[0062] Table 1

[0063] The specific implementation steps of this embodiment are:

[0064] S1. Acquisition of vibration acceleration response signals of rotating machinery containing fault characteristic information;

[0065] Paste the piezoelectric acceleration sensor on the faulty bearing, connect the data acquisition syst...

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Abstract

The invention discloses a local type fault diagnosis method of a rotating machine based on a sparse decomposition optimization algorithm, which comprises the following steps: S1, acquiring a rotatingmachine vibration acceleration response signal containing fault characteristic information; S2, intercepting a signal with an appropriate length, performing noise reduction pretreatment by applying ahigh-pass filter and singular value decomposition, and improving the signal-to-noise ratio; S3, setting an atom optimization criterion, and establishing a time-frequency domain correlation coefficientconstraint function of atoms and an actually-measured signal; S4, updating parameters of an initial random dictionary by using a particle swarm hybrid gradient descent algorithm to obtain an optimized impact response dictionary; S5, solving a sparse coefficient by using a segmented Lagrange contraction algorithm, reconstructing the fault characteristic signal according to the coefficient and thedictionary; S6, analyzing the impact response time interval of the reconstructed signal and demodulation of the characteristic frequency of the amplitude spectrum, identifying the position of the fault to finish the fault diagnosis. The dictionary has the advantages of high precision, high speed, stronger noise resistance and more accurate reconstruction characteristic signal.

Description

technical field [0001] The invention belongs to the field of mechanical fault diagnosis, and specifically relates to a partial fault diagnosis method for rotating machinery based on sparse decomposition and using a particle swarm hybrid gradient descent optimization algorithm, which can be used to extract fault characteristic signals of rotating machinery such as bearings and gearboxes, and perform Troubleshooting. Background technique [0002] Rotating parts such as bearings and gears are an essential general-purpose component in mechanical equipment and one of the most easily damaged components. Fault diagnosis for them is of great significance for prolonging the service life of equipment and reducing maintenance costs. How to extract the characteristic signal containing fault information from the noise signal is the key to diagnosis. [0003] Commonly used fault feature signal extraction methods mainly include amplitude demodulation based on Fourier transform, wavelet t...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G01M13/045
Inventor 瞿蔚丁康何国林杨小青
Owner SOUTH CHINA UNIV OF TECH
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