Planetary gear box fault diagnosis method

A planetary gearbox and fault diagnosis technology, which is applied in computing models, testing of mechanical components, artificial life, etc., can solve problems such as multi-domain feature information redundancy, achieve strong generalization ability, overcome poor decomposition effect, and improve The effect on classification performance

Active Publication Date: 2020-06-05
B TOHIN MACHINE JIANGSU
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Problems solved by technology

[0005] The purpose of the present invention is to provide a planetary gearbox fault diagnosis method, which overcomes the problem of parameter selection in the VMD algorithm and solves the problem of information redundancy in multi-domain features

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Embodiment Construction

[0042] The technical solution of the present invention will be specifically described below in conjunction with the accompanying drawings.

[0043] Such as figure 1 As shown, the present invention provides a planetary gearbox fault diagnosis method, specifically a planetary gearbox fault diagnosis method based on parameter optimization variational mode decomposition and multi-domain manifold learning, including the following steps:

[0044] Step 1: Use the acceleration sensor to collect vibration acceleration signals of the planetary gearbox in the normal state, wear state, crack state and broken tooth state of the sun gear, and obtain its time-domain signal sample set (such as figure 2 shown);

[0045] Step 2: Use the salp swarm optimization (SSO) algorithm to optimize the parameters K and a in the variational mode decomposition (VMD) algorithm, then decompose the collected vibration acceleration signals, and obtain several eigenmode components ( IMF) for refactoring;

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Abstract

The invention relates to a planetary gear box fault diagnosis method. The method comprises the following steps: firstly, decomposing and reconstructing a signal by utilizing salp swarm group optimization-variational mode decomposition (SSO-VMD); then, extracting fault features from multiple domains, and carrying out dimension reduction processing by adopting improved supervised self-organizing incremental learning neural network landmark point isometric mapping (ISSL-Isomap); and finally, using an artificial bee colony optimization support vector machine (ABC-SVM) classifier to carry out diagnosis and identification. According to the method, the problem of parameter selection in the VMD algorithm is solved, and the problem of information redundancy of multi-domain features is solved. A planetary gear box fault diagnosis experiment result shows that the method can effectively identify each fault type and has a great practical value.

Description

technical field [0001] The invention relates to a fault diagnosis method for a planetary gearbox. Background technique [0002] As a key component of rotating machinery, planetary gearboxes are widely used in complex transmission systems such as helicopter main reducers and wind turbines. However, in the actual operation process, since the vibration signal of the planetary gearbox is easily affected by noise pollution and complex vibration, it is more difficult to diagnose its fault. [0003] At present, common fault signal noise reduction methods mainly include: wavelet transform, empirical mode decomposition (EMD) and local mean decomposition. However, the wavelet transform needs to select the wavelet base and the number of decomposition layers in advance, which lacks adaptability; EMD has limitations such as frequency confusion, over-envelope, under-envelope, and endpoint effects; local mean decomposition has defects such as slow operation speed and signal conflicts. . ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G01M13/021G01M13/028G06K9/00G06K9/62G06N3/00
CPCG01M13/021G01M13/028G06N3/006G06F2218/08G06F2218/12G06F18/214
Inventor 姚立纲王振亚蔡永武王博
Owner B TOHIN MACHINE JIANGSU
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