A Fault Diagnosis Method for Planetary Gearbox

A planetary gearbox and fault diagnosis technology, applied in the direction of instruments, biological models, calculation models, etc., can solve the problems of multi-domain characteristic information redundancy, achieve strong generalization ability, fast operation speed, and overcome poor decomposition effect Effect

Active Publication Date: 2021-09-28
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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  • A Fault Diagnosis Method for Planetary Gearbox
  • A Fault Diagnosis Method for Planetary Gearbox
  • A Fault Diagnosis Method for Planetary Gearbox

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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 fault diagnosis method for a planetary gearbox. First, the signal is decomposed and reconstructed using the salp swarm-optimized variational mode decomposition (SSO‑VMD). Then, fault features are extracted from multiple domains, and the improved supervised self-organizing incremental learning neural network landmark point isometric mapping (ISSL‑Isomap) is used for dimensionality reduction. Finally, artificial bee colony optimization support vector machine (ABC‑SVM) classifier was used for diagnostic identification. The invention overcomes the problem of parameter selection in the VMD algorithm and solves the problem of information redundancy in multi-domain features. The experimental results of planetary gearbox fault diagnosis show that the proposed method can effectively identify each fault type and has 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. . ...

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Application Information

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Patent Type & Authority Patents(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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