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Planetary gear degradation state identification method

A planetary gear and state recognition technology, applied in special data processing applications, instruments, biological neural network models, etc., can solve problems such as local optimum and overfitting, inability to handle multi-dimensional data, poor recognition effect, etc., and achieve reliable results , The recognition process is fast and the effect of versatility is strong

Inactive Publication Date: 2019-09-10
HOHAI UNIV CHANGZHOU
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AI Technical Summary

Problems solved by technology

Traditional pattern recognition methods, such as support vector machines, backpropagation neural networks, and fuzzy clustering, cannot handle multi-dimensional data, the recognition effect is poor, the training samples are small, and it is easy to fall into local optimum and overfitting

Method used

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  • Planetary gear degradation state identification method
  • Planetary gear degradation state identification method
  • Planetary gear degradation state identification method

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Experimental program
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Embodiment

[0121] Combined with the experiment, the above methods are comprehensively used to identify the degradation state of the planetary gear. The planetary gear failure experiment was carried out on the DDS mechanical failure comprehensive simulation test bench of Spectra Quest Company in the United States. In this experiment, a total of 9 degradation states of normal planetary gears, fractured planetary gears, and 4-stage degradation states of pitted planetary gears were measured. By analyzing the collected vibration signals, the established degradation state identification method was tested.

[0122] The planetary gear fault simulation experiment was carried out on the DDS mechanical fault comprehensive simulation test bench, and a total of 9 degradation states of normal planetary gears, fractured planetary gears, and pitting planetary gears were extracted. According to the preliminary analysis of the basic parameters of the planetary gearbox on the test bench and its early vibra...

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Abstract

The invention discloses a planetary gear degradation state identification method. The method comprises the following steps of carrying out the optimized arrangement of a plurality of vibration sensorsand the original vibration signal acquisition; decomposing an original vibration signal into a plurality of SSDC components by adopting the adaptive improved singular spectrum decomposition, extracting the multi-scale enhanced information dimensions of the plurality of SSDC components to form a planetary gear degradation state characteristic matrix, and constructing a planetary gear degradation state recognition model based on a convolutional neural network. According to the method, the characteristic information matrix generated by the degradation state of the planetary gear can be extracted, the degradation state of the planetary gear is recognized according to the extracted characteristic information matrix, and the method is an effective planetary gear degradation state recognition method.

Description

technical field [0001] The invention relates to a planetary gear degradation state identification method, which belongs to the technical field of planetary gear fault diagnosis. Background technique [0002] Planetary gear transmission has the advantages of small size, light weight, and large transmission ratio. It is widely used in the transmission system of large and complex machinery. Its long-term operation under harsh working conditions is prone to local damage and undergoes different degradation states. It is a fatal failure that directly affects the operational reliability of electromechanical equipment. Therefore, accurately identifying the current degradation state of the planetary gear is of great significance for the purposeful maintenance of the planetary gear train, further predicting the development trend of the planetary gear train failure, and avoiding sudden failures. [0003] In actual engineering, the working environment of the planetary gear is relativel...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/04
CPCG06F30/17G06N3/045
Inventor 陈曦晖杨泽南杨子豪
Owner HOHAI UNIV CHANGZHOU
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