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Metro and metro bogie operation state identification method and system

A technology of operating status and recognition method, applied in the direction of neural learning method, character and pattern recognition, neural architecture, etc., can solve the problems of effective recognition of subway bogies and low reliability of recognition results, and achieve reliable models, good reliability, The effect of reliable data sources

Active Publication Date: 2021-07-23
ZHUZHOU ELECTRIC LOCOMOTIVE CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] The object of the present invention is to provide a method and system for identifying the operating state of subway and subway bogies, so as to overcome the difficulty of effectively identifying the operating state of subway bogies in traditional single-feature domain state identification methods, resulting in low reliability of identification results question

Method used

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  • Metro and metro bogie operation state identification method and system
  • Metro and metro bogie operation state identification method and system
  • Metro and metro bogie operation state identification method and system

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no. 1 approach

[0070] The first embodiment: the first intermediate layer is connected in parallel with the second intermediate layer, and then connected in series with the third intermediate layer and the fourth intermediate layer in sequence, such as figure 2 As shown; the output end of the input layer is connected to the input ends of the first intermediate layer and the second intermediate layer, and the output end of the fourth intermediate layer is connected to the input end of the output layer.

no. 2 approach

[0071] Second embodiment: the second middle layer and the third middle layer are connected in parallel to form a combination layer, and then the first middle layer, the combination layer and the fourth middle layer are connected in series in sequence, as image 3 As shown; the output end of the input layer is connected to the input end of the first intermediate layer, and the output end of the fourth intermediate layer is connected to the input end of the output layer.

[0072] Third embodiment: the third middle layer and the fourth middle layer are connected in parallel to form a combination layer, and the first middle layer, the second middle layer and the combination layer are connected in series in sequence, as Figure 4 As shown; the output end of the input layer is connected to the input end of the first intermediate layer, and the output end of the combination layer is connected to the input end of the output layer.

no. 4 approach

[0073] Embodiment 4: The first middle layer and the second middle layer are connected in parallel to form the first combination layer, the third middle layer and the fourth middle layer are connected in parallel to form the second combination layer, and the first combination layer and the second combination layer are connected in series, such as Figure 5 As shown; the output end of the input layer is connected to the input end of the first combination layer, and the output end of the second combination layer is connected to the input end of the output layer.

[0074] Fifth embodiment: the first middle layer, the second middle layer, the third middle layer and the fourth middle layer are connected in parallel to form a combined layer, such as Figure 6 As shown; the output end of the input layer is connected to the input end of the combination layer, and the output end of the combination layer is connected to the input end of the output layer.

[0075] 5. Take the input featur...

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Abstract

The invention discloses a metro and metro bogie operation state identification method and system, and the method comprises the steps: collecting the operation data of a metro bogie through a plurality of sensors, carrying out the denoising of the operation data, carrying out the multi-domain feature extraction of the operation data, training a convolutional neural network model through the extracted multi-domain features and operation states, and optimizing parameters of the model by adopting a particle swarm optimization algorithm while training; adopting multiple sensors to obtain operation data, so that the data source is more reliable; training the model by multi-domain features, so that a more reliable recognition result can be obtained; and optimizing model parameters and classifier parameters by adopting a particle swarm optimization algorithm, so that the model is more reliable, and the identification result of the running state of the subway bogie has good reliability.

Description

technical field [0001] The invention belongs to the technical field of subway bogie state recognition, and in particular relates to a subway bogie operating state recognition method, system, and subway based on a multi-feature domain convolutional neural network. Background technique [0002] Subway is a useful supplement to urban rail transit. The bogie in the subway vehicle is located at the bottom of the subway vehicle, between the car body and the track. It bears and transmits various loads from the car body and the line, and at the same time eases its dynamic effect. The subway bogie is mainly composed of frame (no bolster, H type), wheel set, primary suspension, secondary suspension, anti-roll torsion bar, foundation braking system, central traction unit, wheel flange lubrication system and auxiliary devices It is one of the most important components of subway vehicles, and the timely identification of its operating status is of great significance to the safe and reli...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G01M17/08
CPCG06N3/08G01M17/08G06N3/045G06F18/2415G06F18/214
Inventor 刘翊谢锋云沈意平沈龙江王雪芳任科生覃事东李书
Owner ZHUZHOU ELECTRIC LOCOMOTIVE CO LTD