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A method for detecting the loss of assembly bolts of a rail vehicle motor-sensing hanger

A rail vehicle and loss detection technology, applied in neural learning methods, neural architecture, image analysis, etc., can solve the problems of false positives and false positives, high cost, low efficiency, etc., to reduce energy and time, achieve good results, and improve training The effect of precision

Active Publication Date: 2021-10-22
HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0004] The purpose of the present invention is to solve the problems of high cost, low efficiency, and false positives and false positives in manually checking whether the assembly bolts of the machine sense hanger coil are missing on the spot, and to provide a method for detecting the loss of assembly bolts of the machine sense hanger for rail vehicles

Method used

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  • A method for detecting the loss of assembly bolts of a rail vehicle motor-sensing hanger
  • A method for detecting the loss of assembly bolts of a rail vehicle motor-sensing hanger
  • A method for detecting the loss of assembly bolts of a rail vehicle motor-sensing hanger

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specific Embodiment approach 1

[0079] Specific implementation mode one: the following combination Figure 1 to Figure 7 Describe this embodiment, a method for detecting the loss of assembly bolts of a rail vehicle machine-sensing hanger described in this embodiment, the method includes the following steps:

[0080] Step 1. Taking the machine-sensing hanger as the target, continuously collect multiple passing images to obtain continuous frame passing images, and use the SSD detection network to obtain the target area sub-image of the U-shaped groove of the machine-sensing hanger, and then remove the blurred images;

[0081] What is acquired is a set of images, which are multiple images sorted in time series.

[0082] Step 2: Input multiple clear U-groove target area sub-images of the machine-sensing hanger into the fault target segmentation model in time series, and output the predicted image of the U-shaped groove of the machine-sensing hanger used to represent the information of the assembled bolts;

[00...

specific Embodiment approach 2

[0087] Embodiment 2: This embodiment further explains Embodiment 1. The fault target segmentation model includes a convolutional neural network CNN encoder, a convolutional long-short-term memory artificial neural network ConvLSTM and a convolutional neural network CNN decoder. The faulty target segmentation The model building process includes:

[0088] Step 21, establish a training data set; output images in groups to the encoder, each group of images is n consecutive images sorted by time sequence;

[0089] Step 22, using the Resnet network as a reference network to construct a convolutional neural network CNN encoder, the convolutional neural network CNN encoder will generate n feature maps corresponding to n pieces of continuous images input by time series;

[0090] The convolutional neural network CNN encoder inputs consecutive n images X t＝1 ,X t＝2 ,...,X t＝n , output n feature maps Enc t＝1 ,Enc t＝2 ,...,Enc t＝n ;

[0091] Step 2 and 3: Construct a convolutional l...

specific Embodiment approach 3

[0102] Specific implementation mode three: this implementation mode further explains implementation mode two, and the process of establishing a training data set includes the following steps:

[0103] Step A1, collecting a large number of images of passing vehicles with the machine sense hanger as the target;

[0104] Step A2, establishing an original image data set, specifically:

[0105] The SSD detection network is used to train the passing image, the U-shaped groove of the machine-sensing hanger and the area where the machine-sensing hanger is marked, and the sub-map of the target area of ​​the U-shaped groove of the machine-sensing hanger is obtained through the SSD detection network training, and then the machine is established. The original image data set of the U-shaped groove of the sense hanger;

[0106] Step A3, removing blurred images, specifically:

[0107] Edge gradient detection is performed on each U-shaped groove target area sub-image in the original data se...

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Abstract

A method for detecting the loss of assembly bolts of a rail vehicle machine sense hanger belongs to the technical field of rail vehicle detection. The present invention solves the problems of high cost, low efficiency, and false positives and false positives in manually checking whether the machine sense hanger coil assembly bolts are missing. question. The method of the present invention comprises the following steps: step 1, taking the machine sense hanger as the target, continuously collecting multiple passing images, obtaining continuous frame passing images, and using the SSD detection network to obtain the target area sub-image of the U-shaped groove of the machine sense hanger , and then remove the fuzzy image; Step 2: Input multiple clear U-groove target area submaps into the fault target segmentation model in time series, and output the U-shaped Groove prediction image; step 3, according to the prediction image, count the number of bolts and bolt cotter pins of the assembled bolts to determine whether the assembled bolts are missing and the type of missing failure. The invention is used for detecting whether the assembly bolts of the rail vehicle machine sense hanger are lost.

Description

technical field [0001] The invention belongs to the technical field of rail vehicle detection. Background technique [0002] For a long time, the electric service section has used manual on-site inspection of freight trains for inspection, which has always had problems such as high cost and low efficiency. At the same time, due to the influence of component installation location and weather factors, the efficiency is low, there will be missing components, false alarms, etc., and it is difficult to guarantee the accuracy. [0003] The machine-sensing hanger coil, that is, the main locomotive signal locomotive sensor, is developed to adapt to the general main locomotive signal equipment of the existing electrified and non-electrified sections and various locomotive models, and to receive information sent by ground signal equipment of various standards. The technical principle is to receive the current signal transmitted in the rail of the track circuit through electromagnetic...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/00G06T7/10G06T7/136G06N3/04G06N3/08
CPCG06T7/0008G06T7/10G06T7/136G06N3/08G06T2207/10016G06T2207/20081G06T2207/20084G06T2207/30248G06N3/044G06N3/045
Inventor 孙晶
Owner HARBIN KEJIA GENERAL MECHANICAL & ELECTRICAL CO LTD
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