A high-speed rail braking system fault detection svm method based on gwo oversampling

A braking system and fault detection technology, applied in railway vehicle testing, measuring devices, measuring ultrasonic/sonic/infrasonic waves, etc., can solve the problem that the fault diagnosis method is no longer applicable, avoid complex modeling process, and reduce detection errors Effect

Active Publication Date: 2021-11-23
BEIHANG UNIV
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Problems solved by technology

However, due to the nonlinearity and electromechanical coupling of the braking system of high-speed trains, the traditional model-based fault diagnosis method is no longer applicable.

Method used

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  • A high-speed rail braking system fault detection svm method based on gwo oversampling
  • A high-speed rail braking system fault detection svm method based on gwo oversampling
  • A high-speed rail braking system fault detection svm method based on gwo oversampling

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Embodiment Construction

[0044] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be further described in detail and in-depth below in conjunction with the accompanying drawings.

[0045] A kind of high-speed rail brake system fault detection SVM method based on GWO oversampling of the present invention, first utilize gray wolf algorithm to carry out oversampling to fault data, on this basis carry out SVM model training and test, such as image 3 As shown, the specific steps include:

[0046] Step 1. For a high-speed rail train, install sensors at different positions in the braking system of the train;

[0047] in such as figure 1 The high-speed train braking system shown is equipped with sensors to measure the GPS position, speed, external energy supply, line voltage, circuit current, brake pipe pressure, and measured slippage of the high-speed train braking system under normal and fault conditions. Shift rate, in...

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Abstract

The invention discloses a high-speed rail braking system fault detection SVM method based on GWO oversampling, which belongs to the technical field of fault diagnosis. First, a signal sensor is mounted on the train; After vibrating the signal and synthesizing the vector, label it as; then merge all the labeled vectors into the data set, divide the training set and the test set; in the training set, select n fault vectors and m normal vectors, and use the gray wolf algorithm to classify the fault vectors Oversampling is performed to obtain m‑n new fault state vectors. Utilize the new fault state vector, n fault vectors and m normal vectors, a total of 2m data to train the SVM fault detection model, and carry out fault detection to the test set; use the test results to calculate the index G-mean for verification; the present invention The gray wolf algorithm is introduced for oversampling, which reduces detection errors caused by data imbalance.

Description

technical field [0001] The invention relates to fault diagnosis of a braking system of a high-speed rail train, and belongs to the technical field of fault diagnosis, in particular to a high-speed rail braking system fault detection SVM method based on GWO (Grey Wolf Algorithm) oversampling. Background technique [0002] The railway is an important national infrastructure, the main artery of the national economy and a popular means of transportation. It is the backbone of the comprehensive transportation system and plays an important role in promoting the sound and rapid development of my country's economy and society. By the end of 2013, my country's high-speed railway business mileage reached 11,152 kilometers, including 6,354 kilometers with a speed of 300-350 kilometers per hour, and 4,798 kilometers with a speed of 200-250 kilometers per hour. [0003] When the speed of high-speed rail trains often exceeds 300 kilometers per hour, an efficient and reliable braking syste...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06K9/62G06N3/00G01M17/08G01H17/00
CPCG06N3/006G01M17/08G01H17/00G06F18/2411
Inventor 张辉石谦
Owner BEIHANG UNIV
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