Multi-period steel rail damage trend prediction method based on data mining

A data mining and trend forecasting technology, applied in data mining, structured data retrieval, digital data processing, etc., to achieve the effect of saving regular maintenance costs

Pending Publication Date: 2019-08-27
梁帆 +1
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  • Application Information

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Problems solved by technology

[0004] In view of this, aiming at the problems existing in the existing damage analysis methods, the present invention proposes a mu

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  • Multi-period steel rail damage trend prediction method based on data mining
  • Multi-period steel rail damage trend prediction method based on data mining
  • Multi-period steel rail damage trend prediction method based on data mining

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Embodiment Construction

[0023] The present invention will be further described below through specific embodiments in conjunction with the accompanying drawings. These embodiments are only used to illustrate the present invention, and are not intended to limit the protection scope of the present invention.

[0024] Such as figure 1 As shown, the specific implementation steps of the multi-period rail damage trend prediction method based on data mining described in the present invention are as follows:

[0025] S1, using a convolutional neural network to establish a location marker signal.

[0026] S11, combined with the original data of A-ultrasound and B-ultrasound, using a deep learning model to identify position marks such as hole joints, thermite welds, factory welding, on-site welding, guide holes, screw holes, etc., and establish position mark signals. The artificially marked lesion A was viewed as a 12-channel binary matrix superimposed into an image. Such as figure 2 As shown, the neural ne...

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Abstract

The invention relates to the field of rail transit and steel rail flaw detection, in particular to a multi-period steel rail damage trend prediction method based on data mining, which comprises the following steps: S1, training and judging a position mark A display waveform based on a deep learning model, and establishing a corresponding position mark signal; s2, combining position mark signals with geographic information such as mileage, rice blocks and GPS, and carrying out coarse alignment on all the position marks; s3, determining ultrasonic signal sequences C and Q which need to be aligned through the aligned position marks; s4, performing fine alignment on the ultrasonic signal sequences C and Q through a dynamic time warping method; s5, tracing the damaged position back according tothe aligned ultrasonic wave sequence; and S6, quantifying the development trend of the same damage in different periods, and analyzing the development trend of the damage. According to the method, based on a big data mining algorithm, the periodic change of various damage type characteristic data can be captured, a reliable damage growth data model is formed, and the damage condition of the railis effectively estimated and judged. Based on the prediction model, effective predictive maintenance work can be performed on the damage growth point appearing in the specific station section rail ina targeted manner, so that gradual change is prevented, and unnecessary periodic maintenance cost is saved to a great extent.

Description

technical field [0001] The invention relates to the field of rail transit and rail flaw detection, in particular to a multi-period rail damage trend prediction method based on data mining. Background technique [0002] In the traditional flaw detection work, the flaw detector will conduct a comprehensive "physical examination" on the rail every 28-32 days, and conduct a comprehensive inspection of the condition of the rail. This flaw detection cycle is set according to certain objective laws of damage, but the traditional The current flaw detection work can only analyze the damage data in each cycle, and cannot analyze and utilize the damage data of the same track in multiple cycles, and cannot make full use of the past flaw detection data to analyze the damage development trend. At present, the bottleneck of rail damage trend analysis lies in the multi-period alignment of damage data. Multi-period alignment now mainly relies on manual alignment based on mileage. It manual...

Claims

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Application Information

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IPC IPC(8): G06F17/50G06N3/04G06F16/21
CPCG06F16/212G06F2216/03G06F2111/04G06F30/20G06N3/045
Inventor 梁帆余旸
Owner 梁帆
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