Method for predicting derailment coefficients

A technology of derailment coefficient and prediction method, applied in neural learning methods, biological neural network models, etc., can solve problems such as high cost and poor real-time performance

Inactive Publication Date: 2012-07-11
BEIJING JIAOTONG UNIV
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Problems solved by technology

[0010] Aiming at the deficiencies such as high cost and poor real-time performance in the existing derailment coefficient measurem

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  • Method for predicting derailment coefficients
  • Method for predicting derailment coefficients
  • Method for predicting derailment coefficients

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Embodiment Construction

[0076] The preferred embodiments will be described in detail below in conjunction with the accompanying drawings. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0077] The object of the present invention is to realize accurate modeling from track irregularities to derailment coefficients, and to predict the derailment coefficients through track irregularities, so as to make up for the deficiencies of the above methods in terms of cost and accuracy. The NARX neural network improved by the algorithm can realize the accurate prediction of the derailment coefficient.

[0078] The present invention is to the prediction method of derailment coefficient:

[0079] (1) Collect input data

[0080] The data of left track irregularity, left track direction irregularity, right track level irregularity and right track direction irregularity detected by the track inspection vehicle are sor...

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Abstract

The invention discloses a method for predicting derailment coefficients in the technical field of railway safety. The method comprises the following steps of: firstly, acquiring left-rail height irregularity data, left-rail rail direction irregularity data, right-rail height irregularity data and right-rail rail direction irregularity data of rails by using a rail inspection vehicle; secondly, by using professional automatic dynamic analysis of mechanical system (ADAMS) / Rail software, simulating the acquired data to obtain data of wheel-rail forces including a vertical wheel-rail force and a horizontal wheel-rail force so as to obtain the derailment coefficients, and normalizing the derailment coefficients; thirdly, by using a selected training sample, training a non-linear auto-regressive with exogenous input (NARX) neural network prediction model; fourthly, testing the trained NARX neural network prediction model, and outputting derailment coefficient data which are tested; and finally, analyzing the derailment coefficient data in a test sample and the derailment coefficient data which are obtained through a tested neural network, and evaluating the performance of the NARX neural network prediction model. By adoption of the method, the derailment coefficients can be accurately predicted, the accuracy in evaluation of railway running safety is improved, and great practical significance is provided for rail traffic safety control.

Description

technical field [0001] The invention belongs to the technical field of railway safety, and in particular relates to a method for predicting a derailment coefficient. Background technique [0002] As the world's railways are moving towards high speed, heavy load, large capacity and high density, the safety of train operation has attracted much attention. Train derailment will cause heavy loss of life and property. Therefore, the most basic requirement for safe operation of trains is to ensure that train derailment accidents do not occur. Since the French scholar Nadal proposed the famous derailment coefficient criterion in 1908, the derailment coefficient has become an important index for the study of train derailment. Therefore, obtaining the derailment coefficient has important theoretical and practical significance. [0003] The basic index for judging whether a vehicle is derailed at home and abroad is the derailment coefficient Q / P, that is, the ratio of the lateral fo...

Claims

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Application Information

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IPC IPC(8): G06N3/08
Inventor 秦勇贾利民张媛陈皓张道于朱跃邢宗义
Owner BEIJING JIAOTONG UNIV
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