Train weight identification method based on high-speed railway bridge strain monitoring data and computer system

Abstract

The invention discloses a train weight identification method based on high-speed railway bridge strain monitoring data, which comprises the following steps of: firstly, selecting a reference train bridge crossing event, and acquiring the train weight, the train length value and the strain monitoring data during the bridge crossing period; for a section of strain monitoring data containing a train gap bridge signal to be detected, filtering to remove noise and trend components; intercepting a bridge crossing signal of the train to be detected according to the change characteristics of the processed strain signal and the passing time range of the train to be detected; respectively calculating integral average effect indexes IMS of the gap bridge signals of the train to be detected and the reference train; and calculating the train weight of the train to be measured according to the relationship between the parameter IMS and the train weight, and completing train weight identification. According to the invention, the strain response of a single bridge crossing event can be identified from continuous strain monitoring data, and the weight of the train running on the high-speed railway bridge can be identified based on the strain response.

Description

Technical Field

[0001] The present invention relates to the field of structure monitoring, and in particular to a train weight identification method and a computer system based on high-speed railway bridge strain monitoring data. Background Art

[0002] When a high-speed train passes through a high-speed railway bridge, the train's weight information will be reflected in real time in the signal collected by the sensors installed on the bridge. By accurately identifying this parameter, we can have a deeper understanding of the impact of the train on the bridge structure and provide key data support for the maintenance and management of the bridge.

[0003] In existing applications, some researchers use pre-weighing or dynamic weighing methods to obtain vehicle weight information for highway bridges to reduce the number of unknown quantities in structural inverse analysis. However, unlike cars running on highway bridges, trains on high-speed railway bridges are not only more regular in shape, but also have fairly standardized driving rules; at the same time, high-speed EMUs have more stringent requirements on the track configuration of high-speed railway bridges, and conventional weighing equipment cannot be installed and used on high-speed railway bridges. The identification of train weight on high-speed railway bridges has become a problem to be solved, which is the focus of this application. Summary of the invention

[0004] The technical problem to be solved by the present invention is to provide a method for identifying the weight of a train based on the strain monitoring data of a high-speed railway bridge, by identifying the strain response of each single bridge crossing event from the continuous strain monitoring data, based on which the weight of the train running on the high-speed railway bridge can be identified.

[0005] In order to solve the above technical problems, the present invention provides a train weight identification method based on high-speed railway bridge strain monitoring data, firstly, a reference train crossing bridge event is selected, and its vehicle weight, vehicle length and strain monitoring data during the crossing bridge are obtained; for a section of strain monitoring data containing the bridge crossing signal of the train to be tested, the noise and trend components are filtered to remove; according to the change characteristics of the processed strain signal and the passage time range of the train to be tested, the bridge crossing signal of the train to be tested is intercepted; the integrated average effect index IMS of the bridge crossing signal of the train to be tested and the reference train is calculated respectively; the weight of the train to be tested is calculated according to the relationship between the parameter IMS and the vehicle weight, and the vehicle weight identification is completed. Specifically, the following steps are included:

[0006] Step S1: Collection and preprocessing of strain signals;

[0007] Step S11: deploying strain sensors on the high-speed railway bridge to collect real-time strain monitoring data of the bridge during the passage of trains;

[0008] Step S12: filtering the collected original strain signal to remove noise and unnecessary frequency components to obtain a processed strain signal;

[0009] Step S2: Setting of benchmark bridge crossing events and data preparation;

[0010] Select a benchmark bridge crossing event and record the weight W of the benchmark vehicle 0 , Vehicle length 0 , bridge length L and strain monitoring signal of the benchmark bridge crossing event;

[0011] Step S3: Extraction of single bridge crossing events and calculation of indicators;

[0012] Step S31: identifying each bridge crossing event of a train passing through the bridge according to the variation characteristics of the strain signal after detrending;

[0013] Step S32: intercepting the bridge crossing signal of the train to be tested according to the travel time range of the train to be tested;

[0014] Step S33: calculating the integrated average effect index IMS of the intercepted bridge signal;

[0015]

[0016] In the formula, S(t) is the bridge signal, T = t 2 -t 1 It is the bridge crossing period;

[0017] Step S4: Calculation of the weight of the train to be tested;

[0018] Step S41: Calculate the integrated average effect index IMS of the benchmark bridge crossing event 0 ;

[0019] Step S42: Calculate the vehicle weight W of the train to be tested;

[0020]

[0021] The present invention also provides a computer system, comprising a memory, a processor and a computer program stored in the memory, characterized in that: the processor executes the computer program to implement the steps of the train weight identification method based on high-speed railway bridge strain monitoring data.

[0022] The beneficial effects of the present invention are:

[0023] 1) The present invention utilizes the characteristics of the strain effect of the bridge power system during the running period of the train, and can better adapt to the real-time and quasi-real-time identification requirements of the weight of the train on the bridge under the strain monitoring environment, so that the identification method of the present invention can better serve the analysis and processing of the strain monitoring signal, and therefore has broad engineering application prospects;

[0024] 2) Numerical examples show that the identification method of the present invention can control the train weight identification error to less than 1%, has high computational efficiency, and can operate stably and continuously;

[0025] 3) The present invention can identify the strain response of each single bridge crossing event from the continuous strain monitoring data, based on which the weight of the train running on the high-speed railway bridge can be identified. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] The drawings constituting a part of the present application are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute an improper limitation of the present invention. In the drawings:

[0027] Figure 1 is a flow chart of a specific embodiment of the present invention; Figure 2 This is a schematic diagram obtained by taking the train weight recognition of 10 bridge crossing events on a two-dimensional finite element continuous beam bridge model as an example. DETAILED DESCRIPTION

[0028] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0029] like Figure 1 As shown, the present invention provides a train weight identification method based on high-speed railway bridge strain monitoring data. First, a reference train crossing a bridge event is selected to obtain its weight, length and strain monitoring data during the crossing period; for a section of strain monitoring data containing the bridge crossing signal of the train to be tested, the noise and trend components are filtered to remove; according to the change characteristics of the processed strain signal and the travel time range of the train to be tested, the bridge crossing signal of the train to be tested is intercepted; the integrated average effect index IMS of the bridge crossing signal of the train to be tested and the reference train is calculated respectively; the weight of the train to be tested is calculated according to the relationship between the parameter IMS and the weight, and the weight identification is completed. The following specific steps are included:

[0030] Step S1: Collection and preprocessing of strain signals;

[0031] Step S11: deploying strain sensors on the high-speed railway bridge to collect real-time strain monitoring data of the bridge during the passage of trains;

[0032] Step S12: filtering the collected original strain signal to remove noise and unnecessary frequency components to obtain a processed strain signal;

[0033] Step S2: Setting of benchmark bridge crossing events and data preparation;

[0034] Select a benchmark bridge crossing event and record the weight W of the benchmark vehicle 0 , Vehicle length0 , bridge length L and strain monitoring signal of the benchmark bridge crossing event;

[0035] Step S3: Extraction of single bridge crossing events and calculation of indicators;

[0036] Step S31: identifying each bridge crossing event of a train passing through the bridge according to the variation characteristics of the strain signal after detrending;

[0037] Step S32: intercepting the bridge crossing signal of the train to be tested according to the travel time range of the train to be tested;

[0038] Step S33: calculating the integrated average effect index IMS of the intercepted bridge signal;

[0039]

[0040] In the formula, S(t) is the bridge signal, T = t 2 -t 1 It is the bridge crossing period;

[0041] Step S4: Calculation of the weight of the train to be tested;

[0042] Step S41: Calculate the integrated average effect index IMS of the benchmark bridge crossing event 0 ;

[0043] Step S42: Calculate the vehicle weight W of the train to be tested;

[0044]

[0045] like Figure 2 As shown, taking the train weight identification of 10 bridge crossing events on a two-dimensional finite element continuous beam bridge model as an example, the error between the identified train weight and the set train weight true value is less than 1%, and the running time is 0.001 milliseconds, which has high computational efficiency.

[0046] The invention also provides a computer system, comprising a memory, a processor and a computer program stored in the memory, characterized in that: the processor executes the computer program to implement the steps of the train weight identification method based on high-speed railway bridge strain monitoring data.

[0047] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A train weight identification method based on high-speed railway bridge strain monitoring data, characterized in that: First, a reference train crossing a bridge event is selected to obtain its vehicle weight, vehicle length and strain monitoring data during the crossing period; for a section of strain monitoring data containing the bridge crossing signal of the train to be tested, the noise and trend components are filtered to remove; according to the change characteristics of the processed strain signal and the travel time range of the train to be tested, the bridge crossing signal of the train to be tested is intercepted; the integrated average effect index IMS of the bridge crossing signals of the train to be tested and the reference train is calculated respectively; the weight of the train to be tested is calculated according to the relationship between the parameter IMS and the vehicle weight, and the train weight identification is completed.

2. The train weight identification method based on high-speed railway bridge strain monitoring data according to claim 1 is characterized by: The specific steps include the following: Step S1: Collection and preprocessing of strain signals; Step S11: deploying strain sensors on the high-speed railway bridge to collect real-time strain monitoring data of the bridge during the passage of trains; Step S12: filtering the collected original strain signal to remove noise and unnecessary frequency components to obtain a processed strain signal; Step S2: Setting of benchmark bridge crossing events and data preparation; Select a benchmark bridge crossing event, record the vehicle weight W0, vehicle length l0, bridge length L of the benchmark vehicle, and the strain monitoring signal of the benchmark bridge crossing event; Step S3: Extraction of single bridge crossing events and calculation of indicators; Step S31: identifying each bridge crossing event of a train passing through the bridge according to the variation characteristics of the strain signal after detrending; Step S32: intercepting the bridge crossing signal of the train to be tested according to the travel time range of the train to be tested; Step S33: calculating the integrated average effect index IMS of the intercepted bridge signal; Where, S(t) is the bridge signal, T=t2-t1 is the bridge period; Step S4: Calculation of the weight of the train to be tested; Step S41: Calculate the integrated average effect index IMS0 of the benchmark bridge crossing event; Step S42: Calculate the vehicle weight W of the train to be tested; 3. A computer system comprising a memory, a processor and a computer program stored in the memory, characterized in that: The processor executes the computer program to implement the steps of the train weight identification method based on high-speed railway bridge strain monitoring data as described in any one of claims 1-2.

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