Method and system for predicting noise near rails due to underfloor noise from railway vehicles

Abstract

To provide a noise prediction method and a noise prediction system in the vicinity of a rail, capable of highly accurately predicting noise in the vicinity of the rail due to noise of a lower part of a vehicle, which is changed in accordance with conditions such as a rail surface state for each traveling section of the railway vehicle by a simple measurement method.SOLUTION: A noise prediction method in the vicinity of a rail due to underfloor noise of a railway vehicle 10, includes: function deriving step S11 of previously measuring and obtaining a noise transmission function, which indicates a relation between the underfloor noise during traveling of the railway vehicle 10 and the noise in the vicinity of the rail in a position at a specific distance from the rail 20 for each environment condition of a track or the vicinity of the track; noise measurement step S21 of measuring the underfloor noise during traveling at a prediction point where the noise in the vicinity of the rail is predicted in the railway vehicle 10; and noise prediction step S22 of predicting the noise in the vicinity of the rail during traveling at the prediction point of the railway vehicle 10 according to the measured value of the underfloor noise measured in noise measurement step S21 by using the noise transmission function.SELECTED DRAWING: Figure 3

Description

[Technical Field]

[0001] The present invention relates to prediction of noise generated under the floor of a railway vehicle while it is running, in the vicinity of a rail. [Background technology]

[0002] As a means of predicting noise along railway lines caused by the running of railway vehicles, Non-Patent Document 1 describes a method of determining the generation locations and power levels of major noises for each vehicle type, and predicting the overall noise along railway lines from the sum of the noises from these sound sources. Non-Patent Document 1 classifies major noises into four types: undercarriage noise, structural noise, upper-carriage aerodynamic noise, and current collection system noise. Of these noises, the power level of undercarriage noise is determined from the results of measurements of near-rail noise using microphones installed near the rails on the ground, as described in Non-Patent Document 2 (see Section 2.4 (1)). Then, using the procedure described in Non-Patent Document 1, the power level of undercarriage noise corresponding to the prediction method of Non-Patent Document 1 is determined.

[0003] Furthermore, Patent Document 1 discloses a technology for noise prediction that utilizes underfloor noise. The technology disclosed in Patent Document 1 predicts the noise of gear devices, the actual state of which has been difficult to grasp in the past, by using underfloor noise.

[0004] On the other hand, at train speeds of 200 km / h or more, undercarriage noise is mainly composed of aerodynamic noise from the bogie and rolling noise. Rolling noise is generated when the wheel and rail vibrate due to interference between irregularities of micron order amplitude that exist on the wheel tread and the top surface of the rail. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Patent No. 4676811 [Non-patent literature]

[0006] [Non-Patent Document 1] Kiyoshi Nagakura and Yasuo Zenda: "Noise Prediction Method along Shinkansen Lines," Railway Technical Research Institute Report, Vol. 14, No. 9, 2000 [Non-patent document 2] Kiyoshi Nagakura: "Sound Source Analysis Method for Shinkansen Noise", Railway Technical Research Institute Report, Vol. 10, No. 2, 1996 Summary of the Invention [Problem to be solved by the invention]

[0007] Conventional railway line noise prediction methods predict rolling noise from undercarriage noise by assuming that the surface irregularities of the rail head are in a standard state. However, actual rolling noise can differ from this standard state when traveling over rail joints or around curves. As a result, conventional prediction methods cannot predict near-rail noise from undercarriage noise that corresponds to the track conditions at each location.

[0008] Furthermore, the technology in Patent Document 1 predicts the noise of the gearing of electric cars, which is one of the multiple sound sources contained in underfloor noise in railways, but does not predict rolling noise or the associated noise near the rails. Therefore, it cannot be applied to predicting wayside noise of high-speed trains traveling at 200 km / h or more, where undercarriage noise is composed of aerodynamic noise from the bogie and rolling noise.

[0009] The present invention has been made in consideration of the above circumstances, and aims to provide a method and system for predicting noise near the rails that can predict with high accuracy, using a simple measurement technique, noise near the rails caused by undercarriage noise, which varies depending on conditions such as the rail surface condition for each section of railway vehicle travel. [Means for solving the problem]

[0010] In order to solve the above problems, the present invention provides a method for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: Firsta function deriving step of determining a noise transfer function that indicates the relationship between underfloor noise during running of a railway vehicle and near-rail noise at a position a certain distance from the rail by measuring the function in advance for each environmental condition of the track or the vicinity of the track; The second railcar is different from the first. railway vehicles of Prediction points for near-rail noise of the first railway vehicle in a noise measurement step of measuring underfloor noise; and using the noise transfer function, calculating the underfloor noise from the measured values of the underfloor noise measured in the noise measurement step. First a noise prediction step of predicting near-rail noise when the railway vehicle is traveling at the prediction point; a difference derivation step of measuring and determining in advance the difference in near-rail noise between the first railway vehicle and the second railway vehicle; and a correction step of adding the difference to the predicted value of the near-rail noise of the first railway vehicle predicted in the noise prediction step, to predict the near-rail noise of the second railway vehicle when traveling at the prediction location. It is characterized by having:

[0011] The present invention also provides a method for predicting near-rail noise caused by underfloor noise of railway vehicles, comprising: a function derivation step of measuring in advance for each environmental condition of the track or the area around the track a noise transfer function indicating the relationship between the underfloor noise of a first railway vehicle while it is running and the near-rail noise at a position a certain distance from the rail; a noise measurement step of measuring the underfloor noise of the first railway vehicle at a prediction location where the near-rail noise of a second railway vehicle of a different vehicle type to the first railway vehicle is to be predicted; a noise prediction step of using the noise transfer function to predict the near-rail noise of the first railway vehicle while it is running at the prediction location from the measured value of the underfloor noise measured in the noise measurement step; a difference derivation step of measuring in advance the difference between the near-rail noise of the first railway vehicle and the second railway vehicle; and a correction step of predicting the near-rail noise of the second railway vehicle while it is running at the prediction location by adding the difference to the predicted value of the near-rail noise of the first railway vehicle predicted in the noise prediction step.

[0012] The environmental conditions may be the type of track, the condition of the soundproof wall, the internal structure of the bogie cavity of the railway vehicle, or the installation position of a measurement unit for measuring underfloor noise.

[0013] The railway vehicle is preferably traveling at a speed of 150 km / h or more. The underfloor noise is preferably measured by attaching a microphone inside a bogie cavity of the railway vehicle.

[0014] According to another aspect, the present invention provides a system for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: First The underfloor noise is measured by a measurement unit provided under the floor of the railway vehicle, and a noise transfer function is used to measure the underfloor noise and the near-rail noise at a certain distance from the rail for each environmental condition of the track or the area around the track. The second railcar is different from the first. Prediction points for near-rail noise from railway vehicles The first railway vehicle in From the measurement values of the underfloor noise during driving, First Predict the noise near the rails when a railway vehicle is traveling at the predicted location. and adding a difference in near-rail noise between the first railway vehicle and the second railway vehicle, which has been obtained by measuring in advance, to the predicted value of near-rail noise of the first railway vehicle, to predict the near-rail noise of the second railway vehicle when traveling at the predicted location. The system is characterized by comprising a calculation unit. [Effects of the Invention]

[0015] According to the present invention, noise near the rail caused by noise from the underside of a vehicle can be predicted with high accuracy using a simple measurement method, depending on conditions such as the state of the rail surface, which changes for each section of the railway vehicle that it is running. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is an explanatory diagram showing an example of a schematic configuration of a noise prediction system according to an embodiment of the present invention; [Figure 2] FIG. 2 is a block diagram illustrating an example of a hardware configuration of a device. [Figure 3] 1 is a flowchart showing a procedure for predicting noise along a Shinkansen line using a noise prediction method according to an embodiment of the present invention. [Figure 4] 1A and 1B are diagrams illustrating the noise transfer functions of underfloor noise and near-rail noise of a railway vehicle, where FIG. 1A is a graph showing the relationship between underfloor noise and near-rail noise, and FIG. 1B is a graph showing the noise transfer functions. [Figure 5] FIG. 1 is an explanatory diagram showing an example of a schematic configuration of a noise prediction system according to another embodiment of the present invention. [Figure 6] 1 is a flowchart showing a procedure for predicting noise along a Shinkansen line using a noise prediction method according to another embodiment of the present invention. [Figure 7] 1A and 1B are diagrams illustrating differences due to vehicle type, where FIG. 1A is a graph showing near-rail noise for vehicle type A and vehicle type B, and FIG. 1B is a graph showing the difference between vehicle type A and vehicle type B. DETAILED DESCRIPTION OF THE INVENTION

[0017] Hereinafter, embodiments of the present invention will be described with reference to the drawings. In this specification and the drawings, elements having substantially the same functional configurations are designated by the same reference numerals, and redundant description will be omitted.

[0018] The present invention uses a noise prediction system to measure underfloor noise of actually running railway vehicles and, based on the measurement results, predicts near-rail noise, which varies from location to location depending on factors such as rail surface conditions. The underfloor noise here refers to noise measured with a microphone installed near the bogie under the floor of the railway vehicle, and includes aerodynamic noise and rolling noise from the bogie of the railway vehicle. In this invention, preliminary measurements are performed in advance, and the near-rail noise and underfloor noise are simultaneously measured to determine the noise transfer functions of both noises. The near-rail noise at a prediction location is then predicted using the noise transfer functions based on the underfloor noise of the railway vehicle being the target of near-rail noise prediction while it is traveling at the prediction location. Furthermore, if the vehicle being subjected to the preliminary measurement (hereinafter referred to as the first railway vehicle) and the vehicle being predicted (hereinafter referred to as the second railway vehicle) are different vehicle types, the difference in near-rail noise between the vehicle types is added to predict the near-rail noise of the second railway vehicle.

[0019] <Near-rail noise prediction system> First, an example of a system for predicting noise generated near rails by a railway vehicle according to an embodiment of the present invention will be described.

[0020] 1 is an explanatory diagram showing the outline of the configuration of a noise prediction system 1 according to this embodiment. In this embodiment, a first railway vehicle on which preliminary measurements are performed and a second railway vehicle that is the prediction target are the same railway vehicle 10. The noise prediction system 1 has a microphone 11 provided on the railway vehicle 10, a microphone 21 provided near a rail 20, and a device 30. The microphones 11 and 21 and the device 30 are configured to be connectable via a network 40.

[0021] (Microphone 11) The microphone 11 measures underfloor noise of the railway vehicle 10 in the function derivation process and noise measurement process described below. The microphone 11 is attached to the underside of the floor 12 of the railway vehicle 10, near the bogie, at a position on the center line of the track. Note that the microphone 11 is preferably installed in, for example, a pre-made microphone storage holder inside the bogie cavity 13, so that the noise is not affected by turbulence in the airflow caused by the installation of the microphone 11. Furthermore, the microphone 11 is preferably installed in a position where the measured values are not affected by noise from the traction motor, in order to accurately measure the aerodynamic noise and rolling noise of the bogie. Furthermore, it is preferable to install the microphone 11 in a position where the airflow is stable, for example, from the third car from the front.

[0022] The microphone 11 that measures underfloor noise corresponds to the measurement unit in the present invention, but the measurement unit is not limited to a microphone as long as it can measure noise.

[0023] (Microphone 21) The microphone 21 measures near-rail noise when the railway vehicle 10 passes. The near-rail noise is noise at a position a certain distance from the rail 20. The microphone 21 is installed at a horizontal distance L from the rail along the way where a soundproof wall or the like is installed, and at a height H from the rail 20, at a position where the near-rail noise can be appropriately evaluated. As an example, the distance L is about 2 m, and the height H is about 45 cm. The microphones 21 are also installed in the direction of the track, at intervals of 20 to 25 m, the length of the vehicle, for example.

[0024] (Device 30) The device 30 may be, for example, a computer, or may be, for example, a mobile terminal such as a smartphone or a tablet. As shown in Fig. 2, the device 30 has a bus 31, a processor 32, a memory 33, a storage 34, a communication device 35, an input device 36, and an output device 37. The processor 32, the memory 33, the storage 34, the communication device 35, the input device 36, and the output device 37 are connected to each other via the bus 31.

[0025] The processor 32 functions as a calculation unit and executes instructions contained in a program to control all or part of the operations within the device 30. The processor 32 may be, for example, a CPU (Central Processing Unit), a microprocessor, or the like.

[0026] The memory 33 temporarily stores programs, data to be processed by the programs, etc. The memory 33 may be, for example, a RAM (Random Access Memory), a ROM (Read Only Memory), etc.

[0027] The storage 34 stores programs and various data. For example, a flash memory, a hard disc drive (HDD), or a solid state drive (SSD) is used for the storage 34.

[0028] The communication device 35 is a communication interface configured with a communication device for connecting to the network 40 and the like.

[0029] The input device 36 is an input device for receiving input operations from the rally manager, and is composed of, for example, hardware keys such as a keyboard, a pointing device such as a mouse, a touch panel, a touch pad, and the like.

[0030] The output device 37 is an output device for presenting information to the rally manager, and is composed of, for example, a display device such as a display, and an audio output device such as a speaker and headphones.

[0031] The input device 36 and the output device 37 may be integrated in part or in whole, and in such a case, a touch panel display in which the touch panel and the display are integrated may be used.

[0032] It should be noted that the components of the device 30 of this embodiment are not limited to the above examples. For example, each component may be configured using general-purpose components, or may be configured using hardware specialized for the function of each component. The hardware configuration used can be changed as appropriate depending on the technical level at the time of implementing this embodiment.

[0033] The program of the present embodiment may be provided in a state stored in a computer-readable storage medium. The storage medium may be, for example, a hard disk drive (HDD), a solid state drive (SSD), or various types of memory. The program may also be distributed, for example, via a network without using a storage medium.

[0034] (Network 40) The network 40 is not particularly limited as long as it is capable of carrying out communication. The network 40 may be, for example, the Internet, a wired LAN (Local Area Network), a wireless LAN, a public line network, or a mobile data communication network.

[0035] <Method for predicting noise near rails> Next, an example of a method for predicting noise near rails caused by railway vehicles according to an embodiment of the present invention will be described. In this embodiment, noise near rails caused by railway vehicles (e.g., Shinkansen railway vehicles) traveling at speeds of 200 km / h or more is predicted.

[0036] Figure 3 is a flowchart showing the procedure for predicting noise along Shinkansen lines using the prediction method according to this embodiment. In Figure 3, the steps surrounded by solid lines are steps according to this embodiment of the present invention, and the steps surrounded by dotted lines are noise prediction steps described in Non-Patent Document 1. Of the noise sources along Shinkansen lines listed in Non-Patent Document 1, namely, undercarriage noise, structural noise, upper car aerodynamic noise, and current collection system noise, this invention relates to predicting undercarriage noise.

[0037] (Function derivation process) First, a noise transfer function is calculated in advance, which indicates the relationship between underfloor noise when the railway vehicle 10 is running and near-rail noise at a position a certain distance from the rail (step S11).

[0038] Specifically, while the railway vehicle 10 is running, the underfloor noise is measured by the microphone 11, and the near-rail noise at that time is measured by the microphone 21. The measured underfloor noise and near-rail noise are output from the microphones 11 and 21 to a communication device 35 of the device 30, and stored in a storage 34. The device 30 derives a noise transfer function based on the underfloor noise and near-rail noise.

[0039] Specifically, the device 30 calculates the noise transfer function as shown in Figure 4. Figure 4 is a schematic diagram illustrating the noise transfer function. Figure 4(a) shows measurement results for each frequency of underfloor noise and near-rail noise of a railway vehicle 10. That is, Figure 4(a) shows a graph obtained by plotting the A-weighted sound pressure level of the underfloor noise measured by the microphone 11 for each frequency and complementing it, and a graph obtained by plotting the near-rail noise measured by the microphone 21 for each frequency and complementing it. Figure 4(b) shows the noise transfer function. The noise transfer function is defined as the difference in sound pressure level between the underfloor noise and the near-rail noise at each frequency. For example, the sound pressure level difference at frequency f in Figure 4 is ΔL A (f), and the noise transfer function is calculated for each frequency.

[0040] The noise transfer function is calculated for each environmental condition of the track or the area around the track that may affect the sound propagation characteristics from under the vehicle floor to the vicinity of the rail. Examples of environmental conditions include the type of track, the condition of the soundproof walls, the internal structure of the bogie cavity of the railway vehicle, and the installation locations of the microphones 11 and 21. The type of track includes whether it is ballast or non-ballast, the type of track structure (such as ballasted track or concrete vibration-isolating slab track) on the ballast bed, and rail joints and curves. The condition of the soundproof walls includes the presence or absence of soundproof walls beside the rail 20, the structure of the soundproof walls, the presence or absence of sound-absorbing panels, and the type of sound-absorbing panels. The noise transfer function also differs depending on the internal structure of the bogie cavity 13 and the installation locations of the microphones 11 and 21.

[0041] (Noise measurement process) A microphone 11 is installed as a measurement unit under the floor of the railway vehicle 10, and the railway vehicle 10 travels on the rails 20 to measure underfloor noise (step S21). Specifically, the microphone 11 measures underfloor noise at a prediction point. The prediction point is generally a location different from the measurement location in the function derivation step described above, and is a location where noise along the railway line is predicted. The measured underfloor noise is output from the microphone 11 to the communication unit 35 of the device 30 and stored in storage 34.

[0042] (Noise prediction process) Next, in the device 30, the processor 32 uses the noise transfer function obtained in step S11 to predict near-rail noise from the measured underfloor noise (step S22).

[0043] In this embodiment, the predicted power level of the vehicle underside, as described in Non-Patent Document 2, is calculated based on the near-rail noise predicted as described above. Furthermore, the predicted power level of the vehicle underside noise corresponding to Non-Patent Document 1 is calculated using the procedures described in Non-Patent Document 2 and Non-Patent Document 1 (Step S23). The subsequent steps are as described in Non-Patent Document 1. Specifically, the acoustic power levels of structural noise, upper-car aerodynamic noise, and current collection system noise other than the vehicle underside noise are predicted (Step S24), and the single-shot sound exposure level and maximum level value in time-weighting characteristics are calculated in accordance with the Shinkansen wayside noise prediction method (Step S25). Then, for example, the noise at 1.2 m above ground level, 25 m from the center of the track, is calculated and predicted (Step S26). The specific method for these steps S24 to S26 is as described in Non-Patent Document 1, as described above, and therefore will not be described here.

[0044] According to the above-described embodiment, it is possible to accurately predict near-rail noise that corresponds to environmental conditions such as unevenness of the rail surface, which changes depending on the section the railway vehicle is traveling in, and when passing through curves, etc. Therefore, it is possible to improve the accuracy of predictions of railway wayside noise.

[0045] Other Embodiments 5 is an explanatory diagram showing the outline of the configuration of a noise prediction system 100 according to another embodiment of the present invention. In this embodiment, a first railway vehicle 50 on which preliminary measurements are performed and a second railway vehicle 60 that is the prediction target are different types of railway vehicles. The noise prediction system 100 of this embodiment has a microphone 51 provided on the first railway vehicle 50, a microphone 21 provided near the rails 20, and a device 30. The microphones 21 and 51 and the device 30 are configured to be connectable via a network 40.

[0046] (Microphone 51) Microphone 51 measures underfloor noise of first railway vehicle 50. The installation position of microphone 51 is the same as that of microphone 11 in the embodiment shown in Fig. 1. That is, microphone 51 is attached to the underside of floor 52 of first railway vehicle 50, and is preferably housed in a ready-made microphone storage holder or the like and installed inside bogie cavity 53. Furthermore, microphone 51 is preferably installed in a position where the measured value is not affected by noise from the traction motor, for example, it is installed in the third or subsequent railway vehicle from the front.

[0047] The microphone 21, device 30, and network 40 are the same as those in the embodiment of FIG. 1, and therefore a description thereof will be omitted.

[0048] FIG. 6 is a flowchart showing the procedure for predicting noise along a Shinkansen line by a prediction method according to another embodiment of the present invention using the noise prediction system 100 shown in FIG.

[0049] (Function derivation process) First, as a preliminary measurement, a noise transfer function indicating the relationship between underfloor noise during running and near-rail noise at a position a certain distance from the rail at that time is obtained using the first railway vehicle 50 (step T11).

[0050] The first railway vehicle 50 is a railway vehicle capable of running on rails 20 for the railway vehicle whose near-rail noise is to be predicted, and measures the underfloor noise and near-rail noise at appropriate positions within the running range of the second railway vehicle 60. Specifically, while the first railway vehicle 50 is running, the underfloor noise is measured by microphone 51, and simultaneously the near-rail noise at that time is measured by microphone 21. The measured underfloor noise and near-rail noise are output from microphones 51, 21 to communication device 35 of device 30, respectively, and stored in storage 34. Device 30 derives a noise transfer function based on the underfloor noise and near-rail noise. The measurement locations in the function derivation step and the specific method for determining the noise transfer function are the same as those described above, and therefore will not be described again.

[0051] (Noise measurement process) The underfloor noise when the first railway vehicle 50 travels through a prediction point where near-rail noise of the second railway vehicle 60 is predicted is measured by a microphone 51 attached to the first railway vehicle 50 (step T21). The prediction point is generally a location different from the measurement location used in the function derivation step described above, and is a location where noise along the track is predicted. The underfloor noise measured by the microphone 51 is output from the microphone 51 to the communication unit 35 of the device 30 and stored in the storage 34.

[0052] (Noise prediction process) Next, in the device 30, the processor 32 uses the noise transfer function obtained in step T11 to predict near-rail noise from the measured underfloor noise (step T22).

[0053] Furthermore, in this embodiment, for two different types of railway vehicles, the difference in near-rail noise due to the difference in type is calculated.

[0054] (Difference derivation process) Before carrying out the noise measurement step, the difference in near-rail noise between the first railway vehicle 50 and the second railway vehicle 60 is calculated (step T12). The near-rail noises of the first railway vehicle 50 and the second railway vehicle 60 are output from the microphone 21 to the communication device 35 of the device 30 and stored in the storage 34, and the difference between these near-rail noises is derived in the device 30. Note that the difference deriving step only needs to be carried out at least before the correction step described below.

[0055] FIG. 7 is a schematic diagram illustrating the difference used to correct the near-rail noise of two different types of railway vehicles. FIG. 7(a) shows the measurement results for each frequency of the near-rail noise of vehicle type A (first railway vehicle 50) and vehicle type B (second railway vehicle 60). That is, FIG. 7(a) shows a graph interpolated by plotting the A-weighted sound pressure level of the near-rail noise measured by microphone 21 for each frequency. FIG. 7(b) shows the difference between vehicle type A and vehicle type B. The difference is defined as the difference in sound pressure level between vehicle type A and vehicle type B at each frequency. For example, in FIG. 7, the sound pressure level difference ΔL at frequency f is A (f) is the difference. Note that, since near-rail noise is speed-dependent (see Non-Patent Document 1), the difference is defined as the difference in sound pressure levels under conditions where the vehicle traveling speed is the same.

[0056] (correction process) Next, device 30 corrects the predicted value of the near-rail noise obtained in step T22 by adding the difference obtained in step T12, and predicts the near-rail noise of second railway vehicle 60 at the prediction point (step T23).

[0057] As described above, in this embodiment, the value predicted in the noise prediction step is corrected in the correction step to determine the near-rail noise, and the predicted power level value for the undercarriage area described in Non-Patent Document 2 is determined based on the predicted near-rail noise. Furthermore, the predicted power level value for the undercarriage sound corresponding to Non-Patent Document 1 is determined using the procedures described in Non-Patent Document 2 and Non-Patent Document 1 (step T24). The subsequent steps (steps T25 to T27) are similar to steps S24 to S26 in the embodiment described above and shown in Fig. 3, and therefore will not be described here.

[0058] This embodiment also provides the same effects as the previous embodiment. That is, it is possible to accurately predict near-rail noise that corresponds to environmental conditions such as unevenness of the rail surface, which changes depending on the section the railway vehicle is traveling in, and when passing through a curve. Moreover, it is also possible to predict near-rail noise generated by railway vehicles of different models.

[0059] The present invention is suitable for use in predicting near-rail noise of railway vehicles in speed ranges where the impact of main motor noise is minimal, such as when the vehicle is traveling at a speed of approximately 150 km / h to 300 km / h. Furthermore, when the vehicle is traveling at a speed of 200 km / h or more, the impact of main motor noise is even less, making this even more preferable.

[0060] Furthermore, at running speeds of 200 km / h or above, the contribution of aerodynamic noise from the bogie section to undercarriage noise becomes apparent, and aerodynamic noise increases as the running speed increases, but at running speeds up to about 300 km / h, the influence of rolling noise becomes dominant. In other words, at running speeds of 200 km / h or above, the present invention is useful for accurately predicting near-rail noise that varies depending on environmental conditions such as rail surface irregularities that change for each section the railway vehicle is traveling in, and when passing through curves.

[0061] While the preferred embodiments of the present invention have been described above, the present invention is not limited to these examples. It is clear that those skilled in the art can conceive of various modifications and alterations within the scope of the technical ideas described in the claims, and it is understood that these also fall within the technical scope of the present invention.

[0062] Furthermore, the effects described herein are merely descriptive or exemplary and are not limiting. In other words, the technology according to the present disclosure may achieve other effects that are apparent to those skilled in the art from the description of this specification, in addition to or in place of the above-described effects.

[0063] The following configurations also fall within the technical scope of the present disclosure. (1) A method for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: a function deriving step of determining a noise transfer function that indicates the relationship between underfloor noise during railway vehicle operation and near-rail noise at a position a certain distance from the rail, by measuring the function in advance for each environmental condition of the track or the area around the track; a noise measurement step of measuring underfloor noise while the railway vehicle is traveling at a prediction point where near-rail noise is predicted; and a noise prediction step of predicting near-rail noise while the railway vehicle is traveling at the prediction point, based on the measured values of underfloor noise measured in the noise measurement step, using the noise transfer function. (2) A method for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: a function deriving step of determining a noise transfer function that indicates the relationship between underfloor noise while the first railway vehicle is running and near-rail noise at a position a certain distance from the rail by measuring the function in advance for each environmental condition of the track or the area around the track; a noise measurement step of measuring underfloor noise of the first railway vehicle at a prediction point for predicting near-rail noise of a second railway vehicle of a different vehicle type from the first railway vehicle; a noise prediction step of predicting near-rail noise while the first railway vehicle is traveling at the prediction point, based on the underfloor noise measured in the noise measurement step, using the noise transfer function; a difference deriving step of measuring and determining in advance a difference in near-rail noise between the first railway vehicle and the second railway vehicle; a correction step of adding the difference to the predicted value of the near-rail noise of the first railway vehicle predicted in the noise prediction step, thereby predicting the near-rail noise of the second railway vehicle when it is traveling at the prediction location. (3) The method for predicting noise near rails according to (1) or (2), characterized in that the environmental conditions are the type of track, the condition of soundproof walls, the internal structure of the bogie cavity of the railway vehicle, or the installation location of a measurement unit for measuring underfloor noise. (4) The method for predicting noise near rails according to any one of (1) to (3), characterized in that the running speed of the railway vehicle is 150 km / h or more. (5) A method for predicting noise near rails according to any one of (1) to (4), characterized in that the underfloor noise is measured by attaching a microphone inside the bogie cavity of the railway vehicle. [Industrial Applicability]

[0064] The present invention is applicable to predicting noise generated under the floor of a railway vehicle near the rails while the vehicle is running, and is particularly useful for high-speed railway vehicles traveling at speeds of 200 km / h or more. [Explanation of symbols]

[0065] 1.100 Noise Prediction System 10. Railway vehicles 11, 51, 61 microphones 12, 52 Floor 13, 53 Bogie cavity 20 Rail 21 Microphone 30 devices 31 Bus 32 processors 33 Memory 34 Storage 35 Communication equipment 36 Input Devices 37 Output Device 40 Network 50 First Railroad Car 60 Second Railway Car

Claims

1. A method for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: a function deriving step of determining a noise transfer function that indicates the relationship between underfloor noise while the first railway vehicle is running and near-rail noise at a position a certain distance from the rail by measuring the function in advance for each environmental condition of the track or the area around the track; a noise measurement step of measuring underfloor noise of the first railway vehicle at a prediction point for predicting near-rail noise of a second railway vehicle different from the first railway vehicle; a noise prediction step of predicting near-rail noise while the first railway vehicle is traveling at the prediction point, based on the underfloor noise measured in the noise measurement step, using the noise transfer function; a difference deriving step of measuring and determining in advance a difference in near-rail noise between the first railway vehicle and the second railway vehicle; a correction step of adding the difference to the predicted value of the near-rail noise of the first railway vehicle predicted in the noise prediction step, to predict the near-rail noise of the second railway vehicle when it is traveling at the prediction location.

2. A method for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: a function deriving step of determining a noise transfer function that indicates the relationship between underfloor noise while the first railway vehicle is running and near-rail noise at a position a certain distance from the rail by measuring the function in advance for each environmental condition of the track or the area around the track; a noise measurement step of measuring underfloor noise of the first railway vehicle at a prediction point for predicting near-rail noise of a second railway vehicle of a different vehicle type from the first railway vehicle; a noise prediction step of predicting near-rail noise while the first railway vehicle is traveling at the prediction point, based on the underfloor noise measured in the noise measurement step, using the noise transfer function; a difference deriving step of measuring and determining in advance a difference in near-rail noise between the first railway vehicle and the second railway vehicle; a correction step of adding the difference to the predicted value of the near-rail noise of the first railway vehicle predicted in the noise prediction step, to predict the near-rail noise of the second railway vehicle when it is traveling at the prediction location.

3. 3. The method for predicting noise near rails according to claim 1, wherein the environmental conditions are the type of track, the condition of soundproof walls, the internal structure of the bogie cavity of the railway vehicle, or the installation location of a measurement unit for measuring underfloor noise.

4. 3. The method for predicting noise near rails according to claim 1, wherein the traveling speed of the railway vehicle is 150 km / h or more.

5. 3. The method for predicting noise near rails according to claim 1, wherein the underfloor noise is measured by attaching a microphone inside a bogie cavity of the railway vehicle.

6. A system for predicting noise near rails due to underfloor noise of a railway vehicle, comprising: a measurement unit provided under the floor of the first railway vehicle and configured to measure underfloor noise; a calculation unit that predicts the near-rail noise of a second railway vehicle different from the first railway vehicle when it is traveling at a prediction point, based on measured values of the underfloor noise while the first railway vehicle is traveling at the prediction point, using a noise transfer function that indicates the relationship between the underfloor noise and near-rail noise at a position a certain distance from the rail, for each environmental condition of the track or the area around the track, and that predicts the near-rail noise of the second railway vehicle when it is traveling at the prediction point by adding a difference between the near-rail noise of the first railway vehicle and the second railway vehicle, which has been determined by measuring in advance, to the predicted value of the near-rail noise of the first railway vehicle.

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