CN107817038B - Rail vehicle wheel load passing type intelligent detection device and method - Google Patents
Rail vehicle wheel load passing type intelligent detection device and method Download PDFInfo
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- CN107817038B CN107817038B CN201711003865.3A CN201711003865A CN107817038B CN 107817038 B CN107817038 B CN 107817038B CN 201711003865 A CN201711003865 A CN 201711003865A CN 107817038 B CN107817038 B CN 107817038B
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- 238000001514 detection method Methods 0.000 title claims abstract description 32
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- G01G19/00—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups
- G01G19/02—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles
- G01G19/04—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing railway vehicles
- G01G19/045—Weighing apparatus or methods adapted for special purposes not provided for in the preceding groups for weighing wheeled or rolling bodies, e.g. vehicles for weighing railway vehicles for weighing railway vehicles in motion
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Abstract
The invention discloses a rail vehicle wheel weight passing type intelligent detection device and a method, which are characterized in that a weighing unit is used for guiding and lifting a wheel rim to weigh, so that the structure is light, the installation is convenient, the use is flexible, the lifting amount of the wheel rim is adjusted through different cushion block combinations, the use requirement of the worn time of steel rails and wheel sets is met, the lifting amount of the wheel rim is ensured to be in a safety range, the dynamic characteristic of the wheel weight of a vehicle is fully considered, secondary self-adaptive filtering is adopted for processing a wheel weight dynamic signal, the error of a wheel weight detection result is small, the stability is good, in order to ensure the real-time performance and the accuracy of the wheel weight detection under the condition that a plurality of wheels of a train pass through, a two-stage operation mode of a single chip microcomputer and a PC is designed, computing resources are reasonably distributed, the wheel weight dynamic data processing speed is effectively improved, three-dimensional digital software is adopted for modeling and finite, the strength, reliability, etc. of the structure are ensured.
Description
Technical Field
The invention relates to a passing type intelligent detection device and method for wheel load of a railway vehicle.
Background
With the development of high speed and heavy load of railway transportation, higher requirements for ensuring transportation safety are met. The overload, unbalance loading and unbalance weight in the railway freight transportation process can cause great harm. When the railway vehicle is in an overload condition for a long time, faults such as chassis sinking, hook height change and unhooking are caused frequently. The device not only has serious damage to the freight train, the locomotive, the track, the turnout and the like, but also is very easy to cause serious accidents such as the axle cutting, the axle burning, the vehicle overturn and the like of the vehicle, and brings great threat to the railway transportation safety. At present, 2/3 railway lines are still in the same line for both passengers and goods in China, and the line-doubling rate is not high. Once accidents happen, not only can vehicles be damaged, but also lines can be stopped, and people can be injured, so that whether the transportation of goods is safe or not can also influence the safety of passenger transportation.
At present, the equipment widely applied in the railway industry comprises equipment such as a track scale, an overload and unbalance loading instrument, a wheel load instrument and the like. The requirements for field installation of the rail weighbridge are harsh, and the transformation of a rail line in the installation process is large. The railway freight car overload and unbalance load detection device can simultaneously measure the unbalance load and unbalance load of the car, but the measurement precision is lower and the manufacturing cost is high. The wheel weight instrument is limited in practical application, and operators need to lift and drop wheels one by one, so that the operation is very complicated. Therefore, the research on the portable, multifunctional and low-cost wheel weight detection equipment has great significance.
Disclosure of Invention
The invention provides a passing-through type intelligent detection device and method for wheel weight of a rail vehicle, which can realize rapid and accurate wheel weight detection on the premise of low cost, and aims to solve the technical problems of complex operation, low precision of the detection device and high cost when the wheel weight of the vehicle is detected in the railway industry at present.
In order to achieve the technical purpose, the technical scheme of the invention is that,
the utility model provides a rail vehicle wheel is heavy through-type intelligent detection device, includes weighing unit, data acquisition unit and host computer, weighing unit pass through data acquisition unit communication connection host computer, weighing unit is including massive weighing member, mounting and the pressure sensor who is equipped with the slot type hole, pressure sensor set up in the slot type hole of weighing member, weighing member passes through the mounting and sets up in the inboard of train track rail to the rim that makes train wheel contacts the upper surface of weighing member and the tread of train wheel is unsettled when passing through, pressure sensor gathers the rim and conveys pressure signal to the host computer.
The rail vehicle wheel load pass-through type intelligent detection device is characterized in that two ends of the upper top surface of the weighing piece are cambered surfaces.
The fixing piece comprises a supporting block and a U-shaped clamp, the supporting block is L-shaped, the outer side shape of the supporting block is matched with the inner side shape of the steel rail and is arranged on the inner side of the steel rail, the width of the bottom surface of the inner side of the U-shaped clamp is matched with the width of the bottom of the steel rail and is arranged at the bottom of the steel rail, and a space for accommodating the weighing piece is formed by the inner side of the supporting block and the side wall of one side of the inner side of the U-shaped clamp.
The fixing piece further comprises a height-adjusting cushion block, the height-adjusting cushion block comprises at least one gasket, and the gasket is arranged at the bottom of the weighing piece and is located between the weighing piece and the supporting block.
The rail vehicle wheel load pass-through type intelligent detection device is characterized in that the data acquisition unit comprises an analog-to-digital conversion module, a control module, a communication module and a power module, the analog-to-digital conversion module and the communication module are respectively in communication connection with the control module, and the power module supplies power for other modules.
The rail vehicle wheel load pass-through type intelligent detection device is characterized in that the analog-to-digital conversion module comprises an A/D conversion circuit, the control module comprises a single chip microcomputer and a keyboard operation module in communication connection with the single chip microcomputer, the communication module comprises a USB communication circuit and an RS232 communication circuit, the power supply module comprises a storage battery and a voltage stabilizing circuit electrically connected to an output port of the storage battery, the input end of the A/D conversion circuit is connected with the pressure sensor, the output end of the A/D conversion circuit is connected to the single chip microcomputer, the output end of the single chip microcomputer is respectively connected to the USB communication circuit and the RS232 communication circuit, and the voltage stabilizing circuit is electrically connected to the A/D conversion circuit and.
A rail vehicle wheel load passing type intelligent detection method adopts the device and comprises the following steps:
the vibration characteristic analysis of the weighing cell comprises the following steps:
the stress of the weighing unit is mainly the step force f of the dead weight of the train1(t) and train undamped free running oscillation force f2(t) the expressions of both are as follows
Self-propelled trainStep force f of gravity1(t)：
In the formula: w is the self weight of the train;
undamped free oscillation force f of train2(t)：
In the formula: a. thetIs the amplitude of the oscillating force, omegantThe natural frequency of train vibration;
the vibration characteristic of the weighing sensor is equivalent to a single-degree-of-freedom second-order system, x is the upper and lower displacement of the sensor, m, c and k are respectively the equivalent mass, the equivalent damping and the equivalent elastic coefficient of the sensor, and the system has a vibration equation:
in the formula: omegansIn order to be the natural frequency of the load cell,
solving the above equation to obtain:
in the formula:
the input signal of the load cell therefore comprises: the weight stability signal, the low-frequency self-vibration interference component and the external interference component of the vehicle are divided into a transition process and a stability process;
the method comprises the following steps of two-stage self-adaptive wheel weight dynamic signal data processing based on sliding filtering and self-adaptive Kalman filtering:
(1) the data acquisition unit performs sliding filtering, for the acquired dynamic signals of the wheel weight of the vehicle, a sequence with the length of N is taken, the average value of the sequence replaces the output of the sequence, and the filtering result is transmitted to the upper computer;
(2) the upper computer performs adaptive Kalman filtering, firstly, a posterior estimation value x (k-1) of the previous moment of the wheel weight signal is used for calculating the state of the current moment, and the updating equation is as follows: x is the number ofk＝Axk-1+Buk-1，xk＝Mxk-1+Nuk-1Wherein A (M) is a state transition matrix, ukB (N) is a control input matrix;
(3) calculating the posterior estimation covariance of k time: p is a radical ofk＝Mpk-1MT+ Q, Q represents the excitation noise covariance; wherein p iskIs the posterior estimated covariance at time k, and T represents the matrix M transposition;
(4) the estimated value is corrected by using the measured value of the current time, and a filter gain array is calculated firstly
Wherein H is a measurement matrix and R is a measurement noise covariance;
(5) obtaining the state update value at the k moment:
updating value of a posteriori estimated covariance
Wherein z iskIs the filtered input;
(6) subjecting the product obtained in step (5)
As an error correction carry-over to step (3),
as the most important of filteringAnd (6) final output.
The invention has the technical effects that the weighing device is used for weighing the wheel rim by guiding and lifting the arc-shaped top surface of the weighing unit, so that the structure is light, the installation is convenient, the use is flexible, the lifting amount of the wheel rim is adjusted by combining different cushion blocks, the use requirement of the abrasion time of a steel rail and a wheel set is met, the lifting amount of the wheel rim is ensured to be in a safety range, the dynamic characteristic of the wheel weight of a vehicle is fully considered, the wheel weight dynamic signal is processed by adopting secondary self-adaptive filtering, the error of the wheel weight detection result is small, the stability is good, in order to ensure the real-time performance and the accuracy of the wheel weight detection under the condition that a train passes through multiple wheels, a two-stage operation mode of a singlechip and a PC is designed, the computing resources are reasonably distributed, the wheel weight dynamic data processing speed is effectively improved, and three-dimensional digital software is adopted, the strength, reliability, etc. of the structure are ensured. The invention has wide market application prospect and can be popularized to all stations in the country in a large range. The method is grabbed from the source, so that loading and detection are synchronously carried out, the problems of overload and unbalance loading of the freight train are solved from the source, the method can be used for wheel weight detection under the loading condition of the railway freight train and wheel weight balance detection after production and maintenance of the motor train unit are finished, the use is flexible, the installation is convenient, the method can be further interconnected with a railway vehicle identification system and a road network database, a railway vehicle overload and unbalance loading detection and management system is formed, and complete informatization management of railway freight is realized.
The invention will be further explained with reference to the drawings.
Drawings
FIG. 1 is a schematic view of the general structure of the apparatus of the present invention;
FIG. 2 is a schematic view of the mounting of the weighing cell of the present invention;
FIG. 3 is a schematic structural view of a weighing member according to the present invention;
FIG. 4 is a wheel rim stress cloud for finite element analysis of a wheel rim by ANSYS software;
FIG. 5 is a strain cloud of the rim for finite element analysis of the rim by ANSYS software;
FIG. 6 is a schematic waveform of a dynamic weighing signal;
FIG. 7 is a diagram of an original wheel weight dynamic signal;
FIG. 8 is a diagram illustrating the result of the sliding filtering process;
FIG. 9 is a diagram illustrating the result of an adaptive Kalman filtering process;
FIG. 10 is a schematic diagram of error estimation for a posteriori estimation;
FIG. 11 is a device reliability verification experiment error scatter plot;
FIG. 12 is an interface diagram of software developed using the solution of the present invention;
the device comprises a train wheel 1, a steel rail 2, a weighing part 3, a supporting block 4, a U-shaped clamp 5, a height-adjusting cushion block 6, a groove-shaped hole 7, a pressure sensor 8 and a cambered surface 9.
Detailed Description
The embodiment lifts the wheel rim by a certain height through the arc-shaped guide surface of the weighing unit, so that the wheel weight information of the vehicle is obtained. The strain type pressure weighing sensor generates voltage change, four paths of voltage signals of the weighing sensor are converted into digital signals through the high-speed A/D module and sent to the ARM single chip microcomputer, and the single chip microcomputer main control chip integrates data, performs primary filtering and then sends the data to a computer. And the computer analyzes and calculates the acquired data to obtain the final wheel weight information of each vehicle, and the wheel weight information is visually displayed. Meanwhile, the printer can be operated to print the detection result in real time.
The general scheme of the rail vehicle wheel weight passing type intelligent detection device is shown in figure 1, an STM32F407ZGT6 single-chip microcomputer is used as a main control chip, a L CS-D8T high-precision cantilever beam sensor is used for acquiring wheel weight dynamic signals, acquired data are sent to the main control chip through a high-resolution A/D conversion module, and a L CD display screen, an embedded printer and a microcomputer are connected externally to achieve wheel weight detection and output of an overload and unbalance loading judgment result.
The weighing unit is directly contacted with the wheel rim, consists of a cantilever beam sensor and an approach bridge with an arc guide surface and is mainly used for acquiring dynamic wheel weight signals. By using the operating principle of a vice for reference, a U-shaped clamping device with a fastening screw is designed, and a weighing sensor fixing device shown in figure 2 is formed by matching a weighing sensor supporting block and a heightening cushion block. The wheel tread is separated from the steel rail by lifting the wheel rim by 1-6mm, so that the wheel weight of the vehicle is converted into the deformation of the weighing sensor, and a weight signal is output. The weighing sensor fixing device needs to stably fix the weighing unit on the rail, and the fixing device has certain adaptability and can meet the installation requirements of rails of different specifications.
The weighing part is a main bearing part and has a structure shown in figure 3, the weighing part is directly contacted with the wheel pair, the wheel rim of the train wheel pair is lifted by 1-6mm, and the weight of the wheel pair is reflected into the deformation of the sensor and is transmitted to the A/D conversion module. Due to the limitation of the steel rail space, the sensor module needs to have a long and narrow appearance and a guide head with a combination of an arc and an inclined plane, so that the huge impact of the train wheel on the weighing platform is conveniently reduced, and the accuracy and the stability of data acquisition are improved.
In order to explore the scientificity and feasibility of a rim bearing mode, finite element analysis is carried out on a rim through ANSYS software, and a stress cloud chart and a strain cloud chart of the rim are respectively shown in figures 4 and 5. Under the condition that the wheel weight is 12.5 tons, the stress borne by the wheel rim is 518MPa, which is far lower than the maximum tensile strength of a steel grinding wheel, namely 910 MPa. Therefore, the wheel weight detection is carried out in a wheel rim bearing mode, and the wheel pair cannot be damaged.
The stress of the weighing unit is mainly the step force f of the dead weight of the train1(t) and train undamped free running oscillation force f2(t) the expressions of both are as follows
Step force f of self weight of train1(t)：
In the formula: w is the self weight of the train;
undamped free oscillation force f of train2(t)：
In the formula: a. thetIs the amplitude of the oscillating force, omegantThe natural frequency of train vibration;
the vibration characteristic of the weighing sensor is equivalent to a single-degree-of-freedom second-order system, x is the upper and lower displacement of the sensor, m, c and k are respectively the equivalent mass, the equivalent damping and the equivalent elastic coefficient of the sensor, and the system has a vibration equation:
in the formula: omegansIn order to be the natural frequency of the load cell,
solving the above equation to obtain:
in the formula:
from the above analysis, the input signal of the load cell comprises: the weight stability signal, the low-frequency self-vibration interference component of the vehicle and the external interference component can be divided into a transition process and a stability process. Fig. 6 is a waveform of a dynamic weighing signal obtained by an experiment, and it can be seen from the graph that when a vehicle contacts a weighing instrument, a shock vibration is generated on a table and gradually shows a damping phenomenon.
Considering the limited processing capacity of a singlechip, the ADAMS software modeling method adopted at present[6]Weighing algorithm by using neural network[7]This work cannot be applied. In order to effectively ensure the real-time performance and the accuracy of wheel weight detection under the condition of multi-wheel passing of the train and reasonably distribute computing resources, the method designs the condition based on the weight detection of the trainAnd a secondary adaptive round weight dynamic signal data processing algorithm of sliding filtering and adaptive Kalman filtering. The algorithm mainly comprises the following steps:
(1) and the main control chip performs sliding filtering. And for the collected dynamic vehicle wheel weight signals, taking a sequence with the length of N, replacing the output of the sequence with the average value of the sequence, and transmitting the filtering result to a PC (personal computer) end.
(2) And the PC terminal performs adaptive Kalman filtering. Firstly, the posterior estimation value x (k-1) of the previous moment of the wheel weight signal is used for calculating the state of the current moment, and the updating equation is as follows: x is the number ofk＝Axk-1+Buk-1，xk＝Mxk-1+Nuk-1Wherein A (M) is a state transition matrix, ukB (N) is a control input matrix.
(3) Calculating the posterior estimation covariance of k time: p is a radical ofk＝Mpk-1MT+ Q. Q represents the excitation noise covariance.
(4) The estimated value is corrected by using the measured value of the current time, and a filter gain array is calculated firstly
Where H is the measurement matrix and R is the measurement noise covariance.
(5) Obtaining the state update value at the k moment:
updating value of a posteriori estimated covariance
Wherein z iskIs the filtered input.
(6) Subjecting the product obtained in step (5)
As an error correction carry-over to step (3),
as the final output of the filtering.
By adopting the algorithm, the requirement of real-time processing of the wheel weight signals is met. FIG. 7 shows the original dynamic wheel weight signals collected at an experimental speed of 1.5m/s and a wheel weight of 7000 g. Fig. 8 shows the result of the moving average filtering, and it can be seen that the first-stage filtering makes the signal smoother and the amount of invalid data is reduced. Fig. 9 shows the adaptive kalman filter result, where the second-order filtering makes the result almost close to the true value, and fig. 10 shows the filter a posteriori estimation error.
In order to verify the reliability of the detection result of the device, 60 times of wheel weight detection experiments are performed under the conditions that the speed of the bogie is kept to be 1m/s and the total weight is 40.393Kg on a dynamic wheel weight experiment platform, the error percentage between the test result and the true value of each time is recorded, and the statistical result of the error of the device reliability verification experiment is shown in fig. 11. As can be seen from the error statistical results, the error data of 60 experiments are within +/-3%, which indicates that the system has higher reliability.
As a complete system, the data visualization platform with strong interactivity cannot be separated. As shown in fig. 12, the value of the train passing through the load cell exhibits strong characteristics: the sudden rise of the signal from zero to a maximum point corresponds to the process of the load from touching the sensor to completely entering the sensor, and the change from the last maximum point to zero reflects the process of the load completely leaving the sensor. By adopting the scheme of the invention, a software interaction platform is developed by utilizing WPF at the PC end, and the process is completely recorded. And the calculated values of the wheel weight, the axle weight, the unbalance loading and the like of the train are visually displayed in the interface. And in the operation box part, displaying each calculated parameter of the train on a display screen, and printing and outputting data through a printer module.
Claims (1)
1. A rail vehicle wheel weight passing type intelligent detection method is characterized in that a rail vehicle wheel weight passing type intelligent detection device is adopted and comprises a weighing unit, a data acquisition unit and an upper computer, wherein the weighing unit is in communication connection with the upper computer through the data acquisition unit, the weighing unit comprises a block-shaped weighing piece provided with a groove-shaped hole, a fixing piece and a pressure sensor, the pressure sensor is arranged in the groove-shaped hole of the weighing piece, the weighing piece is arranged on the inner side of a train rail through the fixing piece, a rim of a train wheel is in contact with the upper surface of the weighing piece when passing, a tread of the train wheel is suspended, and the pressure sensor acquires pressure when the rim passes and transmits a pressure signal to the upper computer;
both ends of the upper top surface of the weighing piece are cambered surfaces;
the fixing piece comprises a supporting block and a U-shaped clamp, the supporting block is L-shaped, the outer side shape of the supporting block is matched with the inner side shape of the steel rail and is arranged on the inner side of the steel rail, the width of the bottom surface of the inner side of the U-shaped clamp is matched with the width of the bottom of the steel rail and is arranged at the bottom of the steel rail, and a space for accommodating the weighing piece is formed by the inner side of the supporting block and one side wall of the inner side of the U;
the fixing piece further comprises a height adjusting cushion block, the height adjusting cushion block comprises at least one gasket, and the gasket is arranged at the bottom of the weighing piece and is positioned between the weighing piece and the supporting block;
the data acquisition unit comprises an analog-to-digital conversion module, a control module, a communication module and a power module, wherein the analog-to-digital conversion module and the communication module are respectively in communication connection with the control module, and the power module supplies power to other modules;
the analog-to-digital conversion module comprises an A/D conversion circuit, the control module comprises a single chip microcomputer and a keyboard operation module in communication connection with the single chip microcomputer, the communication module comprises a USB communication circuit and an RS232 communication circuit, the power supply module comprises a storage battery and a voltage stabilizing circuit electrically connected to an output port of the storage battery, the input end of the A/D conversion circuit is connected with the pressure sensor, the output end of the A/D conversion circuit is connected to the single chip microcomputer, the output end of the single chip microcomputer is respectively connected to the USB communication circuit and the RS232 communication circuit, and the voltage stabilizing circuit is respectively electrically connected to the A/D conversion circuit and;
the method comprises the following steps:
the method comprises the following steps of firstly, analyzing the vibration characteristics of a weighing unit, wherein the method comprises the following steps:
the stress of the weighing unit is mainly the step force f of the dead weight of the train1(t) and train undamped free running oscillation force f2(t)，The expressions of the two are respectively as follows
Step force f of self weight of train1(t)：
In the formula: w is the self weight of the train;
undamped free oscillation force f of train2(t)：
In the formula: a. thetIs the amplitude of the oscillating force, omegantThe natural frequency of train vibration;
the vibration characteristic of the weighing sensor is equivalent to a single-degree-of-freedom second-order system, x is the upper and lower displacement of the sensor, m, c and k are respectively the equivalent mass, the equivalent damping and the equivalent elastic coefficient of the sensor, and the system has a vibration equation:
in the formula: omegansIn order to be the natural frequency of the load cell,
solving the above equation to obtain:
in the formula:
the input signal of the load cell therefore comprises: a weight stable signal, a low-frequency self-vibration interference component and an external interference component of the vehicle are divided into a transition process and a stable process;
and secondly, processing secondary self-adaptive wheel weight dynamic signal data based on sliding filtering and self-adaptive Kalman filtering:
(1) the data acquisition unit performs sliding filtering, for the acquired dynamic signals of the wheel weight of the vehicle, a sequence with the length of N is taken, the average value of the sequence replaces the output of the sequence, and the filtering result is transmitted to the upper computer;
(2) the upper computer performs the self-adaptive Kalman filtering, firstly uses the posterior estimated value x of the previous moment of the wheel weight signalj-1To calculate the state of the current time, the update equation is: x is the number ofj＝Mxj-1+Nuj-1Where M is a state transition matrix, uj-1N is a control input matrix;
(3) calculating the posterior estimated covariance at jk time: p is a radical ofj＝Mpj-1MT+ Q, Q represents the excitation noise covariance; wherein p isjIs the posterior estimated covariance at time j, and T represents the transposition of the matrix M;
(4) the estimated value is corrected by using the measured value of the current time, and the filter gain array of the j time is firstly calculated
Wherein H is a measurement matrix and R is a measurement noise covariance;
(5) get the state update value at jk:
updating value of a posteriori estimated covariance
Wherein z isjIs the filtered input;
(6) subjecting the product obtained in step (5)
As an error correction carry-over to step (3),
as the final output of the filtering.
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| CN109945958A (en) * | 2019-03-31 | 2019-06-28 | 唐山百川智能机器股份有限公司 | Detachable rolling stock wheel's load measurement device |
| CN111964768B (en) * | 2020-08-05 | 2021-09-14 | 青岛英泰软件技术有限公司 | Filtering method for increasing anti-interference effect of electronic scale |
| CN112345053B (en) * | 2020-11-18 | 2022-07-01 | 台誉传感科技(杭州)有限公司 | Portable rail balance and device |
| CN112816048B (en) * | 2021-01-05 | 2022-09-20 | 北京经纬信息技术有限公司 | System and method for detecting wheel weight of motor train unit |
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| CN2443899Y (en) * | 2000-10-24 | 2001-08-22 | 朱广秀 | Load over-inclined dynamic tester for vehicle of railway |
| CN2463258Y (en) * | 2000-12-27 | 2001-12-05 | 张定尊 | Overload and bias load detecting device for railway vehicle |
| CN2807221Y (en) * | 2005-07-27 | 2006-08-16 | 北京华横新技术开发公司 | Rim bearing type rail weighing device |
| CN2821543Y (en) * | 2005-09-09 | 2006-09-27 | 朱广才 | Dynamic electronic wheel weighing instrument for railway freight train |
| CN201497556U (en) * | 2009-01-21 | 2010-06-02 | 成都成运科技有限责任公司 | Weighing structure component of overload and unbalanced load detection system |
| CN202433072U (en) * | 2011-12-08 | 2012-09-12 | 西安昂科光电有限公司 | Weighing structure assembly for detecting overload and offset load |
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| CN107817038A (en) | 2018-03-20 |
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