CN118289059A - Train braking failure detection and parking spot prediction method based on failure coefficient - Google Patents
Train braking failure detection and parking spot prediction method based on failure coefficient Download PDFInfo
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Abstract
The invention provides a train braking failure detection and parking spot prediction method based on failure coefficients, which comprises the following steps: when the braking of the train fails, establishing a train dynamics equation under the braking failure state based on train operation data; based on a train dynamics equation under a braking failure state, obtaining a failure coefficient of the train through calculation; based on a train dynamics equation under a braking failure state and a failure coefficient of a train, a train braking process and a prediction result of a final stopping point are obtained through calculation. The method provided by the invention can effectively detect the failure coefficient of the braking failure train and forecast the stopping point of the train, and provides powerful support for the output safety measure scheme of the railway train dispatching system.
Description
Technical Field
The invention relates to the technical field of railway safety, in particular to a train braking failure detection and parking spot prediction method based on failure coefficients.
Background
The control of the station route is mainly that a station autonomous machine automatically generates a train operation instruction according to a train operation plan issued by a dispatching center, and the train operation instruction is converted into a command after being checked for legitimacy, timeliness, integrity and no conflict, and the command is issued to interlocking equipment of the station in time for execution.
The manner in which the train receives the inbound command is different due to the different levels of the train control system. When the train adopts a CTCS-2 level control system, a station computer interlocking system (Computer Based Interlocking, CBI) sends the route information in front of the train to the train through a train control center (Train Control Center, TCC), and the vehicle-mounted equipment combines the route information obtained by analysis and the route information uploaded by the transponder and calculates a common braking curve and an emergency braking curve, so that the train is controlled to run at a safe limiting speed. When the train adopts a CTCS-3 level control system, a dispatching centralized system (Centralized Traffic Control, CTC) issues a command to a wireless blocking system (Radio Block Center, RBC), the wireless blocking system RBC generates driving permission, and the driving permission is sent to the train through a communication system (Global System for Mobile Communications-Railway, GSM-R) to generate a train control curve through calculation of vehicle-mounted equipment. The flow of delivery of the train inbound command is shown in fig. 2. In the figure, the dotted line represents the information transfer flow direction of the CTCS-2 level train control system, and the solid line represents the information transfer flow direction of the CTCS-3 level train control system.
The inbound control curve of a train can be divided into two parts: a ceiling speed Monitor (CEILING SPEED Monitor, CSM) zone and a target speed Monitor (TARGET SPEED Monitor, TSM) zone. Calculating a train control curve of the CSM area according to the train braking performance; the control curve of the TSM zone is determined according to the front target point and the ceiling speed. The calculation of the braking curve should take into account the most unfavorable deceleration when the braking performance is reduced, on the basis of the nominal deceleration of the vehicle. The value of the deceleration should consider the influence of different speeds and different gradients on the deceleration. When the front target point is a parking spot, the train control vehicle-mounted device calculates and generates a service braking mode curve and an emergency braking mode curve, as shown in fig. 3. The braking mode in the figure is continuous primary braking speed control, and the right side annunciator in the figure is an outbound annunciator on a stock track, and an inbound train needs to stop in front of the outbound annunciator. Under normal conditions, the actual running curve of the train is below the service braking curve, and a certain speed allowance exists between the two curves, and the value is usually 5km/h. The approach process shown in fig. 3 is a train side-track connection via a switch, L b is the braking distance of the train from the braking start to the stopping point; l p is a safe distance, which is a protection zone that the train operation control system requires to set in consideration of system allowable errors, so that the train stays within the safe distance and is in a normal condition.
Disclosure of Invention
The embodiment of the invention provides a train braking failure detection and parking spot prediction method based on failure coefficients, which is used for solving the technical problems in the prior art.
In order to achieve the above purpose, the present invention adopts the following technical scheme.
A train brake failure detection and parking spot prediction method based on failure coefficients comprises the following steps:
s1, when a train brake fails, establishing a train dynamics equation under a brake failure state based on train operation data;
s2, obtaining a train failure coefficient through calculation based on a train dynamics equation under a brake failure state;
S3, based on a train dynamics equation in a braking failure state and a train failure coefficient, obtaining a train braking process and a prediction result of a final stopping point through calculation;
The execution result of step S3 is used for input to the railroad train scheduling system so that the railroad train scheduling system can output the safety measures for the target section damage control.
Preferably, the train dynamics equation includes:
sum type train kinetic energy
Wherein E T is the sum of energy, and the unit is KJ; m t is the total mass of the train, and the unit is KG; v is the current speed of the train, and the unit is m/s; j n is moment of inertia; omega n is the angular velocity; n is the number of train sections; r is the radius of the track curve, and the unit is m;
Through type
And combining Newton's second law to obtain
Wherein, gamma is a rotation coefficient; m agg is the converted total mass; a c is braking deceleration in m/s 2; f is the resultant force of braking force, and the unit is KN; c is the unit braking force of the resultant force, and the unit is N/KN; g is the gravity acceleration, and the unit is 9.8m/s 2;
Conversion of c
Wherein b is the unit braking force of the train, and the unit is N/KN; w n is the running unit resistance of the train, and the unit is N/KN; i is gradient thousand points;
By integrating conditions Obtained by
The formula (5) is a formula
Wherein Deltas n+1 is the position interval between two adjacent speed points, and the unit is m; s n+1 is the position of the train at the n+1th point; s n is the position of the train at the nth point; v n+1 is the speed of the train at the n+1th point, in m/s; v n is the speed of the train at the nth point in m/s;
a refinement formula for the interval between two adjacent speed points is obtained through the combined type (4) and (6)
Obtaining the speed of the train in the decelerating state by the speed v=v 0 +at and the braking force use coefficient in the decelerating state of the train
Wherein v n is the current speed of the train, m/s; v n+1 is the speed at the next moment, m/s; f is a brake failure coefficient; a 1 is deceleration, m/s 2;ξx is braking force use coefficient, and three desirable values are respectively: deceleration coefficient ζ 1 corresponding to the emergency braking force, deceleration coefficient ζ 2 corresponding to the maximum service braking force, deceleration coefficient ζ 3 corresponding to the service braking force;
When the train speed changes, the distance increment formula corresponding to the train is
The step S2 comprises the following steps:
Time calculation method based on speed-time data and pass-through type
Calculating to obtain a first failure coefficient f T;
Distance calculation method based on speed-distance data and pass-through type
Calculating to obtain a second failure coefficient f S; in the formulas (10) and (11), xi x is a braking force use coefficient, and the calculated priority order is xi 1>ξ2>ξ3;
Through type
Reducing the influence of measurement errors of a train speed measurement and distance measurement unit;
Through type
ER＝|(fdetection-freal)/freal| (13)
Evaluating the accuracy of the first failure coefficient and the second failure coefficient; wherein f detection is the average value of failure coefficients obtained by multiple times of calculation; and f real is a set value of the failure coefficient.
Preferably, step S3 includes:
Through type
And calculating to obtain the prediction results of the train braking process and the final stopping point.
As can be seen from the technical solution provided by the above embodiment of the present invention, the present invention provides a method for predicting a train brake failure detection and a stopping point based on a failure coefficient, including: when the braking of the train fails, establishing a train dynamics equation under the braking failure state based on train operation data; based on a train dynamics equation under a braking failure state, obtaining a failure coefficient of the train through calculation; based on a train dynamics equation under a braking failure state and a failure coefficient of a train, a train braking process and a prediction result of a final stopping point are obtained through calculation. The method provided by the invention can effectively detect the failure coefficient of the braking failure train and forecast the stopping point of the train, and provides data powerful support for the railway train dispatching system to output the safety measure scheme.
Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a process flow diagram of a method for predicting a train brake failure detection and stopping point based on failure coefficients;
FIG. 2 is a schematic diagram of a prior art command issuing flow for a pickup operation;
FIG. 3 is a schematic diagram of a train normally braking inbound;
FIG. 4 is a schematic diagram of a train brake failure inbound;
FIG. 5 is a schematic diagram of a failure coefficient based detection-prediction method for a failure coefficient based train brake failure detection and stopping point prediction method according to the present invention;
Fig. 6 is a schematic diagram of a yard and a schematic diagram of a calculation point in an application scenario of a method for predicting a train braking failure detection and a stopping point based on a failure coefficient provided by the invention;
FIG. 7 is a predictive diagram of a failure coefficient-based method for predicting a failure detection and stopping point of train braking based on a failure coefficient provided by the invention;
FIG. 8 is a logic block diagram of a train brake failure monitoring architecture of the failure coefficient-based train brake failure detection and stopping point prediction method provided by the invention;
Fig. 9 is a schematic diagram of a transmission flow of train brake failure information according to the failure coefficient-based train brake failure detection and parking spot prediction method provided by the invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below by referring to the drawings are exemplary only for explaining the present invention and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless expressly stated otherwise, as understood by those skilled in the art. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. The term "and/or" as used herein includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the purpose of facilitating an understanding of the embodiments of the invention, reference will now be made to the drawings of several specific embodiments illustrated in the drawings and in no way should be taken to limit the embodiments of the invention.
The invention provides a method for predicting train braking failure detection and stopping points, which is used for solving the following technical problems in the prior art:
When a train is ready to enter its front stop, it is usually run at a reduced speed before entering the stop, but if there is some failure of the braking system resulting in an unexpected output of braking force, the actual movement profile of the train must reach or even exceed the speed specified by the braking profile. As shown in fig. 4, the solid line represents the emergency braking curve of the train, the dash-dot line represents the maximum normal braking curve of the train, and the dotted line represents the actual running curve of the train. In the initial state, the train is in a uniform motion stage, and the train is braked at a certain moment because of the need of stopping when the train enters the station, but the actually output braking force is smaller than the braking force when the train control curve is calculated due to certain faults, the actual running curve can be intersected with the service braking curve, and then the train can trigger the maximum service braking. Since the brake is still in failure, the operating curve may not remain below the emergency braking curve and then intersect the emergency braking curve. Finally, the train triggers emergency braking until the speed becomes zero.
The trains put into operation at present are equipped with a large number of sensing elements for monitoring various data in the operation of the trains, such as the speed, the running distance, the mileage, the traction force data, the braking force data and the like of the trains, and the data which can be monitored and recorded provide a basis for the monitoring and calculation of the braking failure coefficient. The running process after the brake failure of the train is predicted by adopting a detection-prediction method based on failure coefficients.
The train control curve of the train is calculated by a train control vehicle-mounted system according to the information of the front stop end point, the idle section information, the train braking performance and the like, and a train driver operates a braking device to finish the process of train braking according to the indication of the train control curve. If the train control vehicle-mounted system can timely find out the braking failure and timely acquire the braking failure rate, the train control vehicle-mounted system can recalculate the braking process of the train in the failure state, which is the logic basis of a failure coefficient-based detection-prediction algorithm. In order to achieve the purpose, a train dynamics equation and a specific braking model under a braking fault state are required to be determined, and the braking model is combined to achieve detection of a braking failure coefficient and prediction of a stopping point.
Referring to fig. 1, the invention provides a train brake failure detection and parking spot prediction method based on failure coefficients, which comprises the following steps:
s1, when a train brake fails, establishing a train dynamics equation under a brake failure state based on train operation data;
s2, obtaining a train failure coefficient through calculation based on a train dynamics equation under a brake failure state;
S3, based on a train dynamics equation in a braking failure state and a train failure coefficient, obtaining a train braking process and a prediction result of a final stopping point through calculation;
The execution result of the step S3 is used for uploading to a railway train dispatching system, data support is provided for the railway train dispatching system to output a safety measure scheme, and the jurisdiction where the train is subjected to risk avoidance when braking fails can take corresponding safety measures according to the safety measure scheme.
In the preferred embodiment provided by the invention, the specific process of each step is as follows.
The train control curve of the train is calculated by a train control vehicle-mounted system according to the information of the front stop end point, the idle section information, the train braking performance and the like, and a train driver operates a braking device to finish the process of train braking according to the indication of the train control curve. If the train control vehicle-mounted system can timely find out the braking failure and timely acquire the braking failure rate, the train control vehicle-mounted system can recalculate the braking process of the train in the failure state, which is the logic basis of a failure coefficient-based detection-prediction algorithm. In order to achieve the purpose, a train dynamics equation and a specific braking model under a braking fault state are required to be determined, and then the braking model is combined to detect a braking failure coefficient and predict a stopping point.
The train can pass through a straight road or a curve in the running process, so that the kinetic energy of the train comprises translational kinetic energy on the straight road and rotational kinetic energy on the curve, and the total kinetic energy of the running train is as follows:
Wherein: e T is the sum of the energies in KJ; m t is the total mass of the train, and the unit is KG; v is the current speed of the train, and the unit is m/s; j n is the moment of inertia, i.e. a measure of the constant velocity circular motion of the train as it rotates about its axis; omega n is the angular velocity; n is the number of train sections; r is the radius of the track curve, and the unit is m.
And (3) making:
Wherein: gamma is the rotation coefficient; m agg is the converted total mass.
Then it is available according to newton's second law f=ma;
Wherein: a c is braking deceleration, m/s 2; f is the resultant force of braking force, and the unit is KN; c is the unit braking force of the resultant force, and the unit is N/KN; g is the gravitational acceleration, and the unit is 9.8m/s 2.
During braking of the train, the resultant force c contains the braking force of the train itself, the running resistance of the train and gravity. For ease of calculation and analysis, the resultant force c is divided into two parts:
c＝c1+c2＝b+(wn+i)
Namely:
wherein: b is the unit braking force of the train, and the unit is N/KN; w n is the running unit resistance of the train, and the unit is N/KN; i is gradient thousand points.
Finally according to the integral conditionThe method can obtain:
the results obtained were:
Wherein: deltas n+1 is the position interval between two adjacent speed points, and the unit is m; s n+1 is the position of the train at the n+1th point; s n is the position of the train at the nth point; v n+1 is the speed of the train at the n+1th point, in m/s; v n is the speed of the train at the nth point in m/s.
The formula (4) is combined with the formula (6), and a refinement formula of the interval between two adjacent speed points can be obtained:
in this formula, the speed units have been converted to km/h.
In the actual running process of the train, the basic resistance is influenced by various factors. From practical experience, the unit resistance w n can be expressed approximately as a quadratic function of the speed v n. The basic resistance calculation method for each model is shown in table 1 below. The formulas are obtained by a vehicle manufacturer through a large amount of experiments and fitting a large amount of data, and parameters a, b and c of different vehicle types are different, but basically are quadratic functions, and the fitting type of the functions is w=a+b.v+c.v 2.
Table 1 basic running resistance calculation table corresponding to different vehicle models
Since a braking system is found to be faulty during braking to cause braking failure, the braking failure coefficient should be added during deceleration. Combining the speed formula v=v 0 +at in the train deceleration state and the braking force use coefficient (corresponding to different braking gears), the formula of the speed of the train in the state can be obtained:
Wherein: v n is the current speed of the train, and the unit is m/s; v n+1 is the speed at the next moment, in m/s; f is a brake failure coefficient; a 1 is deceleration, m/s 2;ξx is braking force use coefficient, and three desirable values are respectively: the deceleration coefficient ζ 1 corresponding to the emergency braking force, the deceleration coefficient ζ 2 corresponding to the maximum service braking force, and the deceleration coefficient ζ 3 corresponding to the service braking force. The brakes taken by the train during normal operation are typically service brakes.
When the speed changes, the corresponding distance increment is:
The method provided by the invention predicts based on the position, speed and time data recorded by the train in the actual running process, and can be divided into three stages according to the execution sequence, and the three stages are mutually matched to finally finish the prediction of the stopping point.
In the first stage, daily acceleration is monitored. Failure to brake a train is not always enough, and in most cases the braking process of the train is in line with the original design of the manufacturer. Therefore, the deceleration of the train during daily braking execution does not deviate from the design value too much, basically, the train slightly fluctuates around the value, and therefore the vehicle-mounted equipment can clearly know the normal deceleration value under braking forces of different grades, and lays a foundation for subsequent calculation.
And in the second stage, detecting the failure coefficient in the braking failure state. If the braking system of the train fails, the variation of the deceleration value can be obviously perceived through daily monitoring. In this state, a specific brake failure coefficient can be obtained through data comparison and iterative calculation. Two calculation methods can be deduced from the formula (8) and the formula (9), wherein the first is a failure coefficient f T obtained by a time calculation method based on speed-time (V-T) data, and the second is a failure coefficient f S obtained by a distance calculation method based on speed-distance (V-S) data, and the two calculation methods respectively correspond to the formula (10) and the formula (11).
Wherein: ζ x is the braking force usage coefficient, and the priority order of calculation is ζ 1>ξ2>ξ3. The brakes taken by the train during normal operation are typically service brakes.
Because the speed and distance measuring units all have errors, the influence of the errors is reduced by adopting a mode of calculating and averaging for a plurality of times:
in order to evaluate the accuracy of the detected failure coefficient, an Error Rate (ER) is used as a measurement index, and the formula is as follows:
ER＝|(fdetection-freal)/freal| (13)
wherein: f detection is the average value of failure coefficients obtained by multiple calculation;
and f real is a set value of the failure coefficient.
The smaller the ER value, the higher the accuracy of the calculation result.
And a third stage, predicting the subsequent braking information. The failure coefficient predicted by the method is combined with the kinematic equation of the train, so that the subsequent braking process of the train can be predicted. The execution is as shown in fig. 5. And calculating all subsequent operation data from the current moment until the actual stopping point of the train. In this process, the priority order of use of ζ is ζ 3>ξ2>ξ1, and the prediction formula group is shown in the following formula (14):
in a possible embodiment, based on the method proposed by the invention, a detection-prediction software based on failure coefficients is used. The algorithm of the software is input into train operation record data and daily monitoring values in the first stage, and output into a brake failure coefficient, a predicted braking process and a final stopping point.
The invention also provides an embodiment for exemplarily displaying the execution process of the method provided by the invention.
The site situation of this embodiment is illustrated in a schematic form in fig. 6. For consistency of experimental data, the complete data recorded is from 3000 meters from the train inbound signaller until final stop. Before the train arrives at a station and when the braking operation starts, the detection system monitors the numerical value generated in the braking process. When the abnormal braking force output of the train is detected and confirmed, the prediction system can complete the prediction work of the stopping point according to the recorded braking data of the train. The starting point of the experimental data adopted in the embodiment is a position point 3000 m away from the arrival signal of the train, and the end point is a position point of the arrival signal. This approach has two considerations, one of which is that the starting points of braking of the trains of the present embodiment are not fixed, but they are all substantially within this range, and the data of this range is selected to fully satisfy the later calculations or requirements. In addition, since the braking starting point is not fixed, the data amount used by the model in fitting is slightly different, which has a certain test on the effectiveness of the model provided by the invention, and the model is also intended to be verified through experiments. Secondly, the invention aims to detect the braking failure coefficient and forecast the stopping point, and the work is completed before the train enters the station in consideration of the reaction time of the station staff and the time for taking precautionary measures, so that the data end point used by the embodiment cannot exceed the entering signal machine. For simplicity of description herein, the actual parking spots in the complete data are abbreviated as APs, and the parking spots predicted by the model are abbreviated as PPs.
The experiment of the embodiment is carried out based on the braking data of the CRH2 type 16 grouped motor train unit, the recorded data are extracted, the retention time T, the position S and the corresponding speed information V are reserved, and the highest running speed of the train is limited within 250 km/h. In order to match the situation of braking failure, the scheme is adopted that the braking force output of a certain train is cut off, and the braking of the certain train is invalidated. In order to explore the rules under the condition of various braking failures, the braking force output of 4 trains is cut off at most in experiments. Finally, in this embodiment, 4 sets of (v, s, t) -containing data records are selected, wherein one record (R1) is braking data after a train braking force is cut off, the starting point of the data is a position point 3000 m away from a train arrival signal, and the end point is an arrival signal position point. Recording two (R2) is brake data after braking force of two trains is cut off, and so on until recording four (R4). The failure coefficient information corresponding to the four sets of data and the parking spot information are shown in table 2.
Table 2 basic information of four sets of operational data
The failure coefficient was detected using detection-prediction software developed based on the failure coefficient, and the results obtained are shown in table 3. The data recorded by train measurements will typically have minor errors, so the detected failure coefficients will also have errors. As can be seen from table 3, for each set of records, the difference between the failure coefficient f S calculated based on the velocity-distance (V-S) data and the failure coefficient f T calculated based on the velocity-time (V-T) data is not large, nor is the deviation rate significantly different. Therefore, in this embodiment, any method for calculating the failure coefficient may be adopted.
Table 3 results of detection calculation of failure coefficient
Taking the second record as an example, a detection-prediction model based on failure coefficients is adopted to predict the state of the train in the later stage of braking. When the detection system detects and confirms that the braking force output of the train is abnormal, the prediction system can complete the detection work of the failure coefficient according to the recorded braking data of the train, and predicts the subsequent braking process of the train, and the obtained speed-distance (V-S) curve is shown in fig. 7. As can be seen from fig. 7, an efficient prediction can be made from the data before the train enters the station, and the predicted curve is smooth and continuous. The predicted stopping point of the data of the group is 4543.37, the actual stopping point is 4447.00, and the difference value is 96.37 meters.
Table 4 is the prediction result of the parking spot for four sets of data based on the detection-prediction model of the failure coefficient, and the failure coefficient employed in the prediction for each set of data is the maximum failure coefficient detected (the result in the worst case). As can be seen from Table 4, the difference between the predicted parking spot and the actual parking spot is within 200 meters, the length of a single track section of the experimental station is not exceeded, the prediction effect of the detection-prediction model on the data of the third group and the fourth group is better, and the error is only about 10 meters. This is because the present detection-prediction model predicts based on the failure coefficient, and the accuracy of the result is positively correlated with the detection accuracy of the failure coefficient. The failure coefficient detection error rate of the third set of data and the fourth set of data is lower, so that the predicted parking spot is closer to the actual parking spot.
TABLE 4 predicted parking Point and Difference values based on failure coefficient method
The detection-prediction method based on the failure coefficient has the advantage that a person skilled in the art can clearly and directly know the failure degree of the current train. The vehicle-mounted system can predict the follow-up driving data according to the current braking state and the failure coefficient. In this approach, the accuracy of predicting the stop point will be highly correlated with the detection accuracy of the failure coefficient. Because the time required by the emergency plan of the station is considered, the time for calculating the failure coefficient for the train is limited, and the basic data trained and calculated by the invention is cut off before the arrival signal machine in combination with the special requirement.
In combination with the study of the above embodiments, in some preferred embodiments provided by the present invention, a train brake failure monitoring architecture is built, as shown in fig. 8, and mainly comprises three major parts.
The first part is the daily monitoring of train operation data, including data collection, data monitoring and judgment. The main work of the part is to monitor key values of the train in the braking process, including whether the wind pressure of a braking unit is normal, whether a valve is normal, whether the value of the braking deceleration is normal, whether the variation is separated from control, whether the mileage and the position of the train have larger deviation, and the like, so as to judge whether the braking state of the train is normal.
The daily monitoring system for train operation data has two roles. First, once a train brake failure is found, the predictive software is immediately activated by judgment and provided with the basic data necessary for calculation. In the daily maintenance and overhaul, the brake system can be checked and maintained in a targeted mode through the monitoring result of the checking and monitoring system, so that the brake system is in a good working state, and the safety and reliability of the train are improved.
The second part is the prediction of the braking process after the braking failure of the train, the braking failure coefficient is calculated through detection based on a failure coefficient detection-prediction model, and the stopping end point is predicted by combining with a kinematic equation under the failure state. In addition, the algorithm provided by the invention adopts an iterative calculation mode when being executed, so that the data can be updated in real time and the software can be calculated in real time, and the calculation software can correct the prediction result in real time according to the real operation data added in the braking process, so that the prediction accuracy is ensured to the greatest extent possible.
And the third part is the transmission and display of train brake failure information and prediction results. The detection is means, and the final purpose is to solve the influence caused by faults and prevent accidents. In order to ensure that related personnel can timely take related emergency plans, the vehicle-mounted subsystem must timely and automatically and timely transfer the key information upwards through the train control system, and effort is made to shorten the influence caused by time delay.
The information transfer flow is shown in fig. 9. The driver is the first contact of information, and the brake failure information and the system prediction data can be directly seen through the display of a human-computer interface (DRIVER MACHINE INTERFACE-DMI) of the train control vehicle-mounted equipment. Then, the information is sequentially transmitted to a dispatching system of a dispatching control center and the autonomous extension of the station through a data transmission channel. The information is automatically transmitted after being checked by the dispatching system, so that the time delay caused by manual operation is avoided. If no other train is in front of the braking fault train, no tracking scene is involved, and only the safety of the receiving operation of the braking fault train is considered, at the moment, the execution right and the adjustment right of the emergency measure are put down to the station autonomous extension along with the fault information. If other running trains exist in front of the braking fault train, the safety of the two trains needs to be considered, at the moment, the executive right of the emergency measure and the main right of the adjusting right are on a dispatching system of a dispatching control center, and the station autonomous extension can assist to act station equipment according to a command automatically issued by the dispatching system so as to complete implementation work of the emergency measure in cooperation with the dispatching system. In addition, the dispatching system and the station self-discipline extension are required to acquire the state information of the station equipment to take emergency measures to assist a driver in handling faults, so that the equipment state information is also required to be acquired and transmitted. By combining all the above, the brake fault information transmission link is perfected, and the train brake fault monitoring system is built.
In summary, the present invention provides a method for predicting a train braking failure detection and a stopping point based on a failure coefficient, including: when the braking of the train fails, establishing a train dynamics equation under the braking failure state based on train operation data; based on a train dynamics equation under a braking failure state, obtaining a failure coefficient of the train through calculation; based on a train dynamics equation under a braking failure state and a failure coefficient of a train, a train braking process and a prediction result of a final stopping point are obtained through calculation. The method provided by the invention can effectively detect the failure coefficient of the braking failure train and forecast the stopping point of the train, and provides powerful support for the output safety measure scheme of the railway train dispatching system.
Those of ordinary skill in the art will appreciate that: the drawing is a schematic diagram of one embodiment and the modules or flows in the drawing are not necessarily required to practice the invention.
From the above description of embodiments, it will be apparent to those skilled in the art that the present invention may be implemented in software plus a necessary general hardware platform. Based on such understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art in the form of a software product, which may be stored in a storage medium, such as a ROM/RAM, a magnetic disk, an optical disk, etc., including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method described in the embodiments or some parts of the embodiments of the present invention.
In this specification, each embodiment is described in a progressive manner, and identical and similar parts of each embodiment are all referred to each other, and each embodiment mainly describes differences from other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, the description is relatively simple, with reference to the description of method embodiments in part. The apparatus and system embodiments described above are merely illustrative, wherein the elements illustrated as separate elements may or may not be physically separate, and the elements shown as elements may or may not be physical elements, may be located in one place, or may be distributed over a plurality of network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment. Those of ordinary skill in the art will understand and implement the present invention without undue burden.
The present invention is not limited to the above-mentioned embodiments, and any changes or substitutions that can be easily understood by those skilled in the art within the technical scope of the present invention are intended to be included in the scope of the present invention. Therefore, the protection scope of the present invention should be subject to the protection scope of the claims.
Claims (3)
1. The train brake failure detection and parking spot prediction method based on the failure coefficient is characterized by comprising the following steps of:
s1, when a train brake fails, establishing a train dynamics equation under a brake failure state based on train operation data;
s2, obtaining a train failure coefficient through calculation based on a train dynamics equation under a brake failure state;
S3, based on a train dynamics equation in a braking failure state and a train failure coefficient, obtaining a train braking process and a prediction result of a final stopping point through calculation;
The execution result of step S3 is used for input to the railroad train scheduling system so that the railroad train scheduling system can output the safety measures for the target section damage control.
2. The method of claim 1, wherein the train dynamics equation includes:
sum type train kinetic energy
Wherein E T is the sum of energy, and the unit is KJ; m t is the total mass of the train, and the unit is KG; v is the current speed of the train, and the unit is m/s; j n is moment of inertia; omega n is the angular velocity; n is the number of train sections; r is the radius of the track curve, and the unit is m;
Through type
And combining Newton's second law to obtain
Wherein, gamma is a rotation coefficient; m agg is the converted total mass; a c is braking deceleration in m/s 2; f is the resultant force of braking force, and the unit is KN; c is the unit braking force of the resultant force, and the unit is N/KN; g is the gravity acceleration, and the unit is 9.8m/s 2;
Conversion of c
Wherein b is the unit braking force of the train, and the unit is N/KN; w n is the running unit resistance of the train, and the unit is N/KN; i is gradient thousand points;
By integrating conditions Obtained by
The formula (5) is a formula
Wherein Deltas n+1 is the position interval between two adjacent speed points, and the unit is m; s n+1 is the position of the train at the n+1th point; s n is the position of the train at the nth point; v n+1 is the speed of the train at the n+1th point, in m/s; v n is the speed of the train at the nth point in m/s;
a refinement formula for the interval between two adjacent speed points is obtained through the combined type (4) and (6)
Obtaining the speed of the train in the decelerating state by the speed v=v 0 +at and the braking force use coefficient in the decelerating state of the train
Wherein v n is the current speed of the train, m/s; v n+1 is the speed at the next moment, m/s; f is a brake failure coefficient; a 1 is deceleration, m/s 2;ξx is braking force use coefficient, and three desirable values are respectively: deceleration coefficient ζ 1 corresponding to the emergency braking force, deceleration coefficient ζ 2 corresponding to the maximum service braking force, deceleration coefficient ζ 3 corresponding to the service braking force;
When the train speed changes, the distance increment formula corresponding to the train is
The step S2 comprises the following steps:
Time calculation method based on speed-time data and pass-through type
Calculating to obtain a first failure coefficient f T;
Distance calculation method based on speed-distance data and pass-through type
Calculating to obtain a second failure coefficient f S; in the formulas (10) and (11), xi x is a braking force use coefficient, and the calculated priority order is xi 1>ξ2>ξ3;
Through type
Reducing the influence of measurement errors of a train speed measurement and distance measurement unit;
Through type
ER＝(fdetection-freal)/freal| (13)
Evaluating the accuracy of the first failure coefficient and the second failure coefficient; wherein f detection is the average value of failure coefficients obtained by multiple times of calculation; and f real is a set value of the failure coefficient.
3. The prediction method according to claim 2, wherein step S3 includes:
Through type
And calculating to obtain the prediction results of the train braking process and the final stopping point.
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