Method for predicting service life of curved switch rail

Abstract

The invention provides a method for predicting the service life of a curved switch rail, which comprises the following steps of: regularly acquiring abrasion and damage data of a curved switch rail piece by using equipment such as a turnout rail inspection trolley and an ultrasonic flaw detector, and predicting the service life of the curved switch rail from two factors of abrasion and damage. Through statistical analysis of historical data, an initial wear life value and an initial damage life value are set, then the life is iterated by using data collected in real time, and the smaller one of the wear life and the damage life is taken as a final prediction result. According to the method, accurate prediction and maintenance guidance of the service life of the curved switch rail can be realized.

Description

Technical Field

[0001] The invention relates to the field of railway engineering, and in particular to a method for predicting the service life of a curved point rail. Background Art

[0002] The curved rail is an important part of railway turnouts, and its service life directly affects the safety and operational efficiency of the railway. At present, the prediction of the service life of the curved rail mostly relies on experience and regular manual inspections. This method has certain limitations and cannot accurately reflect the actual use status and wear degree of the curved rail. Therefore, it is particularly necessary to develop a curved rail service life prediction method based on data collection and analysis. Summary of the invention

[0003] The present invention provides a method for predicting the service life of a curved point rail, which uses equipment such as a turnout track inspection trolley and an ultrasonic flaw detector to regularly obtain wear and damage data of curved point rail parts, and predicts the service life of the curved point rail from the two factors of wear and damage. Through statistical analysis of historical data, the initial values ​​of the wear life and the initial values ​​of the damage life are set, and then the life is iterated using the real-time collected data, and the final prediction result is the smaller of the wear life and the damage life.

[0004] The present invention adopts the following specific technical solutions:

[0005] A method for predicting the service life of a curved point rail comprises the following steps:

[0006] Step 1. Use the turnout track inspection trolley, ultrasonic flaw detector and other equipment to regularly obtain the wear and damage data of the curved rail parts;

[0007] Step 2. Predict the service life of the curved point rail based on the acquired wear and damage data, wherein the factors involved in the prediction include wear and damage;

[0008] Step 3. Set the initial value of wear life and initial value of damage life through statistical analysis of historical data;

[0009] Step 4. Iteratively calculate the life using the data collected in real time to correct and update the predicted values ​​of wear life and damage life;

[0010] Step 5. The final prediction result is the smaller of the wear life and the damage life, so as to achieve accurate prediction of the service life of the curved rail and provide maintenance and repair guidance.

[0011] Furthermore, the wear life prediction is specifically as follows:

[0012] Initial value setting of wear life: Statistics on the service life of the curved point rails that were worn out in the first five years of the switch position, and calculate the average value T wavg, as the initial value of wear life:

[0013]

[0014] Wear data collection: Use laser profile sensors to collect the profile data of curved rail parts on a monthly basis and calculate the wear amount; rail wear is divided into vertical wear and lateral wear, and only the lateral wear value is extracted;

[0015] Wear data fitting: After the side wear values ​​are collected for 3 months, the wear values ​​of the first 3 months are interpolated and fitted using a logarithmic function:

[0016] T(t)=a*log(t+b)+c

[0017] Assume that the wear of the curved point rail in the first three months of operation, i.e. the side wear, is: 1 , 2nd month 2 , 3rd month 3 ; Set t = 1, T(1) = l 1 ; t = 2, T(2) = 1 2 ; t=3,T(3)=1 3 , substitute into the above formula, solve the values ​​of the three parameters a, b, and c, and you can determine all the parameters of the function;

[0018] Wear life calculation: According to the wear lower limit set in the "Rules for Repair of Conventional Railway Lines", the wear calculation formula is used to calculate the service life T w , and the calculated value T w Replacement wear life initial value T wavg ;

[0019] Wear life calculation is updated and iterated monthly: the wear value is measured monthly. Assuming that the rail is in use for the nth month, the wear values ​​of the three months n-2, n-1, and n are taken and re-substituted into the formula to calculate the new a, b, and c values. According to the wear lower limit set in the "Rules for Repair of Conventional Railway Lines", the wear calculation formula is used to calculate the service life T w , and replace the old value, updating and iterating every month.

[0020] Furthermore, the damage life prediction is specifically as follows:

[0021] Setting the initial value of the damaged life: Count the service life of the lower curved rail within the first five years of the switch position and select the maximum value T dmax As the initial value of damage life;

[0022] T dmax =Max(T 1 、T 2 ,,,T n )

[0023] Damage data collection: Use ultrasonic flaw detectors and high-definition camera image processing methods to obtain damage data of curved rail parts on a monthly basis, including: chipping, fish scales, and abrasions;

[0024] Damage life calculation:

[0025] Assume: L T is the total length of the curved track;

[0026] is the average rail head thickness at the i-th dropped block of the curved point rail;

[0027] is the length of the i-th dropped block;

[0028] is the depth of the i-th dropped block;

[0029] k s is the block drop damage coefficient, obtained by statistics or experience;

[0030] is the average rail head thickness of the fish scale pattern area at the jth position of the curved rail;

[0031] A T It is the total area above the wheel-rail contact point (16 mm below the rail top surface) of the curved rail;

[0032] is the maximum depth of the fish scale pattern at the jth position of the curved rail;

[0033] is the surface area of ​​the jth fish scale pattern of the curved track;

[0034] k c is the fish scale damage coefficient, obtained by statistics or experience.

[0035] is the average thickness of the abrasion rail head at the zth position of the curved rail;

[0036] is the maximum scratch depth at the zth position of the curved rail;

[0037] k a is the abrasion damage coefficient, obtained by statistics or experience;

[0038] T 0 is the number of days the curved track has been in use;

[0039] The life formula of a damaged curved rail is:

[0040]

[0041] Some minor damage can be eliminated by grinding. After the minor damage is eliminated, the damage life is recalculated. d Replace the old value and update it monthly;

[0042] when If it is not true, it means that the curved rail has reached a serious damage and cannot be used any longer. It needs to be replaced immediately, and the remaining service life is assigned to 0, that is, T d =T 0 , and trigger the alarm mechanism, prompting relevant personnel to immediately replace the set of curved rails.

[0043] Furthermore, the service life prediction result determines:

[0044] Combine the wear life prediction results and the damage life prediction results, and take the smaller one as the final service life prediction value of the curved point rail;

[0045] T=Min(T w ,T d )

[0046] Output the prediction results and issue a warning message in time when the actual service life reaches T so that the curved rail can be replaced or repaired in time.

[0047] Furthermore, the curved point rail service life prediction method may further include the following steps:

[0048] Step 6. According to the life value obtained by iterative calculation, formulate a corresponding maintenance plan, including but not limited to replacement, repair and other measures;

[0049] Step 7. Apply the revised life value and maintenance plan to actual maintenance work to improve the efficiency and safety of the curved rail.

[0050] The present invention adopts the above technical solution, which has the following advantages:

[0051] It provides a method to accurately predict the service life of curved point rails, effectively guides maintenance work, and improves the efficiency and safety of railway equipment. By comprehensively analyzing wear and damage data and using advanced sensors and analysis technology, it can accurately predict the service life of curved point rails, which will help railway departments formulate scientific maintenance and replacement plans and improve the safety and operational efficiency of railway transportation. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 This is a comparison chart between the profile measured by the laser profile sensor and the designed profile

[0053] Figure 2 The surface damage diagram of the curved rail

[0054] Figure 3 Comparison of different sections of curved track

[0055] Figure 4 Curved point rail service life prediction flow chart DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the embodiment of the present invention clearer, the technical solution of the embodiment of the present invention will be clearly and completely described below in conjunction with the drawings of the embodiment of the present invention. Obviously, the described embodiment is a part of the embodiment of the present invention, not all of the embodiments. Based on the described embodiment of the present invention, all other embodiments obtained by ordinary technicians in this field belong to the scope of protection of the present invention.

[0057] It should be noted that the terms used herein are only for describing specific embodiments and are not intended to limit the exemplary embodiments according to the present application. As used herein, unless the context clearly indicates otherwise, the singular form is also intended to include the plural form. In addition, it should be understood that when the terms "comprise" and / or "include" are used in this specification, it indicates the presence of features, steps, operations, devices, components and / or combinations thereof.

[0058] Example:

[0059] 1. Data collection and initial value setting

[0060] 1. Data collection:

[0061] Equipment: Use a switch track inspection trolley and an ultrasonic flaw detector.

[0062] Collection frequency: once a month.

[0063] Data types: wear data (mainly lateral wear) and damage data (chip drop, fish scale pattern, abrasion, etc.).

[0064] 2. Initial value setting:

[0065] Initial value of wear life: Calculate the service life of the curved point rail due to wear in the first five years before the switch position, and calculate its average value T wavg .

[0066] Calculation formula:

[0067]

[0068] Initial value of damaged life: Count the service life of the curved point rails that were damaged in the first five years before the switch, and select the maximum value T dmax .

[0069] Calculation formula:

[0070] Tdmax =Max(T 1 、T 2 ,,,T n )

[0071] 2. Wear life prediction

[0072] 1. Wear data fitting:

[0073] Fitting model: Logarithmic function fitting.

[0074] Model formula:

[0075] T(t)=a*log(t+b)+c

[0076] Initial data: set t = 1, T(1) = l 1 ; t = 2, T(2) = 1 2 ; t =

[0077] 3, T(3) = l 3 , substitute into the above formula and solve for the values ​​of the three parameters a, b, and c.

[0078] 2. Wear life calculation:

[0079] Life calculation formula: Calculate the service life T according to the wear limit set in the "Rules for Repair of Conventional Railway Lines" w .

[0080] Updated monthly and wear values ​​measured monthly.

[0081] The model is refitted using the latest 3 months of wear data and the parameters a, b, and c are updated.

[0082] Calculate the new wear life T w and overwrite the old value.

[0083] 3. Damage life prediction

[0084] Damage life calculation:

[0085]

[0086] When the damage value meets the conditions Indicates minor injury, and the injury life expectancy is calculated using the above formula.

[0087] Otherwise, it indicates serious damage and the curved rail needs to be replaced immediately. The life value is set to T 0 .

[0088] 4. Comprehensive service life prediction

[0089] 1. Final service life prediction:

[0090] Combined wear life and damage life: the smaller of the two predicted values ​​is taken as the final service life prediction value.

[0091] Calculation formula: T = Min (T w , T d )

[0092] 2. Output and early warning:

[0093] Early warning mechanism: When the actual service life reaches T, the system will issue an early warning to prompt relevant personnel to replace or repair it.

[0094] Maintenance plan: Develop a detailed maintenance plan based on the life prediction results to improve the efficiency and safety of the curved rail.

[0095] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting the service life of a curved point rail. It is characterized in that The following steps are involved: Step 1. Use the turnout track inspection trolley, ultrasonic flaw detector and other equipment to regularly obtain the wear and damage data of the curved rail parts; Step 2. Predict the service life of the curved point rail based on the acquired wear and damage data, wherein the factors involved in the prediction include wear and damage; Step 3. Set the initial value of wear life and initial value of damage life through statistical analysis of historical data; Step 4. Iteratively calculate the life using the data collected in real time to correct and update the predicted values ​​of wear life and damage life; Step 5. The final prediction result is the smaller of the wear life and the damage life, so as to achieve accurate prediction of the service life of the curved rail and provide maintenance and repair guidance.

2. A method for predicting the service life of a curved point rail according to claim 1, It is characterized in that The wear life prediction is specifically as follows: Initial value setting of wear life: Statistics on the service life of the curved point rails that were worn out in the first five years of the switch position, and calculate the average value T wavg , as the initial value of wear life: Wear data collection: Use laser profile sensors to collect the profile data of curved rail parts on a monthly basis and calculate the wear amount; rail wear is divided into vertical wear and lateral wear, and only the lateral wear value is extracted; Wear data fitting: After the side wear values ​​are collected for 3 months, the wear values ​​of the first 3 months are interpolated and fitted using a logarithmic function: T(t)=a*log(t+b)+c Assume that the wear of the curved point rail in the first three months of operation, i.e. the side wear, is: 1 , 2nd month 2 , 3rd month 3 ; Set t = 1, T(1) = l 1 ; t = 2, T(2) = 1 2 ; t=3,T(3)=1 3 , substitute into the above formula, solve the values ​​of the three parameters a, b, and c, and you can determine all the parameters of the function; Wear life calculation: According to the wear lower limit set in the "Rules for Repair of Conventional Railway Lines", the wear calculation formula is used to calculate the service life T w , and the calculated value T w Replacement wear life initial value T wavg ; Wear life calculation is updated and iterated monthly: the wear value is measured monthly. Assuming that the rail is in use for the nth month, the wear values ​​of the three months n-2, n-1, and n are taken and re-substituted into the formula to calculate the new a, b, and c values. According to the wear lower limit set in the "Rules for Repair of Conventional Railway Lines", the wear calculation formula is used to calculate the service life T w , and replace the old value, updating and iterating every month.

3. A method for predicting the service life of a curved point rail according to claim 1, It is characterized in that The damage life prediction is specifically: Setting the initial value of the damaged life: Count the service life of the lower curved rail within the first five years of the switch position and select the maximum value T dmax As the initial value of damage life; T dmax =Max(T 1 、T 2 、、、T n ) Damage data collection: Use ultrasonic flaw detectors and high-definition camera image processing methods to obtain damage data of curved rail parts on a monthly basis, including: chipping, fish scales, and abrasions; Damage life calculation: Assume: L T is the total length of the curved track; is the average rail head thickness at the i-th dropped block of the curved point rail; is the length of the i-th dropped block; is the depth of the i-th dropped block; k s is the block drop damage coefficient, obtained by statistics or experience; is the average rail head thickness of the fish scale pattern area at the jth position of the curved rail; A T It is the total area above the wheel-rail contact point (16 mm below the rail top surface) of the curved rail; is the maximum depth of the fish scale pattern at the jth position of the curved rail; is the surface area of ​​the jth fish scale pattern of the curved track; k c is the fish scale damage coefficient, obtained by statistics or experience. is the average thickness of the abrasion rail head at the zth position of the curved rail; is the maximum scratch depth at the zth position of the curved rail; k a is the abrasion damage coefficient, obtained by statistics or experience; T 0 is the number of days the curved track has been in use; The life formula of a damaged curved rail is: Some minor damage can be eliminated by grinding. After the minor damage is eliminated, the damage life is recalculated. d Replace the old value and update it monthly; when If it is not true, it means that the curved rail has reached a serious damage and cannot be used any longer. It needs to be replaced immediately, and the remaining service life is assigned to 0, that is, T d =T 0 , and trigger the alarm mechanism, prompting relevant personnel to immediately replace the set of curved rails.

4. A method for predicting the service life of a curved point rail as claimed in claim 1, It is characterized in that The service life prediction results determine: Combine the wear life prediction results and the damage life prediction results, and take the smaller one as the final service life prediction value of the curved point rail; T=Min(T w ,T d ) Output the prediction results and issue a warning message in time when the actual service life reaches T so that the curved rail can be replaced or repaired in time.

5. A method for predicting the service life of a curved point rail as claimed in claim 1, It is characterized in that The curved point rail service life prediction method may further include the following steps: Step 6. According to the life value obtained by iterative calculation, formulate a corresponding maintenance plan, including but not limited to replacement, repair and other measures; Step 7. Apply the revised life value and maintenance plan to actual maintenance work to improve the efficiency and safety of the curved rail.

6. A method for predicting the service life of a curved point rail according to claim 1, It is characterized in that Step 1 also includes using a laser scanner to perform a three-dimensional scan of the curved rail surface to obtain more accurate geometric change data, which serves as supplementary information to assist in the detailed assessment of wear and damage.

7. The method according to claim 5, It is characterized in that It also includes displaying the prediction results to maintenance personnel in real time through a visualization platform and providing an early warning mechanism. When it is predicted that the service life of the curved point rail is about to reach a preset threshold, an early warning notification is automatically triggered so that maintenance work can be arranged in time to reduce the operational risks caused by the failure of the curved point rail.

8. The method according to claim 7, It is characterized in that The early warning mechanism also includes formulating personalized maintenance and repair recommendations, providing targeted treatment solutions for different types of wear and damage based on prediction results and current operating conditions, so as to optimize the allocation of maintenance resources and improve maintenance and repair efficiency.

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