A method for optimizing maintenance strategy of corrosion wind barrier based on gamma process

Abstract

The application discloses a kind of based on gamma process's rust wind barrier maintenance strategy optimization method, belong to rail transit infrastructure maintenance technical field, including S1, pitting characteristic parameter extraction and fatigue performance evaluation, S2, wind load time history simulation and structural stress analysis, S3, strength degradation modeling based on gamma process, S4, time-varying failure probability calculation and reliability evaluation, S5, preventive maintenance strategy optimization, S6, maintenance effect evaluation and strategy optimization.The application adopts the above-mentioned one based on gamma process's rust wind barrier maintenance strategy optimization method, especially suitable for the fatigue life evaluation and maintenance decision of trackside equipment under complex environment such as high salt, high humidity.

Description

Technical Field

[0001] This invention relates to the field of rail transit infrastructure maintenance technology, and in particular to an optimization method for maintenance strategies of rust-resistant wind barriers based on gamma processes. Background Technology

[0002] With the rapid expansion of my country's high-speed railway network, the mileage of railway lines in complex environments such as coastal areas, high humidity, and low temperatures continues to increase. As a core trackside protection facility that ensures train operation safety and reduces the impact of aerodynamic loads, wind barriers are exposed to the natural environment for extended periods. Especially under the combined effects of high salinity, high humidity, and strong wind loads, their metal structures are prone to corrosion damage. The formation and expansion of pitting corrosion will continuously weaken the structural strength, significantly shorten service life, and even cause safety hazards.

[0003] Current wind barrier maintenance technologies and strategies still have many shortcomings, making it difficult to meet the precise maintenance needs in complex environments. Existing maintenance mostly adopts a fixed pattern of periodic inspections, failing to consider the randomness and uncertainty of corrosion damage evolution, lacking dynamic modeling capabilities, and unable to accurately predict structural failure time; traditional fatigue performance assessment relies on the classic SN curve, which does not fully couple the influence of pitting corrosion geometry, resulting in insufficient prediction accuracy; at the same time, there is a lack of quantitative relationship models between environmental factors and corrosion rate, making it difficult to achieve differentiated maintenance, and no balancing mechanism between maintenance costs and failure risks has been established, which easily leads to over-maintenance or under-maintenance.

[0004] Therefore, there is an urgent need for a wind barrier maintenance strategy optimization method that can accurately describe the random evolution of corrosion damage, fully consider the influence of pitting corrosion geometry, and adapt to different environmental conditions, so as to provide scientific support for the maintenance decision-making of trackside equipment in complex environments and achieve the unity of maintenance economy and structural safety. Summary of the Invention

[0005] The purpose of this invention is to provide an optimization method for the maintenance strategy of corroded wind barriers based on gamma processes, which can accurately predict the fatigue life of wind barriers under different environmental conditions, accurately determine the timing of preventive maintenance, and effectively balance maintenance costs and failure risks.

[0006] To achieve the above objectives, this invention provides a method for optimizing the maintenance strategy of a rust-prone wind barrier based on gamma processes, comprising the following steps:

[0007] S1. Obtain three-dimensional morphological data of the corroded surface of the wind barrier, extract the pitting depth, width, number and spacing, and calculate the corrosion rate; based on the depth-to-width ratio of the pitting, establish a modified SN curve model that considers the influence of pitting, and evaluate the structural fatigue performance.

[0008] S2. The harmonic superposition method is used to simulate the time history of natural wind pulsating wind load, and the computational fluid dynamics method is used to simulate the time history of vehicle-induced pulsating wind load caused by train passing. A finite element model of wind barrier considering pitting geometry is established, and the wind load time history is used as the load input for transient dynamic analysis. The stress time history of key parts is extracted, and the equivalent stress amplitude is calculated using the rainflow counting method.

[0009] S3. Define the strength degradation rate as a random variable and establish a residual strength model that considers the coupling effect of pitting depth and vehicle-induced pulsating wind load; based on the gamma random process, establish a probability density function model of the strength degradation rate evolving over time, and calibrate the model parameters for different environmental conditions.

[0010] S4. Set the failure threshold for strength degradation rate, and calculate the cumulative failure probability and corresponding reliability index of the wind barrier at any time during its service life based on the gamma process degradation model established in step S3.

[0011] S5. Based on the reliability index change curve over time calculated in step S4, determine multiple differentiated maintenance time nodes; based on the time nodes, formulate a phased maintenance strategy that includes routine inspection, preventive maintenance, enhanced maintenance, and reinforcement replacement.

[0012] S6. After maintenance, remeasure the pitting parameters, update the gamma process model parameters in step S3 using the Bayesian method, recalculate the failure probability and reliability index, evaluate the maintenance effect, and dynamically adjust the subsequent maintenance strategy.

[0013] Preferably, step S1 specifically includes:

[0014] S11. Conduct on-site inspections of key parts of the H-shaped steel columns of the wind barrier, measure the residual thickness using an electronic thickness gauge, and obtain three-dimensional morphological data of the corroded surface using three-dimensional laser scanning technology or a probe-type surface profilometer.

[0015] S12. Extract the geometric feature parameters of pitting pits from the three-dimensional topography data, including pitting depth. (mm), Pitting width (mm), number of pitting corrosions and pitting spacing (mm);

[0016] S13. Calculate the corrosion rate based on the extracted pitting corrosion parameters. The calculation formula is: ,in, The total volume of all erosion pits. This represents the original volume of the corroded area.

[0017] S14, Aspect Ratio Based on Pitting Erosion A modified SN curve model considering the effect of pitting corrosion is established, and its expression is: ,in, To take into account the fatigue life (times) after pitting corrosion. The stress amplitude is expressed in MPa. Aspect ratio, The stress amplitude coefficient, The fatigue performance of wind barriers was evaluated based on a modified SN curve model.

[0018] Preferably, step S2 specifically includes:

[0019] S21. Using the harmonic superposition method, the fluctuating wind field of natural wind is simulated based on the Kaimal wind speed spectrum to generate the time history of random fluctuating wind pressure acting on the wind barrier. The power spectral density of the fluctuating wind pressure is: ,in, Atmospheric density, Let be the power spectral density function of random fluctuating wind pressure. for Wind speed at altitude The power spectrum of fluctuating wind speed;

[0020] S22. The computational fluid dynamics (CFD) numerical simulation method is used to simulate the time history of vehicle-induced pulsating wind pressure caused by the passing of a high-speed train, and the SST k-ε turbulence model is used for transient solution.

[0021] S23. Establish a three-dimensional finite element model of the wind barrier that includes the geometric features of pitting corrosion, and locally refine the mesh in the pitting area.

[0022] S24. Using the time histories of natural wind and vehicle-induced pulsating wind loads obtained from steps S21 and S22 as load inputs into the finite element model, transient dynamic analysis is performed, and stress time history curves of key parts of the wind barrier are extracted. ;

[0023] S25. Using the rainflow counting method to analyze the stress time history curve Cyclic counting and statistical analysis were performed to calculate the equivalent stress amplitude used for fatigue assessment. The calculation formula is: ,in, For the first Level stress amplitude, Stress amplitude The number of loops, The negative reciprocal slope of the SN curve for the material. This represents the number of stress amplitude levels.

[0024] Preferably, step S3 specifically includes:

[0025] S31, Define the intensity degradation rate As a random variable, the expression is: ,in, The initial strength of the uncorroded wind barrier. for Residual strength at any given moment;

[0026] S32. Establish a residual strength model that simultaneously considers the time-varying development of pitting depth and the coupling effect of vehicle-induced pulsating wind load, with the following expression: ,in, The pitting corrosion influence coefficient is... for Pitting depth at any given time (mm) , For corrosion rate, Indicates a time index. This is the wind load amplification factor. for The vehicle's pulsating wind load is constantly being normalized.

[0027] S33. Based on gamma-stochastic processes, establish service... Post-holiday strength degradation rate The probability density function model: ,in, For non-negative continuous random variables characterizing the degree of material strength degradation, For scale parameters, For shape parameters, , The degradation rate coefficient, These are the initial shape parameters;

[0028] S34. For four typical environmental conditions—normal, high-salt, high-humidity, and low-temperature—calibrate the parameter set of the gamma process degradation model. ,in, The intensity parameters of the Poisson process used to describe sudden shock events (such as typhoons and rainstorms);

[0029] The calibrated parameter values ​​are:

[0030] Normal environment: =0.05, =1.5, =150;

[0031] High-salt environment: =0.08, =2.2, =175;

[0032] High humidity environment: =0.06, =1.8, =160;

[0033] Low temperature environment: =0.04, =1.3, =150.

[0034] Preferably, step S4 specifically includes:

[0035] S41. Set failure criteria: When the strength degradation rate reaches the critical value of 30%, the wind barrier is deemed to have failed.

[0036] S42. Based on the gamma process degradation model established in step S3, calculate the wind barrier at any time during its service life. Cumulative failure probability The calculation formula is: = ,in, For an incomplete gamma function, It is a complete gamma function;

[0037] S43. Based on cumulative failure probability Calculate the reliability index at the corresponding time point. The calculation formula is: ,in, It is the inverse function of the standard normal distribution;

[0038] S44. According to reliability indicators As reliability changes over time, multiple levels of reliability thresholds are set, and based on... The time it takes for the temperature to drop to each threshold level determines the key maintenance decision-making time points:

[0039] The multi-level reliability thresholds and corresponding maintenance decision time points include:

[0040] When the reliability drops to 3.7, the target reliability threshold is reached, and the corresponding time is determined as the preventive maintenance initiation time. ;

[0041] When the level drops to 3.0, it enters the medium security level, and the corresponding time is determined as the enhanced detection time. ;

[0042] When the strength drops to 2.5, the safety margin is insufficient, and the corresponding time is determined as the mandatory reinforcement time. ;

[0043] Cumulative failure probability When 50% is reached, the corresponding time is determined as the median lifetime. .

[0044] Preferably, step S5 specifically includes:

[0045] S51. Key maintenance decision-making time nodes determined in step S4 , , Develop phased maintenance strategies adapted to the structural degradation stages, specifically including:

[0046] Phase 1: 0 to During the annual routine maintenance period, annual visual inspections and records are performed, periodic anti-corrosion coating testing and integrity assessments are conducted, regular cleaning is carried out to remove accumulated water and salt stains, and a wind barrier health record is established.

[0047] Phase Two: to During the critical period of preventive maintenance, a detailed semi-annual inspection and pitting parameter measurement are carried out, internal damage is assessed using non-destructive testing techniques, coating repair is performed on rusted areas, connections are inspected and tightened, and stress monitoring is implemented in key areas.

[0048] Phase Three: to During the year, the intensive maintenance period includes quarterly comprehensive inspections and three-dimensional corrosion morphology scanning, structural reinforcement of local pitting corrosion areas, replacement of severely corroded connectors, addition of cathodic protection systems, and, where necessary, the addition of auxiliary support structures.

[0049] Phase Four: Years later, during the comprehensive reinforcement or replacement period, a comprehensive safety assessment and residual bearing capacity calculation will be carried out, and the main load-bearing components will be replaced or the entire wind barrier unit will be replaced based on the assessment results. At the same time, a long-term health monitoring system will be established or upgraded.

[0050] S52. Different critical maintenance time points are adopted for different environmental conditions. , , This results in differentiated maintenance cycles, including:

[0051] Under normal conditions: =12 years, =17 years, =22 years;

[0052] High-salt environment: =8 years, =11 years, =15 years;

[0053] In high humidity environments: =15 years, =22 years, =28 years;

[0054] In low-temperature environments: =14 years, =19 years, =25 years;

[0055] S53. Establish a life-cycle cost model: ,in, For total life cycle cost, For initial construction costs, For the first Maintenance costs For failure loss costs, Discount rate For the first The timing of the next maintenance operation; by optimizing maintenance time and intensity, Minimize, while satisfying reliability constraints: and ,in, The probability of target failure. The target reliability index.

[0056] Preferably, step S6 specifically includes:

[0057] S61. After implementing maintenance measures, re-inspect the corroded area of ​​the wind barrier, obtain the pitting corrosion parameters after maintenance, and calculate the current corrosion rate.

[0058] S62. Based on the detection data obtained after maintenance, the shape parameters of the gamma process degradation model established in step S3 are analyzed using a Bayesian method. and scale parameters Update;

[0059] S63. Using the updated gamma process model parameters, recalculate the failure probability and reliability index to quantitatively evaluate the actual effect of this maintenance measure.

[0060] S64. Based on the assessed reliability indicators, dynamically adjust subsequent maintenance strategies:

[0061] like If the system recovers to 3.7 or higher, the next maintenance cycle will proceed according to the original maintenance plan.

[0062] like If the value is still below 3.0, the maintenance effect is deemed unsatisfactory, and enhanced maintenance measures need to be taken.

[0063] S65. Establish a wind barrier maintenance database to record environmental conditions, maintenance measures, testing data, and corresponding performance indicators; based on the continuously accumulated field data, continuously optimize the parameters of the gamma process degradation model to improve prediction accuracy and strategy applicability.

[0064] Therefore, this invention employs the aforementioned gamma process-based optimization method for the maintenance strategy of corroded wind barriers, achieving accurate prediction and optimization of fatigue life and maintenance strategies for corroded wind barriers. By integrating precise quantification of pitting geometry characteristics with gamma stochastic process modeling, the predictive capability for damage evolution is significantly improved. A phased maintenance decision-making system is constructed based on reliability theory, enabling the formulation of differentiated maintenance schemes for different environmental conditions. This method effectively ensures the long-term safety of the structure while significantly optimizing the economic efficiency of life-cycle maintenance, providing a systematic solution for intelligent preventive maintenance of rail transit infrastructure.

[0065] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0066] Figure 1 This is a flowchart of a method according to an embodiment of the present invention;

[0067] Figure 2 This is a graph showing the comparison between the deterministic model and the stochastic model of this invention, as well as the measured results.

[0068] Figure 3 This is a graph showing the change of reliability index over time in an embodiment of the present invention;

[0069] Figure 4 This is a comparison chart of the reliability of different maintenance strategies in embodiments of the present invention;

[0070] Figure 5 This is the damage accumulation process simulated by the gamma function under normal conditions in the embodiments of the present invention;

[0071] Figure 6 This is the damage accumulation process simulated by gamma function under high-salt environment in an embodiment of the present invention;

[0072] Figure 7 This is the damage accumulation process simulated by gamma function under high humidity conditions in an embodiment of the present invention;

[0073] Figure 8 This is a damage accumulation process simulated by a gamma function under low-temperature conditions according to an embodiment of the present invention.

[0074] Figure 9 These are the failure probability time history curves and key threshold points under normal conditions in embodiments of the present invention;

[0075] Figure 10These are the failure probability time history curves and key threshold points under high-salt environments according to embodiments of the present invention;

[0076] Figure 11 These are the failure probability time history curves and key threshold points under high humidity conditions according to embodiments of the present invention.

[0077] Figure 12 These are the failure probability time history curves and key threshold points under low-temperature conditions in embodiments of the present invention.

[0078] Figure 13 This is a comparison curve of failure probability and reliability under four operating conditions in the embodiments of the present invention. Detailed Implementation

[0079] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0080] Unless otherwise defined, the technical or scientific terms used in this invention shall have the ordinary meaning understood by one of ordinary skill in the art to which this invention pertains. The terms "first," "second," and similar terms used in this invention do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Terms such as "comprising" or "including" mean that the element or object preceding the word encompasses the elements or objects listed following the word and their equivalents, without excluding other elements or objects. Terms such as "connected" or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; when the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0081] like Figure 1 As shown, an optimization method for maintenance strategy of rust-causing wind barriers based on gamma processes includes the following steps:

[0082] S1. Obtain three-dimensional morphological data of the corroded surface of the wind barrier, extract the pitting depth, width, number and spacing, and calculate the corrosion rate; based on the depth-to-width ratio of the pitting, establish a modified SN curve model that considers the influence of pitting, and evaluate the structural fatigue performance.

[0083] S11. Conduct on-site inspections of key parts of the H-shaped steel columns of the wind barrier, measure the residual thickness using an electronic thickness gauge, and obtain three-dimensional morphological data of the corroded surface using three-dimensional laser scanning technology or a probe-type surface profilometer.

[0084] S12. Extract the geometric feature parameters of pitting pits from the three-dimensional topography data, including pitting depth. (mm), Pitting width (mm), number of pitting corrosions and pitting spacing (mm);

[0085] S13. Calculate the corrosion rate based on the extracted pitting corrosion parameters. The calculation formula is: ,in, The total volume of all erosion pits. The original volume of the rusted area is given. In the embodiments listed in this invention, the rust pit is exemplified by a semi-ellipsoidal shape, where the volume of a single semi-ellipsoidal rust pit is... When there is When there is a pitting, ;

[0086] S14, Aspect Ratio Based on Pitting Erosion A modified SN curve model considering the effect of pitting corrosion is established, and its expression is: ,in, To take into account the fatigue life (times) after pitting corrosion. The stress amplitude is expressed in MPa. Aspect ratio, The stress amplitude coefficient, The fatigue performance of wind barriers was evaluated based on a modified SN curve model.

[0087] S2. The harmonic superposition method is used to simulate the time history of natural wind pulsating wind load, and the computational fluid dynamics method is used to simulate the time history of vehicle-induced pulsating wind load caused by train passing. A finite element model of wind barrier considering pitting geometry is established, and the wind load time history is used as the load input for transient dynamic analysis. The stress time history of key parts is extracted, and the equivalent stress amplitude is calculated using the rainflow counting method.

[0088] S21. When a wind barrier is subjected to wind load, the wind pressure at each height is usually not equal. Therefore, the wind pressure of random natural wind fluctuations is simulated to obtain the wind pressure time history curves at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier.

[0089] S211, the wind speed spectrum adopts the Kaimal fluctuating wind speed spectrum, and its formula is:

[0090] ;

[0091] In the formula: The power spectrum of fluctuating wind speed; Friction speed is related to surface roughness; for Average wind speed at altitude The circular frequency of the pulsating wind. Calculate the height for the target.

[0092] ;

[0093] In the formula: Kármán's constant, For ground roughness length, different underlying surfaces The values ​​are different.

[0094] ;

[0095] In the formula: The wind speed power exponent is related to ground roughness. The average wind speed at 10m.

[0096] S212. According to the power spectrum transformation law of stochastic processes, the power spectrum of fluctuating wind pressure and the power spectrum of fluctuating wind speed satisfy the following equation:

[0097] ;

[0098] Substituting the Kaimal wind speed spectrum into the formula yields the fluctuating wind pressure power spectrum under the Kaimal spectrum.

[0099] ;

[0100] Average wind pressure at height Z At the same time, the friction speed Substitute and get The form of expression

[0101] ;

[0102] S213. According to Shinozuka's harmonic synthesis theory, the time history formula for random natural wind fluctuation pressure is expressed as follows:

[0103] ;

[0104] In the formula: N is a sufficiently large positive integer; For the j-th frequency point The corresponding pulsating wind pressure power spectral density function; For frequency increment; To be evenly distributed in the interval The random phase angle within the N-axis, according to the central extremum theorem, when N is sufficiently large, the simulated random process approaches a Gaussian random process.

[0105] S214. The time history function of fluctuating wind load can be derived from the time history formula of random natural wind pressure fluctuation:

[0106] ;

[0107] In the formula: for High body coefficient, for High windward area and for Highly random, pulsating wind pressure.

[0108] S22. The computational fluid dynamics (CFD) numerical simulation method is used to simulate the time history of vehicle-induced pulsating wind pressure caused by the passage of a high-speed train. The simulation range of train speed is 250km / h to 400km / h, the center distance of the trackside is 3.8m, and the SST k-ε turbulence model is used for transient solution.

[0109] S23. Establish a three-dimensional finite element model of the wind barrier containing pitting geometry. Input the obtained natural wind pulsation time history curves and vehicle-induced pulsation wind time history into the finite element model. The random natural wind pulsation pressure is applied in a partitioned loading method, with the wind pressure time history curves at the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier respectively applied to the top, 3 / 4, 1 / 2, and 1 / 4 of the wind barrier. The vehicle-induced pulsation wind time history is uniformly applied to the entire windward surface. Calculate the stress time history curve of the corroded railway wind barrier during its service life, and locally refine the mesh in the pitted area (the mesh size of the column and the wind barrier panel is 10mm and 30mm respectively, and the mesh size of the pitted area is locally refined to 0.1mm).

[0110] S24. Using the time histories of natural wind and vehicle-induced pulsating wind loads obtained from steps S21 and S22 as load inputs into the finite element model, transient dynamic analysis is performed, and stress time history curves of key parts of the wind barrier are extracted. ;

[0111] S25. Using the rainflow counting method to analyze the stress time history curve Cyclic counting and statistical analysis were performed to calculate the equivalent stress amplitude used for fatigue assessment. The calculation formula is: ,in, For the first Level stress amplitude, Stress amplitude The number of loops, The negative reciprocal slope of the SN curve for the material. The number of stress amplitude levels is used as the basis for basic verification of the stress of wind barrier column structures, in conjunction with the wind-resistant design specifications for highway / railway bridges and steel structure design standards.

[0112] S3. Define the strength degradation rate as a random variable and establish a residual strength model that considers the coupling effect of pitting depth and vehicle-induced pulsating wind load; based on the gamma random process, establish a probability density function model of the strength degradation rate evolving over time, and calibrate the model parameters for different environmental conditions.

[0113] S31, Define the intensity degradation rate As a random variable, the expression is: ,in, The initial strength of the uncorroded wind barrier. for Residual strength at any given moment;

[0114] S32. Establish a residual strength model that simultaneously considers the time-varying development of pitting depth and the coupling effect of vehicle-induced pulsating wind load, with the following expression: ,in, The pitting corrosion influence coefficient is... for Pitting depth at any given time (mm) , For corrosion rate, Indicates a time index. This is the wind load amplification factor, according to the "Technical Standard for Noise Barrier Structures" (GB / T 51335-2018). The value is 0.1. for The normalized vehicle-induced pulsating wind load is calculated using the following formula:

[0115] ;

[0116] in, The time history of the pulsating wind load when the train passes. The maximum wind load during the design service life, The minimum wind load during the design service life;

[0117] S33. Based on gamma-stochastic processes, establish service... Post-holiday strength degradation rate The probability density function model: ,in, For non-negative continuous random variables characterizing the degree of material strength degradation, For scale parameters, For shape parameters, , The degradation rate coefficient, These are the initial shape parameters;

[0118] S34. For four typical environmental conditions—normal, high-salt, high-humidity, and low-temperature—collect historical data from at least 30 actual service sound barriers, record the strength degradation rate for different service years, establish a degradation database, calibrate degradation parameters using the maximum likelihood method, and calibrate the parameter set of the gamma process degradation model respectively. ,in, The intensity parameters of the Poisson process used to describe sudden shock events (such as typhoons and rainstorms);

[0119] The calibrated parameter values ​​are:

[0120] Normal environment: =0.05, =1.5, =150;

[0121] High-salt environment: =0.08, =2.2, =175;

[0122] High humidity environment: =0.06, =1.8, =160;

[0123] Low temperature environment: =0.04, =1.3, =150.

[0124] S4. Set the failure threshold for strength degradation rate, and calculate the cumulative failure probability and corresponding reliability index of the wind barrier at any time during its service life based on the gamma process degradation model established in step S3.

[0125] S41. Set failure criteria: When the strength degradation rate reaches the critical value of 30%, the wind barrier is deemed to have failed.

[0126] S42. Based on the gamma process degradation model established in step S3, calculate the... Failure probability within a time unit: Calculate the wind barrier at any time during its service life. Cumulative failure probability The calculation formula is: = ,in, For an incomplete gamma function, It is a complete gamma function;

[0127] S43, such as Figure 3 As shown, based on the cumulative failure probability Calculate the reliability index at the corresponding time point. The calculation formula is: ,in, It is the inverse function of the standard normal distribution;

[0128] S44. According to reliability indicators As reliability changes over time, multiple levels of reliability thresholds are set, and based on... The time it takes for the temperature to drop to each threshold level determines the key maintenance decision-making time points:

[0129] The multi-level reliability thresholds and corresponding maintenance decision time points include:

[0130] When the reliability drops to 3.7, the target reliability threshold is reached, and the corresponding time is determined as the preventive maintenance initiation time. ;

[0131] When the level drops to 3.0, it enters the medium security level, and the corresponding time is determined as the enhanced detection time. ;

[0132] When the strength drops to 2.5, the safety margin is insufficient, and the corresponding time is determined as the mandatory reinforcement time. ;

[0133] Cumulative failure probability When 50% is reached, the corresponding time is determined as the median lifetime. .

[0134] S5. Based on the reliability index change curve over time calculated in step S4, determine multiple differentiated maintenance time nodes; based on the time nodes, formulate a phased maintenance strategy that includes routine inspection, preventive maintenance, enhanced maintenance, and reinforcement replacement.

[0135] S51. Key maintenance decision-making time nodes determined in step S4 , , Develop phased maintenance strategies adapted to the structural degradation stages, specifically including:

[0136] Phase 1: 0 to During the annual routine maintenance period, annual visual inspections and records are performed, periodic anti-corrosion coating testing and integrity assessments are conducted, regular cleaning is carried out to remove accumulated water and salt stains, and a wind barrier health record is established.

[0137] Phase Two: to During the critical period of preventive maintenance, a detailed semi-annual inspection and pitting parameter measurement are carried out, internal damage is assessed using non-destructive testing techniques, coating repair is performed on rusted areas, connections are inspected and tightened, and stress monitoring is implemented in key areas.

[0138] Phase Three: to During the year, the intensive maintenance period includes quarterly comprehensive inspections and three-dimensional corrosion morphology scanning, structural reinforcement of local pitting corrosion areas, replacement of severely corroded connectors, addition of cathodic protection systems, and, where necessary, the addition of auxiliary support structures.

[0139] Phase Four: Years later, during the comprehensive reinforcement or replacement period, a comprehensive safety assessment and residual bearing capacity calculation will be carried out, and the main load-bearing components will be replaced or the entire wind barrier unit will be replaced based on the assessment results. At the same time, a long-term health monitoring system will be established or upgraded.

[0140] S52, such as Figure 4 Different critical maintenance time points are adopted for different environmental conditions. , , This results in differentiated maintenance cycles, including:

[0141] Under normal conditions: =12 years, =17 years, =22 years;

[0142] High-salt environment: =8 years, =11 years, =15 years;

[0143] In high humidity environments: =15 years, =22 years, =28 years;

[0144] In low-temperature environments: =14 years, =19 years, =25 years;

[0145] S53. Establish a life-cycle cost model: ,in, For total life cycle cost, For initial construction costs, For the first Maintenance costs For failure loss costs, Discount rate For the first The timing of the next maintenance operation; by optimizing maintenance time and intensity, Minimize, while satisfying reliability constraints: and ,in, The probability of target failure. The target reliability index.

[0146] S6. After maintenance, remeasure the pitting parameters, update the gamma process model parameters in step S3 using the Bayesian method, recalculate the failure probability and reliability index, evaluate the maintenance effect, and dynamically adjust the subsequent maintenance strategy.

[0147] S61. Within one month after the maintenance measures are implemented, the corroded area of ​​the wind barrier shall be re-inspected to obtain the pitting corrosion parameters after maintenance and calculate the current corrosion rate.

[0148] S62. Based on the detection data obtained after maintenance, the shape parameters of the gamma process degradation model established in step S3 are analyzed using a Bayesian method. and scale parameters Update;

[0149] The Bayesian method for updating the parameters of the gamma process model specifically includes the following steps:

[0150] S621. Set prior distribution parameters based on historical data;

[0151] S622. Input the test data after maintenance;

[0152] S623. Use the Metropolis-Hastings algorithm for sampling;

[0153] S624. Calculate the mean and variance of the posterior distribution;

[0154] S625, Update gamma process parameters;

[0155] S63. Using the updated gamma process model parameters, recalculate the failure probability and reliability index to quantitatively evaluate the actual effect of this maintenance measure.

[0156] S64. Based on the assessed reliability indicators, dynamically adjust subsequent maintenance strategies:

[0157] like If the system recovers to 3.7 or higher, the next maintenance cycle will proceed according to the original maintenance plan.

[0158] like If the value is still below 3.0, the maintenance effect is deemed unsatisfactory, and enhanced maintenance measures need to be taken.

[0159] S65. Establish a wind barrier maintenance database to record environmental conditions, maintenance measures, testing data, and corresponding performance indicators; based on the continuously accumulated field data, continuously optimize the parameters of the gamma process degradation model to improve prediction accuracy and strategy applicability.

[0160] The following two examples illustrate this approach. To further verify the effectiveness of the method and the accuracy of the model, the model results and experimental data are analyzed in conjunction with the accompanying figures: Figure 2 As shown, by comparing the stochastic model based on the gamma process of this invention with the deterministic model and measured data, it is verified that the gamma process model has higher fitting accuracy and prediction reliability in describing the stochastic evolution of corrosion damage. Figures 5-8 The study demonstrates the cumulative process of strength degradation rate over time under four typical environments: normal, high salinity, high humidity, and low temperature, based on gamma process simulation, revealing the differentiated impact of different environments on corrosion rate. Figures 9-12 Failure probability time history curves are presented under four different environments, and the corresponding reliability thresholds are indicated. Key maintenance timelines for versions 3.7, 3.0, and 2.5 , , This provides an intuitive basis for formulating differentiated maintenance strategies. Figure 13 The graphs show the comparison between failure probability and reliability for four different operating conditions.

[0161] Example 1: Optimization of wind barrier maintenance strategy under normal conditions

[0162] A high-speed railway line is located in a non-coastal area with normal environmental conditions: an average annual temperature of 15-25℃, relative humidity of 60-70%, and atmospheric salinity <0.1mg / cm³. 2 •d. The wind barrier parameters are as follows: H-shaped steel columns are HW175×175, material is Q235-B, height is 3m; aluminum alloy panels are 5A03, size is 2m×3m×127mm; design wind speed: 17.89 m / s; train speed: 250 km / h, 210 pairs per day.

[0163] Step 1: Extraction of Pitting Corrosion Characteristic Parameters. After 10 years of service, on-site inspection revealed pitting corrosion damage within a 0-500mm range at the bottom of the column. A 3D laser scanner was used to scan the corroded surface and extract pitting corrosion parameters, including the pitting depth. =0.8-2.5mm, average 1.5mm; pitting width W =8-15mm, average number of pits per 10mm: n =45; Pitting spacing L =15-30mm.

[0164] Single pit volume ;

[0165] Total pit volume ;

[0166] Corrosion area volume ;

[0167] Corrosion rate ;

[0168] Pitting aspect ratio ;

[0169] The stress amplitude coefficient of the corrected SN curve is:

[0170] ;

[0171] The revised fatigue life formula is as follows:

[0172] ;

[0173] Fatigue life: Second-rate;

[0174] Step 2: Simulation of natural wind fluctuation pressure using the Kaimal wind speed spectrum. The average wind speed at a height of 10m is 17.89 m / s, and the ground roughness is... The simulation duration was 60 seconds with a time step of 0.01 seconds. Vehicle-induced pulsating wind pressure was simulated using CFD technology at a train speed of 250 km / h. The calculated maximum positive pressure was 1200 Pa, the maximum negative pressure was 800 Pa, and the dominant pulsating frequency was 2.5 Hz. Finite element analysis was used to calculate the maximum Von Mises stress at the bottom of the column. stress amplitude The equivalent stress amplitude was calculated using the rainflow counting method. Number of loops Second-rate.

[0175] Step 3: Gamma process parameter calibration, normal environmental parameters are as follows Strength degradation rate ( =10 years) is Residual strength: ;

[0176] Step 4: Calculate the failure probability and reliability. The failure probability is... hour, =12 years, , ; =17 years, , ; =22 years, , ; =28 years, , The maintenance timeline is determined. Year, Year, Year, Year.

[0177] Step 5: Phase 1 (Years 0-12): Annual visual inspection revealed minor rust; coating adhesion was good. Cleaning and maintenance were performed quarterly. Phase 2 (Years 12-17): Semi-annual detailed inspection was conducted. Pitting corrosion deepened to 2.0mm. The coating was repaired, first removing rust to Sa 2.5 level, then applying epoxy zinc-rich primer, and finally fluorocarbon topcoat. 20% of bolt preload was insufficient and was retightened. Strain gauges were installed at the bottom of the wind barrier columns and other critical areas. Phase 3 (Years 17-22): Quarterly comprehensive inspection was conducted. Pitting corrosion depth reached 2.8mm, and the corrosion rate was 0.28%. The bottom 500mm area of ​​the columns was reinforced with three layers of carbon fiber cloth, and zinc anodes were installed at 5m intervals. The load-bearing capacity was increased compared to... =0.75. In the fourth phase (22 years later), a comprehensive assessment will be conducted, and the carrying capacity ratio will be... =0.68, a 10mm thick Q345 steel plate is pasted on the bottom 500mm of the column, and a monitoring system is installed for real-time monitoring around the clock. Without maintenance, the lifespan is about 20 years. Preventive maintenance can extend the lifespan to more than 28 years, with a lifespan extension rate of 40%.

[0178] Example 2: Optimization of wind barrier maintenance strategy in high-salt environment

[0179] A certain coastal high-speed railway line is 5km from the coastline and operates in a high-salinity environment with an average annual atmospheric salinity of 1.5mg / cm³. 2 •d, relative humidity 75-85%. The wind barrier parameters are the same as in Example 1.

[0180] Step 1: Extraction of pitting corrosion characteristic parameters. Severe pitting corrosion was detected only 5 years after the product was put into service. Pitting depth =1.5-4.0mm, average 2.8mm; pitting width =10-18mm, average 12mm; number of pitting corrosions 78; rust rate The calculated value is 0.28%.

[0181] Step 2: Gamma process parameter calibration in a high-salt environment: Strength degradation rate ( =5 years): Compared to the normal environment ( =10 years, In just five years, the degradation rate of high-salinity environments reached 18.5%, which is more than four times the rate of degradation in normal environments.

[0182] Step 3: Calculate the failure probability and determine the maintenance nodes. =8 years, , ; =11 years, , ; =15 years, , ; =13.5 years, (Median lifespan). Maintenance timeline: Year, Year, Compared to normal conditions, the maintenance cycle is shortened by about 40% per year.

[0183] Step 4: For high-salt environments, replace ordinary Q235 steel with weathering steel, improving corrosion resistance by 3-5 times. Strengthen the coating by applying an epoxy zinc-rich primer with a film thickness of 80μm. Apply a fluorocarbon topcoat with a film thickness of 60μm, for a total film thickness of 140μm. Install a sacrificial anode protection system with zinc anodes, each weighing 5kg and spaced 5m apart, with a service life of 5-8 years. Increase cleaning and maintenance from quarterly to monthly, with mandatory cleaning after the coastal typhoon season. Increase coating inspection from once every two years to annually, and comprehensive inspection from annual to semi-annual. Add special inspections before and after the typhoon season, including coating integrity, bolt preload, and salt stain removal, with a frequency of monthly (June-September). Without maintenance and reinforcement, the service life is approximately 12 years; with standard maintenance, it is approximately 14 years; with enhanced maintenance, the service life can be extended to 18 years, a 50% extension rate.

[0184] Therefore, the present invention employs the above-mentioned optimization method for rust wind barrier maintenance strategy based on gamma processes, which has the following significant advantages and beneficial effects:

[0185] 1. Applying the Gamma stochastic process theory to the prediction of wind barrier corrosion damage can accurately describe the monotonically increasing and stochastic nature of damage accumulation. Compared with deterministic models, the prediction accuracy is improved, providing a scientific basis for preventive maintenance decisions.

[0186] 2. By introducing the corrosion rate , established The quantitative relationship between fatigue life and the model error is significantly better than the traditional mass loss rate method;

[0187] 3. Different gamma process parameters were calibrated for four typical environments: normal, high salinity, high humidity, and low temperature, and differentiated maintenance time nodes were determined to avoid a "one-size-fits-all" maintenance mode and reduce maintenance costs.

[0188] 4. Based on reliability indicators A three-level maintenance threshold was established, and a progressive maintenance strategy from routine maintenance to mandatory reinforcement was defined to ensure a balance between structural safety and economy.

[0189] 5. Preventive maintenance strategies extend the service life of wind barriers, reduce the total life cycle cost over 30 years, and reduce the probability of failure, thus avoiding operational interruptions caused by sudden damage.

[0190] 6. Not only is it suitable for wind barriers, but it can also be applied to the maintenance decisions of other trackside equipment such as sound barriers, catenary supports, and signal frames, and has broad application prospects.

[0191] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the technical solutions of the present invention, and these modifications or equivalent substitutions cannot cause the modified technical solutions to deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for optimizing a corrosion wind barrier maintenance strategy based on a gamma process, characterized by, Includes the following steps: S1. Obtain three-dimensional morphological data of the corroded surface of the wind barrier, extract the pitting depth, width, number and spacing, and calculate the corrosion rate; based on the depth-to-width ratio of the pitting, establish a modified SN curve model that considers the influence of pitting, and evaluate the structural fatigue performance. S2. The harmonic superposition method is used to simulate the time history of natural wind pulsating wind load, and the computational fluid dynamics method is used to simulate the time history of vehicle-induced pulsating wind load caused by train passing. A finite element model of wind barrier considering pitting geometry is established, and the wind load time history is used as the load input for transient dynamic analysis. The stress time history of key parts is extracted, and the equivalent stress amplitude is calculated using the rainflow counting method. S3. Define the strength degradation rate as a random variable and establish a residual strength model that considers the coupling effect of pitting depth and vehicle-induced pulsating wind load; based on the gamma random process, establish a probability density function model of the strength degradation rate evolving over time, and calibrate the model parameters for different environmental conditions. S4. Set the failure threshold for strength degradation rate, and calculate the cumulative failure probability and corresponding reliability index of the wind barrier at any time during its service life based on the gamma process degradation model established in step S3. S5. Based on the reliability index change curve over time calculated in step S4, determine multiple differentiated maintenance time nodes; based on the time nodes, formulate a phased maintenance strategy that includes routine inspection, preventive maintenance, enhanced maintenance, and reinforcement replacement. S6. After maintenance, remeasure the pitting parameters, update the gamma process model parameters in step S3 using the Bayesian method, recalculate the failure probability and reliability index, evaluate the maintenance effect, and dynamically adjust the subsequent maintenance strategy. Step S3 specifically includes: S31, defining the intensity degradation rate As a random variable, the expression is: where, is the initial intensity of the non-corroded wind barrier, is the instantaneous residual intensity; S32. Establish a residual strength model that simultaneously considers the time-varying development of pitting depth and the coupling effect of vehicle-induced pulsating wind load, with the following expression: ,in, The pitting corrosion influence coefficient is... for Pitting depth at any time , For corrosion rate, Indicates a time index. This is the wind load amplification factor. for The vehicle's pulsating wind load is constantly being normalized. S33. Based on gamma-stochastic processes, establish service... Post-holiday strength degradation rate The probability density function model: ,in, For non-negative continuous random variables characterizing the degree of material strength degradation, For scale parameters, For shape parameters, , The degradation rate coefficient, These are the initial shape parameters; S34, For normal, high salt, high humidity, low temperature four typical environmental conditions, respectively, the parameter set of gamma process degradation model is calibrated wherein, is the Poisson process intensity parameter describing the sudden shock event; Step S4 specifically includes: S41. Set failure criteria: When the strength degradation rate reaches the critical value of 30%, the wind barrier is deemed to have failed. S42. Based on the gamma process degradation model established in step S3, calculate the wind barrier at any time during its service life. Cumulative failure probability The calculation formula is: = ,in, For an incomplete gamma function, It is a complete gamma function; S43、According to the cumulative failure probability , the reliability index at the corresponding time is calculated , and the calculation formula is: , wherein, is the inverse function of the standard normal distribution; S44, according to the reliability index Over time, set multi-level reliability thresholds, and according to The time of falling to each level threshold determines the key maintenance decision time node: The multi-level reliability thresholds and corresponding maintenance decision time points include: corresponding time is determined as preventive maintenance start time when the value drops to 3.7 ; When the decrease is to 3.0, the corresponding time is determined as the time of the enhanced detection ; corresponding time is determined as the mandatory reinforcement time when the value drops to 2.5 ; cumulative failure probability At 50%, the time is determined as median life .

2. The method of claim 1, wherein the method is characterized by, Step S1 specifically includes: S11. Conduct on-site inspections of key parts of the H-shaped steel columns of the wind barrier, measure the residual thickness using an electronic thickness gauge, and obtain three-dimensional morphological data of the corroded surface using three-dimensional laser scanning technology or a probe-type surface profilometer. S12. Extract the geometric feature parameters of pitting pits from the three-dimensional topography data, including pitting depth. Pitting width Pitting quantity and pitting spacing ; S13. Calculate the corrosion rate based on the extracted pitting corrosion parameters. The calculation formula is: ,in, The total volume of all erosion pits. This represents the original volume of the corroded area. S14, Aspect Ratio Based on Pitting Erosion A modified SN curve model considering the effect of pitting corrosion is established, and its expression is: ,in, To account for fatigue life after pitting corrosion, For stress amplitude, Aspect ratio, The stress amplitude coefficient, The fatigue performance of wind barriers was evaluated based on a modified SN curve model.

3. The method of claim 2, wherein the method is characterized by, Step S2 specifically includes: S21. Using the harmonic superposition method, the time history of random pulsating wind pressure acting on the wind barrier is generated by simulating the pulsating wind field of natural wind based on the Kaimal wind speed spectrum. S22. The computational fluid dynamics numerical simulation method is used to simulate the time history of vehicle-induced pulsating wind pressure caused by a high-speed train passing by. S23. Establish a three-dimensional finite element model of the wind barrier that includes the geometric features of pitting corrosion, and locally refine the mesh in the pitting area. S24. Using the time histories of natural wind and vehicle-induced pulsating wind loads obtained from steps S21 and S22 as load inputs into the finite element model, transient dynamic analysis is performed, and stress time history curves of key parts of the wind barrier are extracted. ; S25. Using the rainflow counting method to analyze the stress time history curve Cyclic counting and statistical analysis were performed to calculate the equivalent stress amplitude used for fatigue assessment. The calculation formula is: ,in, For the first Level stress amplitude, Stress amplitude The number of loops, The negative reciprocal slope of the SN curve for the material. This represents the number of stress amplitude levels.

4. The method according to claim 3, wherein, In step S34, the calibrated parameter values ​​are as follows for four typical environmental conditions: normal, high salinity, high humidity, and low temperature: Normal environment: = 0.05, = 1.5, = 150; High salt environment: = 0.08, = 2.2, = 175; High humidity environment: = 0.06, = 1.8, = 160; Low temperature environment: = 0.04, = 1.3, = 150.

5. The method of claim 4, wherein the method is characterized by, Step S5 specifically includes: S51. Key maintenance decision-making time nodes determined in step S4 , , Develop phased maintenance strategies adapted to the structural degradation stages, specifically including: Phase 1: 0 to During the annual routine maintenance period, annual visual inspections and records are performed, periodic anti-corrosion coating testing and integrity assessments are conducted, regular cleaning is carried out to remove accumulated water and salt stains, and a wind barrier health record is established. Phase Two: to During the critical period of preventive maintenance, a detailed semi-annual inspection and pitting parameter measurement are carried out, internal damage is assessed using non-destructive testing techniques, coating repair is performed on rusted areas, connections are inspected and tightened, and stress monitoring is implemented in key areas. Phase Three: to During the year, the intensive maintenance period includes quarterly comprehensive inspections and three-dimensional corrosion morphology scanning, structural reinforcement of local pitting corrosion areas, replacement of severely corroded connectors, and the addition of a cathodic protection system. Phase Four: Years later, during the comprehensive reinforcement or replacement period, a comprehensive safety assessment and residual bearing capacity calculation will be carried out, and the main load-bearing components will be replaced or the entire wind barrier unit will be replaced based on the assessment results. At the same time, a long-term health monitoring system will be established or upgraded. S52, different key maintenance time nodes are adopted for different environmental conditions , , , to form differentiated maintenance periods, wherein: Under normal circumstances: = 12 years, = 17 years, = 22 years; In high salt environment: = 8 years, = 11 years, = 15 years; In a high humidity environment: = 15 years, = 22 years, = 28 years; At low temperatures: = 14 years, = 19 years, = 25 years; S53. Establish a life-cycle cost model: ,in, For total life cycle cost, For initial construction costs, For the first Maintenance costs For failure loss costs, Discount rate For the first The timing of the next maintenance operation; by optimizing maintenance time and intensity, Minimize, while satisfying reliability constraints: and ,in, The probability of target failure. The target reliability index.

6. The method for optimizing the maintenance strategy of a rust-prone wind barrier based on gamma processes according to claim 5, characterized in that, Step S6 specifically includes: S61. After implementing maintenance measures, re-inspect the corroded area of ​​the wind barrier, obtain the pitting corrosion parameters after maintenance, and calculate the current corrosion rate. S62, based on the detection data obtained after maintenance, updating the shape parameter and scale parameter of the gamma process degradation model established in step S3 using a Bayesian method. ​​ S63. Using the updated gamma process model parameters, recalculate the failure probability and reliability index to quantitatively evaluate the actual effect of this maintenance measure. S64. Based on the assessed reliability indicators, dynamically adjust subsequent maintenance strategies: If If the voltage is restored to above 3.7, then the next maintenance cycle is entered according to the original maintenance plan. If If it is still lower than 3.0, it is determined that the maintenance effect is not expected, and the strengthening maintenance measures need to be taken. S65. Establish a wind barrier maintenance database to record environmental conditions, maintenance measures, testing data, and corresponding performance indicators; based on the continuously accumulated field data, continuously optimize the parameters of the gamma process degradation model to improve prediction accuracy and strategy applicability.

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