Bridge bearing slide wear and life prediction method and system based on monitoring data

Abstract

The present invention belongs to the technical field of bridge monitoring, and specifically discloses a method and system for predicting the wear and life of a bridge bearing slide based on monitoring data. The method comprises the following steps: constructing a mathematical model of the actual wear rate of the bridge bearing slide; obtaining the time-series data of the actual wear rate of the bearing slide according to the mathematical model of the actual wear rate, the bearing surface pressure, and the time-series data of the longitudinal movement speed of the bearing, and obtaining the daily wear data of the bearing slide according to the actual wear rate time-series data and the cumulative displacement increment time-series data of the bearing slide; constructing a Gamma random process model of the daily wear of the slide according to the daily wear data, and predicting the long-term wear and remaining service life of the bearing slide based on the model and the remaining thickness of the bearing slide. The present invention simulates the Gamma random process of daily wear based on the existing distribution characteristics of the slide wear data, and then predicts the long-term wear of the slide and the life of the bearing taking into account the influencing factors and randomness, and the wear and life prediction has high accuracy.

Description

Technical Field

[0001] The present invention belongs to the technical field of bridge monitoring, and specifically, relates to a method and system for predicting wear and life of a bridge bearing slide based on monitoring data, and more specifically, relates to a method and system for predicting wear and life of a bridge bearing slide driven by monitoring data. Background Art

[0002] As the service time of bridges increases, their key components will age and be damaged under the external environment and various loads. As one of the important load-bearing components of long-span bridge structures, the deformation, wear and reduced bearing capacity of the bearings in actual operation will directly affect the safe operation of the bridge. In the actual service environment of long-span bridges, the displacement of the bearings continues to accumulate due to the long-term reciprocating action of vehicle loads, causing serious wear of the polytetrafluoroethylene slide plates inside the bearings, thereby reducing the service life of the bearings. Therefore, in order to grasp the actual service life of the bearings in real time and ensure the safe and reliable service status of the bearings, it is particularly important to conduct long-term monitoring of the wear status of the polytetrafluoroethylene slide plates of the bearings.

[0003] The wear of the bearing slide is mainly caused by the large amount of cumulative sliding displacement of the bearing. The cumulative sliding displacement is mainly composed of dynamic displacement caused by dynamic loads such as vehicles and wind. The cumulative static displacement caused by temperature accounts for only a small part of the total cumulative sliding displacement. For example, the cumulative static displacement of the Sutong Bridge bearing is only about 0.469m, and the cumulative dynamic displacement is as high as 4.481m. At present, the research on the methods of slide wear status assessment, early warning and bearing life prediction based on bearing dynamic displacement data has made great progress. The cumulative displacement of the bearing has become an important indicator for slide wear status assessment, early warning and bearing life prediction.

[0004] In the existing bearing sliding life prediction method, the wear rate of the slide plate is considered as a constant value, and the upper limit of the bearing cumulative displacement is calculated in combination with the specified limit of bearing wear to predict the remaining life of the bearing. However, this method ignores the influence of the change of the slide plate wear rate with the change of sliding speed and bearing pressure, resulting in the upper limit of the bearing sliding cumulative displacement not being in line with reality, and the prediction accuracy of bearing sliding life needs to be further improved.

[0005] Based on the above defects and shortcomings, there is an urgent need in this field to propose a bridge bearing slide wear and life prediction method to improve the wear amount and life prediction accuracy of the bridge bearing slide. Summary of the invention

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides a bridge bearing slide plate wear and life prediction method and system based on monitoring data, wherein a bridge bearing slide plate wear and life prediction method driven by monitoring data is designed in combination with the characteristics of the bridge bearing slide plate itself and its load, wear and other characteristics, taking into account the variability and randomness of the slide plate wear rate, based on the slide plate wear rate test data under different surface pressures and different sliding speeds, a linear regression method is used to establish a mathematical relationship model between the benchmark wear rate of the tetrafluoroethylene slide plate and the contact surface pressure and sliding speed, and at the same time, combined with the actual bridge condition, the actual wear rate mathematical model is obtained; based on the bridge bearing monitoring position The bearing sliding velocity and cumulative displacement increment time history are calculated by the method of center difference and adjacent value difference; according to the actual vertical pressure and sliding velocity time history of the bearing, based on the actual wear rate mathematical model, the wear amount of the bearing slide plate within a certain time period can be calculated by the product integral of the bearing cumulative displacement increment time history and the corresponding actual wear rate; considering the randomness of the bearing slide plate wear process, based on the existing single-day slide plate wear amount data distribution characteristics of some bearings, the Monte Carlo method is used to simulate the Gamma random process of daily wear amount, and then the long-term wear amount of the slide plate and the bearing life considering the influencing factors and randomness are predicted, which improves the prediction accuracy of wear amount and life.

[0007] To achieve the above object, according to one aspect of the present invention, a method for predicting the wear and life of a bridge bearing slide based on monitoring data is proposed, comprising the following steps:

[0008] Step 1: construct a mathematical model of the actual wear rate of the bridge bearing slide plate;

[0009] Step 2: obtaining the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtaining the daily wear amount data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data;

[0010] Step three, construct a Gamma random process model of daily slide wear according to the daily wear data, and predict the long-term wear and remaining service life of the support slide based on the model and the remaining thickness of the support slide.

[0011] As a further preferred embodiment, in step one, a mathematical relationship model between the benchmark wear rate and the product of the surface pressure and the speed is established through the test results of the slide plate wear rate under different surface pressures and different sliding speeds. At the same time, the correction coefficient of the wear rate mathematical model is adjusted in combination with the actual bridge conditions to determine the mathematical model of the actual wear rate of the bridge bearing slide plate.

[0012] As further preferred, the actual wear rate mathematical model includes:

[0013] W t =α·W

[0014] Among them, α is the correction coefficient, W is the reference wear rate, W t is the actual wear rate.

[0015] As further preferred, the step 2 comprises the following steps:

[0016] S201 obtains the longitudinal displacement of the bridge bearing, and uses the central difference and difference calculation methods to obtain the longitudinal motion velocity time history data and the cumulative displacement increment time history data of the bearing;

[0017] S202 establishes a random normal distribution of bearing surface pressure based on bearing design parameters or obtains bearing surface pressure value through sensor monitoring;

[0018] S203 obtains the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtains the daily wear data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data.

[0019] As a further preferred embodiment, in step S201, the longitudinal displacement of the bridge bearing is obtained, and the longitudinal motion velocity time history data and the cumulative displacement increment time history data of the bearing are obtained by using the central difference and difference calculation method, including:

[0020] The longitudinal displacement of the bridge bearing is obtained, and the finite element difference method is used to differentiate the time to obtain the longitudinal motion velocity of the bearing. The time interval of the longitudinal displacement data is equal to the time step, so the central difference of the longitudinal motion velocity of the bearing is approximately:

[0021]

[0022] In the formula, is the longitudinal motion speed of the support, u i+1 is the longitudinal displacement value of the i+1th sampling, u i-1 is the longitudinal displacement value of the i-1th sampling, and Δt is the displacement sampling time interval.

[0023] The longitudinal cumulative displacement of the support actually reflects the travel amount of the support that changes over time. The cumulative displacement increment time history data is the longitudinal cumulative displacement increment of the support at t seconds. The specific calculation formula is:

[0024] W(t)=|u i+1 -u i |

[0025] In the formula, u i+1 is the longitudinal displacement value measured in t seconds; u i is the longitudinal displacement value measured at the previous sampling point, and W(t) is the cumulative displacement increment time history.

[0026] As a further preferred method, obtaining the daily wear data of the support slide plate includes:

[0027] S211 calculates the actual wear rate at t seconds based on the actual wear rate mathematical model by using the bearing movement speed and bearing surface pressure at t seconds;

[0028] S212 calculates the daily wear data by multiplying and integrating the cumulative displacement increment and the actual wear rate time history data. The calculation formula is as follows:

[0029] T w =∫D·W t ·dD

[0030] Where: D is the cumulative displacement increment, W t is the actual wear rate.

[0031] As further preferred, step three comprises the following steps:

[0032] S301 establishes its Gamma random process model through the daily wear data of some bearing slides;

[0033] S302 predicts the wear amount of the slide plate at a specified time by simulating the Gamma random process of the daily wear amount by using the Monte Carlo method;

[0034] S303 predicts the remaining sliding life of the bearing through the wear amount of the slide plate and the remaining thickness of the slide plate at a specified time based on the critical value specified by the slide plate wear standard.

[0035] As further preferred, in step S302, the Gamma random process includes:

[0036] Assume that The random process must meet the following conditions:

[0037] (1)

[0038] (2) For any τ>t,

[0039] (3) It is an independent incremental process, that is, for any time t1<t2<…<t n (n>2) increment Independent of each other;

[0040] in, is the wear amount of the support slide at time t.

[0041] As a further preferred embodiment, when the wear amount of the support slide at time t is It obeys the Gamma distribution G(α, β), and its probability density function is:

[0042]

[0043] Among them, Γ(α) is the Gamma function of α, and α and β are constant terms.

[0044] According to another aspect of the present invention, a bridge bearing slide wear and life prediction system based on monitoring data is provided, comprising:

[0045] The first main control module is used to construct a mathematical model of the actual wear rate of the bridge bearing slide plate;

[0046] The second main control module is used to obtain the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtain the daily wear amount data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data;

[0047] The third main control module is used to construct a Gamma random process model of daily slide wear according to the daily wear data, and predict the long-term wear and remaining service life of the support slide based on the model and the remaining thickness of the support slide.

[0048] In general, the above technical solution conceived by the present invention has the following technical advantages compared with the prior art:

[0049] 1. The present invention provides a bridge bearing slide plate wear and life prediction method driven by monitoring data. Through multiple groups of slide plate wear test results, a mathematical model of the slide plate benchmark wear rate is established in combination with a piecewise linear regression method; the longitudinal movement speed of the bearing and the cumulative displacement increment time series data are calculated by monitoring the displacement of the bridge bearing; the actual bearing surface pressure data is determined by actual monitoring of the bearing surface pressure or a bearing surface pressure normal distribution model established based on the bearing design parameters; according to the slide plate wear rate model, a database of daily wear of existing bearing slide plates is obtained by calculating the bearing surface pressure, movement speed and cumulative displacement data; a slide plate wear random process is established based on the slide plate wear database, and the Monte Carlo method is used to predict the long-term slide plate wear and remaining service life of the bearing. In order to take into account the influence of material properties, sliding speed, contact surface pressure and other factors on the wear rate, the present invention uses the skateboard wear test data to establish a mathematical model of the skateboard wear rate, sliding speed and contact surface pressure; in order to consider the randomness of the wear amount and life prediction of the support skateboard, according to the distribution characteristics of the existing support skateboard daily wear amount database, the Monte Carlo method is used to simulate the Gamma random process of the daily wear amount, and further predict the long-term wear amount and remaining service life of the support skateboard, thereby improving the accuracy and reliability of the prediction results, and being able to solve the defects of low precision and misjudgment in the existing support wear / life prediction methods due to not considering the variability of the actual wear rate of the skateboard and the randomness of the skateboard wear process.

[0050] 2. The present invention can solve the problems of low precision and misjudgment in existing bearing wear / life prediction methods due to failure to consider the variability of the actual wear rate of the slide and the randomness of the slide wear process, and further effectively improve the prediction accuracy of the bearing slide wear and life.

[0051] 3. The present invention predicts the wear and life of the bearing slide of a bridge in China, and verifies its effectiveness based on measured data. The verification results show that the actual wear of the bearing slide in one year is 0.12861mm, and the predicted wear in one year is 0.12852mm. The predicted result is basically close to the actual result, with a prediction error of only 0.09%. The remaining sliding life of the bearing is predicted to be 11 to 15 years. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] Figure 1 It is a logic flow chart of a bridge bearing slide wear and life prediction method based on monitoring data involved in an embodiment of the present invention;

[0053] Figure 2 The invention relates to a method for predicting the wear and life of a bridge bearing slide plate based on monitoring data in accordance with an embodiment of the present invention;

[0054] Figure 3It is a flow chart of a bridge bearing slide wear and life prediction method based on monitoring data according to an embodiment of the present invention;

[0055] Figure 4 It is a flow chart of establishing a mathematical model of the bearing plate wear rate of a bridge bearing plate wear and life prediction method based on monitoring data in an embodiment of the present invention;

[0056] Figure 5 It is a schematic diagram of obtaining a daily wear amount database of a bridge bearing slide plate according to a bridge bearing slide plate wear and life prediction method based on monitoring data in an embodiment of the present invention;

[0057] Figure 6 It is a schematic diagram of slide plate wear and life prediction of a bridge bearing slide plate wear and life prediction method based on monitoring data in an embodiment of the present invention;

[0058] Figure 7 A mathematical model of a reference wear rate of a tetrafluoroethylene slide plate of a bridge bearing slide plate wear and life prediction method based on monitoring data driven in an embodiment of the present invention;

[0059] Figure 8 A bearing sliding velocity time history curve of a bridge bearing slide wear and life prediction method based on monitoring data driven according to an embodiment of the present invention;

[0060] Fig. 9 The daily / monthly cumulative displacement status of the bearing of a bridge bearing slide plate according to a method for predicting wear and life of the bridge bearing slide plate driven by monitoring data according to an embodiment of the present invention;

[0061] Fig.10 A bearing surface pressure normal distribution model of a bridge bearing slide wear and life prediction method based on monitoring data driven according to an embodiment of the present invention;

[0062] Fig.11 The daily / monthly wear condition of the bearing slide plate of a bridge bearing slide plate wear and life prediction method driven by monitoring data according to an embodiment of the present invention;

[0063] Fig.12 The daily / annual wear condition of the bearing slide plate of a bridge bearing slide plate wear and life prediction method driven by monitoring data according to an embodiment of the present invention;

[0064] Fig.13 A Gamma random process model of bearing plate wear for a bridge bearing plate wear and life prediction method based on monitoring data driven according to an embodiment of the present invention;

[0065] Fig.14 A schematic diagram of the gamma distribution of a bridge bearing slide wear and life prediction method driven by monitoring data according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0067] like Figures 1 to 6 As shown, an embodiment of the present invention provides a bridge bearing slide wear and life prediction method based on monitoring data, comprising the following steps:

[0068] S1: The mathematical relationship model between the reference wear rate and the product of the surface pressure and the speed is established through the test results of the slide plate wear rate under different surface pressures and different sliding speeds. At the same time, the correction coefficient of the wear rate mathematical model is adjusted according to the actual bridge conditions to determine the mathematical model of the actual wear rate of the bridge bearing slide plate;

[0069] S2: The longitudinal displacement of the bridge bearing is obtained through the structural health monitoring system, and the longitudinal motion velocity time history data and cumulative displacement increment time history data of the bearing are obtained by using the central difference and adjacent data difference calculation method;

[0070] S3: Based on the bearing design parameters, a random normal distribution of the bearing surface pressure is established through normal distribution or the bearing surface pressure value is obtained through sensor monitoring;

[0071] S4: The actual wear rate time history data is obtained by calculating the actual wear rate model, the bearing surface pressure and the velocity time history, and the daily wear amount data is obtained by multiplying and integrating the cumulative displacement and the actual wear rate time history data;

[0072] S5: A Gamma random process model of daily slide wear is established through daily wear data. Based on this model and the remaining thickness of the support slide, the long-term wear and remaining service life of the support slide are predicted.

[0073] The present invention considers the influencing factors of wear rate and the randomness of the skateboard wear process, and proposes a bridge bearing skateboard wear and life prediction method driven by monitoring data. First, based on multiple sets of skateboard wear rate test data, a piecewise linear regression is used to establish a mathematical relationship model between the skateboard wear rate and the contact surface pressure and sliding speed; then, the bearing sliding speed, cumulative displacement increment data and skateboard daily wear amount database are calculated according to the bridge bearing monitoring displacement; in order to consider the randomness of the bearing skateboard wear process, based on the distribution characteristics of the skateboard daily wear amount database, the Monte Carlo method is used to simulate the Gamma random process of daily wear amount to predict the long-term wear amount and remaining service life of the bearing skateboard, which further effectively improves the accuracy and reliability of the bearing skateboard wear and life prediction.

[0074] As a further solution of the present invention: the mathematical relationship model of the reference wear rate and the product of the surface pressure and the speed is established through the test results of the slide plate wear rate under different surface pressures and different sliding speeds described in step S1, and the correction coefficient of the wear rate mathematical model is adjusted in combination with the actual bridge conditions to determine the mathematical model of the actual wear rate of the bridge bearing slide plate, including:

[0075] S11: Wear rate test under different surface pressure and sliding speed for specific skateboard materials;

[0076] S12: Based on the test data, a mathematical model of the baseline wear rate of a specific skateboard material is established by performing linear regression in two stages: low wear and high wear;

[0077] S13: The correction coefficient α of the mathematical model of wear rate is adjusted according to the actual life of the bridge bearing and the daily cumulative displacement, and the mathematical model of the bearing slide wear rate that conforms to the actual situation is obtained. The expression is as follows:

[0078] W t =α·W

[0079] Wherein, α is correction coefficient, W is reference wear rate.

[0080] In this embodiment, the mathematical model of the reference wear rate of the slide plate established in step S12 is as follows: Figure 7 As shown, the specific formula of the mathematical model is as follows:

[0081]

[0082] Where: x = PV; P is the bearing surface pressure (MPa); V is the bearing longitudinal movement speed (cm / min).

[0083] In this embodiment, the model correction coefficient α in step S13 is set to 1.

[0084] As a further solution of the present invention, in step S2, before calculating the support movement speed and cumulative displacement, it is necessary to perform preprocessing such as abnormal elimination and noise filtering on the displacement data, and then use the center difference and adjacent value difference method to calculate the support sliding speed and cumulative displacement increment. The speed and cumulative displacement increment calculation formulas are as follows:

[0085]

[0086] W(t)=|u i+1 -u i |

[0087] Where: u i+1 is the longitudinal displacement value measured in t seconds; u iis the longitudinal displacement value measured at the previous sampling point; is the support movement speed; W(t) is the cumulative displacement measured in t seconds; Δt is the displacement data sampling time step. The results are shown in Figure 8 and Fig. 9 shown.

[0088] As a further solution of the present invention, in the absence of a bearing pressure sensor, the bearing surface pressure value is obtained by establishing a normal distribution of the bearing surface pressure based on the bearing design parameters in step S3, and the probability density function expression of the normal distribution is:

[0089]

[0090] In this embodiment, the average surface pressure of the bearing slide is 80% of the rated bearing capacity of the vertical bearing, and the standard deviation is 0.5 MPa. Fig.10 As shown, the normal distribution model of the support surface pressure is displayed.

[0091]

[0092] σ=0.5MPa

[0093] As a further solution of the present invention, the actual wear rate time history data is obtained by calculating the actual wear rate model, the bearing surface pressure and the speed time history in step S4, and the daily wear amount data is obtained by multiplying and integrating the cumulative displacement and the actual wear rate time history data, which specifically includes the following steps:

[0094] S41: According to the actual wear rate mathematical model, the actual wear rate at t seconds is calculated by the bearing movement speed and bearing surface pressure at t seconds;

[0095] S42: The daily wear data is obtained by multiplying and integrating the cumulative displacement increment and the actual wear rate time history data. The calculation formula is as follows:

[0096] T w =∫D·W t ·dD

[0097] Where: D is the cumulative displacement increment, w t - Actual wear rate.

[0098] Furthermore, if Fig.11 As shown, the daily and monthly wear conditions of the support slide are shown; Fig.12 The figure shows the actual daily and annual wear of the bearing slide. The actual daily wear of the bearing slide is 0.1×10 -3 mm~0.25×10 -3 The daily wear volume at different times varies greatly; the actual monthly wear volume is about 6×10 -3mm; the actual annual wear amount is about 0.12861 mm.

[0099] As a further solution of the present invention, in order to improve the accuracy of the prediction of the wear amount of the skateboard and its life, the inevitable randomness in the wear process of the support skateboard is considered in step S5, and the Gamma random process is used to describe the skateboard wear process. The daily wear amount Gamma random process model is established through the partial daily wear amount data of the skateboard, that is, the daily wear amount prediction model, thereby achieving the purpose of predicting the long-term wear amount and remaining life of the skateboard.

[0100] Furthermore, the Gamma random process in step S5 is assumed to be , the random process must meet the following conditions:

[0101] i) 2) For any τ>t,

[0102] j) It is an independent incremental process, that is, for any time t1<t2<…<t n (n>2) increment Independent of each other.

[0103] The random process is a Gamma random process about time t, α(t) and β are shape parameters and inverse scale parameters respectively. When α(t) and β are fixed parameters, It is a Gamma process with fixed effects, which is generally applicable to the case where the mean and variance of the wear amount are linear functions of time. When the wear amount is nonlinear with time t, it is usually assumed that the inverse scale parameter is a fixed parameter and the shape parameter is a time-varying parameter. In fact, the wear amount is linear with time t, so when describing the wear process of the skateboard, the shape parameter of the Gamma random process is a fixed parameter α.

[0104] When the wear amount of the structure at time t It obeys the Gamma distribution G(α, β), and its probability density function is

[0105]

[0106] Among them, Γ(α) is the Gamma function of α; the unknown parameters α and β can be estimated by maximum likelihood estimation method.

[0107] Further, step S5 includes the following steps:

[0108] S51: Establish a Gamma random process model based on the daily wear data of some bearing slides, and calculate the mean and variance of the model;

[0109] S52: The wear of the skateboard in one year is predicted by simulating the Gamma random process of daily wear using the Monte Carlo method;

[0110] S53: Based on the critical value specified in the slide wear standard, the remaining sliding life of the bearing is predicted by the slide wear amount and the remaining thickness of the slide in one year.

[0111] In this embodiment, in the daily wear data of the support slide for one year, the data of 3 days in each month is taken as the basis to establish the Gamma random process model of the daily wear of the support slide, such as Fig.13 As shown, the mean and variance of the model are as follows:

[0112] Mean:

[0113] variance:

[0114] Furthermore, the Monte Carlo method is used to simulate the Gamma random process of daily wear, such as Fig.14 As shown, the predicted value of the skateboard wear in one year is 0.12852mm. Based on the existing thickness of the bearing skateboard of 2.5-3.13mm and the critical value of 1mm specified in the skateboard wear standard, the remaining sliding life of the bridge bearing is predicted to be 11-15 years.

[0115] Finally, by comparing the actual wear of the bridge bearing slide plate in one year with the predicted wear, the relative error between the two was only 0.09%, and the predicted value was basically close to the actual value, which verified the accuracy and reliability of the bearing slide plate wear and life prediction method.

[0116] As a better solution of the above embodiment, step S1 specifically includes the following steps:

[0117] (1) Obtain the design parameters of the bridge bearing, the load data of the bridge bearing and the wear amount of the slide under the corresponding load, the load data includes dynamic load data and static load data. Normalize the above data to generate a data set, divide the data set into a training set and a test set, input the training set into the LSTM model, and start training the model; (2) Calculate the fitness of each particle in the DMPSO model, update the individual extreme value of each particle and the global extreme value of the population according to the fitness of each particle, update the speed and position of each particle according to the individual extreme value and the global extreme value, update the inertia weight of the DMPSO model, and perform adaptive mutation on each particle; (3) Iterate step (2) to determine whether the maximum number of iterations of the DMPSO model is reached. If the iteration is not completed, continue to loop step (2). If the maximum number of iterations has been reached, find the optimal values ​​of the number of neurons in the first hidden layer of the LSTM model, the number of neurons in the second hidden layer, the learning rate, and the number of iterations, and finally complete the DMPSO-LSTM model training, thereby obtaining the characteristic parameters that affect the wear of the slide. (4) Weight the above characteristic parameters. (5) Based on the characteristic parameters after weighted processing, a mathematical relationship model between the benchmark wear rate and the product of surface pressure and speed is established through the test results of the slide plate wear rate under different surface pressures and different sliding speeds. At the same time, the correction coefficient of the wear rate mathematical model is adjusted according to the actual bridge conditions to determine the mathematical model of the actual wear rate of the bridge bearing slide plate.

[0118] In the above calculation process, the inertia weight is updated by the following formula:

[0119]

[0120] Among them, ω max is the maximum inertia weight, ω min is the minimum inertia weight, k max is the maximum number of iterations, k is the number of current iterations, and ω is the inertia weight.

[0121] Adaptive mutation of each particle includes:

[0122]

[0123] Among them, p is the adaptive mutation probability, which is a random value in the interval [0, 1], k is the current iteration number, k max is the maximum number of iterations.

[0124] That is, the present invention also analyzes the relationship between the wear of the bearing slide and the design parameters of the bridge bearing, the bridge bearing load, etc. Starting from the original wear data, the inertia weight in DMPSO is improved so that it can change dynamically to quickly seek the characteristic parameters that affect the wear of the slide.

[0125] According to another aspect of the present invention, a bridge bearing slide wear and life prediction system based on monitoring data is also provided, which is used to implement any of the above-mentioned embodiments and methods, including:

[0126] The first main control module is used to construct a mathematical model of the actual wear rate of the bridge bearing slide plate;

[0127] The second main control module is used to obtain the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtain the daily wear amount data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data;

[0128] The third main control module is used to construct a Gamma random process model of daily slide wear according to the daily wear data, and predict the long-term wear and remaining service life of the support slide based on the model and the remaining thickness of the support slide.

[0129] The present invention considers the influencing factors of the wear rate and the randomness of the skateboard wear process, and proposes a bridge bearing skateboard wear and life prediction method driven by monitoring data. First, based on multiple sets of skateboard wear rate test data, a piecewise linear regression is used to establish a mathematical relationship model between the skateboard wear rate and the contact surface pressure and sliding speed; then, the bearing sliding speed, cumulative displacement increment data and skateboard daily wear amount database are calculated according to the bridge bearing monitoring displacement; in order to consider the randomness of the bearing skateboard wear process, based on the distribution characteristics of the skateboard daily wear amount database, the Monte Carlo method is used to simulate the Gamma random process of daily wear amount to predict the long-term wear amount and remaining service life of the bearing skateboard, which further effectively improves the accuracy and reliability of the bearing skateboard wear and life prediction, and can solve the problems of low precision and misjudgment in the existing bearing wear / life prediction method due to the failure to consider the variability of the actual wear rate of the skateboard and the randomness of the skateboard wear process.

[0130] It will be easily understood by those skilled in the art that the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A bridge bearing slide wear and life prediction method based on monitoring data, characterized in that: The following steps are involved: Step 1: construct a mathematical model of the actual wear rate of the bridge bearing slide plate; Step 2: obtaining the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtaining the daily wear amount data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data; Step 3: construct a Gamma random process model of daily slide wear according to the daily wear data, and predict the long-term wear and remaining service life of the support slide based on the model and the remaining thickness of the support slide; In step 1, a mathematical relationship model between the reference wear rate and the product of the surface pressure and the speed is established through the test results of the slide plate wear rate under different surface pressures and different sliding speeds. At the same time, the correction coefficient of the wear rate mathematical model is adjusted in combination with the actual bridge conditions to determine the mathematical model of the actual wear rate of the bridge bearing slide plate; The actual wear rate mathematical model includes: W t =α·W Among them, α is the correction coefficient, w is the reference wear rate, W t is the actual wear rate.

2. The method for predicting wear and life of bridge bearing slide based on monitoring data according to claim 1 is characterized in that: The step 2 comprises the following steps: S201 obtains the longitudinal displacement of the bridge bearing, and uses the central difference and difference calculation methods to obtain the longitudinal motion velocity time history data and the cumulative displacement increment time history data of the bearing; S202 establishes a random normal distribution of bearing surface pressure based on bearing design parameters or obtains bearing surface pressure value through sensor monitoring; S203 obtains the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtains the daily wear data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data.

3. The method for predicting wear and life of bridge bearing slide based on monitoring data according to claim 2 is characterized in that: In step S201, the longitudinal displacement of the bridge bearing is obtained, and the longitudinal motion velocity time history data and the cumulative displacement increment time history data of the bearing are obtained by using the central difference and difference calculation method, including: The longitudinal displacement of the bridge bearing is obtained, and the finite element difference method is used to differentiate the time to obtain the longitudinal motion velocity of the bearing. The time interval of the longitudinal displacement data is equal to the time step, so the central difference of the longitudinal motion velocity of the bearing is approximately: In the formula, is the longitudinal motion speed of the support, u i+1 is the longitudinal displacement value of the i+1th sampling, u i-1 is the longitudinal displacement value of the i-1th sampling, and Δt is the displacement sampling time interval.

4. The method for predicting wear and life of bridge bearing slide based on monitoring data according to claim 3 is characterized in that: The longitudinal cumulative displacement of the support actually reflects the travel amount of the support that changes over time. The cumulative displacement increment time history data is the longitudinal cumulative displacement increment of the support at t seconds. The specific calculation formula is: W(t)=|u i+1 -u i | In the formula, u i+1 is the longitudinal displacement value measured in t seconds; u i is the longitudinal displacement value measured at the previous sampling point, and W(t) is the cumulative displacement increment time history.

5. The method for predicting wear and life of bridge bearing slide based on monitoring data according to claim 1 is characterized in that: Obtaining daily wear data of the support slide includes: S211 calculates the actual wear rate at t seconds based on the actual wear rate mathematical model by using the bearing movement speed and bearing surface pressure at t seconds; S212 calculates the daily wear data by multiplying and integrating the cumulative displacement increment and the actual wear rate time history data. The calculation formula is as follows: T w =∫D·W t ·dD Where: D is the cumulative displacement increment, W t is the actual wear rate.

6. The method for predicting wear and life of bridge bearing slide based on monitoring data according to claim 1 is characterized in that: Step three includes the following steps: S301 establishes its Gamma random process model through the daily wear data of some bearing slides; S302 predicts the wear amount of the slide plate at a specified time by simulating the Gamma random process of the daily wear amount by using the Monte Carlo method; S303 predicts the remaining sliding life of the bearing through the wear amount of the slide plate and the remaining thickness of the slide plate at a specified time based on the critical value specified by the slide plate wear standard.

7. The method for predicting wear and life of bridge bearing slide based on monitoring data according to claim 6 is characterized in that: In step S302, the Gamma random process includes: Assume that The random process must meet the following conditions: (1) (2) For any τ>t, (3) It is an independent incremental process, that is, for any time t1 <t2<…<t n (n>2) increment Independent of each other; in, is the wear amount of the support slide at time t.

8. The method for predicting wear and life of bridge bearing slide plates based on monitoring data according to claim 7 is characterized in that: When the wear amount of the support slide at time t It obeys the Gamma distribution G(α,β), and its probability density function is: Among them, Γ(α) is the Gamma function of α, and α and β are constant terms.

9. A bridge bearing slide wear and life prediction system based on monitoring data, characterized in that: include: The first main control module is used to construct a mathematical model of the actual wear rate of the bridge bearing slide plate; Among them, the mathematical relationship model between the benchmark wear rate and the product of surface pressure and speed is established through the test results of the slide plate wear rate under different surface pressures and different sliding speeds. At the same time, the correction coefficient of the wear rate mathematical model is adjusted according to the actual bridge conditions to determine the mathematical model of the actual wear rate of the bridge bearing slide plate. The actual wear rate mathematical model includes: W t =α·W Among them, α is the correction coefficient, W is the reference wear rate, W t is the actual wear rate; The second main control module is used to obtain the actual wear rate time history data of the bearing slide according to the actual wear rate mathematical model, the bearing surface pressure and the bearing longitudinal movement speed time history data, and obtain the daily wear amount data of the bearing slide according to the actual wear rate time history data and the bearing slide cumulative displacement increment time history data; The third main control module is used to construct a Gamma random process model of daily slide wear according to the daily wear data, and predict the long-term wear and remaining service life of the support slide based on the model and the remaining thickness of the support slide.

Images