Thermal-mechanical coupling analysis method for elevator safety tongs

Abstract

The invention provides a thermal-mechanical coupling analysis method for elevator safety tongs, and relates to the technical field of elevator safety tongs analys.According to the thermal-mechanical coupling analysis method, the difference between simulation and actual working conditions is effectively reduced through multi-source data fusion in combination with sensor data and microscopic damage information in the actual braking process of an elevator; a time-varying friction coefficient is adopted to replace a traditional constant value, and the influence of surface temperature change of a friction pair on friction performance is accurately reflected; a heat-force-wear coupling UMAT subprogram is embedded, so that the physical trueness of wear and performance degradation simulation is improved; an evolution curve of a key performance index is generated based on finite element simulation data, a physical information neural network is constructed in an auxiliary mode, multi-physical information and a microscopic damage equivalent value are fused, intelligent prediction and advanced safety early warning of the braking efficiency decline rate are achieved, and therefore the accuracy and the real-time performance of safety tongs performance evaluation are improved, operation optimization is supported, and the safety tongs performance evaluation efficiency is improved. And the elevator safety guarantee level is enhanced, and the method is suitable for intelligent safety management under complex working conditions.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of elevator safety gear analysis, in particular to a thermal coupling analysis method of elevator safety gear. BACKGROUND

[0002] With the development of urban construction and high-rise buildings, as an important equipment for vertical transportation, the safety performance of the elevator has attracted much attention. As a key safety device, the braking efficiency of the elevator safety gear is directly related to the safety of passengers and equipment. The traditional performance evaluation of the safety gear mainly relies on static test and empirical parameters, ignoring the multi-physical field coupling effect of the friction pair during braking, especially the influence of thermal effect on friction characteristics, material performance degradation and micro-damage. In addition, the existing multi-body dynamics model usually uses a constant friction coefficient, which cannot accurately reflect the temperature change of the friction pair during braking and its dynamic influence on the friction coefficient, resulting in a large deviation between the simulation results and the actual working conditions. Moreover, the micro-damage such as micro-cracks and hard phase degradation is not systematically included in the performance prediction, making it difficult to realize multi-level, real-time and dynamic monitoring and prediction of the braking efficiency of the safety gear, and limiting the reliability evaluation and maintenance decision of the safety gear.

[0003] In the prior art, the publication number CN117332605A discloses a thermal coupling analysis method for elevator progressive safety gear, which includes: collecting parameters and geometric information to facilitate subsequent accurate establishment of thermal model; establishing thermal model to facilitate analysis of internal heat conduction process and temperature distribution of elevator safety gear; setting boundary conditions to facilitate simulation of heat conduction behavior under real conditions; formulating heat conduction equation to facilitate description of heat conduction process, prediction of temperature distribution and analysis of thermal stability; adding energy equation to facilitate more accurate simulation of energy transfer and energy loss under actual conditions; and performing grid division to facilitate discretization of geometric shape and balance between accuracy and calculation efficiency. Although the detailed thermal model of the elevator progressive safety gear is constructed, it focuses on micro-fractal surface modeling and multi-physical boundary condition setting, lacks coupling analysis of dynamic friction coefficient temperature dependence and material performance degradation, and thus cannot accurately predict and warn the performance degradation caused by temperature rise due to insufficient description of thermal-mechanical-wear interaction effect.

[0004] The above information disclosed in the background section is only intended to enhance the understanding of the background of the present disclosure, and thus it can include information that does not constitute the prior art known to those of ordinary skill in the art. SUMMARY

[0005] The present application aims to provide a thermal coupling analysis method of elevator safety gear to solve the problems raised in the background.

[0006] To achieve the above-mentioned purpose, the present application provides the following technical solutions: A thermal coupling analysis method of an elevator safety gear, the specific steps comprising: S1: performing a brake test on the elevator, and collecting sensor data and micro-damage data of the safety gear friction pair during the braking process of the elevator; S2: constructing a multi-body dynamics model of the elevator counterweight system containing time-varying friction coefficient, and quantifying the influence of the braking impact load on the surface temperature rise of the friction pair; S3: embedding a thermal-force-wear coupling UMAT subprogram in a finite element analysis platform, dynamically correcting the material hardness according to the transient temperature field of the friction pair surface, and simulating the bidirectional coupling effect of friction heat accumulation and material performance degradation during the braking process; S4: collecting simulation data during the finite element simulation process, and generating evolution curves of the braking deceleration fluctuation, thermal-elastic instability critical temperature and wear depth during the braking process of the elevator; S5: data fusion of the sensor data and the simulation data based on a physical information neural network model, generating a predicted value of the braking efficiency degradation rate, and issuing a safety warning when the predicted value exceeds a preset safety threshold.

[0007] Preferably, in step S1, the sensor data includes: speed, acceleration, braking force, local temperature, pressure; The micro-damage data includes: micro-crack density, hard phase content reduction rate.

[0008] Preferably, the micro-damage data collection method is: Based on the SEM system, three-dimensional point cloud data of the friction pair surface of the elevator safety gear is collected, the length of each micro-crack is calculated according to the three-dimensional point cloud data, and the micro-crack density is calculated according to the length of each micro-crack and the scanning area of the SEM system; Based on the XRD diffraction pattern, the characteristic peak intensity of the hard phase before and after the braking test of the elevator is identified respectively, and the hard phase content reduction rate is calculated according to the characteristic peak intensity of the hard phase before and after the braking test; The micro-crack density and the hard phase content reduction rate are weighted to quantify the micro-damage data.

[0009] Preferably, the micro-crack density calculation method is: ; In the formula, The micro-crack density is represented by The length of the th micro-crack is represented by The scanning area is represented by subscript The micro-crack index in the scanning area is represented by The total number of micro-cracks in the scanning area is represented by The calculation method of the hard phase content reduction rate is: ; In the formula represents the hard phase content reduction rate, 、 represent the characteristic peak intensities of the hard phase before and after the braking test, respectively; The calculation method for quantifying micro damage data is: ; In the formula represents the equivalent value of micro damage data, 、 Both represent calculation weights, both are greater than 0, and .

[0010] Preferably, in step S2, when constructing the multi-body dynamics model of the elevator counterweight system, a dynamic time-varying friction coefficient is used to replace the preset friction coefficient. The time-varying friction coefficient is calculated as follows: ; In the formula express The friction coefficient at time represents the initial friction coefficient, 、 represents the attenuation coefficient, Indicates the friction surface of the elevator safety clamp The average temperature at the time, represents the reference temperature, 、 Indicates the temperature threshold, 、 They respectively represent the maximum friction coefficient and minimum friction coefficient of the elevator friction pair at high temperature.

[0011] Preferably, in step S3, the logic of the UMAT subroutine to dynamically correct the material hardness according to the transient temperature field on the friction pair surface is: The average temperature at that moment is calculated based on the transient temperature field of the friction pair surface, and different material hardness calculation methods are used according to the average temperature: When the average temperature of the transient temperature field on the friction pair surface When , the material hardness is calculated as: ; In the formula express The material hardness at the moment, Indicates the hardness of the material at the reference temperature, represents the material softening coefficient, represents the phase transition temperature; When the average temperature of the transient temperature field of the friction pair surface The calculation method of the material hardness is: ; In the formula represents the material hardness at the phase transition temperature, represents the material softening coefficient at the phase transition stage.

[0012] Preferably, in the step S4, the generation logic of the evolution curve of the brake deceleration fluctuation, the thermal-elastic instability critical temperature and the wear depth during the elevator braking process is respectively: For the evolution curve of the brake deceleration fluctuation: from the multi-body dynamics model of the elevator counterweight system, the acceleration-time curve during the elevator braking process is extracted, and then the fast Fourier transform is performed to identify the main vibration frequency component, and the gradient of the evolution curve is calculated, and when the gradient exceeds the preset mutation threshold, it is determined as a resonance risk point; For the evolution curve of the thermal-elastic instability critical temperature: according to the output of the highest temperature of the friction pair surface of the elevator safety gear transient temperature field, the second derivative of the highest temperature with respect to time is calculated, and when the second derivative of the highest temperature with respect to time exceeds the preset thermal instability critical threshold, the surface area corresponding to the highest temperature is marked as a thermal instability area; For the evolution curve of the wear depth: first, the instantaneous wear rate of the elevator safety gear friction pair is calculated, then the instantaneous wear rate is integrated to obtain the wear depth, and finally the evolution curve of the wear depth is generated according to the change of the wear depth with time, wherein the calculation method of the instantaneous wear rate is: ; In the formula represents the wear depth, represents the loss wear rate, represents the wear constant, represents the pressure of the safety gear friction pair contact surface, represents the speed of the elevator braking at the moment, represents an empirical coefficient.

[0013] Preferably, in the step S5, when constructing the physical information neural network, the evolution curves of the brake deceleration fluctuation, the thermal-elastic instability critical temperature, the wear depth, and the equivalent value of the micro-damage data are taken as inputs, and the predicted value of the brake efficiency degradation rate is taken as output.

[0014] Preferably, in the step S5, when constructing the physical information neural network, the physical constraint is embedded in the loss function, and the expression is: ; In the formula represents a physical constraint term, represents a brake performance degradation rate prediction value, represents a material thermal diffusivity, represents the normal coordinate of the safety gear friction pair.

[0015] Compared with the prior art, the present application has the following beneficial effects: The present application realizes multi-level diagnosis from material microstructure to system overall performance by multi-source data fusion, combining sensor data and micro-damage information in the actual braking process of the elevator, effectively reducing the gap between simulation and actual working conditions. The time-varying friction coefficient is used to replace the traditional constant value, accurately reflecting the influence of friction pair surface temperature change on friction performance, realizing dynamic simulation of thermal-mechanical coupling; the UMAT subprogram of thermal-mechanical- wear coupling is embedded, dynamically correcting the material hardness, improving the physical authenticity of wear and performance degradation simulation; based on the finite element simulation data, the evolution curve of the key performance index is generated, auxiliary construction of the physical information neural network is realized, multi-physical information and micro-damage equivalent value are fused, intelligent prediction and early safety warning of the brake performance degradation rate are realized, thereby improving the accuracy and real-time performance of the safety gear performance evaluation, supporting scientific maintenance decision and operation optimization, enhancing the safety guarantee level of the elevator, and being suitable for intelligent safety management under complex working conditions. BRIEF DESCRIPTION OF DRAWINGS

[0016] Fig. 1 is a schematic diagram of the overall method of the present application; Fig. 2 is a graph of the change trend of the friction coefficient with time in the present application; Fig. 3 is a graph of the change trend of the friction coefficient with temperature in the present application. DETAILED DESCRIPTION

[0017] In order to make the purpose, technical scheme and advantages of the present application clearer, the present application is further described in detail below in combination with specific embodiments.

[0018] It should be noted that the technical terms or scientific terms used in the present application should be understood as the general meaning understood by those skilled in the art to which the present application belongs, unless otherwise defined. The terms "first", "second", and similar words used in the present application do not represent any order, quantity or importance, but are only used to distinguish different components. The terms "include" or "contain" and similar words mean that the elements or objects appearing before the words cover the elements or objects listed after the words and their equivalents, without excluding other elements or objects. The terms "connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. The terms "upper", "lower", "left", "right" and the like are only used to represent relative positional relationships, which may change accordingly when the absolute position of the described object changes.

[0019] Embodiments: Please refer to Figs. 1-3 The present application provides a technical solution: A thermal coupling analysis method of an elevator safety gear, the specific steps comprising: S1: performing a brake test on the elevator, collecting sensor data and micro-damage data of the safety gear friction pair during the braking process of the elevator, wherein the sensor data includes: speed, acceleration, braking force, local temperature, pressure; the micro-damage data includes: micro-crack density, hard phase content reduction rate.

[0020] The micro-crack is a small crack generated in the material during the stress, friction and thermal cycle process, and is a direct manifestation of the material microstructure fatigue damage and degradation. The micro-crack density refers to the total length of the micro-cracks per unit surface area, reflecting the number and distribution density of cracks on the material surface. An increase in micro-crack density means that the surface structure of the friction pair is deteriorating, the friction characteristics are unstable, and the braking force may decrease or the friction pair may fail. The hard phase generally refers to hard phase particles or hard compounds (such as carbides, nitrides, etc.) in the friction pair material, which endow the material with high hardness and wear resistance. The hard phase content reduction rate represents the relative reduction ratio of the hard phase before and after braking, which is a quantitative index of the decomposition, dissolution or transformation of the hard phase under the action of friction heat and mechanical load. It can be understood that the micro-crack density can reflect the structural damage caused by mechanical fatigue and stress concentration during the braking process of the elevator, and the hard phase content reduction rate can reflect the degradation of material composition and performance under the influence of heat and chemistry. Both of them together reflect the overall micro-damage of the friction pair under complex thermal interaction.

[0021] Experiments are conducted here to obtain key physical parameters of the elevator during actual braking (speed, acceleration, braking force, local temperature, and pressure), as well as microscopic damage information of the friction pair of the safety clamp (microcrack density and hard phase content reduction rate). This allows the combination of macroscopic sensor data and microscopic damage information to achieve multi-level diagnosis from material damage to system performance, narrowing the deviation between simulation and actual working conditions, thereby providing real and effective input data for subsequent modeling, simulation, and intelligent prediction, ensuring the accuracy and reliability of the analysis.

[0022] The method for collecting micro damage data is: Using a scanning electron microscope (SEM) system to collect 3D point cloud data of the friction pair surface of the elevator safety gear, the length of each microcrack is calculated based on the 3D point cloud data, and the microcrack density is calculated based on the length of each microcrack and the scanning area of ​​the SEM system. Based on the XRD diffraction pattern (i.e., X-ray diffraction), the characteristic peak intensity of the hard phase before and after the braking test of the elevator is identified, and the hard phase content reduction rate is calculated based on the characteristic peak intensity of the hard phase before and after the braking test; The microcrack density and hard phase content reduction rate are weighted to complete the quantification of micro damage data.

[0023] The microcrack density is calculated as: ; In the formula represents the microcrack density, Indicates the Microcrack length, Indicates the scan area, subscript represents the microcrack index in the scanned area, Represents the total number of microcracks in the scanned area. It can be seen that the microcrack density is defined as the total length of microcracks per unit area. It is used to reflect the distribution and severity of defects and quantify the cumulative damage of microcracks, which helps in subsequent fatigue life prediction.

[0024] The calculation method of the hard phase content reduction rate is: ; In the formula represents the hard phase content reduction rate, 、 These represent the characteristic peak intensities of the hard phase before and after the braking test, respectively. XRD peak intensity represents the proportion of a specific phase. A decrease in intensity indicates a reduction in the hard phase content and a decrease in material hardness. This quantitatively reflects hard phase degradation and reveals the phase transformation and softening of the material during braking.

[0025] The calculation method for quantifying micro damage data is: ; In the formula represents the equivalent value of micro damage data, 、 Both represent calculation weights, representing the relative importance of microcrack density and hard phase content reduction rate in the material damage mechanism, and both are greater than 0, and The specific size can be determined based on expert experience. Here, the equivalent value of microscopic damage data is constructed by fusing two types of damage indicators through weighted linear combination to construct a comprehensive damage evaluation quantity, which can quantify multidimensional damage information, reduce the deviation of a single indicator, and improve the comprehensiveness and accuracy of damage description.

[0026] S2: Construct a multi-body dynamics model of the elevator counterweight system with a time-varying friction coefficient to quantify the effect of braking shock load on the surface temperature rise of the friction pair.

[0027] When constructing the multi-body dynamics model of the elevator counterweight system, a dynamic time-varying friction coefficient is used instead of the preset friction coefficient. The calculation method of the time-varying friction coefficient is: ; In the formula express The friction coefficient at time represents the initial friction coefficient, that is, the friction coefficient of the friction pair at the reference temperature, 、 It represents the attenuation coefficient, reflecting the attenuation sensitivity of the friction coefficient with increasing temperature. It is usually obtained through experimental fitting, and can also be set according to expert experience. Indicates the friction surface of the elevator safety clamp The average temperature at the time, Indicates the reference temperature, which is usually set to 25°C. 、 Represents the temperature threshold, which is used to divide the temperature zone of the elevator friction pair and is determined according to the material of the elevator friction pair, that is, For the low temperature zone, The medium temperature zone, It is a high temperature area. 、 They represent the maximum friction coefficient and minimum friction coefficient of the elevator friction pair at high temperature, which can be measured through high temperature test. In the low temperature zone, the friction coefficient is less affected by temperature and changes more slowly, so the constant To avoid unnecessary complexity, the stable performance of materials at room temperature is reflected; then, as the temperature rises, the surface of the friction pair begins to soften, and the lubricating film is destroyed, leading to a significant decrease in the friction coefficient (affected by thermal softening, lubricating film destruction, etc.), reflecting the significant impact of thermal softening on the friction performance. The exponential function is simple and can adjust the decay rate by adjusting the decay coefficient, thus adapting to different materials and working conditions; beyond a certain temperature, the friction coefficient may tend to be stable (such as the oxidation layer causing the friction coefficient to rise), reflecting the non-monotonic change in physical friction performance. The model divides the temperature interval, decomposes the complex thermal-mechanical-chemical interaction into three representative stages, and uses different function forms to express them, ensuring that the model has good fitting ability in each stage.

[0028] In traditional multi-body dynamics models, the friction between friction pairs is usually described by the Coulomb friction model, as follows: ; where represents the friction force, represents the friction coefficient, represents the normal pressure, represents the relative sliding speed of the friction surface, represents the sign function, indicating that the direction of the friction force is opposite to that of the relative speed; Further, the specific motion equation in the traditional multi-body dynamics model can be expressed as: ; where , , respectively represent the mass matrix, damping matrix, and stiffness matrix, , , respectively represent the acceleration, velocity, and displacement vectors at time , , respectively represent the driving force and friction force at time .

[0029] From the traditional multi-body dynamics model, the friction coefficient is preset as a constant value, representing the friction performance of the friction pair under specific materials and surface conditions. This method has the advantages of simplicity, small calculation amount, good model convergence, and is suitable for cases where the state of the friction pair does not change significantly or the research target is not sensitive to the friction coefficient. The disadvantage is that it ignores the influence of temperature change on the friction performance during the friction process, and cannot accurately reflect the dynamic change process of the friction pair performance under the impact load of the brake. Therefore, under the working condition of the elevator brake which generates a lot of heat, the surface temperature of the friction pair rises significantly, causing the friction material to soften, the lubrication state to change, and the friction coefficient to decrease, resulting in a deviation between the traditional multi-body dynamics model and the actual situation, affecting the authenticity and reliability of the simulation results.

[0030] Therefore, in this embodiment, the time-varying friction coefficient is used to replace the fixed friction coefficient in the traditional multi-body dynamics model. From the calculation formula of the time-varying friction coefficient, it can be seen that it decreases exponentially with the increase of temperature, reflecting the objective law of the decrease of friction resistance due to factors such as softening of friction material, change of lubrication state, or formation of oxidation layer, which can not only reflect the nonlinear attenuation, but also facilitate parameter adjustment and numerical calculation. By introducing the time-varying friction coefficient, the physical phenomenon of the decrease of friction coefficient due to the softening of friction material caused by temperature rise can be reflected, making up for the shortcomings of the constant friction coefficient model, and the simulation results are closer to the actual working condition. In addition, the feedback of the thermal field can be incorporated into the dynamics calculation to form a preliminary expression of thermal-mechanical coupling, providing boundary conditions and load input for subsequent finite element thermal-mechanical wear coupling calculation.

[0031] In this embodiment, the elevator safety gear made of high chromium cast iron is taken as an example for testing, and the temperature threshold is about 80℃ and 150℃, respectively. The corresponding attenuation coefficients are set to 0.025 and 0.005, respectively. The maximum and minimum values of the friction coefficient in the high temperature zone are about 0.3 and 0.15, respectively. The theoretical value of the friction coefficient is calculated in real time through the calculation model of the time-varying friction coefficient, and the real value of the friction coefficient is calculated by measuring the pressure, speed, acceleration and other parameters in the test in real time through various sensors. The obtained data are as follows: Table 1: Friction coefficient data table

[0032] From the above table data and the change curve of Fig. 2 , Fig. 3 , it can be seen that the friction coefficient in the low temperature zone is basically stable, the friction coefficient in the medium temperature zone decays rapidly, and the friction coefficient in the high temperature zone tends to be stable or even slightly rises, which is consistent with the actual friction characteristics of high chromium cast iron material. Moreover, the friction coefficient values calculated by the theoretical calculation and the measured friction coefficient maintain a small error during the whole temperature rise process, indicating that the model not only can accurately describe the trend change of the friction coefficient, but also can reasonably cover the fluctuation range of the field measurement.

[0033] S3: Embedding the thermo-mechanical-tribological coupled UMAT subroutine into the finite element analysis platform, dynamically modifying the material hardness according to the transient temperature field of the friction pair surface, simulating the bidirectional coupling effect of friction heat accumulation and material performance degradation during braking.

[0034] The logic of the UMAT subroutine for dynamically modifying the material hardness according to the transient temperature field of the friction pair surface is as follows: According to the transient temperature field of the friction pair surface, the average temperature at this moment is calculated, and different material hardness calculation methods are used according to the average temperature: When the average temperature of the transient temperature field of the friction pair surface is The calculation method of the material hardness is: ; In the formula, represents the material hardness at this moment, represents the material hardness at the reference temperature, represents the material softening coefficient, represents the phase transition temperature.

[0035] Here, it represents the material hardness model in the low temperature zone, at this time, the material hardness decreases with temperature, reflecting the thermal softening effect of the material, suitable for the stable stage when the temperature does not exceed the phase transition point, where the material hardness can be measured by standard hardness test (such as Rockwell, Vickers hardness) at room temperature, and the material softening coefficient is obtained by high temperature hardness test, usually by testing hardness at different temperature gradients and linear fitting.

[0036] When the average temperature of the transient temperature field of the friction pair surface is The calculation method of the material hardness is: ; In the formula, represents the material hardness at the phase transition temperature, represents the material softening coefficient in the phase transition stage.

[0037] Here, it represents the material hardness model in the phase transition zone, considering the hardness mutation and subsequent softening caused by material phase transition or structural transformation, reflecting the complex nonlinear softening behavior of the material in the thermal phase transition zone, improving the physical rationality of the material performance simulation at high temperature, where the measurement method of the parameters is similar to the case of low temperature, the material hardness at the phase transition temperature is measured by high temperature hardness test, and the material softening coefficient in the phase transition stage is obtained by fitting the high temperature hardness data in the phase transition zone, usually showing different softening trends from the case of low temperature.

[0038] ​In traditional finite element simulation, material properties (such as hardness, elastic modulus) are usually assumed to be constant or discrete values divided by fixed temperature. In this embodiment, continuous dynamic hardness correction based on temperature field is realized through UMAT subroutine, which reflects the temperature-dependent performance change of the material in real time, makes the wear model and the mechanical model coupled with the temperature field, realizes two-way feedback, improves the simulation accuracy, makes the simulation model closer to the physical reality, and thus builds accurate thermal-mechanical-wear coupling data, which is used as the training input of PINN model in the subsequent process, helps the model learn the internal physical law of material performance degradation, and improves the prediction accuracy and generalization ability.

[0039] S4: Collect simulation data during finite element simulation to generate evolution curves of brake deceleration fluctuation, thermal-elastic instability critical temperature, and wear depth during elevator braking.

[0040] The generation logic of the evolution curves of brake deceleration fluctuation, thermal-elastic instability critical temperature, and wear depth during elevator braking is as follows: For the evolution curve of brake deceleration fluctuation: from the multi-body dynamics model of the elevator counterweight system, the acceleration-time curve during elevator braking is extracted, and then the main vibration frequency component is identified by fast Fourier transform, and the gradient of the evolution curve is calculated, and when the gradient exceeds the preset mutation threshold, it is determined as a resonance risk point.

[0041] Brake deceleration is a direct reflection of the response of the elevator counterweight system to braking force, and its fluctuation reflects the vibration state of the system during braking, including resonance of mechanical structure and impact load. Therefore, the evolution curve of brake deceleration fluctuation can reflect the dynamic vibration characteristics and stability during elevator braking. By analyzing the deceleration fluctuation, the resonance risk point that may cause equipment fatigue damage can be identified, and the abnormal vibration state of the mechanical system can be predicted.

[0042] For the evolution curve of thermal-elastic instability critical temperature: the highest temperature curve of the friction pair surface is output according to the surface transient temperature field of the elevator safety gear friction pair, the second derivative of the highest temperature with respect to time is calculated, and when the second derivative of the highest temperature with respect to time exceeds the preset thermal instability critical threshold, the surface area corresponding to the highest temperature is marked as a thermal instability zone. Thermal-elastic instability refers to the stress change caused by thermal expansion exceeding the stability threshold, leading to surface deformation or instability, which may cause accelerated wear or structural damage. Therefore, the evolution curve of thermal-elastic instability critical temperature can reflect the evolution of thermal-elastic instability risk of the friction pair surface caused by braking heat. By monitoring the highest temperature and its accelerated change, the thermal instability critical region and critical time can be accurately identified, thereby helping to improve the selection of heat-resistant materials and the design of heat dissipation structure.

[0043] For the evolution curve of the wear depth: first, the instantaneous wear rate of the elevator safety gear friction pair is calculated, then the instantaneous wear rate is integrated to obtain the wear depth, and finally the evolution curve of the wear depth is generated according to the change of the wear depth with time, wherein the calculation method of the instantaneous wear rate is: ; In the formula, represents the wear depth, represents the loss wear rate, represents the wear constant, represents the pressure of the safety gear friction pair contact surface, represents the speed of the elevator brake at the moment, represents the empirical coefficient.

[0044] The wear depth is a direct indicator of the cutting, peeling or deformation of the material surface, directly affecting the braking efficiency and service life of the safety gear, so the evolution curve of the wear depth is a direct indicator of the cutting, peeling or deformation of the material surface, directly affecting the braking efficiency and service life of the safety gear, reflecting the cumulative wear degree of the surface material of the friction pair over time, and through dynamic calculation of the wear depth, the degradation speed and remaining life of the friction pair can be quantitatively evaluated.

[0045] As can be seen from the calculation method of the instantaneous wear rate, it belongs to a modified form of the Archard wear model, and the expression of the Archard wear model is as follows: ; It can be seen that, compared with the traditional Archard wear model, the material hardness in this embodiment changes dynamically instead of being a fixed value, which can better simulate the process of wear intensifying caused by material performance degradation under real working conditions, so that the wear rate dynamically responds to the change of the braking speed, and is suitable for transient analysis of complex working conditions. At the same time, the empirical coefficient is introduced to reflect the nonlinear influence of pressure on the wear rate, which can be determined by expert experience or fitted by experiment, and is used to fit the complexity of the pressure effect under different materials and working conditions.

[0046] S5: Based on the physical information neural network model (PINN), the sensor data and simulation data are fused to generate a predicted value of the braking efficiency degradation rate, and a safety alarm is issued when the predicted value exceeds a preset safety threshold.

[0047] When constructing the physical information neural network, the braking deceleration fluctuation, the thermal elastic instability critical temperature, the evolution curve of the wear depth, and the equivalent value of the micro-damage data are taken as inputs, and the predicted value of the braking efficiency degradation rate is taken as output.

[0048] When constructing the physical information neural network, a physical constraint is embedded in the loss function, and its expression is: ; In the formula , represents the physical constraint term, , represents the predicted value of the brake performance degradation rate, , represents the thermal diffusivity of the material, , represents the normal coordinate of the safety gear friction pair.

[0049] The brake performance degradation rate is usually defined as the relative decline rate of the brake force or the brake response index of the brake system under certain conditions per unit time or per cycle, which is used to reflect the degradation trend of brake performance with factors such as wear, temperature rise, and mechanical fatigue. When obtaining the real brake performance degradation rate, the brake performance parameters can be recorded by repeatedly performing a certain number of brake cycles on the experimental bench of the elevator safety gear to simulate the real braking conditions, and the relative change rate of the brake force can be calculated to obtain the brake performance degradation rate.

[0050] As can be seen from the expression of the physical constraint, it is mainly used to reflect the relationship between the time variation rate of the performance degradation rate and the heat diffusion process in the material, that is, the dynamic change of performance degradation is driven and restricted by the heat diffusion (i.e. the spatial variation of the surface temperature of the friction pair), which can force the PINN prediction results to meet the physical laws similar to the heat diffusion equation, avoiding random fitting without physical basis.

[0051] The logic of the physical information neural network, which takes the brake deceleration fluctuation, the thermal elastic instability critical temperature, the evolution curve of the wear depth, and the equivalent value of the microscopic damage data as input, and the predicted value of the brake performance degradation rate as output, is as follows: First, after preprocessing such as normalization and time alignment of multi-dimensional data from various sources, the multi-input feature vectors of the PINN are formed, and the input data set with time series characteristics is formed; Then, based on the deep learning framework, a multi-layer feedforward network or recurrent network structure is designed, which is suitable for processing time series and multi-dimensional input. The network input layer receives the fused input features, the multi-layer hidden layer extracts the nonlinear features and potential physical laws in the input through the activation function, and the network output layer predicts the brake performance degradation rate at the corresponding time to describe the degradation trend of the performance over time; Then, a composite loss function is constructed, including data fitting loss, physical constraint, and regularization term, and its expression is: ; In the formula , represents the composite loss function, , respectively represent the data fitting loss and the regularization term, 、 represents a hyper parameter. Wherein the data fitting loss can be selected from a variety of different types of mathematical models such as mean square error, mean absolute error, Huber loss function, etc., the regularization term can be selected from a variety of different types of mathematical models such as L2 regularization, L1 regularization, etc., the specific selection of the two can be determined according to expert experience, and the hyper parameter can be determined according to expert experience to obtain an initial value, and then automatically optimized using Hyperopt, Optuna, Ray Tune, etc. Automatic hyper parameter optimization library for automatic optimization; Then the neural network is iteratively trained using optimization algorithms such as gradient descent, and in the training process, the network not only minimizes the prediction error, but also meets the physical constraints to ensure the physical reasonableness of the prediction results, and the derivative term is calculated through automatic differentiation technology to accurately evaluate the physical constraint loss, and the training data needs to cover multiple working conditions and states to improve the robustness and applicability of the model. After training, input new real-time or historical data, and output the predicted value of the brake efficiency degradation rate to describe the current and future performance degradation level of the safety clamp.

[0052] The above formulas are all dimensionless numerical calculations, and the formulas are obtained by software simulation of a large amount of data to obtain a formula of the latest real situation, and the preset parameters in the formula are set by a person skilled in the art according to the actual situation.

[0053] The above embodiments can be realized wholly or partially by software, hardware, firmware or any combination thereof. When realized by software, the above embodiments can be realized wholly or partially in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of the examples described in connection with the embodiments disclosed herein can be realized by electronic hardware or a combination of computer software and electronic hardware. Whether the functions are realized by hardware or software methods depends on the specific application and design constraints of the technical solutions.

[0054] The units described as separate components can or can not be physically separated, and the components shown as units can or can not be physical units, which can be located in one place or distributed on multiple network units. Part or all of the units can be selected to achieve the purpose of the embodiments according to actual needs.

[0055] The above is merely a specific implementation of the present application, but the protection scope of the present application is not limited thereto, and any person skilled in the art can easily think of changes or replacements within the technical scope disclosed in the present application, which should be covered within the protection scope of the present application.

Claims

1. A thermal coupling analysis method for elevator safety gear, characterized in that: The specific steps include: S1: Perform a braking test on the elevator to collect sensor data and micro-damage data of the friction pair of the safety gear during the braking process; S2: Construct a multi-body dynamics model of the elevator counterweight system including a time-varying friction coefficient to quantify the effect of braking shock load on the temperature rise of the friction pair surface; S3: Embed the thermal-mechanical-wear coupling UMAT subroutine in the finite element analysis platform to dynamically correct the material hardness according to the transient temperature field on the friction pair surface, simulating the bidirectional coupling effect of friction heat accumulation and material performance degradation during braking; S4: Collect simulation data during the finite element simulation process to generate evolution curves of brake deceleration fluctuation, thermoelastic instability critical temperature, and wear depth during elevator braking; S5: Based on the physical information neural network model, sensor data and simulation data are fused to generate a predicted value of the braking efficiency degradation rate, and a safety alarm is issued when the predicted value exceeds the preset safety threshold.

2. The thermal-mechanical coupling analysis method for an elevator safety clamp according to claim 1, characterized in that: In step S1, the sensor data includes: speed, acceleration, braking force, local temperature, and pressure; Micro damage data include: micro crack density and hard phase content reduction rate.

3. The thermal-mechanical coupling analysis method for an elevator safety clamp according to claim 2, characterized in that: The method for collecting the microscopic damage data is as follows: The SEM system is used to collect 3D point cloud data of the friction pair surface of the elevator safety gear. The length of each microcrack is calculated based on the 3D point cloud data, and the microcrack density is calculated based on the length of each microcrack and the scanning area of ​​the SEM system. Based on the XRD diffraction patterns, the characteristic peak intensities of the hard phase before and after the braking test of the elevator were identified, and the hard phase content reduction rate was calculated based on the characteristic peak intensities of the hard phase before and after the braking test. The microcrack density and hard phase content reduction rate are weighted to complete the quantification of micro damage data.

4. The thermal-mechanical coupling analysis method for an elevator safety clamp according to claim 3, characterized in that: The microcrack density is calculated as follows: ； In the formula represents the microcrack density, Indicates the Microcrack length, Indicates the scan area, subscript represents the microcrack index in the scanned area, Indicates the total number of microcracks in the scanned area; The calculation method of the hard phase content reduction rate is: ； In the formula represents the hard phase content reduction rate, 、 represent the characteristic peak intensities of the hard phase before and after the braking test, respectively; The calculation method for quantifying micro damage data is: ； In the formula represents the equivalent value of micro damage data, 、 Both represent calculation weights, both are greater than 0, and .

5. The thermal-mechanical coupling analysis method for an elevator safety clamp according to claim 1, characterized in that: In step S2, when constructing the multi-body dynamics model of the elevator counterweight system, a dynamic time-varying friction coefficient is used to replace the preset friction coefficient. The time-varying friction coefficient is calculated as follows: ； In the formula express The friction coefficient at time represents the initial friction coefficient, 、 represents the attenuation coefficient, Indicates that the friction surface of the elevator safety clamp is The average temperature at the time, represents the reference temperature, 、 Indicates the temperature threshold, 、 They respectively represent the maximum friction coefficient and minimum friction coefficient of the elevator friction pair at high temperature.

6. The thermal-mechanical coupling analysis method for an elevator safety gear according to claim 5, characterized in that: In step S3, the logic of the UMAT subroutine to dynamically correct the material hardness according to the transient temperature field on the friction pair surface is: The average temperature at that moment is calculated based on the transient temperature field of the friction pair surface, and different material hardness calculation methods are used according to the average temperature: When the average temperature of the transient temperature field on the friction pair surface When , the material hardness is calculated as: ； In the formula express The material hardness at the moment, Indicates the hardness of the material at the reference temperature, represents the material softening coefficient, represents the phase transition temperature; When the average temperature of the transient temperature field on the friction pair surface When , the material hardness is calculated as: ； In the formula Indicates the hardness of the material at the phase transition temperature, Indicates the material softening coefficient in the phase change stage.

7. The thermal-mechanical coupling analysis method for an elevator safety gear according to claim 6, characterized in that: In step S4, the generation logic of the evolution curves of the braking deceleration fluctuation, the thermoelastic instability critical temperature and the wear depth during the elevator braking process is as follows: For the evolution curve of braking deceleration fluctuations: From the multi-body dynamics model of the elevator counterweight system, the time-varying curve of the acceleration during the elevator braking process is extracted. Then, a fast Fourier transform is performed on it to identify the main vibration frequency components and calculate the gradient of the evolution curve. When the gradient exceeds the preset mutation threshold, it is determined to be a resonance risk point; For the evolution curve of the critical temperature of thermoelastic instability: the variation curve of the maximum temperature of the friction pair surface is output based on the transient temperature field of the elevator safety gear friction pair surface, and the second-order derivative of the maximum temperature with respect to time is calculated. When the second-order derivative of the maximum temperature with respect to time exceeds the preset critical threshold of thermal instability, the surface area corresponding to the maximum temperature is marked as the thermal instability zone; For the evolution curve of wear depth: first calculate the instantaneous wear rate of the friction pair of the elevator safety gear, then integrate the instantaneous wear rate to obtain the wear depth, and finally generate the evolution curve of wear depth according to the change of wear depth over time. The calculation method of instantaneous wear rate is: ； In the formula Indicates the wear depth, represents the loss wear rate, represents the wear constant, Indicates the pressure on the contact surface of the friction pair of the safety gear. express The braking speed of the elevator at time Represents the empirical coefficient.

8. The thermal-mechanical coupling analysis method for an elevator safety gear according to claim 7, characterized in that: In step S5, when constructing the physical information neural network, the evolution curve of the braking deceleration fluctuation, the critical temperature of thermoelastic instability, the wear depth, and the equivalent value of the micro damage data are used as input, and the predicted value of the braking performance degradation rate is used as output.

9. The thermal-mechanical coupling analysis method for an elevator safety clamp according to claim 8, characterized in that: In step S5, when constructing the physical information neural network, physical constraints are embedded in the loss function, and its expression is: ； In the formula represents the physical constraint term, Represents the predicted value of braking efficiency degradation rate, represents the thermal diffusivity of the material, Represents the normal coordinate of the friction pair of the safety gear.

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