Intelligent axle monitoring system

Abstract

The invention provides an intelligent axle monitoring system, which relates to the technical field of automobile monitoring and comprises a monitoring point selection module, a data acquisition module, a current damage determination module, a residual life calculation module, a model training module and an alarm module. The monitoring point selection module screens the monitoring point set to obtain a plurality of monitoring point positions; the data acquisition module acquires a current stress sequence at a plurality of monitoring point positions; the current damage determination module is used for processing the current stress sequence to obtain the current damage of each monitoring point position; the remaining life calculation module processes the current damage and the historical damage through an RBF model to obtain the remaining life of the axle; the model training module is used for training a genetic algorithm and an RBF model, and the genetic algorithm and the RBF model are obtained through joint training; the alarm module is used for giving an alarm when the residual life does not meet the safety threshold value; the accuracy of axle life prediction is improved, the axle life condition is more accurately obtained, and driving accidents are avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of automobile monitoring, and in particular to an intelligent monitoring system for vehicle axles. Background Art

[0002] The axle is an important part of the automobile chassis. It is used to connect the left and right wheels of the vehicle and support the weight of the vehicle body. It is one of the core components of the automobile. From the perspective of location, the axle can be divided into the front axle and the rear axle; from the perspective of function, the axle can be divided into the driving axle, the driven axle and the supporting axle. Modern automobile axle monitoring technology mainly relies on a variety of sensors (such as strain gauges, acceleration sensors, temperature sensors and lubricant quality sensors, etc.) to monitor the axle in all directions. The system monitors key parameters such as axle load, vibration frequency, temperature change and stress distribution through real-time data acquisition and wireless transmission. An intelligent analysis system is used for fault warning, life evaluation and abnormality detection, and real-time monitoring and management are achieved through on-board display and remote monitoring platform. However, the existing technology often focuses on the maximum bearing point of the axle when evaluating the life of the axle, while ignoring the damage of other parts. As an integral component, the axle also has the risk of damage in other parts. Therefore, only the bearing point is monitored and analyzed, which makes the axle monitoring blind area too large, and the life prediction is too different from the actual life of the axle.

[0003] In view of this, the present application proposes an intelligent monitoring system for vehicle axles, which monitors multiple points on the vehicle axle and processes the monitoring data of multiple points to obtain the remaining life of the vehicle axle, thereby improving the accuracy of the axle life prediction, making it possible to more accurately obtain the life of the vehicle axle, and to give feedback in time based on the predicted life to avoid traffic accidents. Summary of the invention

[0004] The object of the present invention is to provide an intelligent monitoring system for a vehicle bridge, comprising a monitoring point selection module, a data acquisition module, a current damage determination module, a remaining life calculation module, a model training module and an alarm module; the monitoring point selection module screens a monitoring point set through a genetic algorithm to obtain a plurality of monitoring points; the data acquisition module comprises a plurality of stress sensors, and the stress sensors are arranged at the plurality of monitoring points to obtain the current stress sequence at the plurality of monitoring points; the current damage determination module is used to process the current stress sequence to obtain the current damage of each monitoring point; the remaining life calculation module processes the current damage and historical damage of the plurality of monitoring points through an RBF (Radial Basis Function) neural network) model to obtain the remaining life of the vehicle bridge; the model training module is used to train the genetic algorithm and the RBF model, and the genetic algorithm and the RBF model are obtained through joint training, and the purpose of the joint training is to maximize the objective function, and the objective function is: ;in, represents the objective function; , and Respectively represent the first target parameter, the second target parameter and the third target parameter; i represents the monitoring point variable; I represents the total number of monitoring points; represents the remaining lifespan output by the RBF model obtained using the previous round of training; t represents the time variable of the acquired monitoring point; T represents the total number of time points of the data at the acquired monitoring point; represents the actual remaining life at time point t; It represents the predicted remaining life at time point t output by the RBF model obtained in the current round of training; represents the weight of the jth RBF neuron; the alarm module is used to issue an alarm when the remaining life does not meet the safety threshold.

[0005] Furthermore, the monitoring point selection module includes a construction unit, a screening unit and a loop unit; the construction unit constructs multiple monitoring point selection vectors based on the monitoring point set; the monitoring point selection vector is used to represent the selection of monitoring points in the monitoring point set; the screening unit calculates the fitness value of each of the monitoring point selection vectors, and determines multiple target monitoring point selection vectors; the fitness value is related to the sensitivity of the monitoring point to the remaining life; the loop unit is used to process the target monitoring point selection vector to obtain multiple new monitoring point selection vectors, and repeat the screening operation until the fitness value converges.

[0006] Furthermore, the fitness function for solving the fitness value is: ;in, represents the fitness value; i represents the monitoring point variable; I represents the total number of monitoring points in the monitoring point set; Indicates the selection of the i-th monitoring point; Indicates the position of the i-th monitoring point on the bridge; Indicates the remaining life of the axle; Indicates the sensitivity of the monitoring point to the remaining life.

[0007] Furthermore, the current damage determination module includes a stress sequence decomposition unit, a cycle number determination unit, an equivalent stress calculation unit and a current damage determination unit; the stress sequence decomposition unit is used to process the current stress sequence to obtain a stress cycle set and cycle characteristics of each stress cycle; the cycle characteristics include stress amplitude, average stress and total cycle number; the cycle number determination unit determines the current cycle number at each stress level based on the stress amplitude; the equivalent stress calculation unit determines the equivalent stress based on the current cycle number and the stress level; the current damage determination unit determines the current damage based on the equivalent stress and the current cycle number.

[0008] Furthermore, the current stress sequence is processed by a rainflow counting method to obtain the stress cycle set and the cycle characteristics.

[0009] Further, the stress range of each stress level is: ; ; ;in, represents the stress of the kth stress level; and They represent the minimum and maximum values ​​of the kth level stress respectively; represents the stress value when the vehicle is unloaded; k represents the stress level variable; Indicates the total number of stress levels; Indicates the stress value when the vehicle is fully loaded; the calculation formula of the equivalent stress is: ;in, represents the equivalent stress of the kth level stress; represents the actual magnitude of the kth stress; represents the average stress of the kth level stress; the current damage is: ;in, Indicates current damage; Indicates the actual number of cycles of the kth stress level; represents the first material parameter; m represents the second material parameter.

[0010] Furthermore, the remaining life is: ; Where L represents the predicted remaining life; j represents the hidden layer node variable; J represents the number of hidden layer nodes; represents the weight of the jth RBF neuron; exp represents the exponential function; i represents the monitoring point variable; I represents the total number of monitoring points; Indicates the impact factor of each monitoring point on the remaining life; represents the damage of the i-th monitoring point at the t-th time point; represents the i-th component of the j-th RBF center point; Represents the expansion constant.

[0011] Furthermore, the model training module also includes a training acceleration unit, which includes an initial model determination layer, a genetic algorithm update layer, an RBF model update layer, a first cycle layer, an update acceleration layer and a second cycle layer; the initial model determination layer determines the initial RBF model based on the initial monitoring point influence factor and the initial neuron weight; the genetic algorithm update layer updates the initial genetic algorithm based on the initial RBF model to obtain a first genetic algorithm and a first monitoring point influence factor; the RBF model update layer updates the initial RBF model based on the first monitoring point influence factor to obtain a first RBF model and a first neuron weight; the first cycle layer is used to repeat the update operation of the genetic algorithm and the RBF model until multiple monitoring point influence factors and multiple neuron weights are obtained; the update acceleration layer is used to process the multiple monitoring point influence factors and the multiple neuron weights to obtain accelerated monitoring point influence factors and accelerated neuron weights; the second cycle layer replaces the initial monitoring point influence factors and the initial neuron weights with the accelerated monitoring point influence factors and the accelerated neuron weights respectively, and repeats the update operation of the genetic algorithm and the RBF model until the fitness value of the genetic algorithm converges.

[0012] Furthermore, the multiple monitoring point influencing factors and the multiple neuron weights are three, and the obtained acceleration monitoring point influencing factors and acceleration neuron weights are: ;in, represents the impact factor of the accelerated monitoring point; represents the acceleration neuron weight; , and Respectively represent the influencing factors of the third, second and first monitoring points, and the fitness values ​​of the influencing factors of the third, second and first monitoring points decrease in sequence; , and They represent the third, second and first neuron weights respectively, and the objective function values ​​corresponding to the third, second and first neuron weights decrease in sequence.

[0013] Furthermore, the updated acceleration layer also determines an acceleration objective function value based on the acceleration monitoring point influence factor and the acceleration neuron weight, and compares the acceleration objective function value with a third objective function value; when the acceleration objective function value is greater than or equal to the third objective function value, the second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight respectively with the acceleration monitoring point influence factor and the acceleration neuron weight; otherwise, the second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight respectively with the third monitoring point influence factor and the third neuron weight.

[0014] The technical solution of the embodiment of the present invention has at least the following advantages and beneficial effects:

[0015] The present invention screens the monitoring point set through a genetic algorithm to obtain multiple monitoring points, which can obtain the spatial variation of the stress of the axle and reflect the overall state of the axle with a certain number of monitoring points, avoiding the uncertainty caused by human selection.

[0016] The present invention processes damage at multiple time points through the RBF model, can obtain the temporal variation of the axle life, and determine the remaining life of the axle according to the potential connection of the damage at multiple time points, thereby improving the prediction accuracy of the axle life.

[0017] The model training module of the present invention updates the genetic algorithm and the RBF model through joint training, which can link the vehicle bridge in time and space, and updates the RBF model through the results of the genetic algorithm, and then updates the genetic algorithm through the updated results of the RBF model, so that the RBF model can better process the results output by the genetic algorithm, optimize resource allocation, reduce the noise of data transmission and processing between models, and improve prediction accuracy.

[0018] The training acceleration unit provided by the present invention can increase the speed of model training, reduce the number of training times, and reduce energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] Figure 1 An exemplary schematic diagram of an intelligent monitoring system for vehicle axles provided by the present invention;

[0020] Figure 2 An exemplary schematic diagram of a monitoring point selection module provided by the present invention;

[0021] Figure 3 An exemplary schematic diagram of a current damage determination module provided by the present invention;

[0022] Figure 4 An exemplary schematic diagram of a training acceleration unit provided by the present invention;

[0023] Icons: 1- monitoring point selection module, 2- data acquisition module, 3- current damage determination module, 4- remaining life calculation module, 5- model training module, 6- alarm module, 11- construction unit, 12- screening unit, 13- cycle unit, 31- stress sequence decomposition unit, 32- number of cycles determination unit, 33- equivalent stress calculation unit, 34- current damage determination unit, 311- initial model determination layer, 312- genetic algorithm update layer, 313- RBF model update layer, 314- first cycle layer, 315- update acceleration layer, 316- second cycle layer. DETAILED DESCRIPTION

[0024] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0025] Figure 1 FIG. 1 is an exemplary schematic diagram of an intelligent axle monitoring system provided by the present invention. Figure 1 As shown, the intelligent monitoring system for axles provided by the present invention includes a monitoring point selection module 1, a data acquisition module 2, a current damage determination module 3, a remaining life calculation module 4, a model training module 5 and an alarm module 6.

[0026] The monitoring point selection module 1 selects the monitoring point set through the genetic algorithm to obtain multiple monitoring point positions. The monitoring point refers to the point where the vehicle axle is monitored. The monitoring point can be set at the position of the leaf spring seat, bridge housing, axle tube, bearing seat and brake camshaft bracket. The monitoring point position refers to the location of the monitoring point. For more information about the monitoring point selection module, see Figure 2 and its related description.

[0027] The data acquisition module 2 includes a plurality of stress sensors, and the stress sensors are arranged at the plurality of monitoring points to obtain the current stress sequence at the plurality of monitoring points. The current stress sequence may refer to the stress values ​​obtained in the current cycle and arranged in chronological order. Each monitoring point corresponds to a current stress sequence.

[0028] The current damage determination module 3 is used to process the current stress sequence to obtain the current damage of each monitoring point. The current damage may refer to the damage value of the current axle. For more information about the current damage determination module, see Figure 3 and its related description.

[0029] The remaining life calculation module 4 processes the current damage and historical damage of the multiple monitoring points through the RBF model to obtain the remaining life of the axle. The historical damage refers to the damage value of the axle obtained in the past. The remaining life refers to the time or mileage that the axle can still be used. The remaining life is:

[0030] ;

[0031] Where L represents the predicted remaining life; j represents the hidden layer node variable; J represents the number of hidden layer nodes; represents the weight of the jth RBF neuron; exp represents the exponential function; i represents the monitoring point variable; I represents the total number of monitoring points; Indicates the impact factor of each monitoring point on the remaining life; represents the damage of the i-th monitoring point at the t-th time point; represents the i-th component of the j-th RBF center point; Represents the expansion constant.

[0032] The model training module 5 is used to train the genetic algorithm and the RBF model. The genetic algorithm and the RBF model are obtained through joint training. The purpose of the joint training is to maximize the objective function. The objective function is:

[0033] ;

[0034] in, represents the objective function; , and Respectively represent the first target parameter, the second target parameter and the third target parameter; i represents the monitoring point variable; I represents the total number of monitoring points; represents the remaining lifespan output by the RBF model obtained using the previous round of training; t represents the time variable of the acquired monitoring point; T represents the total number of time points of the data at the acquired monitoring point; represents the actual remaining life at time point t; It represents the predicted remaining life at time point t output by the RBF model obtained in the current round of training; Represents the weight of the jth RBF neuron. For more information about the model training module, see Figure 4 and its related description.

[0035] The alarm module is used to sound an alarm when the remaining life does not meet a safety threshold. The safety threshold may refer to a preset minimum remaining life value. For example, an alarm is sounded when the remaining life is less than 1 week.

[0036] Figure 2This is an exemplary schematic diagram of the monitoring point selection module provided by the present invention. Figure 2 As shown, the monitoring point selection module 1 includes a construction unit 11, a screening unit 12 and a circulation unit 13.

[0037] The construction unit constructs a plurality of monitoring point selection vectors based on the monitoring point set; the monitoring point selection vectors are used to indicate the selection of monitoring points in the monitoring point set, where a selected value is 1 and a non-selected value is 0.

[0038] The screening unit calculates the fitness value of each of the monitoring point selection vectors and determines a plurality of target monitoring point selection vectors; the fitness value is related to the sensitivity of the monitoring point to the remaining life. The monitoring point selection vectors are sorted according to the fitness value, and the monitoring point selection vectors with a preset number or a fitness value greater than a preset fitness threshold are used as target monitoring point selection vectors. The fitness function for solving the fitness value is:

[0039] ;

[0040] in, represents the fitness value; i represents the monitoring point variable; I represents the total number of monitoring points in the monitoring point set; Indicates the selection of the i-th monitoring point; Indicates the position of the i-th monitoring point on the bridge; Indicates the remaining life of the axle; Indicates the sensitivity of the monitoring point to the remaining life.

[0041] The loop unit is used to process the target monitoring point selection vector to obtain multiple new monitoring point selection vectors, and repeat the screening operation until the fitness value converges. The fitness value convergence may refer to the fitness difference between the individual with the best fitness value obtained in the previous cycle and the individual with the best fitness value obtained in the current cycle being less than a preset cycle difference threshold. The preset cycle difference threshold refers to a preset threshold condition for terminating the genetic algorithm cycle. The condition for terminating the cycle may also be in other forms, for example, the cycle reaches a maximum number of cycles.

[0042] Figure 3 FIG. 5 is an exemplary schematic diagram of a current damage determination module provided by the present invention. Figure 3 As shown, the current damage determination module 3 includes a stress sequence decomposition unit 31 , a cycle number determination unit 32 , an equivalent stress calculation unit 33 and a current damage determination unit 34 .

[0043] The stress sequence decomposition unit is used to process the current stress sequence to obtain a stress cycle set and cycle characteristics of each stress cycle; the cycle characteristics include stress amplitude, average stress and total number of cycles. In some embodiments, the current stress sequence can be processed by a rain flow counting method to obtain the stress cycle set and the cycle characteristics. A stress cycle may refer to a cyclic pattern in which stress changes over time when the axle is subjected to static and dynamic loads of the vehicle. Stress amplitude refers to the maximum amplitude of stress change in a stress cycle. Average stress refers to the average value of stress in a stress cycle. The total number of cycles refers to the total number of stress cycles in the stress cycle set.

[0044] The cycle number determination unit determines the current cycle number at each stress level based on the stress amplitude. The current cycle number may refer to the number of stress cycles at each stress level in the current sampling period. The stress range of each stress level is:

[0045] ;

[0046] ;

[0047] ;

[0048] in, represents the stress of the kth stress level; and They represent the minimum and maximum values ​​of the kth level stress respectively; represents the stress value when the vehicle is unloaded; k represents the stress level variable; Indicates the total number of stress levels; Indicates the stress value when the vehicle is fully loaded.

[0049] The equivalent stress calculation unit determines the equivalent stress based on the current number of cycles and the stress level. The calculation formula of the equivalent stress is:

[0050] ;

[0051] in, represents the equivalent stress of the kth level stress; represents the actual magnitude of the kth stress; Represents the average stress of the kth level stress.

[0052] The current damage determination unit determines the current damage based on the equivalent stress and the current number of cycles. The current damage is:

[0053] ;

[0054] in, Indicates current damage; Indicates the actual number of cycles of the kth stress level; represents the first material parameter; m represents the second material parameter.

[0055] Figure 4 The exemplary schematic diagram of the training acceleration unit provided by the present invention. The model training module also includes a training acceleration unit, such as Figure 4 As shown, the training acceleration unit includes an initial model determination layer 311 , a genetic algorithm update layer 312 , an RBF model update layer 313 , a first circulation layer 314 , an update acceleration layer 315 and a second circulation layer 316 .

[0056] The initial model determination layer determines the initial RBF model based on the initial monitoring point influence factor and the initial neuron weight. The initial monitoring point influence factor and the initial neuron weight are obtained by initializing the model. The initial RBF model refers to the RBF model obtained by initializing the initial monitoring point influence factor and the initial neuron weight.

[0057] The genetic algorithm updating layer updates the initial genetic algorithm based on the initial RBF model to obtain a first genetic algorithm and a first monitoring point influencing factor.

[0058] The RBF model updating layer updates the initial RBF model based on the first monitoring point influencing factor to obtain a first RBF model and a first neuron weight.

[0059] The first loop layer is used to repeat the update operation of the genetic algorithm and the RBF model until multiple monitoring point influencing factors and multiple neuron weights are obtained;

[0060] The updating acceleration layer is used to process the multiple monitoring point influencing factors and the multiple neuron weights to obtain the accelerated monitoring point influencing factors and the accelerated neuron weights. The multiple monitoring point influencing factors and the multiple neuron weights are three, and the obtained accelerated monitoring point influencing factors and the accelerated neuron weights are:

[0061] ;

[0062] in, represents the impact factor of the accelerated monitoring point; represents the acceleration neuron weight; , and Respectively represent the influencing factors of the third, second and first monitoring points, and the fitness values ​​of the influencing factors of the third, second and first monitoring points decrease in sequence; , and They represent the third, second and first neuron weights respectively, and the objective function values ​​corresponding to the third, second and first neuron weights decrease in sequence.

[0063] The second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight with the acceleration monitoring point influence factor and the acceleration neuron weight respectively, and repeats the updating operation of the genetic algorithm and the RBF model until the fitness value of the genetic algorithm converges.

[0064] The update acceleration layer also determines the acceleration objective function value based on the acceleration monitoring point influence factor and the acceleration neuron weight, and compares the acceleration objective function value with the third objective function value; when the acceleration objective function value is greater than or equal to the third objective function value, the second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight respectively with the acceleration monitoring point influence factor and the acceleration neuron weight; otherwise, the second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight respectively with the third monitoring point influence factor and the third neuron weight. The acceleration objective function value refers to the objective function value calculated by the objective function based on the acceleration monitoring point influence factor and the acceleration neuron weight. The third objective function value refers to the objective function value calculated by the objective function based on the third monitoring point influence factor and the third neuron weight.

[0065] The above are only preferred embodiments of the present invention and are not intended to limit the present invention. For those skilled in the art, the present invention may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. An intelligent monitoring system for vehicle axles, characterized in that: It includes monitoring point selection module, data acquisition module, current damage determination module, remaining life calculation module, model training module and alarm module; The monitoring point selection module screens the monitoring point set through a genetic algorithm to obtain a plurality of monitoring points; The data acquisition module includes a plurality of stress sensors, and the stress sensors are arranged at the plurality of monitoring points to obtain the current stress sequences at the plurality of monitoring points; The current damage determination module is used to process the current stress sequence to obtain the current damage of each monitoring point; The remaining life calculation module processes the current damage and historical damage of the multiple monitoring points through the RBF model to obtain the remaining life of the axle; The model training module is used to train the genetic algorithm and the RBF model. The genetic algorithm and the RBF model are obtained through joint training. The purpose of the joint training is to maximize the objective function. The objective function is: ； in, represents the objective function; , and Respectively represent the first target parameter, the second target parameter and the third target parameter; i represents the monitoring point variable; I represents the total number of monitoring points; represents the remaining lifespan output by the RBF model obtained using the previous round of training; t represents the time variable of the acquired monitoring point; T represents the total number of time points of the data at the acquired monitoring point; represents the actual remaining life at time point t; It represents the predicted remaining life at time point t output by the RBF model obtained in the current round of training; represents the weight of the jth RBF neuron; The alarm module is used to issue an alarm when the remaining life does not meet the safety threshold.

2. The intelligent monitoring system for vehicle axles according to claim 1, characterized in that: The monitoring point selection module includes a construction unit, a screening unit and a circulation unit; The construction unit constructs a plurality of monitoring point selection vectors based on the monitoring point set; the monitoring point selection vectors are used to represent the selection of monitoring points in the monitoring point set; The screening unit calculates the fitness value of each of the monitoring point selection vectors and determines a plurality of target monitoring point selection vectors; the fitness value is related to the sensitivity of the monitoring point to the remaining life; The cyclic unit is used to process the target monitoring point selection vector to obtain multiple new monitoring point selection vectors, and repeat the screening operation until the fitness value converges.

3. The intelligent axle monitoring system according to claim 2, characterized in that: The fitness function for solving the fitness value is: ； in, represents the fitness value; i represents the monitoring point variable; I represents the total number of monitoring points in the monitoring point set; Indicates the selection of the i-th monitoring point; Indicates the position of the i-th monitoring point on the bridge; Indicates the remaining life of the axle; Indicates the sensitivity of the monitoring point to the remaining life.

4. The intelligent monitoring system for vehicle axles according to claim 1, characterized in that: The current damage determination module includes a stress sequence decomposition unit, a cycle number determination unit, an equivalent stress calculation unit and a current damage determination unit; The stress sequence decomposition unit is used to process the current stress sequence to obtain a stress cycle set and cycle characteristics of each stress cycle; the cycle characteristics include stress amplitude, average stress and total number of cycles; The cycle number determination unit determines the current cycle number at each stress level based on the stress amplitude; The equivalent stress calculation unit determines the equivalent stress based on the current number of cycles and the stress level; The current damage determination unit determines the current damage based on the equivalent stress and the current number of cycles.

5. The intelligent monitoring system for vehicle axles according to claim 4, characterized in that: The current stress sequence is processed by a rainflow counting method to obtain the stress cycle set and the cycle characteristics.

6. The intelligent axle monitoring system according to claim 4, characterized in that: The stress range of each stress level is: ; ; ; in, represents the stress of the kth stress level; and They represent the minimum and maximum values ​​of the kth level stress respectively; represents the stress value when the vehicle is unloaded; k represents the stress level variable; Indicates the total number of stress levels; Indicates the stress value when the vehicle is fully loaded; The calculation formula of the equivalent stress is: ； in, represents the equivalent stress of the kth level stress; represents the actual magnitude of the kth stress; represents the average stress of the kth level stress; The current damage is: ； in, Indicates current damage; Indicates the actual number of cycles of the kth stress level; represents the first material parameter; m represents the second material parameter.

7. The intelligent axle monitoring system according to claim 1, characterized in that: The remaining life is: ； Where L represents the predicted remaining life; j represents the hidden layer node variable; J represents the number of hidden layer nodes; represents the weight of the jth RBF neuron; exp represents the exponential function; i represents the monitoring point variable; I represents the total number of monitoring points; Indicates the impact factor of each monitoring point on the remaining life; represents the damage of the i-th monitoring point at the t-th time point; represents the i-th component of the j-th RBF center point; Represents the expansion constant.

8. The intelligent axle monitoring system according to claim 1, characterized in that: The model training module also includes a training acceleration unit, which includes an initial model determination layer, a genetic algorithm update layer, an RBF model update layer, a first circulation layer, an update acceleration layer, and a second circulation layer; The initial model determination layer determines the initial RBF model based on the initial monitoring point influencing factors and initial neuron weights; The genetic algorithm update layer updates the initial genetic algorithm based on the initial RBF model to obtain a first genetic algorithm and a first monitoring point influencing factor; The RBF model updating layer updates the initial RBF model based on the first monitoring point influencing factor to obtain a first RBF model and a first neuron weight; The first circulation layer is used to repeat the updating operation of the genetic algorithm and the RBF model until multiple monitoring point influencing factors and multiple neuron weights are obtained; The update acceleration layer is used to process the multiple monitoring point influence factors and the multiple neuron weights to obtain accelerated monitoring point influence factors and accelerated neuron weights; The second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight with the acceleration monitoring point influence factor and the acceleration neuron weight respectively, and repeats the updating operation of the genetic algorithm and the RBF model until the fitness value of the genetic algorithm converges.

9. The intelligent axle monitoring system according to claim 8, characterized in that: The number of the multiple monitoring point influencing factors and the number of the multiple neuron weights is three, and the obtained acceleration monitoring point influencing factors and acceleration neuron weights are: ； in, represents the impact factor of the accelerated monitoring point; represents the acceleration neuron weight; , and Respectively represent the influencing factors of the third, second and first monitoring points, and the fitness values ​​of the influencing factors of the third, second and first monitoring points decrease in sequence; , and They represent the third, second and first neuron weights respectively, and the objective function values ​​corresponding to the third, second and first neuron weights decrease in sequence.

10. The intelligent axle monitoring system according to claim 9, characterized in that: The update acceleration layer further determines an acceleration objective function value based on the acceleration monitoring point influence factor and the acceleration neuron weight, and compares the acceleration objective function value with a third objective function value; When the acceleration objective function value is greater than or equal to the third objective function value, the second circulation layer replaces the initial monitoring point influence factor and the initial neuron weight respectively with the acceleration monitoring point influence factor and the acceleration neuron weight; Otherwise, the second recurrent layer replaces the initial monitoring point influence factor and the initial neuron weight with the third monitoring point influence factor and the third neuron weight respectively.

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