CN117656716A - Non-pneumatic tire health monitoring method and system based on improved time series model - Google Patents
Non-pneumatic tire health monitoring method and system based on improved time series model Download PDFInfo
- Publication number
- CN117656716A CN117656716A CN202311627566.2A CN202311627566A CN117656716A CN 117656716 A CN117656716 A CN 117656716A CN 202311627566 A CN202311627566 A CN 202311627566A CN 117656716 A CN117656716 A CN 117656716A
- Authority
- CN
- China
- Prior art keywords
- model
- pneumatic tire
- time series
- order
- health monitoring
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
- 238000000034 method Methods 0.000 title claims abstract description 60
- 230000036541 health Effects 0.000 title claims abstract description 51
- 238000012544 monitoring process Methods 0.000 title claims abstract description 40
- 230000006378 damage Effects 0.000 claims abstract description 49
- 238000012545 processing Methods 0.000 claims abstract description 18
- 230000001133 acceleration Effects 0.000 claims abstract description 15
- 241001123248 Arma Species 0.000 claims description 16
- 230000008569 process Effects 0.000 claims description 14
- 230000006870 function Effects 0.000 claims description 12
- 238000004590 computer program Methods 0.000 claims description 7
- 230000008859 change Effects 0.000 claims description 6
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000003860 storage Methods 0.000 claims description 5
- 238000004088 simulation Methods 0.000 claims description 4
- 238000013016 damping Methods 0.000 claims description 3
- 238000010606 normalization Methods 0.000 claims description 3
- 230000009466 transformation Effects 0.000 claims description 3
- 230000005856 abnormality Effects 0.000 abstract description 4
- 230000015556 catabolic process Effects 0.000 abstract description 4
- 238000006731 degradation reaction Methods 0.000 abstract description 4
- 230000000694 effects Effects 0.000 abstract description 2
- 238000005311 autocorrelation function Methods 0.000 description 7
- 208000027418 Wounds and injury Diseases 0.000 description 6
- 208000014674 injury Diseases 0.000 description 6
- 238000005520 cutting process Methods 0.000 description 5
- YHXISWVBGDMDLQ-UHFFFAOYSA-N moclobemide Chemical compound C1=CC(Cl)=CC=C1C(=O)NCCN1CCOCC1 YHXISWVBGDMDLQ-UHFFFAOYSA-N 0.000 description 5
- 238000009826 distribution Methods 0.000 description 3
- 230000004044 response Effects 0.000 description 3
- 238000003491 array Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 2
- 230000008901 benefit Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000005540 biological transmission Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 230000001747 exhibiting effect Effects 0.000 description 1
- 239000000945 filler Substances 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 239000013307 optical fiber Substances 0.000 description 1
- 238000005457 optimization Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000035945 sensitivity Effects 0.000 description 1
- 208000037974 severe injury Diseases 0.000 description 1
- 230000009528 severe injury Effects 0.000 description 1
- 238000012360 testing method Methods 0.000 description 1
Classifications
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T90/00—Enabling technologies or technologies with a potential or indirect contribution to GHG emissions mitigation
Landscapes
- Tires In General (AREA)
Abstract
The invention discloses a non-pneumatic tire health monitoring method and system based on an improved time sequence model, and relates to the technical field of intelligent non-pneumatic tires, wherein the method comprises the steps of measuring radial acceleration signals of the intelligent non-pneumatic tire under different local injuries, and carrying out differential processing on sample data; establishing an autoregressive integral moving average model for the preprocessed data sample; and establishing a damage sensitive factor according to the first-order coefficient of the AR term, and identifying the health state of the intelligent non-pneumatic tire based on the damage sensitive factor. The non-pneumatic tire health monitoring method based on the improved time sequence model provided by the invention can be used for timely finding out abnormality or monitoring the health state in the initial stage of non-pneumatic tire performance degradation through the improved time sequence model, so that the safety and reliability of a vehicle are improved, and compared with the traditional method, the method is more accurate and reliable, and the method has better effects on the safety, reliability and accuracy.
Description
Technical Field
The invention relates to the technical field of intelligent non-pneumatic tires, in particular to a non-pneumatic tire health monitoring method based on an improved time sequence model.
Background
The tyre is an important component of the chassis of the automobile, is the only medium for the automobile to contact with the road surface and generate interaction, and has important influence on the safety, stability, economy, comfort and the like of the whole automobile. The traditional pneumatic tire has potential safety hazards such as puncture, air leakage, tire burst and the like, and seriously threatens the running safety and reliability of the vehicle. To ameliorate or overcome the shortcomings of conventional pneumatic tires, large tire manufacturers and research institutes have increasingly placed on the development of non-pneumatic tire technology. The non-pneumatic tire uses the elastic support body or the filler to replace the pneumatic structure, thereby fundamentally overcoming the defects of easy puncture, tire burst and the like of the pneumatic tire. However, in the service process, the integral and key component performances inevitably degrade due to the influences of factors such as stress concentration, high temperature, variable load, variable working condition, large disturbance and impact, and once the final failure is caused by the component performance degradation, the safety of the vehicle is still greatly influenced.
However, whether pneumatic or non-pneumatic, it is still a "passive" component in the vehicle itself, and the deformation, stress and health of the tire, the relationship between the tire and the road, etc. are unknown during the running of the vehicle. If the method can discover abnormality or quantitatively evaluate the health state of the non-pneumatic tire in time and predict the residual life of the non-pneumatic tire according to the state monitoring information at the initial stage of performance degradation of the non-pneumatic tire, particularly when the non-pneumatic tire has not greatly influenced the performance of the vehicle, the method has important significance for practically guaranteeing the running safety, reliability and economy of the automatic driving vehicle.
The elastic spokes in the non-pneumatic tire are force transmission media of the hub and the tread, and bear tensile and compressive loads in the actual running process. Localized damage to the spoke structure can directly affect the dynamic response of the tire. By monitoring the running state of the non-pneumatic tire, the state characteristic quantity of the tire at different moments can be obtained. Since the value of the characteristic quantity at a certain moment has a physical correlation with the data measured before, a time series model can be established from the value, and the abnormality or damage of the structure can be excavated according to various changes of model coefficients or residual errors.
Disclosure of Invention
The present invention has been made in view of the above-described problems.
Therefore, the technical problems solved by the invention are as follows: the existing non-pneumatic tires have high limitation, lack of health monitoring methods and optimization problems of how to identify the health state of the tire by using coefficient changes of a time series model.
In order to solve the technical problems, the invention provides the following technical scheme: the non-pneumatic tire health monitoring method based on the improved time sequence model comprises measuring radial acceleration signals of the intelligent non-pneumatic tire under different local injuries, and carrying out differential processing on sample data; establishing an autoregressive integral moving average model for the preprocessed data sample; and establishing a damage sensitive factor according to the first-order coefficient of the AR term, and identifying the health state of the intelligent non-pneumatic tire based on the damage sensitive factor.
As a preferable mode of the non-pneumatic tire health monitoring method based on the improved time series model according to the invention, wherein: the differential processing includes setting A (t) as a radial acceleration signal of the intelligent non-pneumatic tire at time t, and the differential operation is expressed as:
ΔA(t)＝A(t)-A(t-τ)
where τ is the time differential interval, α is the multiplication factor, and ΔA (i) is the radial acceleration signal of the intelligent non-pneumatic tire at time i.
As a preferable mode of the non-pneumatic tire health monitoring method based on the improved time series model according to the invention, wherein: the establishing an autoregressive integral moving average model comprises an AR model, an MA model and a difference method, wherein the ARMA model combined with difference operation is an autoregressive integral moving average model, AR items are used for predicting the past value of the next value and are defined by parameters p in the autoregressive integral moving average model, MA items are used for predicting the number of past prediction errors of the future value, parameters q in the model are used for representing MA items, and the ARMA model with p-order AR items and q-order MA items is set to be ARMA (p, q) and is expressed as:
wherein x is t Epsilon is the value of the plateau time series at time t t Mean 0, variance sigma 2 The positive-going white noise of (2) represents the external interference at the time t which is irrelevant to the past value, p is the order of the autoregressive process,for autocorrelation coefficients, q is the average process order of the autoregressive motion, θ is the moving average coefficient, and μ is a constant coefficient.
As a preferable mode of the non-pneumatic tire health monitoring method based on the improved time series model according to the invention, wherein: the establishing the autoregressive integral moving average model further comprises determining an order, and carrying out parameter order on the ARMA (p, q) model through a red-pool information quantity criterion, wherein the parameter order is expressed as follows:
AIC(n)＝Nlnσ a 2 +2n
wherein AIC (N) is a function of model order, N is time sequence length, N is model order number,for the variance of the fitting error, when n increases, +.>The number of parameters is reduced, the number of 2n is increased, the red pool information quantity criterion balances the fitting precision and the number of parameters, and when AIC (n) takes the minimum value, the model takes the optimal order n.
As a preferable mode of the non-pneumatic tire health monitoring method based on the improved time series model according to the invention, wherein: the establishing of the autoregressive integral moving average model further comprises the steps of determining the coefficient of AR (p), and expressing the relation between the function and the coefficient through Z transformation, parameter physical connotation and polynomial equation root as follows:
the first three coefficients of the time sequence model contain information of structural modal parameters, a function of the first three coefficients of the parameter model is constructed to serve as a damage judging index, the first three coefficients in the model contain information of all modal frequencies and damping, algebraic combinations of the first three coefficients are taken as damage sensitive factors, and coefficients of AR (p) are determined.
As a preferable mode of the non-pneumatic tire health monitoring method based on the improved time series model according to the invention, wherein: the establishing the autoregressive integral moving average model further comprises checking a time sequence model, and based on an AR (p) model, according to a sequence x 1 ，x 2 ，...，x n Calculating parametersIs expressed as:
Y＝[x p+1 ，x p+2 ，…，x n ] T
ε＝[ε p+1 ，ε p+2 ，…，ε n ] T
the resulting matrix form is expressed as:
parameters (parameters)The least squares estimate of (2) is expressed as:
after the time series model is established, whether the established time series model is proper or not is generally judged from three aspects of stability, residual randomness, normalization and stability of model coefficients of the time series model.
As a preferable mode of the non-pneumatic tire health monitoring method based on the improved time series model according to the invention, wherein: the damage sensitive factor comprises a structural characteristic quantity which changes when a structure is damaged, a time sequence shows the dynamic process of the system, a first third-order autoregressive coefficient shows the historical inheritance of time sequence data, the characteristics of the time sequence are represented, in the time sequence model simulation process, a model coefficient fluctuates, a first-order coefficient is selected as a damage index, and the first-order coefficient is represented as:
and judging the health state of the tire according to the change of the damage index.
It is another object of the present invention to provide a non-pneumatic tire health monitoring system based on an improved time series model, which can build an autoregressive integral moving average model through a model building module, predict and analyze time series data, and solve the problem of inaccurate complex dynamics in capturing the time series data at present.
As a preferred embodiment of the non-pneumatic tire health monitoring system based on the improved time series model according to the present invention, wherein: the system comprises a differential processing module, a model building module and a state identification module; the differential processing is used for carrying out differential processing on radial acceleration signals of the intelligent non-pneumatic tire under different local injuries; the model building module builds an autoregressive integral moving average model based on the preprocessed data samples; the state identification module is used for establishing damage sensitive factors and identifying the health state of the tire by analyzing the damage sensitive factors.
A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that execution of the computer program by the processor is the step of implementing a non-pneumatic tire health monitoring method based on an improved time series model.
A computer readable storage medium having stored thereon a computer program, characterized in that the computer program when executed by a processor implements the steps of a non-pneumatic tire health monitoring method based on an improved time series model.
The invention has the beneficial effects that: the non-pneumatic tire health monitoring method based on the improved time sequence model provided by the invention can be used for timely finding out abnormality or monitoring the health state in the initial stage of non-pneumatic tire performance degradation through the improved time sequence model, so that the safety and reliability of a vehicle are improved, the autoregressive integral sliding average model is used for analyzing the dynamic response signals of the non-pneumatic tire, compared with the traditional method, the method is more accurate and reliable, the method provides support for an automatic driving technology, the performance of the whole vehicle system is improved, and the method has better effects on the safety, reliability and accuracy.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without the need of creative efforts for a person of ordinary skill in the art. Wherein:
FIG. 1 is an overall flowchart of a non-pneumatic tire health monitoring method based on an improved time series model, according to a first embodiment of the present invention.
FIG. 2 is a graph of test model results of a non-pneumatic tire health monitoring method based on an improved time series model according to a second embodiment of the present invention.
FIG. 3 is an overall flow chart of a non-pneumatic tire health monitoring system based on an improved time series model provided in accordance with a second embodiment of the present invention.
Detailed Description
So that the manner in which the above recited objects, features and advantages of the present invention can be understood in detail, a more particular description of the invention, briefly summarized above, may be had by reference to the embodiments, some of which are illustrated in the appended drawings. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
Example 1
Referring to fig. 1, for one embodiment of the present invention, a non-pneumatic tire health monitoring method based on an improved time series model is provided, comprising:
s1: and measuring radial acceleration signals of the intelligent non-pneumatic tire under different local injuries, and carrying out differential processing on sample data.
Still further, the differential processing includes processing the radial acceleration signal.
Let a (t) be the radial acceleration signal of the intelligent non-pneumatic tire at time t, and the differential operation is expressed as:
ΔA(t)＝A(t)-A(t-τ)
where τ is the time differential interval, α is the multiplication factor, and ΔA (i) is the radial acceleration signal of the intelligent non-pneumatic tire at time i.
S2: and establishing an autoregressive integral moving average model for the preprocessed data samples.
Further, establishing an autoregressive integral moving average model comprises an AR model, an MA model and a difference method.
It should be noted that the ARMA model combined with the differential operation is an autoregressive integral moving average model, the AR term is used for predicting the past value of the next value, and is defined by a parameter p in the autoregressive integral moving average model, the MA term is used for predicting the number of past prediction errors of the future value, a parameter q in the model is used for representing the MA term, and an ARMA model having a p-order AR term and a q-order MA term is set as ARMA (p, q), and is expressed as:
wherein x is t Epsilon is the value of the plateau time series at time t t Mean 0, variance sigma 2 The positive-going white noise of (2) represents the external interference at the time t which is irrelevant to the past value, p is the order of the autoregressive process,for autocorrelation coefficients, q is the average process order of the autoregressive motion, θ is the moving average coefficient, and μ is a constant coefficient.
Further, establishing the autoregressive integral moving average model also includes determining an order.
It should be noted that, the ARMA (p, q) model is subjected to parameter scaling by using the red-pool information quantity criterion, which is expressed as follows:
AIC(n)＝Nlnσ a 2 +2n
wherein AIC (N) is a function of model order, N is time sequence length, N is model order number,for the variance of the fitting error, when n increases, +.>The number of parameters is reduced, the number of 2n is increased, the red pool information quantity criterion balances the fitting precision and the number of parameters, and when AIC (n) takes the minimum value, the model takes the optimal order n.
Still further, establishing the autoregressive integral moving average model further includes determining coefficients of the AR (p).
It should be noted that, the relationship between the function through Z transformation, parameter physical connotation, polynomial equation root and coefficient is expressed as:
the first three coefficients of the time sequence model contain information of structural modal parameters, a function of the first three coefficients of the parameter model is constructed to serve as a damage judging index, the first three coefficients in the model contain information of all modal frequencies and damping, algebraic combinations of the first three coefficients are taken as damage sensitive factors, and coefficients of AR (p) are determined.
Still further, the establishing the autoregressive integral moving average model further includes examining a time series model.
It should be noted that based on the AR (p) model, according to the sequence x 1 ，x 2 ，...，x n Calculating parametersIs expressed as:
Y＝[x p+1 ，x p+2 ，…，x n ] T
ε＝[ε p+1 ，ε p+2 ，…，ε n ] T
the resulting matrix form is expressed as:
parameters (parameters)The least squares estimate of (2) is expressed as:
after the time series model is established, whether the established time series model is proper or not is generally judged from three aspects of stability, residual randomness, normalization and stability of model coefficients of the time series model.
It should also be noted that, by selecting a suitable model type according to the characteristics of the model after the difference between the auto-correlation function (ACF) and the partial auto-correlation function (PACF), the ACF and the PACF of the observation sequence quickly tend to be k-order tail-cutting after a certain constant k is exceeded, and always have non-zero values, and cannot be trailing after the k is greater than a certain constant and then be equal to zero (or randomly fluctuated around 0).
Table 1 preliminary identification table of stationary time series type
| Model | ACF | PACF |
| AR(p) | Trailing tail | p-order tail cutting |
| MA(q) | q-order tail cutting | Trailing tail |
| ARMA(p，q) | Trailing tail | Trailing tail |
| Unsuitable model | Tail cutting | Tail cutting |
As shown in Table 1, the PACF image of the AR model rapidly tended to 0 after the p-th step, the ACF image of the MA model rapidly tended to 0 after the q-th step, i.e., both had a truncated nature, whereas the ACF image and the PACF image of the ARMA model were decayed sine wave and tended to zero after the q-th and p-th steps, exhibiting a trailing nature, thus determining the ARMA model (ARMA model combined with differential operation is referred to as ARIMA model).
S3: and establishing a damage sensitive factor according to the first-order coefficient of the AR term, and identifying the health state of the intelligent non-pneumatic tire based on the damage sensitive factor.
Further, damage-sensitive factors include when damage to the structure occurs.
It should be noted that, the time series represents the dynamic process of the system, the former three-order autoregressive coefficient represents the historical inheritance of the time series data, the characteristics of the time series are represented, in the time series model simulation process, the model coefficient fluctuates, the first-order coefficient is selected as the damage index, and the first-order coefficient is represented as:
and judging the health state of the tire according to the change of the damage index.
Example 2
Referring to fig. 2, for one embodiment of the present invention, a non-pneumatic tire health monitoring method based on an improved time series model is provided, and in order to verify the beneficial effects of the present invention, scientific demonstration is performed through economic benefit calculation and simulation experiments.
According to structural characteristics and monitoring requirements of the tire, an optimal sensor arrangement scheme is determined, radial acceleration signals of the tire in different damage states are collected, differential processing is carried out, so that data are stable, an ARIMA model is built, preprocessed data are analyzed, damage sensitive factors are built based on first-order autoregressive coefficients of the ARIMA model, and the health state of the tire is identified according to the damage sensitive factors.
As can be seen from fig. 2, the model fitting graph evaluates the degree of difference between the variance or covariance matrix estimated by the model and the observed sample variance or covariance matrix, which is generally the degree of consistency between the assumed theoretical model and the actual data, the higher the model fitting degree is, the higher the degree of consistency between the theoretical model and the actual data is, the line graph representing the predicted value in the model fitting graph of the graph a tends to be consistent with the line graph of the actual value, the residual Q-Q graph observes and judges whether the residual is subject to normal distribution, if the data points are all concentrated near the reference line, it is indicated that the observed data of the sample is approximately subject to normal distribution, the data points of the residual Q-Q graph of the graph b are almost concentrated near the reference line, the residual histogram is represented by the residual in the form of histogram, and whether the residual of the graph c has a certain trend or not is observed, and the residual of the model is subject to normal distribution can be seen in the residual histogram of the graph c.
As shown in Table 2, as the damage degree is deepened, the first-order, second-order and third-order coefficients of the ARIMA model are gradually reduced, which indicates that the dynamic response of the tire is significantly changed, the change is directly reflected on the damage sensitivity factor, the value of the change is reduced along with the increase of the damage degree, and the trend shows that the health state of the intelligent non-pneumatic tire can be effectively identified by monitoring the coefficient change of the ARIMA model.
Table 2 table of experimental data
| Status of | AR1 coefficient | AR2 coefficient | AR3 coefficient | Injury sensitive factor |
| No damage | 0.28 | 0.35 | 0.3 | 0.31 |
| Slight damage | 0.25 | 0.32 | 0.28 | 0.28 |
| General injury | 0.2 | 0.27 | 0.23 | 0.23 |
| Severe injury | 0.15 | 0.22 | 0.18 | 0.18 |
Example 3
Referring to fig. 3, for one embodiment of the present invention, a non-pneumatic tire health monitoring system based on an improved time series model is provided, which includes a differential processing module, a model building module, and a status recognition module.
The differential processing is used for carrying out differential processing on radial acceleration signals of the intelligent non-pneumatic tire under different local injuries; the model building module builds an autoregressive integral moving average model based on the preprocessed data samples; the identification module is used for establishing damage sensitive factors and identifying the health state of the tire by analyzing the damage sensitive factors.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method of the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a random access Memory (RAM, random Access Memory), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
Logic and/or steps represented in the flowcharts or otherwise described herein, e.g., a ordered listing of executable instructions for implementing logical functions, can be embodied in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, processor-containing system, or other system that can fetch the instructions from the instruction execution system, apparatus, or device and execute the instructions. For the purposes of this description, a "computer-readable medium" can be any means that can contain, store, communicate, propagate, or transport the program for use by or in connection with the instruction execution system, apparatus, or device.
More specific examples (a non-exhaustive list) of the computer-readable medium would include the following: an electrical connection (electronic device) having one or more wires, a portable computer diskette (magnetic device), a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium may even be paper or other suitable medium upon which the program is printed, as the program may be electronically captured, via, for instance, optical scanning of the paper or other medium, then compiled, interpreted or otherwise processed in a suitable manner, if necessary, and then stored in a computer memory.
It is to be understood that portions of the present invention may be implemented in hardware, software, firmware, or a combination thereof. In the above-described embodiments, the various steps or methods may be implemented in software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented in hardware, as in another embodiment, may be implemented using any one or combination of the following techniques, as is well known in the art: discrete logic circuits having logic gates for implementing logic functions on data signals, application specific integrated circuits having suitable combinational logic gates, programmable Gate Arrays (PGAs), field Programmable Gate Arrays (FPGAs), and the like. It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that the technical solution of the present invention may be modified or substituted without departing from the spirit and scope of the technical solution of the present invention, which is intended to be covered in the scope of the claims of the present invention.
Claims (10)
1. A method of non-pneumatic tire health monitoring based on an improved time series model, comprising:
measuring radial acceleration signals of the intelligent non-pneumatic tire under different local injuries, and carrying out differential processing on sample data;
establishing an autoregressive integral moving average model for the preprocessed data sample;
and establishing a damage sensitive factor according to the first-order coefficient of the AR term, and identifying the health state of the intelligent non-pneumatic tire based on the damage sensitive factor.
2. The non-pneumatic tire health monitoring method based on an improved time series model of claim 1, wherein: the differential processing includes setting A (t) as a radial acceleration signal of the intelligent non-pneumatic tire at time t, and the differential operation is expressed as:
ΔA(t)＝A(t)-A(t-τ)
where τ is the time differential interval, α is the multiplication factor, and ΔA (i) is the radial acceleration signal of the intelligent non-pneumatic tire at time i.
3. The non-pneumatic tire health monitoring method based on an improved time series model of claim 1, wherein: the establishing an autoregressive integral moving average model comprises an AR model, an MA model and a difference method, wherein the ARMA model combined with difference operation is an autoregressive integral moving average model, AR items are used for predicting the past value of the next value and are defined by parameters p in the autoregressive integral moving average model, MA items are used for predicting the number of past prediction errors of the future value, parameters q in the model are used for representing MA items, and the ARMA model with p-order AR items and q-order MA items is set to be ARMA (p, q) and is expressed as:
wherein x is t Epsilon is the value of the plateau time series at time t t Mean 0, variance sigma 2 The positive-going white noise of (2) represents the external interference at the time t which is irrelevant to the past value, p is the order of the autoregressive process,for autocorrelation coefficients, q is the average process order of the autoregressive motion, θ is the moving average coefficient, and μ is a constant coefficient.
4. A non-pneumatic tire health monitoring method based on an improved time series model as in claim 3, wherein: the establishing the autoregressive integral moving average model further comprises determining an order, and carrying out parameter order on the ARMA (p, q) model through a red-pool information quantity criterion, wherein the parameter order is expressed as follows:
AIC(n)＝N lnσ a 2 +2n
wherein AIC (N) is a function of model order, N is time sequence length, N is model order number,for the variance of the fitting error, when n increases, +.>The number of parameters is reduced, the number of 2n is increased, the red pool information quantity criterion balances the fitting precision and the number of parameters, and when AIC (n) takes the minimum value, the model takes the optimal order n.
5. The method for non-pneumatic tire health monitoring based on an improved time series model as in claim 4, wherein: the establishing of the autoregressive integral moving average model further comprises the steps of determining the coefficient of AR (p), and expressing the relation between the function and the coefficient through Z transformation, parameter physical connotation and polynomial equation root as follows:
the first three coefficients of the time sequence model contain information of structural modal parameters, a function of the first three coefficients of the parameter model is constructed to serve as a damage judging index, the first three coefficients in the model contain information of all modal frequencies and damping, algebraic combinations of the first three coefficients are taken as damage sensitive factors, and coefficients of AR (p) are determined.
6. The non-pneumatic tire health monitoring method based on an improved time series model of claim 5, wherein: the establishing the autoregressive integral moving average model further comprises checking a time sequence model, and based on an AR (p) model, according to a sequence x 1 ,x 2 ,…,x n Calculating parametersIs expressed as:
Y＝[x p+1 ,x p+2 ,…,x n ] T
ε＝[ε p+1 ,ε p+2 ,…,ε n ] T
the resulting matrix form is expressed as:
parameters (parameters)The least squares estimate of (2) is expressed as:
after the time series model is established, whether the established time series model is proper or not is generally judged from three aspects of stability, residual randomness, normalization and stability of model coefficients of the time series model.
7. The non-pneumatic tire health monitoring method based on an improved time series model of claim 6, wherein: the damage sensitive factor comprises a structural characteristic quantity which changes when a structure is damaged, a time sequence shows the dynamic process of the system, a first third-order autoregressive coefficient shows the historical inheritance of time sequence data, the characteristics of the time sequence are represented, in the time sequence model simulation process, a model coefficient fluctuates, a first-order coefficient is selected as a damage index, and the first-order coefficient is represented as:
and judging the health state of the tire according to the change of the damage index.
8. A system employing the improved time series model-based non-pneumatic tire health monitoring method as set forth in any one of claims 1-7, wherein: the system comprises a differential processing module, a model building module and a state identification module;
the differential processing is used for carrying out differential processing on radial acceleration signals of the intelligent non-pneumatic tire under different local injuries;
the model building module builds an autoregressive integral moving average model based on the preprocessed data samples;
the state identification module is used for establishing damage sensitive factors and identifying the health state of the tire by analyzing the damage sensitive factors.
9. A computer device comprising a memory and a processor, the memory storing a computer program, characterized in that the processor, when executing the computer program, implements the steps of the non-pneumatic tire health monitoring method based on an improved time series model of any one of claims 1 to 7.
10. A computer readable storage medium having stored thereon a computer program, which when executed by a processor, implements the steps of the non-pneumatic tire health monitoring method based on an improved time series model of any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311627566.2A CN117656716A (en) | 2023-11-30 | 2023-11-30 | Non-pneumatic tire health monitoring method and system based on improved time series model |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311627566.2A CN117656716A (en) | 2023-11-30 | 2023-11-30 | Non-pneumatic tire health monitoring method and system based on improved time series model |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN117656716A true CN117656716A (en) | 2024-03-08 |
Family
ID=90065506
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311627566.2A Pending CN117656716A (en) | 2023-11-30 | 2023-11-30 | Non-pneumatic tire health monitoring method and system based on improved time series model |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117656716A (en) |
-
2023
- 2023-11-30 CN CN202311627566.2A patent/CN117656716A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| EP2813378B1 (en) | Tire wear state estimation system and method | |
| JP2019011048A (en) | Tire wear state estimation system and method | |
| EP2837510B1 (en) | Torsional mode tire wear state estimation system and method | |
| JP5837341B2 (en) | Road surface condition determination method and apparatus | |
| EP2502759B1 (en) | Apparatus, method and program for vehicle mass estimation | |
| US8207839B2 (en) | Apparatus, method and program for detecting tire having decreased pressure using bayesian estimation means | |
| US8279056B2 (en) | Apparatus, method and computer for detecting decrease in tire air pressure by calculating a gain corresponding to an arbitrary frequency | |
| CN111532090B (en) | Four-wheel indirect tire pressure detection method based on electric vehicle motor wheel speed | |
| JP2018004419A (en) | Road surface state determination method | |
| US20040158414A1 (en) | Method of determining components of forces exerted on a tyre and the self-alignment torque | |
| CN105793687A (en) | Method and device for estimating tire partial wear | |
| CN113761649A (en) | Intelligent automobile tire eccentric wear prediction method based on one-dimensional convolutional neural network | |
| CN113239599A (en) | Intelligent tire wear life estimation method and device based on BP neural network | |
| CN110243598B (en) | Train bearing temperature processing method and device and storage medium | |
| CN114161888B (en) | Dual-tire iTPMS tire pressure monitoring method and system | |
| US20240017729A1 (en) | Vehicle and vehicle load distribution identification method, apparatus, medium and electronic device | |
| CN117656716A (en) | Non-pneumatic tire health monitoring method and system based on improved time series model | |
| CN114235448A (en) | Rail vehicle bogie wheel fatigue damage assessment method and system | |
| CN117591986B (en) | Real-time automobile data processing method based on artificial intelligence | |
| CN114674576B (en) | Forklift braking performance test method and device | |
| CN117725779A (en) | Wheel fatigue evaluation method, device and storage medium | |
| CN115711753A (en) | Method for predicting high-speed uniformity of automobile tire | |
| CN117367775B (en) | Hydraulic braking system simulation test device and method | |
| CN113721304B (en) | Anti-skid chain detection method and device, electronic equipment and storage medium | |
| EP4246117A1 (en) | Tire wear condition predicting system, tire wear condition predicting program, and tire wear condition predicting method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |