Method and system for evaluating tire state through spectral analysis

Abstract

The invention relates to a tire state evaluation method and system through spectral analysis, and belongs to the technical field of vehicle safety. The method comprises the following steps: acquiring a tire state signal, correcting the tire state signal through a correction model to obtain a tire state correction signal, and screening interference factors in the tire state correction signal through a signal screening model to obtain a wheel speed state reference signal; analyzing the wheel speed state reference signal through the signal spectrum to obtain a fusion spectrum; and predicting and evaluating the tire state signal and the fusion frequency spectrum through a prediction and evaluation model to output a tire abnormal state. Accurate evaluation of the tire state is realized.

Description

Technical Field

[0001] The present invention belongs to the technical field of vehicle safety, and particularly relates to a method and system for evaluating tire status through spectrum analysis. Background Art

[0002] As the only component of a vehicle in contact with the ground, the status of the tire is crucial for driving safety and vehicle performance. Tire failures can lead to a decrease in driving stability, an increase in braking distance, and even cause tire blowout accidents. Therefore, establishing a method that can effectively evaluate the tire status and predict failures in advance is of great significance for improving vehicle safety.

[0003] Currently, the evaluation of tire status mainly relies on a tire pressure monitoring system (TPMS) or directly measures parameters such as tire temperature and pressure by sensors. However, these methods have certain limitations: the TPMS mainly focuses on tire pressure and cannot comprehensively reflect the actual operating status of the tire; the direct measurement method requires additional installation of sensors, increasing the system cost and complexity. At the same time, the above methods often ignore the influence of special road conditions on tire status evaluation. Therefore, how to eliminate road condition interference and achieve more accurate tire status evaluation has become a key research direction. Summary of the Invention

[0004] To solve the above problems existing in the prior art, the present invention provides a method and system for evaluating tire status through spectrum analysis,

[0005] The object of the present invention can be achieved by the following technical solutions:

[0006] A method for evaluating tire status through spectrum analysis, comprising:

[0007] 1. A method for evaluating tire status through spectrum analysis, characterized by comprising the following steps:

[0008] S1: Obtain a tire status signal, correct the tire status signal through a correction model to obtain a corrected tire status signal, and screen out interference factors in the corrected tire status signal through a signal screening model to obtain a wheel speed status reference signal;

[0009] S2: Analyze the wheel speed status reference signal through signal spectrum analysis to obtain a fused spectrum;

[0010] S3: Perform prediction and evaluation on the tire status signal and the fused spectrum through a prediction and evaluation model to output a tire abnormal status.

[0011] Preferably, the correction model in step S1 updates and corrects the tire status signal by presetting an ideal gear ring error to obtain the corrected tire status signal.

[0012] Preferably, the screening process of the signal screening model in step S1 is as follows:

[0013] S101: Screen the parameters of the tire state correction signal through a multi-parameter fusion screening model to obtain a wheel speed correction compensation signal;

[0014] S102: Screen the environment of the wheel speed correction compensation signal through a ramp detection model to obtain the wheel speed state reference signal.

[0015] Preferably, the parameter screening process of the multi-parameter fusion screening model in step S101 is as follows:

[0016] S101-1: Preset an evaluation threshold, and perform an exclusion determination on the evaluation threshold and the tire state correction signal to obtain a preliminary screened tire state signal;

[0017] S101-2: Calculate the slip ratio of the tire by performing a slip ratio calculation on the preliminary screened tire state signal;

[0018] S101-3: Obtain the lateral acceleration, and calculate a threshold region based on the tire slip ratio, the lateral acceleration, and the road surface friction coefficient to generate a dynamic threshold interval;

[0019] S101-4: Determine whether the tire slip ratio is within the dynamic threshold interval based on the dynamic threshold interval and the tire slip ratio:

[0020] Yes, then the tire state signal is a normal signal;

[0021] No, then the tire state signal is an abnormal signal and is excluded;

[0022] S101-5: Compensate the normal signal according to the characteristics of the normal data distribution to obtain the wheel speed correction compensation signal.

[0023] Preferably, the modeling process of the ramp detection model in step S102 is as follows:

[0024] S102-1: Obtain the steep slope gradient, the real-time load mass, and the empty vehicle mass, and obtain an adaptive slope threshold through a slope dynamic coupling threshold;

[0025] S102-2: Perform a steep slope filtering process on the wheel speed correction compensation signal to obtain a wheel speed steep slope signal;

[0026] S102-3: Obtain the slope passing time, the steep slope reference gradient, preset a passing time threshold, and determine the wheel speed state reference signal through a slope signal judgment.

[0027] Preferably, the calculation formula of the slope dynamic coupling threshold in step S102-1 is expressed as:

[0028]

[0029] Among them, θ th is the adaptive slope threshold, and θ base is the steep slope, m load is the real-time load mass, m 空载 is the empty vehicle mass, and μ road is the road surface friction coefficient.

[0030] Preferably, the judgment process of the slope signal judgment in the step S102-3 is specifically: judge whether the steep slope reference slope is greater than the adaptive slope threshold:

[0031] If yes, further compare whether the slope passing time is greater than the passing time threshold; if it is greater, eliminate the wheel speed steep slope signal; if it is less, retain the wheel speed steep slope signal;

[0032] If no, do not perform any operation.

[0033] Preferably, the analysis process of the signal spectrum analysis in the step S2 is:

[0034] S201: Jointly decompose the time-frequency domain of the wheel speed state reference signal to obtain multi-scale signal components;

[0035] S202: Calculate the frequency of the multi-scale signal components to obtain the signal resonance frequency;

[0036] S203: Perform weighted calculation on the signal resonance frequency to obtain the fusion spectrum.

[0037] Preferably, the prediction and evaluation process of the prediction and evaluation model in the step S3 is:

[0038] S301: Store the tire state signal and the fusion spectrum to obtain a state evaluation data set;

[0039] S302: Extract the tire state characteristics by extracting the characteristics of the state evaluation data set;

[0040] S303: Evaluate the tire state characteristics through an evaluation network to obtain the abnormal tire state.

[0041] A tire state evaluation system based on spectrum analysis includes a signal preprocessing module, a signal analysis module, and a state evaluation module, including:

[0042] The signal preprocessing module is used to obtain tire status signals, correct the tire status signals through a correction model to obtain corrected tire status signals, and screen out interference factors in the corrected tire status signals through a signal screening model to obtain wheel speed status reference signals;

[0043] The signal analysis module is used to obtain a fusion spectrum by analyzing the wheel speed status reference signal through signal spectrum analysis;

[0044] The status evaluation module is used to perform prediction and evaluation on the tire status signal and the fusion spectrum through a prediction and evaluation model to output tire abnormal status.

[0045] The beneficial effects of the present invention are as follows:

[0046] (1) By using a signal screening model to eliminate interference factors, and adopting multi-parameter fusion screening and a ramp detection model, the accuracy of the wheel speed status reference signal is improved, thereby enhancing the reliability of the evaluation result.

[0047] (2) Through dynamic threshold region calculation and slope dynamic coupling threshold calculation, the system can adaptively adjust the evaluation criteria according to different vehicle loads, road surface friction coefficients, and slope conditions, improving the adaptability to complex working conditions.

[0048] (3) By combining signal spectrum analysis and a prediction and evaluation model, the resonance frequency characteristics in the tire status signal can be accurately identified, and the status data can be deeply evaluated using the fusion spectrum, improving the detection sensitivity to tire abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] For the convenience of those skilled in the art to understand, the present invention will be further described below with reference to the accompanying drawings.

[0050] Figure 1 It is a schematic flow chart of a method for evaluating tire status through spectrum analysis according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0051] In order to further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention purpose, the following will, with reference to the accompanying drawings and preferred embodiments, describe in detail the specific embodiments, structures, features, and effects of the present invention.

[0052] Please refer to Figure 1 , a method for evaluating tire status through spectrum analysis, includes:

[0053] S1: Obtain tire status signals, correct the tire status signals through a correction model to obtain corrected tire status signals, and screen out interference factors in the corrected tire status signals through a signal screening model to obtain wheel speed status reference signals;

[0054] S2: Obtain a fused spectrum by analyzing the wheel speed status reference signal through signal spectrum analysis;

[0055] S3: Perform prediction and evaluation on the tire status signal and the fused spectrum through a prediction evaluation model, and output an abnormal tire status.

[0056] Specifically, the tire status signal is collected by a wheel speed sensor, and the correction process of the correction model includes:

[0057] Update and correct the tire status signal by presetting an ideal gear ring error to obtain the tire status correction signal;

[0058] The correction of the stable speed tire status signal by the gear ring error is specifically expressed as:

[0059]

[0060] where v represents the tire status signal, r is the tire radius, N is the total number of teeth of the gear ring, and δ i(k) represents the error value of the i-th tooth after the k-th iteration, and Δt i represents the passing time of the i-th tooth, and δ i(k-1) represents the error value of the i-th tooth after the (k - 1)-th iteration, and μ is the learning rate, and T i(k) represents the sum of the times of all teeth, that is, the total time for the tire to run one circle.

[0061] Specifically, in step S1, the screening process of the signal screening model in step S1 is as follows:

[0062] S101: Perform parameter screening on the tire status correction signal through a multi-parameter fusion screening model to obtain a wheel speed correction compensation signal;

[0063] S102: Perform environmental screening on the wheel speed correction compensation signal through a ramp detection model to obtain the wheel speed status reference signal.

[0064] Specifically, the parameter screening process of the multi-parameter fusion screening model in step S101 is as follows:

[0065] S101-1: Preset an evaluation threshold, and perform exclusion determination on the evaluation threshold and the tire status correction signal to obtain a preliminary screened tire status signal. The tire status correction signal includes a braking signal, yaw rate, acceleration, and tire temperature. The evaluation threshold includes a rapid acceleration threshold, a tire temperature threshold, and a yaw rate threshold;

[0066] The exclusion determination expression is:

[0067] R = 1 ∨ γ > γ max ∨|ax | > a th ∨T tire > T th ,

[0068] Wherein, B is the braking signal. When the braking signal is 1, it indicates that the braking signal is activated; when it is 0, it indicates that the braking signal is not activated. γ is the yaw rate, and γ max is the yaw rate threshold, a th is the rapid acceleration threshold, a x is the acceleration, T tire is the tire temperature, and T th is the tire temperature threshold;

[0069] S101-2: Calculate the slip ratio of the tire by performing a slip ratio calculation on the preliminarily screened tire status signal;

[0070] The slip ratio calculation formula is:

[0071]

[0072] Wherein, s(i) is the tire slip ratio, v wheel (i) is the preliminarily screened tire status signal, v ref (i) is the reference vehicle speed, and δ road is the correction factor;

[0073] S101-3: Obtain the lateral acceleration, and generate a dynamic threshold interval by performing a threshold region calculation based on the tire slip ratio, the lateral acceleration, and the road surface friction coefficient;

[0074] The threshold region calculation expression is:

[0075]

[0076] Wherein, S max (i) represents the maximum threshold, S w (i) represents the weighted sliding average value, α is the dynamic coupling coefficient, β is the safety margin constant, a y is the lateral acceleration, μ road is the road surface friction coefficient, S min (i) represents the minimum threshold, i is the i-th time point, k represents the historical time point index within the sliding window, ranging from i - L to i, L is the sliding window length, λ is the attenuation factor, and s(i) is the tire slip ratio;

[0077] S101-4: Determine whether the tire slip ratio is within the dynamic threshold interval based on the dynamic threshold interval and the tire slip ratio:

[0078] If so, the tire status signal is a normal signal;

[0079] If not, the tire status signal is an abnormal signal and is excluded;

[0080] S101-5: Compensate the normal signal according to the normal data distribution characteristics to obtain the wheel speed correction compensation signal.

[0081] In this embodiment, taking a vehicle traveling at a constant speed of 60 km / h on a wet road surface (μ road = 0.3) and suddenly encountering a slight turn (yaw rate γ = 0.1 rad / s) as an example, the preset yaw rate threshold γ max is 0.5 rad / s, and the sudden acceleration threshold a th is 2 m / s 2 , and the exclusion judgment is made to retain the data as the initial screening tire status signal; the window length L is 10, and the attenuation factor λ is 0.2, and the weighted sliding average value S w (i) is 0.02, the lateral acceleration is 0.3 m / s 2 , the maximum threshold is 0.125, and the minimum threshold is -0.085. By calculating the current tire slip ratio to be 0.05, the dynamic threshold interval is satisfied and the data is retained; if the current tire slip ratio is 0.15, it is marked as an abnormal signal and excluded, and compensation is triggered at the same time.

[0082] Specifically, the modeling process of the ramp detection model in step S102 is as follows:

[0083] S102-1: Obtain the steep slope gradient, the real-time load mass, the empty vehicle mass, and obtain the adaptive slope threshold through the slope dynamic coupling threshold;

[0084] The calculation formula of the slope dynamic coupling threshold is expressed as:

[0085]

[0086] where θ th is the adaptive slope threshold, θ base is the steep slope gradient, m load is the real-time load mass, m 空载 is the empty vehicle mass, and μ road is the road surface friction coefficient;

[0087] S102-2: Perform steep slope filtering on the wheel speed correction compensation signal to obtain the wheel speed steep slope signal;

[0088] S102-3: Obtain the slope passing time, the steep slope reference gradient, and a preset passing time threshold. Based on the wheel speed steep slope signal, the steep slope reference gradient, the adaptive slope threshold, the slope passing time, and the passing time threshold, determine the wheel speed state reference signal through slope signal judgment;

[0089] The process of the slope signal judgment is as follows:

[0090] Judge whether the steep slope reference gradient is greater than the adaptive slope threshold:

[0091] If yes, further compare whether the slope passing time is greater than the passing time threshold; if it is greater, eliminate the wheel speed steep slope signal; if it is less, retain the wheel speed steep slope signal;

[0092] If no, do nothing.

[0093] Specifically, the steep slope filtering process non-linearly adjusts the gain of the wheel speed correction compensation signal through a preset slope threshold to generate the wheel speed steep slope signal.

[0094] Specifically, the analysis process of the signal spectrum analysis in step S2 is as follows:

[0095] S201: Jointly decompose the time-frequency domain of the wheel speed state reference signal to obtain multi-scale signal components;

[0096] S202: Calculate the frequency of the multi-scale signal components to obtain the signal resonance frequency;

[0097] The frequency calculation expression is:

[0098]

[0099] where f res,j is the signal resonance frequency, T s is the sampling period, a 1,j , a 2,j are coefficients;

[0100] S203: Perform weighted calculation on the signal resonance frequency to obtain the fusion spectrum.

[0101] Specifically, the joint time-frequency domain decomposition is based on wavelet packet decomposition. The decomposition process of the wavelet packet decomposition is to recursively decompose the wheel speed state reference signal into high-frequency detail coefficients and low-frequency approximation coefficients through the stretching and translation of the wavelet basis function to form multi-scale signal components.

[0102] Specifically, the prediction and evaluation process of the prediction and evaluation model in step S3 is as follows:

[0103] S301: Store the tire status signal and the fused spectrum to obtain a status evaluation data set;

[0104] S302: Obtain tire status features by extracting the features of the status evaluation data set;

[0105] S303: Evaluate the tire status features through an evaluation network to obtain the abnormal tire status, where the abnormal tire status includes normal, abnormal tire pressure, overheating, excessive wear, and structural damage.

[0106] In this embodiment, the evaluation network includes a three-layer neural network and a hidden layer. The number of network nodes in the hidden layer is 5. The learning rate of the evaluation network is 0.025, and the number of iterations is 1000 times.

[0107] A tire status evaluation system based on spectrum analysis includes a signal preprocessing module, a signal analysis module, and a status evaluation module, including:

[0108] The signal preprocessing module is used to obtain a tire status signal, correct the tire status signal through a correction model to obtain a corrected tire status signal, and filter out interference factors in the corrected tire status signal through a signal screening model to obtain a wheel speed status reference signal;

[0109] The signal analysis module is used to obtain a fused spectrum by performing signal spectrum analysis on the wheel speed status reference signal;

[0110] The status evaluation module is used to perform predictive evaluation on the tire status signal and the fused spectrum through a predictive evaluation model and output the abnormal tire status.

[0111] As mentioned above, it is only a preferred embodiment of the present invention and does not impose any form of limitation on the present invention. Although the present invention has been disclosed above with preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications or equivalents by using the disclosed technical content without departing from the technical solution of the present invention. However, any simple modification, equivalent change, and modification made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for evaluating tire condition through spectrum analysis, characterized in that, It includes the following steps: S1: Obtain the tire status signal, correct the tire status signal through a correction model to obtain a corrected tire status signal, and screen out interference factors in the corrected tire status signal through a signal screening model to obtain a wheel speed status reference signal; S2: Analyze the wheel speed status reference signal through signal spectrum analysis to obtain a fusion spectrum; S3: Perform prediction and evaluation on the tire status signal and the fusion spectrum through a prediction evaluation model to output an abnormal tire status.

2. The method for evaluating tire condition by spectrum analysis according to claim 1, wherein The correction model in step S1 updates and corrects the tire status signal through a preset ideal gear ring error to obtain the corrected tire status signal.

3. The method for evaluating the tire condition by spectrum analysis according to claim 1, characterized in that, The screening process of the signal screening model in step S1 is as follows: S101: Screen parameters of the corrected tire status signal through a multi-parameter fusion screening model to obtain a wheel speed correction compensation signal; S102: Screen the environment of the wheel speed correction compensation signal through a ramp detection model to obtain the wheel speed status reference signal.

4. The method for evaluating tire condition by spectrum analysis according to claim 3, characterized in that, The parameter screening process of the multi-parameter fusion screening model in step S101 is as follows: S101-1: Preset an evaluation threshold, perform exclusion determination on the evaluation threshold and the corrected tire status signal to obtain a preliminarily screened tire status signal; S101-2: Calculate the slip rate of the tire for the preliminarily screened tire status signal to obtain the tire slip rate; S101-3: Obtain the lateral acceleration, and calculate a threshold region according to the tire slip rate, the lateral acceleration, and the road surface friction coefficient to generate a dynamic threshold interval; S101-4: Judge whether the tire slip rate is within the dynamic threshold interval according to the dynamic threshold interval and the tire slip rate: If yes, the tire status signal is a normal signal; If no, the tire status signal is an abnormal signal and is excluded; S101-5: Compensate the normal signal according to the characteristics of normal data distribution to obtain the wheel speed correction compensation signal.

5. The method for evaluating tire condition by spectrum analysis according to claim 3, characterized in that, The modeling process of the ramp detection model in step S102 is as follows: S102-1: Obtain the steep slope gradient, the real-time load mass, and the empty vehicle mass, and obtain an adaptive slope threshold through a slope dynamic coupling threshold; S102-2: Perform steep slope filtering on the wheel speed correction compensation signal to obtain a wheel speed steep slope signal; S102-3: Obtain the slope passing time, the steep slope reference slope, preset a passing time threshold, and judge through the slope signal to obtain the wheel speed status reference signal.

6. The method for evaluating tire condition by spectrum analysis according to claim 5, wherein The calculation formula of the slope dynamic coupling threshold in step S102-1 is expressed as: where θ th is the adaptive slope threshold, θ base is the steep slope, m load is the real-time load mass, m 空载 is the empty vehicle mass, μ road is the road surface friction coefficient.

7. The method for evaluating tire condition by spectrum analysis according to claim 5, characterized in that, The judgment process of the slope signal judgment in step S102-3 is specifically as follows: Judge whether the steep slope reference slope is greater than the adaptive slope threshold: If yes, further compare whether the slope passing time is greater than the passing time threshold; if it is greater, exclude the wheel speed steep slope signal; if it is less, retain the wheel speed steep slope signal; If no, do nothing.

8. The method for evaluating tire condition by spectrum analysis according to claim 1, wherein The analysis process of the signal spectrum analysis in step S2 is as follows: S201: Jointly decompose the time-frequency domain of the wheel speed state reference signal to obtain multi-scale signal components; the joint time-frequency domain decomposition is based on wavelet packet decomposition, and the decomposition process of the wavelet packet decomposition is to recursively decompose the wheel speed state reference signal into high-frequency detail coefficients and low-frequency approximation coefficients through the stretching and translation of the wavelet basis function, forming multi-scale signal components. S202: Calculate the frequency of the multi-scale signal components to obtain the signal resonance frequency; S203: Perform weighted calculation on the signal resonance frequency to obtain the fusion spectrum.

9. The method for evaluating the tire condition by spectrum analysis according to claim 1, wherein The prediction and evaluation process of the prediction and evaluation model in step S3 is as follows: S301: Store the tire state signal and the fusion spectrum to obtain a state evaluation data set; S302: Extract the characteristics of the state evaluation data set to obtain tire state characteristics; S303: Evaluate the tire state characteristics through an evaluation network to obtain the abnormal tire state.

10. A tire condition assessment system by spectrum analysis, comprising a signal preprocessing module, a signal analysis module, and a condition assessment module, characterized in that, Including: The signal preprocessing module is used to obtain the tire state signal, correct the tire state signal through a correction model to obtain a corrected tire state signal, and screen out the interference factors in the corrected tire state signal through a signal screening model to obtain the wheel speed state reference signal; The signal analysis module is used to obtain the fusion spectrum by analyzing the wheel speed state reference signal through signal spectrum; The state evaluation module is used to perform prediction and evaluation on the tire state signal and the fusion spectrum through a prediction and evaluation model and output the abnormal tire state.

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