A tire wear imbalance early warning method

Abstract

The application relates to the field of tire detection, and discloses a tire eccentric wear early warning method, which comprises the following steps: step one, obtaining tire temperature, tire pressure and acceleration data through an intelligent tire sensor; step two, calculating a tire ideal harmonic spectrum f1 through tire design information; step three, taking an obvious feature of sensor grounding as a demarcation line of one rotation of the tire; step four, performing Fourier series decomposition on a multi-period signal, and performing coherent averaging on a complex spectrum to obtain a frequency spectrum f2; step five, calculating a cross spectrum p1 of the frequency spectrum f2 and the f1; step six, calculating an energy spectrum entropy of the p1 to obtain e1; and step seven, calculating a ratio of the p1 and P, setting an alarm threshold L, and performing eccentric wear alarm when the ratio is greater than the L. The tire eccentric wear early warning method provided by the application does not depend on high sampling rate hardware conditions, takes a physical model of tire design parameters as an algorithm input, is closely combined with tire characteristics, and can recognize and alarm tire non-uniform wear.

Description

Technical Field

[0001] This invention belongs to the field of tire inspection, specifically a method for early warning of uneven tire wear. Background Technology

[0002] As a key component of automobiles, tires are receiving increasing attention for monitoring their condition. Tire wear is closely related to driving safety. Excessive tire wear can lead to tire blowouts, vehicle slippage on wet roads, and deviations in vehicle steering and acceleration, thus affecting driving safety. Tires are affected by various factors such as driving conditions, uneven load, wheel position, and vehicle layout. Tire wear is generally non-uniform, including wear on both sides, wear in the middle, and localized wear.

[0003] Existing tire wear monitoring methods fall into two categories: one is vision-based, which involves complex sensor layouts, high costs, and is greatly affected by the environment, making it difficult to promote and use; the other is algorithms based on intelligent tire sensors, which can only handle ideal conditions of uniform tire wear, and most of them have only proven their feasibility in laboratory benches, with low feasibility in real vehicles. Summary of the Invention

[0004] To address the problems mentioned in the background art, this invention provides a method for early warning of uneven tire wear. The method includes the following steps: Step 1, acquiring tire temperature, tire pressure, and acceleration data through an intelligent tire sensor; Step 2, calculating the ideal harmonic spectrum f1 of the tire using tire design information; Step 3, using the obvious characteristic of the sensor grounding as the dividing line for one revolution of the tire based on the acceleration signal; Step 4, saving time-domain signals of multiple periods, resampling to the same sampling points, performing Fourier series decomposition on the multi-period signals, coherently averaging the complex spectrum to obtain spectrum f2, and storing the average spectrum of the tire under the initial state as F; Step 5, calculating the cross spectrum p1 between spectrum f2 and f1, and storing the cross spectrum P between F and f1 under the initial state of the tire; Step 6, calculating the energy spectral entropy of p1 to obtain e1, and storing the energy spectral entropy of the cross spectrum P under the initial state of the tire; Step 7, calculating the ratio of p1 to P, setting an alarm threshold L, and issuing an uneven wear alarm when the ratio is greater than L.

[0005] Furthermore, the tire sensor in step one is used to detect and output temperature signals, pressure signals, and acceleration signals. The output terminal of the tire sensor is electrically connected to a PLC controller for converting the temperature signals, pressure signals, and acceleration signals into temperature values, pressure values, and acceleration values. The PLC controller is electrically connected to an external device for displaying the temperature values, pressure values, and acceleration values.

[0006] Furthermore, the calculation method for the ideal harmonic spectrum f1 of the tire in step two is as follows: after the tire rotates multiple times, a certain point will be taken as the zero point, and the pressure corresponding to 360 degrees will be measured to obtain the pressure spectrum. Based on the waveform on this pressure spectrum, a Fourier transform is performed to obtain the value.

[0007] Furthermore, the spectral entropy calculation method in step six is ​​as follows: Let y t It is the input of a stable causal system with a transfer function of G(B) and G(f) ≡ G(e). -i2πf )=G(B) B =exp(-i2πf o ), x t If the system output is stationary, then it can be proven that the relationship between the entropy rates of the system's input and output is as follows: This relationship is most commonly used in time series analysis with the input {y} t} is white noise with a value of zero and a variance of σy 2 At this point, the system's transfer function |G(f)| 2 =G X (f) / σy 2 The entropy rate of white noise is Therefore, we obtain {x t The entropy rate of} A linear stationary system with white noise as input is precisely a time-series ARMA model, and the relationship above reflects {x} t The relationship between the spectral density and its entropy rate of} is given by the equation where the first term on the right-hand side is a constant. Compare h... x The magnitude of the integral is equivalent to comparing the magnitude of the second integral, hence the term is called For the sequence {x t The spectral entropy of}.

[0008] Furthermore, it also includes performing multi-segment data processing, calculating p1 values ​​separately, and considering that tire wear is a slow process, therefore it is assumed that the p values ​​obtained from sampling within the same time period are... k =p k-1 +Q, where Q is the state error and the calculated value is y. pk =p k +R, where R is the observation error, and Q ~ N(0,Q), R ~ N(0,R). Based on the above, particle filtering is performed.

[0009] Compared with the prior art, the beneficial effects of the present invention are:

[0010] The tire wear early warning method provided by this invention does not rely on high sampling rate hardware conditions. It uses a physical model based on tire design parameters as algorithm input and is closely integrated with the characteristics of the tire itself to identify and alarm for uneven tire wear. Attached Figure Description

[0011] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0012] Figure 1 This is a schematic diagram of the acceleration signal of the present invention;

[0013] Figure 2 This is a schematic diagram of the tire tread depth test according to the present invention. Detailed Implementation

[0014] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the scope of protection of the present invention.

[0015] In the idle space of this device, all electrical components and their matching drivers are placed, and all the aforementioned driving components, which refer to power elements, electrical components and adapted power supplies, are connected by wires by those skilled in the art. The specific connection method should refer to the following description of the sequential operation of each electrical component to complete the electrical connection. The detailed connection method is a well-known technology in the art.

[0016] The tire wear pre-warning method provided in this specific embodiment includes the following steps:

[0017] Step 1: Obtain tire temperature, tire pressure, and acceleration data through intelligent tire sensors. The output of the tire sensors is electrically connected to a PLC controller for converting the temperature, pressure, and acceleration signals into temperature, pressure, and acceleration values. The PLC controller is electrically connected to an external device for displaying the temperature, pressure, and acceleration values.

[0018] Step 2: Calculate the ideal harmonic spectrum f1 of the tire using the tire design information. The calculation method for the ideal harmonic spectrum f1 is as follows: After the tire rotates multiple times, a certain point will be taken as the zero point. Measure the pressure corresponding to 360 degrees to obtain the pressure spectrum. Perform a Fourier transform on the waveform of this pressure spectrum to obtain the value.

[0019] Step 3, as Figure 1 As shown, based on the acceleration signal, the obvious feature of the sensor grounding is used as the dividing line for one revolution of the tire;

[0020] Step 4: Save the time-domain signal of multiple cycles, resample to the same sampling points, perform Fourier series decomposition on the multi-cycle signal, perform coherent averaging on the complex spectrum to obtain the spectrum f2, and store the average spectrum of the tire in the initial state as F.

[0021] Step 5: Calculate the cross spectrum p1 of the spectra f2 and f1, and store the cross spectrum of F and f1 in the initial state of the tire as P;

[0022] Step 6: Calculate the energy spectral entropy for p1 to obtain e1, and store the energy spectral entropy of the cross spectrum P under the initial state of the tire. The calculation method for spectral entropy is as follows: Let y t It is the input of a stable causal system with a transfer function of G(B) and G(f) ≡ G(e). -i2πf )=G(B) B =exp(-i2πf o ), x t If the system output is stationary, then it can be proven that the relationship between the entropy rates of the system's input and output is as follows: This relationship is most commonly used in time series analysis with the input {y} t} is white noise with a value of zero and a variance of σy 2 At this point, the system's transfer function |G(f)| 2 =G X (f) / σy 2 The entropy rate of white noise is Therefore, we obtain {x t The entropy rate of} A linear stationary system with white noise as input is precisely a time-series ARMA model, and the relationship above reflects {x} t The relationship between the spectral density and its entropy rate of} is given by the equation where the first term on the right-hand side is a constant. Compare h... x The magnitude of the integral is equivalent to comparing the magnitude of the second integral, hence the term is called For the sequence {x t Spectral entropy of};

[0023] Step 7: Calculate the ratio of p1 to P, set the alarm threshold L, and trigger an uneven wear alarm when the ratio is greater than L.

[0024] In addition, during actual operation, multiple data segments can be processed to calculate the p1 value separately. Considering that tire wear is a slow process, the p1 values ​​obtained from sampling within the same time period are considered to be... k =p k-1 +Q, where Q is the state error and the calculated value is y. pk =p k +R, where R is the observation error, and Q ~ N(0,Q), R ~ N(0,R). Based on the above, particle filtering is performed.

[0025] like Figure 2 As shown, by collecting and processing real vehicle data, the tread depth of the tires was measured at different time periods. The variance of the tread depth was 1mm, 2.5mm, and 3.5mm over time. The experimental results show that the method provided by this invention has a significant ability to distinguish tires worn on actual roads. After filtering, the data distinction is more obvious and the index fluctuation is smaller.

[0026] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0027] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for early warning of uneven tire wear, characterized in that, The method includes the following steps: Step 1: Obtain tire temperature, tire pressure, and acceleration data through intelligent tire sensors; Step 2: Calculate the ideal harmonic spectrum f1 of the tire using the tire design information; Step 3: Based on the acceleration signal, use the obvious characteristic of the sensor grounding as the dividing line for one revolution of the tire; Step 4: Save the time-domain signal of multiple cycles, resample to the same sampling points, perform Fourier series decomposition on the multi-cycle signal, perform coherent averaging on the complex spectrum to obtain the spectrum f2, and store the average spectrum of the tire in the initial state as F. Step 5: Calculate the cross spectrum p1 of the spectra f2 and f1, and store the cross spectrum of F and f1 in the initial state of the tire as P; Step 6: Calculate the energy spectral entropy of p1 to obtain e1, and store the energy spectral entropy of the cross spectrum P under the initial state of the tire. Step 7: Calculate the ratio of p1 to P, set the alarm threshold L, and trigger an uneven wear alarm when the ratio is greater than L.

2. The method for early warning of uneven tire wear according to claim 1, characterized in that: The tire sensor in step one is used to detect and output temperature signals, tire pressure signals, and acceleration signals. The output terminal of the tire sensor is electrically connected to a PLC controller for converting the temperature signals, tire pressure signals, and acceleration signals into temperature values, tire pressure values, and acceleration values. The PLC controller is electrically connected to an external device for displaying the temperature values, tire pressure values, and acceleration values.

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