A method for checking deformation of tires subjected to external stress while rolling

Abstract

A method for identifying deformations in a tire is disclosed, the method comprising the steps of: fixing a sensor to the tire, capable of generating a signal representative of its own motion; measuring a time-dependent wheel rotation signal Sig, which includes the amplitude of acceleration, during rolling; TDR Step (201) of acquiring (101); wheel rotation signal Sig TDR A reference speed W associated with a part of reference A step (202) of determining W reference The wheel rotation signal Sig is calculated by the variable F, which is a function of TDR and normalizing the part of the wheel rotation signal Sig TDR and angularly resampling (204) the portion of the normalized angularly resampled wheel rotation signal Sig TDR obtaining (205) a spectral signal of said portion of said tyre; defining (206) a spectral variable; and determining (207) a deformation Def% of said tyre as a function G of said spectral variable.

Description

[Technical field]

[0001] The present invention relates to the field of measurement signals provided during rolling by measuring means mounted on a mounting assembly of a land vehicle. [Background technology]

[0002] Recent developments in coupled mounting assemblies, measuring physical variables of the mounting assembly with sensors mounted on the mounting assembly, provide a determination of the mounting assembly's condition and thus open the door to the development of services related to the monitoring of the mounting assembly's condition. While common variables measured, such as the air pressure of the mounting assembly or the temperature of this mounting assembly, only change slightly during the rotation of the mounting assembly and are therefore less sensitive to the measurement noise generated by the rotation of the mounting assembly on a surface of random roughness, more subtle variables are very sensitive to the physical phenomena related to the rotation of the mounting assembly. Furthermore, the mounting assembly is subjected to external forces. Some are related to the movement of the mounting assembly, such as rolling resistance. Other forces, such as load, are applied at all times, especially while stationary. These applied forces can affect the subtle variables that are to be measured. Finally, new services require cleaning of the directly measured physical variables before useful information can be obtained from the measurement signals, such as the deformation of the tire casing. Summary of the Invention [Problem to be solved by the invention]

[0003] One of the objects of the invention described below is to solve the problem of disturbances in the measurement signal generated by the sensor in order to obtain only measurements free of disturbances of certain physical phenomena, aiming to obtain a scalar value for the deformation of the tire casing.

[0004] For a better understanding of the present invention, the circumferential direction S, the axial direction A, and the radial direction R are directions defined relative to a rotational reference frame about the inherent axis of rotation of the tire casing. The radial direction R is a direction extending perpendicularly away from the inherent axis of rotation. The axial direction A is a direction parallel to the inherent axis of rotation. Finally, the circumferential direction S forms a regular triangle with the predefined radial and axial directions. [Means for solving the problem]

[0005] The present invention relates to a method for identifying deformations in a tire casing, the tire casing being mounted on a wheel so as to form a pneumatic mounting assembly in rolling motion at a rotational speed W and subject to external forces, for example a static load. The tire casing has a crown in contact with the ground and rotating about a natural axis of rotation. The method comprises the steps of: fastening at least one sensor to a crown of a tire casing and generating at least one output signal responsive to movement of the sensor within the tire casing; obtaining at least one first time signal Sig during rolling, the first time signal Sig including at least the amplitude of the movement; Wheel rotation count N is 1 or more TDR The first signal is divided into two parts, and the wheel rotation signal Sig TDR and Wheel rotation signal Sig TdR At least one reference speed W associated with at least one portion of reference determining At least one reference speed W reference normalizing at least a portion of the wheel rotation signal by a variable F that is a function of Wheel rotation signal Sig TDR Angularly resampling at least a portion of obtaining a spectral signal spect(Sig) of at least a portion of the normalized and angularly resampled wheel rotation signal; defining at least one spectral variable related to a spectral signal spect(Sig); determining a deformation Def% of the tire casing as a function G of at least one spectral variable; Includes.

[0006] The term "sensor motion" here refers not only to the motion, velocity, and acceleration of the sensor in absolute terms with respect to the Galilean reference frame, but also to the deformation or deformation rate or deformation acceleration of the sensor, i.e. in relative terms between the various elementary units of the sensor. The sensor output signal is therefore sensitive to at least one of these six components of the sensor motion.

[0007] The signal received from the sensor is the time amplitude of the sensor's movement during the rolling of the mounting assembly under certain conditions. The acquired signal therefore represents the amplitude variations of the movement over a portion of the wheel revolution with respect to the tire casing, which may include not only those related to the traverse of the contact patch by that portion of the tire casing to which the sensor is attached, but also those related to other specific zones of the wheel revolution, for example those corresponding to angular sectors opposite the contact patch that are subject to adverse deflections, or those corresponding to angular sectors located at 90 degrees from the contact patch with respect to the axis of rotation. In all these zones, depending on the sensitivity of the sensor, variations of the sensor's movement may be observed on the output signal.

[0008] This first acquired signal is related to a reference speed that may be determined on this first signal or obtained from another source, such as another signal, or may be obtained from the output of a variable by a system external to the mounting assembly. This reference speed necessarily relates to the same time frame as the portion of the first signal. This reference speed serves to normalize the amplitude of the first signal with a function F of which the reference speed is a variable. The function F may be a linear function, a power function, an exponential function or a constant function. The sensor signal is normalized as a function of the dependency of the sensor signal amplitude on the reference speed, if this dependency is recognized as a parasitic signal of tire casing deformation. The first normalized signal is thus independent of this reference speed. For example, this reference speed may be the rotational speed of the mounting assembly or the translational speed of the mounting assembly in the direction of movement of the mounting assembly. As a result, the first signal can be used independently of the reference speed related to the rotation of the mounting assembly.

[0009] The method also comprises a step of partitioning the first signal Sig over a number of wheel revolutions in order to exploit the periodicity of the sensor signal relative to the natural rotation of the tire casing in rolling conditions. Thus, a high quality spectral analysis can be performed on the data obtained from the wheel rotation signal. However, in this step, it is not essential that the number of wheel revolutions is an integer number, as long as this number is at least greater than 1, the signal can be partitioned over the actual number of wheel revolutions. Preferably, multiple wheel revolutions are used.

[0010] The method also comprises an angular resampling of the first signal or the wheel rotation signal, which can be performed before or after the normalization step. This step allows the conversion of the time signal into a spatial signal by synchronizing the time signal with one or more angular references of the mounting assembly. This angular reference can first be obtained from the first signal by the specific response of the sensor to the individual azimuth angles of the wheel rotation. However, this angular reference can also be obtained from another signal of a sensor that shares a common timer with the first signal. This synchronization of the shared timer or signal is natural when the two sensors are from the same device or when the signals are transmitted to a common device. This angular resampling naturally allows the generation of a spatial signal that is periodic with respect to the wheel rotation. Therefore, to generate a completely angularly periodic signal, it is sufficient to interpolate the signal over a set angular interval. However, this resampling allows the generation of an angularly periodic signal even if the mounting assembly is to undergo a movement with a variable speed.

[0011] The method comprises a step of performing a spectral analysis from said portion of the angularly resampled normalized wheel rotation signal, where it is useful to ensure that said portion of the initial signal is defined at a constant angular pitch, which ensures a regular spatial discretization of the sensor signal. If necessary, a step of angular resampling ensures that the angular pitch is constant to allow a high quality spectral analysis, which may require an interpolation method of the measurement points to redefine the signal at a constant angular pitch.

[0012] The method includes the step of defining a spectral variable or a number of spectral variables related to the spectral signal obtained from the previous step.

[0013] Finally, the method includes determining the deformation of the tire casing by a function G of the spectral variables identified in the previous step, said deformation being expressed in the form of a scalar or vector that is an invariant of the tire casing in rolling state under the application of external forces such as static loads.

[0014] Advantageously, the reference speed W reference The step of determining the wheel rotation signal Sig according to the following formula: TDR From, or the first signal Sig TDR and establishing from the signal synchronized with the ratio of the angular change to the duration separating two azimuthal positions about a unique axis of rotation for the sensor in the tire casing; [Formula 1] W reference =Δ(α) / Δ(t) where α is the angular position and t is the time abscissa related to the angular position.

[0015] If the reference speed corresponds to the rotational angular speed of the tire casing, this reference speed is calculated over the angular change of the signal between two known positions. Preferably, this reference speed is evaluated over a signal duration of less than one wheel revolution, which allows a quick definition of the reference speed and a normalization step to be performed on a portion of the first signal in an electronic device associated with the sensor. Furthermore, this allows an angular resampling of this portion of the first signal with better accuracy when the tire casing moves with a variable angular speed. Indeed, at the level of wheel rotation, the variations in angular speed are necessarily small for tires with a development length that can reach 2 meters for car tires and 3 meters for truck tires. The acceleration or deceleration applied to the tire casing over this length is naturally small in the drive and braking systems of current vehicles. Of course, it is entirely possible to integrate the angular velocity variations during the wheel rotation at finer azimuth settings in order to take into account small variations in angular velocity that occur during the wheel rotation, for example before and after passing over a contact patch, or when encountering discontinuities in the movement on the ground, such as a cross bar on the ground, etc. This precision with respect to the reference speed during the wheel rotation then allows not only to normalize the signal more accurately, but also to improve the angular precision of the angular position with respect to the measurement point of the first signal during the angle resampling step, thus improving the desired precision for sensing the smallest variations during the wheel rotation.

[0016] According to a particular embodiment, the azimuthal position of the tire casing is determined based on the position of the wheel rotation signal Sig. TDR The wheel rotation signal Sig corresponds to any specified angular position from the signal synchronized with TDR The angular positions detectable from are included in the group.

[0017] These are the azimuth positions that affect the signal from the motion sensor and correspond to specific angular positions. These positions are therefore easy to identify on the signal from the sensor. Furthermore, it is easy to assign their azimuth reference. In fact, the center position of the contact patch corresponds to an azimuth position of 0 degrees or 180 degrees with respect to the ground normal. If the length of the contact patch is determined from the entry and exit points into the contact patch, the angle that the contact patch forms can be established as the ratio between the contact patch length and the development length of one revolution or 360 degrees of the tire casing. The sectors that the contact patch forms on both sides of the normal to the ground are divided equally. Naturally, by using signals other than the first signal, angular sectors finer than one revolution of the wheel are also possible, such as angle encoders.

[0018] According to a very particular embodiment, determining entry and exit of the ground plane for the first signal comprises: defining a threshold B which is a function of at least one maximum value for at least a second portion of the first signal; identifying a set of step sizes I corresponding to abscissa values ​​t of at least one first signal at which the first signal crosses a threshold B in a given crossing direction; Includes. An entire step size I, or a step size I of the same parity, represents an entry or exit from a contact patch.

[0019] Advantageously, the threshold B is a value ranging between 0.1 and 0.5 for at least one maximum value for at least one portion of the first signal.

[0020] This is an embodiment associated with the detection of positions related to the entry and exit of the contact patch. The strong changes of movement occurring at these two positions of the wheel rotation allow elementary methods to be applied to determine these two points, for example by direct processing of the first signal in the electronic circuit of the sensor. Depending on the choice of the detection of the direction of crossing the threshold, it is possible to directly determine the entry or exit of the contact patch with various signals. The diversity of signals relates firstly to the observation direction of the variable, the change of the radial or circumferential movement of the tire casing, and secondly to the nature of the signals, the acceleration signals and the radial deformation signals, etc.

[0021] It is not intended to specify, in an absolute sense, the exact location of the contact patch, which depends on the variable nature of the wheel rotation. The method used in the data aggregation step does not necessarily require precision in the absolute positions of the entry and exit of the contact patch. The robustness of the method allows the position of the contact patch centre to be determined reproducibly, regardless of the nature of the first signal resulting from the entry and exit positions of the contact patch.

[0022] Advantageously, the angular pitch is less than 18 degrees.

[0023] It is therefore possible to ensure that one of the measurement points lies on the ground plane, so that a change in motion will be observed at least between this sampling point and the closest point, allowing the entry and exit points of the first signal into and out of the ground plane to be determined.

[0024] Highly advantageously, the angular pitch is less than 6 degrees, preferably less than 3 degrees.

[0025] By using a finer angular pitch, multiple measurement points can be sensed within the contact patch, and thus the first signals of deformation phenomena can be observed no longer at the level of the wheel rotation, but at the scale of the contact patch. This fine observation makes it possible to exploit different observable variables relevant for the specific application. For example, in the case of wet road conditions, a puddle in front of the contact patch changes the geometry of the contact patch. By observing multiple points at the contact patch height, it is possible to measure the width of this puddle and its influence on the dynamics of the tire casing.

[0026] According to a very particular embodiment, the method comprises the steps of: TDR The data from at least one portion of the TDR aggregating over at least one sub-portion of at least one portion of the angularly resampled normalized wheel rotation signal Sig TDR At least one sub-portion of the portion of the angularly resampled normalized wheel rotation signal Sig TDR At least one part of.

[0027] Advantageously, the wheel rotation signal Sig TDR At least one sub-portion of the portion is an integer multiple of the wheel revolutions.

[0028] The method preferably includes a data aggregation step, which allows data on multiple angular periods of wheel rotation to be aggregated over a portion of the wheel rotation, whether a complete wheel rotation or an integer multiple of the wheel rotation. Thus, by doubling the signal data over this partial angular sectorization, all variations on the scale of the wheel rotation period, such as ground granularity or isolated obstacles on the road, are taken into account. The aggregation includes averaging data from various wheel rotations to a single value at a selected azimuth angle of the angular sectorization. This is equivalent to filtering or averaging random phenomena on the wheel rotation over multiple wheel rotations, improving the quality of the sensor signal. Of course, the specific angular pitch of the angular sectorization resulting from the aggregation step is a constant angular pitch, which ensures high quality of the spectrum analysis.

[0029] Also, the fine angular pitch allows for minimal variations in angle to be taken into account, which are periodic with respect to the wheel revolutions, with multiple wheel revolutions in a homogeneous manner. These minimal variations can be obtained without a high degree of time discretization, but the higher the discretization, the shorter the signal required to determine these minimal variations. This step guarantees a value added to the initial signal from the sensor.

[0030] To determine the overall deformation of the tire casing under external forces, the optimal sub-portion defined in the data aggregation step is a wheel revolution or an integer multiple of the wheel revolution, so as to benefit from the angular periodicity of the wheel revolution. The data aggregation step can be performed over one wheel revolution, which is the natural angular period of the tire casing. Therefore, the method is ideal for observing the deformation of the tire casing over a wheel revolution. This allows analyzing signals of reasonable size while focusing on the observation of one angular sector and still taking advantage of the natural periodicity of the tire casing with respect to the wheel revolution.

[0031] According to a preferred embodiment, the data aggregation step includes one of the methods included in the group including average over a decile interval, median, selection of a decile or interval, methods of interpolation, weighted or unweighted average, optimization of a parametric model of the tire deformation.

[0032] The purpose of the aggregation is to adjust the measurements to the new angular distribution of the first signal, making sense of all the raw measurement data without favoring one zone over the others due to the abundance of measurement points. The aggregation step is intended to provide a balanced signal with respect to the measurement points at an angular pitch selected by the operator according to the tire casing deformation observed. For this purpose, a method that optimizes a parametric model of the tire deformation is ideal, since this parametric model is theoretical and may not take into account the measurement noise associated with the entire applied measurement chain. The output signal from the aggregation step is the theoretical output of the parametric model that has the smallest variance with the set of recorded measurement points.

[0033] According to a particular embodiment, at least one spectral variable is identified on the first positive frequency block of the spectral signal spect(Sig).

[0034] Preferably, the at least one identified spectral variable is included in the group consisting of maximum, median, mean, first block passband, first block area under the curve, median frequency, mean frequency, maximum frequency.

[0035] Preferably, the function G is a linear function of at least one spectral variable.

[0036] The applicant was surprised to find that examining the first positive frequency block of the spectral signal spect(Sig) is sufficient to identify one or more variables associated with this first block that are relevant for determining the tire casing deformation with adequate quality at the end of the method. The variables that are most sensitive to the tire casing deformation are specified in the prepared list. These are standard variables of the spectral signal that require little computational resources, which is favorable for the method. Moreover, these variables are primarily sensitive to the tire casing deformation and are less sensitive to secondary variables. As a result, these variables are ideally suited to the general deformation of the tire casing as a whole, for example deformations caused by global forces on the entire tire casing, such as static loads.

[0037] In this case, the function G does not need to be sophisticated, and the applicant has found that a linear function G of one or more spectral variables can adequately determine the deformation of the tire casing in response to various usage conditions of the tire casing subjected to external stresses and especially static load fluctuations.

[0038] According to a preferred embodiment, the sensor is included in the group comprising accelerometers, piezoelectric sensors, magnetic sensors, inductive sensors and capacitive sensors.

[0039] All these sensor types allow observing the change in motion when passing through the ground, specifically a specific zone in the wheel rotation. Some of these sensors, such as accelerometers, provide discrete values ​​over a small spatial range, while others, for example piezoelectric sensors, provide discrete values ​​over a large spatial range, which can suppress the effects of local phenomena. Some sensors are influenced by external physical phenomena, such as the Earth's gravity, as acceleration, allowing the recovery of azimuth information if necessary.

[0040] According to a preferred embodiment, the data aggregation step includes one of the methods included in the group including average over a decile interval, median, selection of a decile or interval, methods of interpolation, weighted or unweighted average, optimization of a parametric model of the tire deformation.

[0041] The purpose of the aggregation is to set up a means to execute on a new angular distribution of the first signal in order to resolve the set of raw measurement data. The aggregation step is intended to provide a balanced signal for the measurement points of the angular pitch selected by the operator according to the tire casing deformation observed. To this end, a method of optimizing a parametric model of the tire deformation is ideal, since this parametric model is theoretical and may not take into account the measurement noise associated with the entire measurement chain to which it is applied. The output signal from the aggregation step is the theoretical output of the parametric model that has the smallest variance with the set of recorded measurement points.

[0042] According to a particular embodiment, the movement of the sensor is described by an acceleration.

[0043] This type of sensor provides local information about the tire's movements, since its attachment to the tire casing is very small. The sensor is therefore hardly disturbed by changes in the tire casing's movements. The miniaturization of the sensor therefore makes it possible to double the observation directions by using two- or three-axis accelerometers that provide several signals in orthogonal directions at the same physical installation point of the sensor. Finally, since acceleration is a motion-sensitive signal, a high sensitivity of the sensor to tire casing movements is guaranteed, allowing for example a detailed analysis of possible local phenomena at the contact patch.

[0044] Advantageously, after synchronising the first signal Sig with respect to the angular position of the tyre casing, and before the normalisation step, a correction Corr is made to the first signal Sig in order to take into account the influence of the earth's gravity.

[0045] A drawback of the acceleration signal is that it is sensitive to the earth's gravity when it is oriented in a direction approximately parallel to the earth's gravity. In the case of a tire casing, the sensor is rotationally coupled to the tire casing. As a result, the amplitude of the sensor signal is affected by the earth's gravity during wheel rotation when the sensor is oriented radially or circumferentially. This is reflected in the signal in the form of a sine function of amplitude coupled to the earth's gravity, with nodes at the azimuth of the tire casing separated by 180 degrees when the sensor is oriented in line with the gravity vector, i.e. substantially perpendicular to the ground. Conversely, the sensor signal is not affected by the earth's gravity when the sensor is oriented parallel to the ground, i.e. corresponding to two azimuth positions approximately ±90 degrees from the gravity vector, separated from each other by 180 degrees. To eliminate this parasitic component of the acceleration signal, it is necessary to couple the amplitude of the signal with the corresponding sine function by synchronizing the first sensor signal with the vertical position relative to the ground, which corresponds to the direction of the gravity vector.

[0046] According to a very particular embodiment, the first signal Sig comprises the amplitude of the acceleration in a direction perpendicular to the crown of the tire casing.

[0047] This is one of the two directions for acceleration that is sensitive to Earth gravity. The orientation of the sensor within the tire casing facilitates this orientation, allowing the effect of Earth gravity to be concentrated at 0 and 180 degrees azimuth positions relative to a line perpendicular to the ground. Thus, azimuth positions at + / - 90 degrees from these positions are not disturbed by Earth gravity, and so in these particular angular sectors the signal from the accelerometer can be used directly without the step of correcting for Earth gravity.

[0048] Very advantageously, the function F is reference It is proportional to the square of .

[0049] For example, in the case of signals from accelerometer type sensors, in the radial or circumferential direction of the tire casing, the sensor signal is affected by a square function of a reference velocity, and therefore the normalization step preferably uses a square function of a reference velocity, where the reference velocity is preferably the angular velocity of the tire casing.

[0050] The invention will be better understood from reading the following description, purely by way of non-limiting example, in connection with the accompanying drawings in which the same reference numbers represent the same parts in all cases, in which: [Brief description of the drawings]

[0051] [Figure 1] 1 shows an outline of the method according to the present invention. [Diagram 2] 1 shows an example of a first signal from a sensor. [Diagram 3] Angular resampling of the wheel rotation signal is shown. [Figure 4] 1 shows an example of a resampled normalized wheel rotation signal. [Diagram 5] 13 shows an example of the final signal after aggregating data over subportions of the wheel rotation signal. [Figure 6] FIG. 2 is an explanatory diagram of a spectrum signal spect(Sig) of wheel rotation. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0052] 1 shows a schematic diagram of the method according to the invention, which performs a number of steps along different possible paths in order to finally obtain, from a first signal Sig obtained by temporal acquisition 201 of the amplitude output of a motion sensor during the rolling of a tire casing equipped with the sensor, a scalar representative of the deformation of the tire casing.

[0053] The first path derives from the time signal at the output of step 201 a reference velocity W of the tire casing in its mounting assembly configuration, i.e., the tire casing mounted on the rim and inflated. referenceHere, the first signal Sig101 is already segmented over a certain number of wheel revolutions (12 to be precise). As a result, the first signal Sig101 is determined based on the wheel revolution signal Sig TDR This reference speed can be the angular speed associated with the inherent rotation of the tire casing about its axis of rotation, but it can also be the translational speed per unit length in the direction of travel of the tire casing. This value corresponds to the wheel rotation signal Sig TDR However, the first signal and therefore the wheel rotation signal Sig TDR It may also be determined from another signal that is time-synchronized with the

[0054] Next, the wheel rotation signal Sig TDR is the variable W obtained in step 2. reference After this step 203, a signal normalized for the motion of the tire casing in the time description is obtained.

[0055] The normalized signal then needs to be angularly resampled to find a signal that is angularly periodic with respect to the wheel revolutions, via step 204. Then, after this step 204, the result is a normalized, angularly resampled signal over several wheel revolutions.

[0056] The second path is the wheel rotation signal Sig resulting from step 201. TDR Angularly resampling the first signal Sig from a first signal Sig that is also periodic with respect to the wheel rotation, by synchronising this first signal with a morphology of the first signal or by synchronising in time with the first signal by another signal coming from another sensor or from another track of the same sensor, such as the circumferential acceleration of a three-dimensional accelerometer. This angular resampling of the first signal results in a signal that is periodic with respect to the wheel rotation at the end of step 204.

[0057] After synchronizing this angular signal with another time signal, a reference velocity is determined from the other time signal synchronized with the first signal, preferably the same other signal that was used to angularly resample the first signal in step 204. Thus, at the end of step 202, a reference velocity W reference is identified.

[0058] The reference velocity then allows normalizing the angularly resampled signal from step 204 using a function of the reference velocity variable, thereby giving at the end of step 203 an angularly resampled normalized signal.

[0059] Optionally, whichever path is taken, data from the angularly resampled normalization resulting from step 204 on the first path or step 203 on the second path is aggregated. This data aggregation is performed over subportions of the input signal that are wheel revolutions, ideally multiples of the wheel revolutions, since the resampled normalized signal is periodic in nature with respect to the wheel revolutions. At this level, it may be necessary to resample the aggregated signal resulting from step 207 at a constant angular pitch in order to perform a high quality spectral analysis.

[0060] Alternatively, if the first signal 101 is contaminated by a known physical phenomenon, such as an accelerometer signal affected by the Earth's gravity, it may be useful (although not essential) to perform a correction of the first signal to this physical phenomenon in order to suppress the parasitic noise caused by the physical phenomenon. This correction can be performed at any step between steps 201 and 204, but necessarily before the data aggregation step 205, thereby improving the quality of the signal regarding the deformation of the tire casing. If the correction is performed after the normalization step, it should also be normalized so as not to introduce any correction errors.

[0061] A spectral analysis 205 is then performed on the wheel rotation signal normalized and resampled in step 204 or 203 depending on the path, which signal is periodic with respect to the wheel rotation. If the angular pitch is not regular, the measurement points should be interpolated onto theoretical points regularly spaced across the signal. The spectral analysis step 205 is possibly performed after a data aggregation step 207 providing a signal with a fixed angular pitch.

[0062] The spectral signal obtained from step 205 is analyzed to extract one or more spectral variables during step 206. This spectral variable(s) will give a function G which in turn will provide a vector, preferably a scalar, as an invariant of the tire casing deformation in rolling conditions subjected to external forces.

[0063] Figures 2 to 4 illustrate the method using the second path outlined in figure 1. An accelerometer is described which is fixed to the crown of a tire casing, mounted on the inner liner of the tire casing. The tire casing is a Michelin CrossClimate, size 265 / 65R17, with a static load of 800 daN when mounted on a vehicle. The mounting assembly was inflated to 3 bar. Measurements were performed while the vehicle was running on a circuit of asphalt with different roughnesses, under standard speed and load conditions according to the tire markings. The mounting assembly was installed on the front axle of the vehicle. Measurements were mainly performed in straight-line driving conditions.

[0064] FIG. 2 shows a time signal 101 acquired with a signal acquisition frequency of 3200 Hz, which allows a very fine discretization of the signal. As a result, it records all the acceleration type movement fluctuations in the crown of the tire casing during rolling. The wheel rotation signal Sig TDR This signal was sectioned over 12 wheel revolutions to construct

[0065] The recording in FIG. 2 was made during the acceleration phase of the vehicle, which is reflected by the increase in the amplitude of the acceleration signal. The sensor here is a 1-axis acceleration sensor mounted radially relative to the crown of the tire casing. The data was transmitted by wireless communication between an electronic device electrically connected to the accelerometer and a second radio frequency device located in the vehicle. In this particular case, post-processing of the measurements was performed in the vehicle. However, it is quite possible to perform these in a first electronic device equipped with a microcontroller or microprocessor and coupled with sufficient memory space to perform the elementary mathematical operations required by the method.

[0066] Here, the first step consists in determining the reference speed, taking the rotational angular velocity as the reference speed. For this purpose, the first time signal 101 must be synchronized with a reference azimuth position of the wheel rotation. For this purpose, the first signal 101 exhibits regular, very strong amplitude dips 111, 112, reflecting the passage of the contact patch through the angular sector in which the accelerometer is mounted. Naturally, these downward and upward slopes for the dips 111, 112 represent the entry and exit of the contact patch, respectively. The center of the contact patch is the middle of the section separating the entry and exit of the contact patch. This center is assigned to the 0° azimuth position, which becomes the azimuth reference. By adopting a second angular reference, for example at the next signal dip 112, the signal 101 is determined for a wheel rotation of 360° and for the time interval related to this wheel rotation. The reference speed W reference is defined as the ratio of the angular change between the two centers of the contact surface to the time interval separating these two azimuth positions. This reference velocity W reference is assigned to the portion of the signal located between these two centers of the contact area. Naturally, taking into account the two non-adjacent drops 111, 115 of the time signal 101, the second reference speed W reference and assigning a second rate to the portion of the signal 101 located between the two drops 111, 115.

[0067] FIG. 3 shows the result of the step of angularly resampling the time signal 101. It is thus easy to synchronize the time signal with a wheel revolution over 360 degrees, using the determination of the center of contact for each drop of the time signal performed in the previous step. The discretized measurement points are then linearly distributed with respect to the wheel revolution. Even if angular positioning errors occur in this step, the result will be smoothed and the angular positioning errors will be minimized, for example by linear interpolation performed during the data aggregation step. In a more sophisticated way, a reference speed is evaluated for each wheel revolution. It is possible to assign an evolving angular speed to the wheel revolution by considering the reference speeds of successive revolutions. For example, if the reference speeds are determined over three successive revolutions, it is possible to assign to the central wheel revolution a first reference speed for the first quarter wheel revolution, which is the center of gravity speed weighted by 2 to the reference speed of the preceding revolution and by 1 to the reference speed of the current revolution. The next quarter will have a reference speed weighted by 2 to the reference speed of the current revolution and by 1 to the reference speed of the preceding revolution. The third quarter of the wheel revolution has a reference speed of the center of gravity weighted by 2 to the reference speed of the current revolution and weighted by 1 to the reference speed of the next revolution. Finally, the last quarter of the wheel revolution has a reference speed of the center of gravity weighted by 1 to the reference speed of the current revolution and weighted by 2 to the reference speed of the next revolution. All the discretized measurement points are distributed in each quarter wheel revolution in proportion to the ratio of the reference speed of each quarter revolution to the reference speed of the current revolution. Other methods of smoothing these points can also be applied. Here, the spatial discretization of the points is not regular due to the variable rolling speed. It is quite possible to make this discretization regular for the points of the signal 102 by applying a method of interpolating the measurement points over a given angular distribution with respect to the wheel revolution. An angularly resampled signal 102 with a regular angular pitch is then obtained. Figure 3 shows an angularly resampled signal 102 that is periodic with respect to the wheel revolution, with an arbitrary discretization of the measurement points.

[0068] FIG. 4 shows the result of a step of normalizing the first angularly resampled signal 102 without interpolation of points. Thus, using the periodicity of the first resampled wheel rotation signal with respect to the wheel revolution, it is easy to resolve the angular signal over a wheel revolution or over a multiple of a wheel revolution (here 12 wheel revolutions) as shown in FIG. 4. The normalization step involves dividing the amplitude of the signal by a function of a reference speed associated with each part of the wheel revolution. For example, the reference speed was determined during the first signal processing step 101. The function used here is the square of the reference speed, which is the angular velocity. The result observed for the curves 103 and 103bis is that the amplitude of the normalized signal is similar for each wheel revolution. There is no longer a strong variation in the amplitude between the various wheel revolutions made at different speeds and on different roads. Also, the signal is centered on a unit value. Then, over the same angular interval length, which is an integer multiple of 360 degrees, the wheel revolution sections are superimposed to form a curve bundle 103, here shown by the grey curve. This takes into account the spread of measurements between wheel revolutions, emphasized by the fact that here the signal is not corrected for the Earth's gravity. However, applying a low-pass filter results in a smoother black curve 103bis, since the parasitic noise is removed. This makes it possible to see that the signal 103bis is periodic with respect to the wheel revolutions, with slight variations between wheel revolutions. At the end of this normalization of the signal 102, an angularly resampled normalized signal 103 is obtained. Figure 4 shows the angularly resampled normalized signal 103, centered on a unit value, as confirmed by the filter applied to the curve 103bis.

[0069] FIG. 5 shows the result of a step of aggregating the data of the signals 103 from the previous step, which is an optional step. Here, the segments of each wheel revolution are superimposed over the same angular interval length of 360 degrees, as shown by the grey curves forming the curve bundle 104. This is emphasized by the fact that the signals are not corrected for the Earth's gravity, taking into account the spread of the measurements between each wheel revolution. However, since the accelerometer is now sensitive to the Earth's gravity, if a correction for the Earth's gravity is applied to each wheel revolution before the normalization step, the data aggregation by the method of averaging over a decile interval determines a very stable curve 104bis for the wheel revolution. This results in a signal for the deformation of the tire casing under external forces, in particular in this case a static load. This signal 104bis is representative of the measurements of the tire casing in rolling condition at variable speeds on ground of any roughness. This curve is an invariant of the tire casing in rolling condition under static load, mounted on a rim and inflated.

[0070] 6 shows the spectrum of the angularly resampled normalized wheel revolution signal with a fixed angular pitch of 0.1 degrees and spaced over 12 wheel revolutions. To suppress high frequency artifacts, the signal resulting from step 203 of the first pass or step 204 of the second pass was first filtered with a low pass filter of 1 / 30th of a wheel revolution.

[0071] After spectral analysis of the filtered signal, i.e. here the signal from the aggregation step of step 207, using a Fourier transform, we obtain a curve 105 which represents the amplitude of the Fourier transform over a limited frequency band. This curve shows various spectral blocks, the first of which have a large amplitude, but the following blocks are not negligible per se.

[0072] It is possible to obtain several spectral variables from this spectral response 105. In this case, we focus on the first block, but the analysis can also be carried out on subsequent blocks.

[0073] To consider the sensitivity of the method, Fig. 6 shows a second dotted curve 106 corresponding to the spectral response of the same sensor fixed to the same mounting assembly to different static loads and different air pressures, where the mounting assembly is swapped between the front and rear axles of the vehicle. Thus, the mechanical response of the tire casing to the two variables, air pressure and static load, is necessarily different. However, the spectral response shows a similarity in shape with a continuous block-shaped response, where the width and height of the blocks are a function of the external forces applied to the tire casing.

[0074] This shows that while the analysis of the first block may not be sufficient for weak fluctuations in the external forces acting on the tire casing, it has sufficient discriminatory power to determine the deformation of the tire casing associated with such fluctuations in external forces.

[0075] Spectral variables such as maximum, median, mean, passband, area under the curve associated with the first block, etc., can all be criteria for identifying tire casing deformation. However, the median frequency, mean frequency, and maximum frequency are also secondary criteria in tire casing deformation and, although still discriminatory, indicate much weaker driving forces.

[0076] Then, a tire casing deformation value can be assigned using a function of one or more spectral variables in vector or scalar form. Preferably, the maxima 105bis and 106bis of the first block are found to be very good indicators of the tire casing deformation, which allows the tire casing deformation to be determined through an affine function of the maxima of the first block. However, the determination of the tire casing deformation can be more sophisticated if other spectral variables associated with the secondary spectral blocks are also taken into account.

Claims

1. A method for checking the deformation of a tire casing that is attached to a wheel in a state of receiving an external force so as to constitute an air intake assembly rolling at a rotational speed W, wherein the tire casing has a crown that contacts the ground and rotates around a natural rotation axis, fixing at least one sensor to the crown of the tire casing to generate at least one output signal sensitive to the movement of the sensor within the tire casing; acquiring (201) at least one first signal Sig including at least the amplitude of the movement during rolling; The number of wheel rotations N for multiple times TDR separating the first signal over the number of wheel rotations N to form a wheel rotation signal Sig TDR and steps of the wheel rotation signal Sig TDR at least one reference speed W related to at least one part of reference determining step (202); said at least one reference speed W reference normalizing, by a variable that is a function F, said at least one portion of said wheel rotation signal (203); angularly resampling (204) at least one part of the wheel rotation signal; acquiring (205) a spectral signal spect(Sig) of at least one part of the normalized and angularly resampled wheel rotation signal; defining (206) at least one spectral variable related to the spectral signal spect(Sig); specifying (207) the deformation Def% of the tire casing as a function G of the at least one spectral variable; A method comprising the steps of.

2. the reference speed W reference The step (202) of determining includes establishing a ratio of an angular change with respect to a duration for separating two azimuthal positions around the eigen-rotation axis related to the sensor in the tire casing from the wheel rotation signal Sig TDR (101), or from a signal synchronized with the wheel rotation signal Sig TDR (101). [Equation 1] where α is the angular position and t is the time abscissa related to the angular position, the method for checking the deformation of the tire casing receiving an external force according to Claim 1.

3. The angular position of the tire casing is an entry into the ground contact surface, an exit from the ground contact surface, or a center position of the ground contact surface, or a specified angular position corresponding to any signal synchronized with the wheel rotation signal Sig TDR from the wheel rotation signal Sig TDR The method for confirming the deformation of the tire casing receiving an external force according to claim 2, which is included in a group including an angular position detectable from

4. The angular pitch is less than 18 degrees, preferably less than 6 degrees, very preferably less than 3 degrees, the method for checking the deformation of the tire casing receiving an external force according to any one of Claims 1 to 3.

5. The at least one spectral variable is specified on the first positive frequency block of the spectral signal spect(Sig), the method for checking the deformation of the tire casing receiving an external force according to any one of Claims 1 to 3.

6. The at least one specified spectral variable is included in a group including a maximum value, a median value, an average value, a passband of the first positive frequency block, an area under the curve of the first positive frequency block, a frequency of the median value, a frequency of the average value, and a frequency of the maximum value, the method for checking the deformation of the tire casing receiving an external force according to Claim 5.

7. The data from the at least one part of the angularly resampled normalized wheel rotation signal Sig TDR is aggregated over at least one sub - part of the at least one part of the angularly resampled normalized wheel rotation signal Sig TDR including the step of aggregating, and the sub - part of the at least one part of the angularly resampled and normalized wheel rotation signal Sig TDR is the at least one part of the angularly resampled and normalized wheel rotation signal Sig TDR A method for identifying deformation of a tire casing subjected to an external force according to any one of claims 1 to 3, wherein the sub - part of the at least one part of the angularly resampled and normalized wheel rotation signal Sig

8. The wheel rotation signal Sig TDR The method for verifying the deformation of a tire casing receiving an external force according to claim 7, wherein the sub - part of the at least one part of TDR is an integer multiple of the wheel rotation.

9. The data aggregation step (205) includes one of the methods included in a group including an average value, a median value, a decile selection or interval, an interpolation method, a weighted or unweighted average, and an optimization of a parametric model of tire deformation, for the method of identifying deformation of a tire casing subjected to an external force according to claim 7.

10. The sensor is included in a group including an accelerometer, a piezoelectric sensor, a magnetic sensor, an inductive sensor, and a capacitive sensor, for the method of identifying deformation of a tire casing subjected to an external force according to any one of claims 1 to 3.

11. The movement of the sensor is described by acceleration, for the method of identifying deformation of a tire casing subjected to an external force according to any one of claims 1 to 3.

12. The wheel rotation signal Sig TDR After synchronizing (101) with respect to the angular position of the tire casing, before the normalization step, the wheel rotation signal Sig TDR is corrected Corr to account for the influence of the earth's gravity, the method for verifying the deformation of a tire casing subject to an external force according to claim 11.

13. The first signal Sig includes an amplitude of the movement in a direction perpendicular to the crown of the tire casing, for the method of identifying deformation of a tire casing subjected to an external force according to claim 11.

14. The function F is proportional to the square of the reference speed W reference A method for checking the deformation of a tire casing that receives an external force according to any one of claims 1 to 3

15. The function G is a linear function of the at least one spectral variable, for the method of identifying deformation of a tire casing subjected to an external force according to any one of claims 1 to 3.

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