JP5227687B2 - Tire pressure drop detection device and method, and tire pressure drop detection program - Google Patents

Description

  The present invention relates to a tire pressure drop detection apparatus and method for detecting a tire pressure drop based on a resonance frequency of a tire of a running vehicle, and a tire pressure drop detection program.

  One of the factors that enable a car to travel safely is the tire air pressure. If the air pressure falls below an appropriate value, the steering stability and fuel consumption are deteriorated, which may cause tire bursts. For this reason, a tire pressure monitoring system (TPMS) that detects a decrease in tire pressure and alerts the driver to take appropriate measures is important from the standpoint of environmental conservation and ensuring driver safety. Technology.

  Conventional alarm devices can be classified into two types, a direct detection type and an indirect detection type. The direct detection type directly measures tire air pressure by incorporating a pressure sensor inside the tire wheel. While it is possible to detect a decrease in air pressure with high accuracy, there are technical and cost issues such as the need for a dedicated wheel and the problem of fault-tolerant performance in the actual environment.

  On the other hand, the indirect detection type is a method for estimating air pressure from tire rotation information, and can be subdivided into a dynamic load radius (DLR) method and a resonance frequency (RFM) method. The DLR method uses a phenomenon in which the dynamic load radius becomes smaller due to the crushed tire being crushed during driving, and as a result, it rotates faster than a normal pressure tire, and the pressure drop is reduced by comparing the rotation speeds of the four tires. This is a detection method. Since calculation processing can be performed relatively easily using only the wheel rotation speed signal obtained from the wheel speed sensor, research has been extensively conducted mainly for the purpose of detecting puncture of one wheel. However, since the rotational speeds of the wheels are merely compared, it is impossible to detect the case where the four wheels are depressurized simultaneously (natural leakage). In addition, since wheel speed differences also occur depending on traveling conditions such as turning of the vehicle, acceleration / deceleration, and load bias, there is a problem that pressure reduction cannot be detected accurately through all traveling states.

  On the other hand, the RFM method is a method for detecting a difference from the normal pressure by utilizing the change in the frequency characteristics of the wheel speed signal due to the reduced pressure. Unlike the DLR method, since it is an absolute comparison with the normal value of each wheel that has been held in advance, it can be used for simultaneous depressurization of four wheels, and is attracting attention as a better indirect detection method. However, depending on the driving conditions, there is a problem that the estimated value of the frequency in the target region is not robust to the vehicle speed and the road surface condition due to strong noise or the like. The present invention relates to a tire state detection device based on an RFM system. Hereinafter, the basic principle of the RFM method will be described in more detail.

  When the vehicle travels, the torsional motion in the front-rear direction that appears when the tire receives a force from the road surface and the motion in the front-rear direction of the suspension cause a coupled resonance. Since this resonance phenomenon also affects the rotational movement of the wheel, information on the resonance phenomenon is also present in the wheel speed signal acquired from the wheel sensor mounted on the anti-lock braking system (ABS). included. In addition, since coupled resonance is an inherent vibration mode due to the torsional rigidity of the tire, its excited state changes only depending on changes in the air pressure that constitutes the physical characteristics of the tire, and changes in vehicle speed and road surface There is almost no dependence. That is, since the dynamics of the torsional motion of the tire changes as the air pressure decreases, when the wheel speed signal is analyzed by frequency, the peak created by the coupled resonance (resonance peak) appears on the lower frequency side during normal pressure than during normal pressure.

  FIG. 6 shows a case where tires with normal air pressure and tires with a pressure reduced by 25% from normal pressure are mounted on a vehicle, and the respective wheel acceleration signals (time difference of wheel speed signals) obtained during a certain period of time are calculated. It represents a power spectrum obtained by applying a Fast Fourier Transform (FFT) to (obtained).

  The component in the vicinity of 40 to 50 Hz is the vibration that occurs when the vibration in the longitudinal direction of the tire and the suspension of the vehicle resonate, and the frequency having the peak value (resonance frequency) moves to the lower frequency side due to the change in internal pressure. I understand that. Because this phenomenon appears independently from the above-mentioned characteristics, such as the type of tire or vehicle, traveling speed, road surface condition, etc., the RFM method focuses on this resonance frequency and is relatively in comparison to the reference frequency estimated at initialization. An alarm is issued when it is judged to be low. Here, it is necessary to estimate the resonance frequency from the wheel speed signal acquired from the ABS, but it is difficult to store the necessary time series data in an in-vehicle computer with limited calculation resources, so frequency analysis by FFT Is difficult to do. For this reason, in the conventional method, the resonance frequency is estimated using an online method described below.

Since vibration can be described by a quadratic model, a time series analysis is performed on the wheel speed signal based on a quadratic autoregressive (AR) model. Specifically, parameters θ = {a ₁ ,..., A _K } in the model represented by the following equation (1) are estimated by a Kalman filter (sequential least square method).

Here, y (t) represents the wheel speed at time t, ε represents white noise, and K is the order of the model. Since the frequency corresponding to the pole of the transfer function representing the AR model is estimated as the resonance frequency, the resonance frequency can be accurately obtained if the resonance peak is correctly extracted by the model.

  However, these methods, which have been proposed so far, have the following problems. First, when traveling on a road with a smooth road surface, the force received from the road surface is weak and the resonance phenomenon is reduced, so that the influence of noise mixed independently of the target signal becomes relatively large. That is, since the signal noise (SN) ratio is deteriorated, it is difficult to accurately estimate the resonance frequency. Second, when traveling at a high speed (80 km / h or more), the signal of the high frequency component becomes strong due to an increase in the vertical vibration of the tire and the resonance peak tends to be unclear. Third, when a strong noise peak is generated in the frequency region in the vicinity where the resonance peak appears, the estimated value of the resonance frequency becomes unstable due to the influence. FIG. 7 shows the result of applying FFT to the travel data obtained under such circumstances. Depending on the driving conditions, these problems may overlap and it is particularly difficult to estimate the resonance frequency stably. In addition, if the estimated value is unstable, it is difficult to set a standard for abnormality determination, which makes it difficult to put the abnormality detection system using the RFM method into practical use.

  In order to solve such a problem of the RFM method, Patent Document 1 proposes that initialization and estimation are performed by the number of combinations of factors that affect estimation of air pressure such as road surface, vehicle speed, and temperature. . Patent Document 2 lists three external factors that affect the resonance frequency: outside air temperature, vehicle speed, and vibration that the tire receives from the road surface. Any two or all of these three external factors are listed. Based on the above, the estimated value is corrected according to the situation by using a predetermined correction function.

JP 2005-14664 A Japanese Patent No. 3152151

However, in the method described in Patent Document 1, for example, assuming three types of each factor, 27 windows are held in total. However, it is only complicated to initialize all of them. In addition, it is not possible to make an abnormality determination for a window whose initialization is not completed. A method of interpolating an uninitialized window from surrounding windows by interpolation is also proposed, but since the interpolation method is not based on the physical characteristics of the tire, the reliability of abnormality detection accuracy is lacking.
Further, since the correction function in the method described in Patent Document 2 is not determined based on physical characteristics, there is a problem in the universality of abnormality detection.

  Meanwhile, in recent years, a technique called independent component analysis (ICA) is being used in various fields. ICA is a technique for restoring an original signal only from a statistical independence assumption when an observed signal is obtained as a linear mixture of unknown original signals. With this technology, even when the influence of unnecessary factors such as noise dominates the contribution from the phenomenon you want to focus on, you can extract the desired information from many complex signals, and you (For example, T. W. Lee), "Independent Component Analysis (Independent Component Analysis)". "Analysis", Kluwer Academic Publishers, 1998). Currently, it is a kind of multivariate analysis technology that is actively researched in the field of information engineering called machine learning.

ICA is applied to fields such as control engineering because it estimates transfer characteristics in the process of restoring unknown input signals from observed signals (for example, Masuda Nitta, “Independent Study on application of component analysis to control engineering ”, Nara Institute of Science and Technology, Doctoral Dissertation, 2007).
However, there is no obvious guarantee of performance for removing noise contained in the wheel speed signal or the like in order to detect a decrease in tire air pressure of a running vehicle, and no proposal or implementation has been made yet.

  The present invention has been made in view of such circumstances, and it is possible to stably estimate a resonance frequency, and thereby to detect a tire pressure drop with high accuracy, and a tire pressure drop detection apparatus and method. In addition, an object of the present invention is to provide a tire pressure drop detection program.

A tire pressure drop detecting device (hereinafter also simply referred to as “detecting device”) according to a first aspect of the present invention periodically transmits tire rotation speed information, an outside air temperature signal, a turning signal, and a wheel torque signal of each wheel of a vehicle. Information detecting means for automatically detecting,
From the rotational speed information obtained by this information detecting means, rotational acceleration information calculating means for calculating the rotational acceleration information of the tire,
From the rotational speed information obtained by the information detecting means, frequency characteristic estimating means for estimating the frequency characteristics of the rotational speed information;
Determining means for determining a decrease in the tire air pressure based on the estimated frequency characteristics,
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for estimating a parameter of the linear model assuming a linear model for each signal decomposed in one step, and
The determination means is configured to determine a decrease in tire air pressure based on the estimated resonance frequency in the torsional direction of the tire.

  In the detection apparatus according to the first aspect of the present invention, the wheel speed signal, the wheel acceleration signal, the outside air temperature signal, the turning signal, and the wheel torque signal are input and decomposed into five types of independent signals by independent component analysis, and these are decomposed. A linear model is assumed for each signal, and parameters of the linear model are estimated. At this time, the decomposed signal includes a signal containing a lot of information regarding torsional resonance, and the resonance frequency can be accurately obtained by paying attention to this. In other words, a state in which it is difficult to detect an abnormality depending on driving conditions can be solved by a technique of decomposing into five types of independent signals by independent component analysis. The “situation in which abnormality detection is difficult” refers to a situation in which the target resonance peak is obscured due to the influence of noise or the like, or the resonance frequency cannot be accurately estimated.

A detection device according to a second aspect of the present invention includes information detection means for periodically detecting rotation speed information, an outside air temperature signal, a turning signal, and a wheel torque signal of a tire of each wheel of a vehicle,
From the rotational speed information obtained by this information detecting means, rotational acceleration information calculating means for calculating the rotational acceleration information of the tire,
From this rotational acceleration information, frequency characteristic estimation means for estimating the frequency characteristic of the rotational acceleration information,
Determining means for determining a decrease in the tire air pressure based on the estimated frequency characteristics,
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for assuming a linear model for each signal decomposed in one step and estimating parameters of the linear model; and
The determination means is configured to determine a decrease in tire air pressure based on the estimated resonance frequency in the torsional direction of the tire.

  Also in the detection device according to the second aspect of the present invention, the resonance frequency can be accurately obtained as in the detection device according to the first aspect. Moreover, in the detection apparatus which concerns on a 2nd viewpoint, although wheel acceleration data are used, since this wheel acceleration data changes less than wheel speed data, it can raise calculation precision.

The parameter estimation means can be configured to estimate a parameter of the linear model by a least square method. Since the parameter is estimated by the method of least squares, an optimum solution for given data can be obtained.

The determination means is configured to estimate the resonance peak as a resonance frequency in a tire twist direction when a waveform having a resonance peak exists in a predetermined frequency range including a reference frequency obtained in advance. Can do.

A tire air pressure drop detection method (hereinafter also simply referred to as “detection method”) according to a third aspect of the present invention is a method of periodically transmitting tire rotational speed information, an outside air temperature signal, a turning signal, and a wheel torque signal of each wheel of a vehicle. A detection step to detect automatically,
From the rotational speed information obtained by this detection step, a calculation step of calculating the rotational acceleration information of the tire,
From the rotation speed information obtained by the detection step, an estimation process for estimating the frequency characteristics of the rotation speed information;
A determination step of determining a decrease in air pressure of the tire based on the estimated frequency characteristic,
The estimation step includes
A first step of decomposing the time series signal including the information into five types of independent signals by independent component analysis using a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal and a wheel torque signal as inputs; and A second step of assuming a linear model for each signal decomposed in step 1 and estimating parameters of the linear model; and
The determination step is characterized in that a decrease in tire air pressure is determined based on the estimated resonance frequency in the torsional direction of the tire.

In the detection method according to the third aspect of the present invention, the wheel speed signal, the wheel acceleration signal, the outside air temperature signal, the turning signal, and the wheel torque signal are input to be decomposed into five independent signals by independent component analysis, and these are decomposed. A second-order linear model is assumed for each signal, and parameters of the linear model are estimated. At this time, the decomposed signal includes a signal containing a lot of information regarding torsional resonance, and the resonance frequency can be accurately obtained by paying attention to this. In other words, a state in which it is difficult to detect an abnormality depending on driving conditions can be solved by a technique of decomposing into five types of independent signals by independent component analysis.

A detection method according to a fourth aspect of the present invention includes a detection step of periodically detecting rotation speed information, an outside air temperature signal, a turning signal, and a wheel torque signal of a tire of each wheel of a vehicle;
From the rotational speed information obtained in this detection step, a calculation step of calculating the rotational acceleration information of the tire,
From the rotational acceleration information obtained in this calculation step, an estimation step for estimating the frequency characteristics of the rotational acceleration information,
A determination step of determining a decrease in air pressure of the tire based on the estimated frequency characteristic,
In the estimation step, the time series signal including the information is first decomposed into five types of independent signals by independent component analysis with a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal as inputs. A second step of assuming a linear model for each signal decomposed in the first step and estimating parameters of the linear model; and
The determination step is characterized in that a decrease in tire air pressure is determined based on the estimated resonance frequency in the torsional direction of the tire.

Also in the detection method according to the fourth aspect of the present invention, the resonance frequency can be obtained accurately as in the detection method according to the third aspect. In the detection method according to the fourth aspect, the wheel acceleration data is used. However, since the wheel acceleration data is less changed than the wheel speed data, the calculation accuracy can be improved.

In the step of estimating the parameter, the linear model parameter may be estimated by a least square method. Since the parameter is estimated by the method of least squares, an optimum solution for given data can be obtained.

In the determination step, when a waveform having a resonance peak exists in a predetermined frequency range including a reference frequency obtained in advance, the resonance peak is estimated to be a resonance frequency in the tire twist direction. Can do.

According to a fifth aspect of the present invention, there is provided a program for detecting a decrease in tire air pressure, a computer for detecting a decrease in tire pressure based on a resonance frequency of a tire in a running vehicle, and a rotation of a tire in each wheel of the vehicle. Rotational acceleration information computing means for computing tire rotational acceleration information from rotational speed information obtained by information detecting means for periodically detecting speed information, outside air temperature signal, turning signal and wheel torque signal, obtained by the information detecting means. From the rotational speed information to be functioned as a frequency characteristic estimation means for estimating the frequency characteristics of the rotational speed information, and a determination means for determining a decrease in the tire air pressure based on the estimated frequency characteristics,
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for estimating a parameter of the linear model assuming a linear model for each signal decomposed in one step, and
The determination means is configured to determine a decrease in tire air pressure based on the estimated resonance frequency in the torsional direction of the tire.

According to a sixth aspect of the present invention, there is provided a program for detecting a decrease in tire air pressure, a computer for detecting a decrease in tire pressure based on a resonance frequency of a tire in a running vehicle, and a rotation of a tire in each wheel of the vehicle. From the rotational speed information obtained by the information detecting means for periodically detecting the speed information, the outside air temperature signal, the turning signal and the wheel torque signal, the rotational acceleration information calculating means for calculating the rotational acceleration information of the tire, from this rotational acceleration information, Function as frequency characteristic estimation means for estimating the frequency characteristics of the rotational acceleration information, and determination means for determining a decrease in the tire air pressure based on the estimated frequency characteristics;
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for estimating a parameter of the linear model assuming a linear model for each signal decomposed in one step, and
The determination means is configured to determine a decrease in tire air pressure based on the estimated resonance frequency in the torsional direction of the tire.

  According to the detection apparatus and method and the tire pressure drop detection program of the present invention, it is possible to stably estimate the resonance frequency and thereby detect the tire pressure drop with high accuracy.

Hereinafter, embodiments of a detection apparatus and method and a tire pressure drop detection program according to the present invention will be described in detail with reference to the accompanying drawings.
As shown in FIG. 1, the detection device according to an embodiment of the present invention includes a left front wheel (FL), a right front wheel (FR), and a left rear wheel (RL) of four tires provided in a four-wheel vehicle. In order to detect the rotational speed information of the right rear wheel (RR), a normal wheel speed detecting means (rotational speed information detecting means) 1 provided in association with each tire is provided.

  The wheel speed detection means 1 generates power using rotation like a wheel speed sensor or dynamo for generating a rotation pulse by using an electromagnetic pickup or the like and measuring a rotation angular speed and a wheel speed from the number of pulses. It is possible to use an angular velocity sensor including that for measuring the rotational angular velocity and the wheel speed from this voltage. The output of the wheel speed detecting means 1 is given to a control unit 2 which is a computer such as ABS. The control unit 2 includes, for example, a liquid crystal display element for displaying that the tire is depressurized, a display 3 composed of a plasma display element or a CRT, an initialization button 4 that can be operated by a driver, An alarm device 5 for notifying the driver of tire decompression and a temperature sensor 6 for detecting the temperature in the vicinity of the tire are connected.

  As shown in FIG. 2, the control unit 2 stores an I / O interface 2a necessary for passing signals to and from an external device, a CPU 2b that functions as a center of arithmetic processing, and a control operation program for the CPU 2b. The ROM 2c and the RAM 2d from which data is temporarily written or the written data is read when the CPU 2b performs a control operation.

  The wheel speed detection means 1 outputs a pulse signal corresponding to the number of rotations of the tire (hereinafter also referred to as “wheel speed pulse”). Then, by sampling this wheel speed pulse every predetermined sampling period ΔT (seconds), for example, ΔT = 0.005 seconds, time-series data of the wheel speed signal can be obtained. Since the resonance frequency in the torsional direction of the tire of interest appears in the vicinity of several tens of Hz, the sampling cycle needs to be longer than that.

The detection device according to the present embodiment includes an information detection means comprising wheel speed detection means (rotational speed information detection means) 1, a temperature sensor 6 for detecting the temperature in the vicinity of the tire, and means for detecting a turning signal and a wheel torque signal. , Frequency characteristic estimating means for estimating the frequency characteristics of the information from the rotational speed information obtained by the information detecting means, the outside air temperature signal, the turning signal and the wheel torque signal, and the tire air pressure based on the estimated frequency characteristics. It is mainly comprised by the determination means which determines a fall. The tire pressure drop detection program causes the control unit 2 to function as a frequency characteristic estimation unit and a determination unit. The frequency characteristic estimation means, as a first step for the time series signal including the information, includes a wheel speed signal, a wheel speed difference signal (wheel acceleration signal), an outside air temperature signal, a turning signal, and a wheel torque signal. As a second step, a second-order linear model is assumed for each signal decomposed in the first step, and a second-order linear model is assumed as the second step. Parameter estimation means for estimating the parameter is included.

  In the present invention, the problem that the estimated value of the resonance frequency becomes unstable due to the influence of noise generated by the running condition of the vehicle is solved by using the ICA described above. In other words, it is assumed that the observation signal is generated by the noise from the parasitic factor mixed in parallel with the signal related to the torsional vibration of the tire, which is the original target signal, and is obtained by independently decomposing these signals. Only get the signal. FIG. 3 is a diagram schematically representing the above, and shows the process from the input to the road surface until the wheel speed signal is output. In the observation signal, influences from unnecessary parasitic factors (Q and R) are mixed in parallel with the element of interest (element P including the information of the torsion signal), but these signals are obtained by using ICA. Decompose and extract only the signals related to the torsional vibration. When the resonance frequency is estimated by the conventional method, the above-mentioned problem (cannot be estimated stably) occurs because the signal is analyzed with unnecessary parasitic factors included. However, in the present invention, since such parasitic factors are removed, the resonance frequency can be estimated robustly to the environment.

In the present invention, five types of wheel speed signals, wheel speed difference signals (wheel acceleration signals), outside air temperature signals, turning signals, and wheel torque signals are used as observation signals used for input. These signals are important features for robust estimation of the resonance frequency. In Patent Document 2 described above, the resonance frequency estimated from the wheel speed signal is corrected according to the situation based on the remaining information. However, in the present invention, the five types of signals are usually used as five-dimensional observation signals. ICA algorithm is applied. That is, when the matrix of the original signal is S and the linear mixing matrix is A, the observation matrix X is
X = AS
From the statistical independence condition,
V = WX
An estimated value V of the original signal and an inverse transformation W of the mixing matrix are obtained. The time-series information included in each dimension of V is independent of each other, and can be regarded as an original signal generated from parallel elements that influence the estimation of the resonance frequency. In the present invention, these independently decomposed signals are analyzed using a conventional analysis method. Specifically, a quadratic linear model is assumed for each signal, and parameters of the linear model are estimated. Among the decomposed signals, there is a signal containing a lot of information regarding torsional resonance, and the resonance frequency can be accurately obtained by paying attention to this. For example, a reference frequency is obtained at the time of initialization, a region in a predetermined range including this reference frequency is monitored, and when a waveform having a resonance peak exists in the region, the resonance peak is estimated as a resonance frequency in the tire twist direction. can do. The region of the predetermined range varies depending on the type of tire or vehicle, but in the case of a general passenger car, for example, it can be set to 6 Hz before and after the reference frequency.

  Of the five types of signals described above, the outside air temperature signal can be obtained by, for example, the temperature sensor 6 that detects the temperature in the vicinity of the tire. ) Can be obtained from. These signals are input to the I / O interface 2a of the control unit 2.

[Examples and Comparative Examples]
Next, examples of the detection method of the present invention will be described, but the present invention is not limited to only the examples described below.
The resonance frequency of the tire used, which was obtained from actual vehicle tests and measurement experiments, was about 48 Hz. A vehicle equipped with this tire is run at a high speed (100 kph) on paved asphalt, and the rotation information of each wheel tire is sampled every 4 ms to obtain the time series data of the wheel speed signal. Wheel acceleration was determined by taking the difference. A case where FFT was applied only to wheel acceleration information (conventional method, comparative example) and a case where the present invention was applied (example) was compared with data for 2 minutes (data number 30000). The results are shown in FIGS.

  FIG. 4 shows a frequency characteristic obtained by analyzing only the wheel acceleration signal by FFT, and a spectrum waveform analyzed by applying the present invention. The horizontal axis represents frequency (Hz), and the vertical axis represents signal strength (dB). Is shown. FIG. 4 shows that when only the wheel acceleration information is frequency-analyzed as in the conventional method, the resonance peak is very unclear and the resonance frequency cannot be obtained accurately. This is because, as described above, when driving on a smooth road, the force received from the road surface is weak and the resonance phenomenon is reduced, so the influence of noise mixed independently of the target signal is relatively The reason is that the signal of the high frequency component becomes strong due to the increase of the signal noise (SN) ratio and the increase in the vertical vibration of the tire when driving at high speed (over 80 km / h). It is done. In addition, strong noise may form a peak in the frequency region in the vicinity where the resonance peak appears, and the estimated value of the resonance frequency may become unstable due to the influence.

  On the other hand, in the embodiment, five types of signals statistically independent of five types of signals (wheel speed signal, wheel acceleration signal, outside air temperature signal, turning signal, and wheel torque signal) in which various information are mixed are provided. Then, these signals are time-series analyzed with a second-order AR model as in the conventional method. The five quadratic curves in FIG. 4 represent the results of time series analysis of each signal. Among the decomposed signals, there is a signal containing a lot of information on torsional resonance (drawn with a solid line in the quadratic curve in FIG. 4), and by accurately paying attention to this, the resonance frequency is accurately obtained. Can do.

  FIG. 5 is a diagram showing the robustness of the estimation of the resonance frequency when the conventional method and the present invention are applied. This figure shows the mean and variance for 120 times when the resonance frequency is estimated with 10 seconds of data for each data of 10 trials (each trial is 2 minutes). The conventional method shows a reasonable estimation result on average. However, in general, run-flat tires have a low pressure reduction sensitivity (difference in resonance frequency between normal pressure and low pressure) of about 2 Hz, so variations in estimation results have a large effect on judgment results. The standard deviation is 1.42, which is large with respect to the pressure reduction sensitivity, and is not practically accurate. On the other hand, if the present invention is applied, the standard deviation of the estimated value is as very small as 0.23, and the variation in estimation can be largely suppressed. This is considered to be because a signal including a lot of information on torsional resonance is extracted by eliminating the cause of the influence of noise and the peak being obscured by independent decomposition of the signal by ICA.

It is a block diagram which shows one Embodiment of the detection apparatus of this invention. It is a block diagram which shows the electrical structure of the detection apparatus shown by FIG. It is a figure which represents typically until it receives an input from a road surface and a wheel speed signal is output. It is a figure which shows the frequency waveform which analyzed only the wheel acceleration signal by FFT, and the spectrum waveform analyzed by applying this invention. It is a figure which shows the robustness of estimation of the resonant frequency in the case where a conventional method and this invention are each applied. It is a figure showing distribution of power spectral density when FFT is applied to a 2-minute wheel acceleration signal. It is a figure showing distribution of the power spectrum density when FFT is applied to the wheel acceleration signal for 2 minutes which carried a high-speed run on the road where a run flat tire was attached, and the road surface is smooth.

Explanation of symbols

1 Wheel speed detection means 2 Control unit 2a Interface 2b CPU
2c ROM
2d RAM
3 Display 4 Initialization button 5 Alarm 6 Temperature sensor

Claims (10)

Information detecting means for periodically detecting tire rotation speed information, outside air temperature signal, turning signal and wheel torque signal of each wheel of the vehicle;
From the rotational speed information obtained by this information detecting means, rotational acceleration information calculating means for calculating the rotational acceleration information of the tire,
From the rotational speed information obtained by the information detecting means, frequency characteristic estimating means for estimating the frequency characteristics of the rotational speed information;
Determining means for determining a decrease in the tire air pressure based on the estimated frequency characteristics,
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for estimating a parameter of the linear model assuming a linear model for each signal decomposed in one step, and
The tire pressure drop detecting device according to claim 1, wherein the judging means is configured to determine a drop in tire air pressure based on the estimated resonance frequency in the torsional direction of the tire. Information detecting means for periodically detecting tire rotation speed information, outside air temperature signal, turning signal and wheel torque signal of each wheel of the vehicle;
From the rotational speed information obtained by this information detecting means, rotational acceleration information calculating means for calculating the rotational acceleration information of the tire,
From this rotational acceleration information, frequency characteristic estimation means for estimating the frequency characteristic of the rotational acceleration information,
Determining means for determining a decrease in the tire air pressure based on the estimated frequency characteristics,
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for assuming a linear model for each signal decomposed in one step and estimating parameters of the linear model; and
The tire pressure drop detecting device according to claim 1, wherein the judging means is configured to determine a drop in tire air pressure based on the estimated resonance frequency in the torsional direction of the tire.   The tire pressure drop detecting device according to claim 1 or 2, wherein the parameter estimating means is configured to estimate a parameter of a linear model by a least square method.   The determination unit is configured to estimate the resonance peak as a resonance frequency in a tire twist direction when a waveform having a resonance peak exists in a predetermined frequency range including a reference frequency obtained in advance. The tire pressure drop detecting device according to claim 1 or 2. A detection step of periodically detecting tire rotation speed information, outside air temperature signal, turning signal and wheel torque signal of each wheel of the vehicle;
From the rotational speed information obtained by this detection step, a calculation step of calculating the rotational acceleration information of the tire,
From the rotation speed information obtained by the detection step, an estimation process for estimating the frequency characteristics of the rotation speed information;
A determination step of determining a decrease in air pressure of the tire based on the estimated frequency characteristic,
The estimation step includes
A first step of decomposing the time series signal including the information into five types of independent signals by independent component analysis using a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal and a wheel torque signal as inputs; and A second step of assuming a linear model for each signal decomposed in step 1 and estimating parameters of the linear model; and
A method for detecting a decrease in tire air pressure, wherein, in the determination step, a decrease in tire air pressure is determined based on an estimated resonance frequency in a tire twist direction. A detection step of periodically detecting tire rotation speed information, outside air temperature signal, turning signal and wheel torque signal of each wheel of the vehicle;
From the rotational speed information obtained in this detection step, a calculation step of calculating the rotational acceleration information of the tire,
From the rotational acceleration information obtained in this calculation step, an estimation step for estimating the frequency characteristics of the rotational acceleration information,
A determination step of determining a decrease in air pressure of the tire based on the estimated frequency characteristic,
In the estimation step, the time series signal including the information is first decomposed into five types of independent signals by independent component analysis with a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal as inputs. A second step of assuming a linear model for each signal decomposed in the first step and estimating parameters of the linear model; and
A method for detecting a decrease in tire air pressure, wherein, in the determination step, a decrease in tire air pressure is determined based on an estimated resonance frequency in a tire twist direction.   The tire pressure drop detection method according to claim 5 or 6, wherein in the step of estimating the parameter, the parameter of the linear model is estimated by a least square method.   In the determination step, when there is a waveform having a resonance peak in a predetermined frequency range including a reference frequency obtained in advance, the resonance peak is estimated as a resonance frequency in the tire twist direction. The tire pressure drop detecting method according to claim 5 or 6. A computer is used to detect a decrease in tire pressure based on the resonance frequency of the tire of the traveling vehicle, and the rotational speed information, outside air temperature signal, turning signal and wheel torque signal of each wheel of the vehicle are periodically Rotational acceleration information calculating means for calculating tire rotational acceleration information from rotational speed information obtained by information detecting means to detect, and frequency for estimating frequency characteristics of the rotational speed information from rotational speed information obtained by the information detecting means Function as characteristic estimation means, and determination means for determining a decrease in the tire air pressure based on the estimated frequency characteristics;
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for estimating a parameter of the linear model assuming a linear model for each signal decomposed in one step, and
The tire pressure drop detection program, wherein the determination means is configured to determine a tire pressure drop based on the estimated resonance frequency in the tire twist direction. A computer is used to detect a decrease in tire pressure based on the resonance frequency of the tire of the traveling vehicle, and the rotational speed information, outside air temperature signal, turning signal and wheel torque signal of each wheel of the vehicle are periodically Rotational acceleration information calculating means for calculating the rotational acceleration information of the tire from rotational speed information obtained by the information detecting means to detect, frequency characteristic estimating means for estimating the frequency characteristic of the rotational acceleration information from the rotational acceleration information, and Function as a determination means for determining a decrease in air pressure of the tire based on the estimated frequency characteristics;
The frequency characteristic estimation means, for the time series signal containing the information,
As a first step, a wheel speed signal, a wheel acceleration signal, an outside air temperature signal, a turning signal, and a wheel torque signal are input, and decomposition means for decomposing the signal into five types of independent signals by independent component analysis. A parameter estimation means for estimating a parameter of the linear model assuming a linear model for each signal decomposed in one step, and
The tire pressure drop detection program, wherein the determination means is configured to determine a tire pressure drop based on the estimated resonance frequency in the tire twist direction.

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