A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber

Abstract

The present application relates to the technical field of sensor accuracy testing, and specifically relates to a method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber, including: obtaining the position data, the acceleration of the shock absorber vibration, and the moving speed of the shock absorber of the in-built position sensor of the motorcycle shock absorber under different working conditions; obtaining the three-dimensional feature vectors corresponding to each working condition, clustering and dividing all the three-dimensional feature vectors, calculating the comprehensive interference eigenvalue of each category after clustering, so as to divide each category into large and small interference sets, and optimizing the covariance matrix when performing Kalman filtering on the position data corresponding to the large and small interference sets; testing the accuracy of the in-built position sensor of the motorcycle shock absorber based on the error between the filtered position data and the standard displacement data of a preset linear motor. The present application can improve the accuracy of testing the in-built position sensor of the motorcycle shock absorber.

Description

Technical Field

[0001] This application relates to the technical field of sensor accuracy testing, and particularly to a method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber. Background Art

[0002] The motorcycle shock absorber is an important component of the motorcycle running system. Installing a position sensor in the motorcycle shock absorber can monitor the working state of the shock absorber in real time, accurately obtain information such as the telescopic displacement and speed of the shock absorber. The accuracy of the position sensor directly affects the performance of the motorcycle shock absorption system. If the sensor accuracy is insufficient, it will cause the electronic control system to receive incorrect information, resulting in improper adjustment of the shock absorber, and further affecting the driving stability, comfort and controllability of the motorcycle. Therefore, testing the sensor accuracy can ensure the reliable performance of the sensor and ensure the normal operation of the motorcycle shock absorption system.

[0003] Currently, most of the accuracy tests of position sensors adopt static measurement methods. High-precision displacement measurement devices such as laser interferometers and grating scales are used to calibrate and test the sensors in a laboratory environment. The sensor is installed on the test bench, and the test bench is driven to generate accurate displacements. By comparing the output data of the sensor with the standard displacement value, the error is calculated. However, this method has limitations. It can only simulate static working conditions and cannot reflect the dynamic performance of the sensor during the actual running of the motorcycle. Moreover, the test environment is idealized, and the influence of complex factors such as vibration, shock, and electromagnetic interference during the operation of the motorcycle is not considered, resulting in a deviation between the test results and the actual use situation. Summary of the Invention

[0004] To solve the above technical problems, this application provides a method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber to solve the existing problems.

[0005] The method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber in this application adopts the following technical solutions:

[0006] An embodiment of this application provides a method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber, including the following steps:

[0007] Obtain various data under different working conditions, including: each position data of the in-built position sensor of the motorcycle shock absorber, the acceleration of the shock absorber vibration, and the moving speed of the shock absorber;

[0008] Analyze the time-series changes of various types of data under each working condition and extract the peaks in various types of data. According to the deviation between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the moving speed, combined with the deviation of the peak amplitudes in the position data and the number of peaks in the position data, obtain the three-dimensional feature vector corresponding to each working condition, and perform clustering on all the three-dimensional feature vectors;

[0009] According to the average level of the same element in all the three-dimensional feature vectors within each category after clustering, and the significant fluctuation of each element in all the three-dimensional feature vectors within each category, obtain the comprehensive interference eigenvalue of each category. Combine the average level of all the comprehensive interference eigenvalues to divide the categories after clustering into large and small interference sets, and optimize the covariance matrix when performing Kalman filtering on the position data corresponding to the large and small interference sets;

[0010] Based on the error between the filtered position data under different working conditions and the standard displacement data of the preset linear motor, obtain a quantization index to test the accuracy of the built-in position sensor of the motorcycle shock absorber.

[0011] Preferably, the method for obtaining the three-dimensional feature vector corresponding to each working condition is as follows:

[0012] For each working condition, respectively perform curve fitting on various types of data to obtain the time-series change curves of various types of data, and extract the peaks of the time-series change curves of various types of data; and according to the difference between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the moving speed, obtain the relative peak offset value of the position data. Analyze the relative peak deviation coefficient of the amplitudes of the peaks in the position data, and use the number of peaks in the position data as the eigenvalue of the interference effect. The normalized relative peak offset value, peak deviation coefficient, and eigenvalue under each working condition form the three-dimensional feature vector corresponding to each working condition.

[0013] Preferably, the method for obtaining the relative peak offset value further includes:

[0014] Calculate the absolute values of the differences between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the speed data respectively, and take the mean of all the absolute values as the relative peak offset value of the position data.

[0015] Preferably, the method for obtaining the peak deviation coefficient further includes: Calculate the difference between the amplitudes of the peaks in the position data and the standard displacement data of the corresponding linear motor at the positions where the peaks are located, calculate the ratio of the difference to the corresponding standard displacement data, and take the sum of all the ratios as the peak deviation coefficient of the position data.

[0016] Preferably, the method for obtaining the comprehensive interference eigenvalue of each category is as follows:

[0017] ; where represents the comprehensive interference eigenvalue of the th category after clustering; represents the mean value of the th element among all three-dimensional feature vectors in the th category after clustering; and respectively represent the weights of the th and th elements among all three-dimensional feature vectors in the th category after clustering; represents the number of elements in the three-dimensional feature vector.

[0018] Preferably, the method for obtaining the weight is further: calculating the coefficient of variation of the same element among all three-dimensional feature vectors in each category after clustering as the weight of the same element.

[0019] Preferably, the method for dividing the large and small interference sets is: using the mean value of all comprehensive interference eigenvalues as the judgment threshold, and dividing the categories with comprehensive interference eigenvalues greater than the judgment threshold into the large interference set, otherwise, dividing them into the small interference set.

[0020] Preferably, the method for optimizing the covariance matrix when filtering the position data corresponding to the large and small interference sets includes:

[0021] For the large interference set, increasing the covariance matrix by a first preset multiple; for the small interference set, reducing the covariance matrix by a second preset multiple, where the value range of the first preset multiple is from 2 to 4, and the value range of the second preset multiple is from 0.2 to 0.4.

[0022] Preferably, the method for obtaining the quantization index is:

[0023] For the filtered position data under each working condition, calculate the absolute error and relative error between the position data of each position and the standard displacement data of the linear motor respectively, and calculate the average value of the absolute errors corresponding to all positions and the average value of the relative errors corresponding to all positions under each working condition respectively, and record them as the first mean value and the second mean value respectively, both of which are used as the quantization index of the position sensor accuracy under each working condition.

[0024] Preferably, the test on the accuracy of the built-in position sensor of the motorcycle shock absorber further includes:

[0025] Preset the accuracy requirements for each quantization index. When the position sensor meets the accuracy requirements for all quantization indexes under all working conditions, the accuracy test of the built-in position sensor of the motorcycle shock absorber is qualified; if there is any quantization index that does not meet the accuracy requirements under any working condition, the test accuracy of the built-in position sensor of the motorcycle shock absorber is unqualified.

[0026] This application has at least the following beneficial effects:

[0027] This application takes into account that the accuracy test of the traditional built-in position sensor of motorcycle shock absorbers mostly uses the static test method. Since the complex dynamic factors such as vibration, shock, and electromagnetic interference during motorcycle operation are not fully analyzed, there is a large deviation between the test results and the actual application scenarios. This application comprehensively collects, analyzes, and precisely processes the data of the built-in position sensor by simulating various working conditions during motorcycle riding;

[0028] Since there are significant differences in the degree of interference of dynamic test data under different working conditions by actual measurements, this application conducts a comparative analysis of the hysteresis and interference influence intensity for the test data under each working condition, and precisely divides the test data based on the analysis results, thereby accurately identifying the comprehensive interference influence characteristics under different working conditions during the dynamic test process;

[0029] Compared with the traditional static test method, this application can not only effectively reduce the deviation between the test results and the actual usage, but also reduce the influence of the measurement interference differences under different working conditions during the actual detection process, and improve the accuracy of the accuracy test of the built-in position sensor of motorcycle shock absorbers. Description of the Drawings

[0030] In order to more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the following will briefly introduce the drawings required for the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of this application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0031] Figure 1 It is a step flowchart of a method for testing the accuracy of a built-in position sensor of a motorcycle shock absorber provided by this application. Detailed Embodiments

[0032] To further elaborate on the technical means and effects adopted by the present application to achieve the intended invention purpose, the following specifically describes, with reference to the accompanying drawings and preferred embodiments, a method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber proposed according to the present application, including its specific implementation manner, structure, features, and effects. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. In addition, the specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0033] Unless otherwise defined, terms such as "including", "comprising", or any other variant thereof are intended to cover non-exclusive inclusion, such that a circuit structure, article, or device including a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such article or device. Without further limitation, an element defined by the statement "including one..." does not exclude the existence of additional identical elements in the article or device including the said element. Additionally, the term "and / or" used herein includes any and all combinations of one or more of the associated listed items. All technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs.

[0034] The following specifically describes the specific solution of a method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber provided by the present application with reference to the accompanying drawings.

[0035] A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber provided by an embodiment of the present application. Specifically, please refer to Figure 1 , including the following steps:

[0036] Step 1: Obtain various types of data under different working conditions, including: position data at various positions of the in-built position sensor of the motorcycle shock absorber, the acceleration of the shock absorber vibration, and the moving speed of the shock absorber.

[0037] In this embodiment, a high-precision linear motor is selected to achieve precise control of the telescopic displacement of the shock absorber and simulate the stroke changes of the shock absorber under different road conditions. At the same time, a vibration generator capable of generating vibrations with a frequency range of 0 - 50 Hz and an acceleration range of 0 - 5g is installed to simulate the road surface vibrations during motorcycle driving, where g is the acceleration due to gravity. On the other hand, a multi-channel data acquisition card is prepared, and the sampling frequency should not be lower than 1 kHz to ensure accurate acquisition of various sensor data. Specifically, the built-in position sensor to be tested is firmly installed on the simulated shock absorber according to the actual installation method of the motorcycle to obtain the position data of the shock absorber at various positions, that is, the displacement data, which is referred to as position data in this embodiment for the convenience of understanding and expression. At the same time, an acceleration sensor and a speed sensor are installed, where the acceleration sensor is used to monitor the acceleration change of the shock absorber vibration; the speed sensor is used to measure the moving speed of the shock absorber.

[0038] Furthermore, in this embodiment, a laser interferometer with a precision of ±0.001 mm is used to calibrate the displacement output of the linear motor to ensure the accuracy and reliability of the output displacement. During the calibration process, multiple measurement points are selected within the stroke range of the linear motor, the deviation between the measurement value of the laser interferometer and the set value of the linear motor is recorded, and corrections are made. And according to the calibration specification of the accelerometer, a standard acceleration source is used to calibrate the acceleration sensor, and the calibration frequency range covers 0 - 50 Hz required for testing. Similarly, the speed sensor is calibrated. In this embodiment, a high-precision tachometer is used as a reference to ensure the accuracy of speed measurement. It should be noted that the implementer can select other methods for the specific calibration process. This embodiment only gives one implementation method and does not make special limitations on this.

[0039] In this embodiment, according to the common driving road conditions of motorcycles, 100 groups of different combinations of displacement, linear motor speed, and vibration parameters are set, that is, multiple groups of different road conditions are simulated to collect data under different working conditions. Specifically, for example, when simulating a flat road surface, the linear motor is set to perform reciprocating displacement of 50 mm at a speed of 0.1 m / s, and the vibration generator outputs vibrations with a frequency of 20 Hz and an acceleration of 1g; when simulating a bumpy road surface, the speed is adjusted to 0.05 m / s, the displacement range is expanded to 80 mm, the vibration frequency is increased to 30 Hz, and the acceleration is increased to 3g. In this embodiment, 5 different operating cycles are set for each working condition, and the duration of each cycle is 30 - 60 seconds to obtain sufficient data for analysis. In the actual application scenario, the implementer can set the specific data acquisition process by himself / herself, and this application does not make special restrictions on this.

[0040] Further initiate data acquisition, synchronously collect the data of the position sensor, acceleration sensor, and speed sensor. Before each working condition runs, collect 10 seconds of static data as the reference benchmark for subsequent data processing. During the operation of each simulated working condition, continuously collect data at the sampling frequency of 1 kHz set in this embodiment to ensure the integrity and accuracy of the data. The collected data is stored in the computer in real time, adopting the HDF5 data storage format for subsequent processing and analysis.

[0041] Under normal circumstances, in the flat road condition, the main interferences may come from the slight vibration of the motor, the thermal noise of electronic components, and the electromagnetic interference of the surrounding environment; while in the bumpy road condition, the vibration intensity increases significantly, which may cause large fluctuations in the sensor measurement values. At the same time, due to the intense vibration, the friction, collision, etc. between mechanical components may also generate additional noise, and the electromagnetic interference may be enhanced due to the vibration of the vehicle electrical system. Therefore, during the simulation of the motorcycle's movement under different working conditions, the accuracy of its test data may be affected by different sources and degrees of interference, resulting in a large deviation in the sensor accuracy test when facing the accuracy test of the built-in sensor of the motorcycle shock absorber due to the difference in dynamic interference.

[0042] Based on the above analysis, in this embodiment, the Kalman filter algorithm is used to filter and denoise the collected data. Among them, the state observation equation of the system is constructed through the data collected by the position sensor and the data collected by the speed sensor; the observation equation is constructed through the relationship between the data tested by the position sensor and the real position. Using the Kalman filter to construct the state equation and observation equation of the system is well-known technology to those skilled in the art and will not be elaborated here.

[0043] In this embodiment, the difference in the influence of state measurement interference in different working conditions during the actual dynamic simulation process will be considered, and the covariance matrix in the data filtering process for the built-in position sensor of the motorcycle shock absorber will be optimized and adjusted.

[0044] Step 2: Analyze the time-series changes of various types of data under each working condition and extract the peaks in various types of data. According to the deviation situation between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the moving speed, combined with the deviation situation of the peak amplitudes in the position data and the number of peaks in the position data, obtain the three-dimensional feature vector corresponding to each working condition, and perform clustering division on all three-dimensional feature vectors.

[0045] First, due to the interference differences under different working conditions in the actual simulation process, the data collected during the actual test will show asynchronous change characteristics due to the differences in working conditions, that is, the different degrees of interference influence, and the response situations of the position, acceleration, and speed sensors are different; during the test of the position sensor, the interference characteristics of the position sensor tested under different working conditions in the filtering process are analyzed by comparing the asynchronous characteristics of the position sensor with those of the acceleration and speed.

[0046] For various working conditions, curve fitting is performed on various types of data respectively to obtain the time-sequence change curves of various types of data. In this embodiment, preferably, each type of data collected under each working condition is mapped to a two-dimensional rectangular coordinate system with the abscissa being the collection time and the ordinate being the corresponding data value, so as to obtain the time-sequence change curve corresponding to each type of data collected; to accurately analyze the interference characteristics generated on the position sensor test under different working conditions, in this embodiment, by comparing the asynchronous characteristic differences of the data changes caused by the interference in the time-sequence change curves between the collected data, the time-sequence change curve corresponding to each type of data is used as the input, and the peak detection algorithm is used to obtain the position and amplitude of the peak in the time-sequence change curve. The position of the peak represents the time corresponding to the peak; specifically, in this embodiment, the peak detection algorithm uses a sliding window method based on local maximum search.

[0047] Furthermore, the relative peak offset values of the peak corresponding times in the position data compared with the peak corresponding times in the acceleration data and the speed data are calculated respectively. Specifically, the absolute values of the differences between the peak corresponding times in the position data and the peak corresponding times in the acceleration data and the speed data are calculated, and the mean value of all the absolute values is used as the relative peak offset value of the position data; the relative peak offset value reflects the lag characteristic of the response during the actual test of the position sensor under different working conditions. It should be noted that during the calculation process of the relative peak offset value, if the number of peaks in the position data is not equal to the number of peaks in the acceleration data and the number of peaks in the speed data, only the situation corresponding to the data with the least number of peaks is involved in the calculation and analysis.

[0048] Further, for each peak in the position data, the difference between the peak amplitude in the position data and the standard displacement data of the linear motor corresponding to the position where each peak is located is calculated, and the ratio of the difference to the corresponding standard displacement data is calculated. The sum of all the ratios is used as the peak deviation coefficient of the position data. It should be noted that during the simulation process, the standard displacement data of the linear motor at each position is fixed and can be obtained by the implementer through self-measurement. The specific process is not described in detail in this embodiment. The larger the peak deviation coefficient, the greater the deviation generated by the position data collected based on the current working condition under the influence of interference.

[0049] Further, to accurately analyze the interference frequency characteristics of position sensor testing under different working conditions, for the current working condition, the number of peaks in the position data is used as the characteristic value of the interference effect. The larger the characteristic value, the greater the degree of interference during the dynamic testing process.

[0050] Therefore, based on the above analysis, a comparative analysis is carried out on the interference effect characteristics of dynamic testing of position sensors under different working conditions. Based on the analysis results, the interference characteristics under different working conditions are clustered and classified to achieve the classification and judgment of interference patterns under different working conditions.

[0051] Specifically, during the dynamic testing of the position sensor under different working conditions, to eliminate the influence of dimension differences on the analysis of interference characteristics under different working conditions, the calculated relative peak offset value, peak deviation coefficient, and characteristic value under each working condition are used as inputs, and a normalization algorithm is used for standardization processing. The specific normalization process is a well-known technology in the art and will not be elaborated in this embodiment. The normalized relative peak offset value, peak deviation coefficient, and characteristic value under each working condition are combined to form a three-dimensional characteristic vector corresponding to each working condition, which is used to represent the measurement interference characteristics of the position sensor under each working condition.

[0052] It should be noted that each three-dimensional characteristic vector contains three elements, and the elements represented by the same position in different three-dimensional characteristic vectors are the same. In this embodiment, the element corresponding to the first position in the three-dimensional characteristic vector is the normalized offset value, the element corresponding to the second position is the normalized deviation coefficient, and the element corresponding to the third position is the normalized characteristic value.

[0053] Taking the three-dimensional characteristic vectors of all working conditions as inputs, an agglomerative hierarchical clustering algorithm is used to classify the measurement interference characteristics under different working conditions. During the clustering process, the Euclidean distance between the three-dimensional characteristic vectors is calculated to measure the similarity of the measurement interference characteristics between different working conditions, and the data with similar measurement interference characteristics are classified into the same category, so as to achieve the accurate classification of the measurement interference characteristics of the position sensor under different working conditions and compare the hysteresis and interference intensity differences between different working conditions.

[0054] Step 3: According to the average level of the same element in all three-dimensional characteristic vectors within each category after clustering, and the significant fluctuation of each element in all three-dimensional characteristic vectors within each category, the comprehensive interference characteristic value of each category is obtained. Combining the average level of all comprehensive interference characteristic values, the categories after clustering are divided into large and small interference sets, and the covariance matrix during Kalman filtering for the various data corresponding to the large and small interference sets is optimized.

[0055] Perform dynamic measurement interference sensitivity analysis under different working conditions for each type of data after clustering; since the same type of working conditions have similar interference characteristics, but there are differences in the response characteristics to hysteresis and interference intensity, it is necessary to further quantitatively analyze the sensitivity of their measurement interference characteristics reflecting different working conditions. Specifically, calculate the coefficient of variation of the same element in all three-dimensional feature vectors in each category after clustering as the weight of the same element, and the weight reflects the stability of specific interference characteristics under the same type of working conditions. The larger the weight, the more significant the fluctuation of the corresponding feature of the element among different working conditions and the worse the stability. The calculation method of the coefficient of variation is a prior art and will not be elaborated in this embodiment.

[0056] Furthermore, the larger the calculated coefficient of variation, the greater the impact on the comprehensive interference feature analysis due to the poor stability of the corresponding element. Therefore, in this embodiment, by analyzing the stability of the measurement interference characteristics under different categories after clustering, calculate the comprehensive interference feature value of each category after clustering. , and the specific calculation relationship is as follows:

[0057] ;

[0058] Wherein, represents the comprehensive interference feature value of the th category after clustering; represents the mean value of the th type of element in all three-dimensional feature vectors in the th category after clustering; and respectively represent the weights of the th type and the th type of element in all three-dimensional feature vectors in the th category after clustering; represents the number of three-dimensional feature vectors in the th category.

[0059] It can be understood that the larger the calculated comprehensive interference feature, the greater the possibility of significant hysteresis and large interference intensity caused by interference during dynamic testing under different working conditions of the current category based on the comprehensive analysis of the interference influence characteristics under different working conditions of each category.

[0060] Furthermore, based on the above analysis results of the comprehensive interference features of different categories after partitioning, adjust the measurement noise covariance matrix during the filtering and noise reduction process to adapt to the differences in interference effects under different working conditions during the position sensor testing process.

[0061] Specifically, for the accuracy test process of position sensors in the same batch, to avoid the problem of large judgment errors caused by interference in the sensor accuracy test process due to system defects during the dynamic test process; during the process of testing sensors in the same batch under different working conditions, the mean value of all calculated comprehensive interference characteristic values is used as the judgment threshold. ; During the process of testing each sensor under different working conditions, the category with a calculated comprehensive interference characteristic value greater than the judgment threshold is classified into the large interference set, and the category with a calculated comprehensive interference characteristic value less than or equal to the judgment threshold is classified into the small interference set.

[0062] Further, in this embodiment, the covariance matrix is dynamically adjusted according to the large interference set and the small interference set divided according to the above process. For the large interference set, the covariance matrix is increased by a first preset multiple; for the small interference set, the covariance matrix is reduced by a second preset multiple. The value range of the first preset multiple is from 2 to 4, and the value range of the second preset multiple is from 0.2 to 0.4. It should be noted that the process of obtaining the initial covariance matrix in the Kalman filtering process is a well-known prior art, and the specific calculation method will not be elaborated in this embodiment. Specifically, in this embodiment, for the large interference set, the covariance matrix is increased by 2 times to reduce the dependence on the measured value of the position data; for the small interference set, it indicates that the credibility of all corresponding measured values of the position data is high, and the covariance matrix can be reduced by 0.4 times to strengthen the measurement data in the filtering process. Based on the above process, the filtering and noise reduction processing of the dynamic test data of the position sensor under different working conditions is completed.

[0063] Step 4: Based on the error between the filtered position data under different working conditions and the standard displacement data of the preset linear motor, obtain a quantization index to test the accuracy of the built-in position sensor of the motorcycle shock absorber.

[0064] After completing the filtering process of the data of the built-in position sensor of the motorcycle shock absorber, the filtered position data is compared and analyzed with the standard displacement data of the linear motor. It should be noted that during the simulation process, the standard displacement data of the linear motor at each position is fixed and can be obtained by the implementer through self-measurement. The specific process is not elaborated in this embodiment and is not specifically limited herein. Specifically, for the filtered position data under each working condition, the absolute error and relative error between the position data at each position and the standard displacement data are calculated respectively. It should be noted that the calculation methods of the absolute error and relative error are prior arts and the specific calculation process is not elaborated in this embodiment. For all the position data under each simulated working condition, the average value of the absolute errors corresponding to all positions and the average value of the relative errors corresponding to all positions under each working condition are calculated respectively, and are denoted as the first average value and the second average value respectively, both of which are used as the quantization indexes of the position sensor accuracy under each working condition.

[0065] Specifically, according to the accuracy index requirements of the position sensor for the motorcycle shock absorption system, the accuracy requirements of each quantization index are preset. In this embodiment, the absolute error is controlled within ±0.5 mm and the relative error does not exceed ±2%. In the actual application scenario, the implementer sets it by himself / herself and this embodiment does not limit it. The calculated error data is compared item by item with the accuracy index requirements. When the position sensor fully meets the accuracy requirements for both quantization indexes under all set simulated working conditions, the accuracy test of the built-in position sensor is qualified; if there is any quantization index that does not meet the accuracy requirements in any working condition, it is determined that the test accuracy of the position sensor is unqualified.

[0066] It can be understood that referring to "one embodiment" or "some embodiments" etc. described in the specification of this application means that specific features, structures or characteristics described in combination with this embodiment are included in one or more embodiments of this application. Thus, if "in one embodiment", "in some embodiments", "in other some embodiments", "in still other embodiments" etc. appear in different places in this specification, it does not necessarily refer to the same embodiment, but means "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "include", "comprise", "have" and their variants all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0067] It should be noted that the above order of the embodiments of the present application is only for description and does not represent the superiority or inferiority of the embodiments. In addition, the specific embodiments of this specification have been described above. Moreover, the processes depicted in the drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous. At the same time, the size of the serial numbers of the steps in the embodiments does not mean the sequence of execution. The execution sequence of each process should be determined by its function and internal logic, and should not constitute any limitation to the implementation process of the embodiments in this specification.

[0068] The above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them; although the present application has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions recorded in the foregoing embodiments, or perform equivalent replacements on some of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present application, and should all be included in the protection scope of the present application.

Claims

1. A method for testing the accuracy of an in - built position sensor of a motorcycle shock absorber, characterized in that, It includes the following steps: Obtain various types of data under different working conditions, including: various position data of the built-in position sensor of the motorcycle shock absorber, the acceleration of the shock absorber vibration, and the moving speed of the shock absorber; Analyze the time-series changes of various types of data under each working condition and extract the peaks in various types of data. According to the deviation situation between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the moving speed, combined with the deviation situation of the peak amplitudes in the position data and the number of peaks in the position data, obtain the three-dimensional feature vector corresponding to each working condition, and perform clustering division on all three-dimensional feature vectors; According to the average level of the same element in all three-dimensional feature vectors within each category after clustering, and the significant fluctuation situation of each element in all three-dimensional feature vectors within each category, obtain the comprehensive interference eigenvalue of each category. Combine the average level of all comprehensive interference eigenvalues to divide the categories after clustering into large and small interference sets, and optimize the covariance matrix when performing Kalman filtering on the position data corresponding to the large and small interference sets; Based on the error between the filtered position data under different working conditions and the standard displacement data of the preset linear motor, obtain a quantization index to test the accuracy of the built-in position sensor of the motorcycle shock absorber.

2. The accuracy testing method of an in-built position sensor for a motorcycle shock absorber according to claim 1, characterized in that The method for obtaining the three-dimensional feature vector corresponding to each working condition is as follows: For each working condition, perform curve fitting on various types of data respectively to obtain the time-series change curves of various types of data, and extract the peaks of the time-series change curves of various types of data; and according to the difference between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the moving speed, obtain the relative peak offset value of the position data, analyze the relative peak deviation coefficient of the amplitudes of the peaks in the position data, and use the number of peaks in the position data as the eigenvalue of the interference effect. The normalized relative peak offset value, peak deviation coefficient, and eigenvalue under each working condition form the three-dimensional feature vector corresponding to each working condition.

3. A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber according to claim 2, characterized in that, The method for obtaining the relative peak offset value further includes: Calculate the absolute values of the differences between the times corresponding to the peaks in the position data and the times corresponding to the peaks in the acceleration data and the speed data respectively, and take the mean of all absolute values as the relative peak offset value of the position data.

4. The accuracy test method for an in-built position sensor of a motorcycle shock absorber as described in claim 2, characterized in that, The method for obtaining the peak deviation coefficient further includes: calculate the difference between the amplitudes of the peaks in the position data and the standard displacement data of the linear motor corresponding to the positions where the peaks are located, calculate the ratio of the difference to the corresponding standard displacement data, and take the sum of all ratios as the peak deviation coefficient of the position data.

5. A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber according to claim 1, characterized in that, The method for obtaining the comprehensive interference eigenvalue of each category is as follows: ; among them, represents the comprehensive interference eigenvalue of the th category after clustering; represents the mean value of the th element among all three-dimensional feature vectors in the th category after clustering; and respectively represent the weights of the th and th elements among all three-dimensional feature vectors in the th category after clustering; represents the number of elements in the three-dimensional feature vector.

6. The accuracy test method for an in-built position sensor of a motorcycle shock absorber according to claim 5, characterized in that, The method for obtaining the weight is further: calculate the coefficient of variation of the same element in all three-dimensional feature vectors in each category after clustering as the weight of the same element.

7. The accuracy testing method of an in-built position sensor for a motorcycle shock absorber according to claim 1, characterized in that The method for dividing the large and small interference sets is: take the mean of all comprehensive interference eigenvalues as the judgment threshold, and divide the categories with comprehensive interference eigenvalues greater than the judgment threshold into the large interference set, otherwise, divide them into the small interference set.

8. A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber according to claim 1, characterized in that, The method for optimizing the covariance matrix of each position data corresponding to the large and small interference sets during filtering includes: For the large interference set, increase the covariance matrix by a first preset multiple; for the small interference set, reduce the covariance matrix by a second preset multiple, where the value range of the first preset multiple is from 2 to 4, and the value range of the second preset multiple is from 0.2 to 0.

4.

9. A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber according to claim 1, characterized in that, The method for obtaining the quantization index is: For each position data after filtering under each working condition, calculate the absolute error and relative error between the position data of each position and the standard displacement data of the linear motor respectively, and calculate the average value of the absolute errors corresponding to all positions and the average value of the relative errors corresponding to all positions under each working condition, which are respectively recorded as the first average value and the second average value, and both are used as the quantization indexes of the position sensor accuracy under each working condition.

10. A method for testing the accuracy of an in-built position sensor of a motorcycle shock absorber according to claim 1, characterized in that, The test for the accuracy of the built-in position sensor of the motorcycle shock absorber further includes: Preset the accuracy requirements for each quantization index. When all quantization indexes of the position sensor meet the accuracy requirements under all working conditions, the accuracy test of the built-in position sensor of the motorcycle shock absorber is qualified; if any quantization index does not meet the accuracy requirements under any working condition, the test accuracy of the built-in position sensor of the motorcycle shock absorber is unqualified.

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