Wheel hub dynamic balance detection method and system based on vibration analysis

Abstract

The present invention relates to the field of wheel hub detection technology, and specifically to a wheel hub dynamic balance detection method and system based on vibration analysis. The detection method comprises the following steps: installing at least one vibration sensor on the wheel hub to be detected, for collecting vibration signals during the rotation of the wheel hub; collecting vibration signals of the wheel hub at different rotation speeds through the vibration sensor, and converting the vibration signals into digital signals; processing the digital signals using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics. The present invention not only overcomes the limitations of traditional denoising technology in the face of complex noise environments, but also improves the detection accuracy of minor imbalance problems, and ensures that the wheel hub can obtain accurate imbalance information even at high speeds. It provides a set of high-precision and high-efficiency dynamic balance detection and correction solutions, reduces tire wear, and improves vehicle driving safety.

Description

Technical Field

[0001] The present invention relates to the technical field of wheel hub detection, and in particular to a wheel hub dynamic balance detection method and system based on vibration analysis. Background Art

[0002] To ensure vehicle performance at high speeds, wheels must be dynamically balanced and effectively corrected before leaving the factory. Traditional methods typically rely on mechanical testing equipment, which has low accuracy for detecting minor imbalances. Especially at high rotational speeds, due to the influence of noise, traditional methods have difficulty accurately extracting the characteristics of wheel imbalance vibration, resulting in deviations in dynamic balancing correction.

[0003] By installing a vibration sensor on the wheel hub, vibration signals during rotation can be collected in real time. Combined with signal processing techniques, this can theoretically more accurately analyze imbalance characteristics. However, existing vibration analysis methods often rely on traditional denoising techniques, such as low-pass filtering or spectral subtraction. These methods are limited in their effectiveness when dealing with high-order nonlinear, non-Gaussian noise, easily causing signal distortion and failing to effectively improve the detection accuracy of minor imbalance issues. Summary of the Invention

[0004] The present invention provides a wheel hub dynamic balance detection method and system based on vibration analysis to improve the detection accuracy of small imbalance problems in wheel hub dynamic balance detection.

[0005] The technical solution of the present invention to achieve the above-mentioned purpose is:

[0006] In one aspect, a wheel hub dynamic balance detection method based on vibration analysis is provided, the detection method comprising the following steps:

[0007] Install at least one vibration sensor on the wheel hub to be tested to collect vibration signals during the rotation of the wheel hub;

[0008] The vibration sensor collects vibration signals of the wheel hub at different rotation speeds and converts the vibration signals into digital signals;

[0009] Processing the digital signal using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics;

[0010] Based on the target vibration characteristics, a multi-dimensional space mapping algorithm is used to determine the unbalance position and unbalance mass of the wheel hub, and obtain a detection result;

[0011] transmitting the detection result to a control unit, wherein the control unit generates a correction instruction according to the unbalanced position and the unbalanced mass;

[0012] The execution unit performs wheel hub dynamic balancing correction according to the correction instruction.

[0013] On the other hand, a wheel hub dynamic balance detection system based on vibration analysis is provided, for implementing the wheel hub dynamic balance detection method based on vibration analysis as described above, the detection system comprising:

[0014] At least one vibration sensor, used to collect vibration signals during the rotation of the wheel hub;

[0015] a signal processing unit, configured to convert the vibration signal into a digital signal, and process the digital signal using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics;

[0016] an analyzing unit, configured to determine an unbalanced position and an unbalanced mass based on the target vibration characteristics by using a multi-dimensional space mapping algorithm;

[0017] a control unit, configured to generate a correction instruction according to the unbalanced position and the unbalanced mass;

[0018] an execution unit, configured to perform a dynamic balancing correction operation according to the correction instruction;

[0019] The feedback unit is used to re-collect the vibration signal of the wheel hub after the dynamic balancing correction operation is completed, and compare the corrected unbalance position and corrected unbalance mass with the unbalance position and unbalance mass determined by the analysis unit to confirm the correction effect.

[0020] The beneficial effects of the present invention are:

[0021] The wheel hub dynamic balance detection method and system based on vibration analysis of the present invention installs at least one vibration sensor on the wheel hub to be detected to collect vibration signals during the rotation of the wheel hub, and then uses high-order spectrum analysis technology to denoise the collected digital signals, thereby accurately extracting vibration characteristics representing wheel hub imbalance. The present invention not only overcomes the limitations of traditional denoising technology in the face of complex noise environments, but also improves the detection accuracy of minor imbalance problems and ensures that the wheel hub can obtain accurate imbalance information even at high speeds. Furthermore, the present invention utilizes a multi-dimensional space mapping algorithm to determine the imbalance position and mass of the wheel hub based on the processed target vibration characteristics. It can effectively map the vibration characteristics into a multi-dimensional space, amplify weak characteristic signals, and reduce processing errors. It also provides a set of high-precision and high-efficiency dynamic balance detection and correction solutions, which reduces tire wear and improves vehicle driving safety.

[0022] The above description is only an overview of the technical solution of the present invention. In order to more clearly understand the technical means of the present invention and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present invention with reference to the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a flow chart of a wheel hub dynamic balance detection method according to one embodiment of the present invention;

[0024] Figure 2 This is a vibration waveform diagram of the wheel hub at different speeds in one embodiment of the present invention;

[0025] Figure 3 A schematic diagram of vibration waveform denoising in one embodiment of the present invention;

[0026] Figure 4 A comparison diagram of denoising of a time spectrum diagram in one embodiment of the present invention;

[0027] Figure 5 A schematic diagram of determining wheel hub imbalance using a mapping algorithm according to an embodiment of the present invention;

[0028] Figure 6 1 is a time-frequency comparison diagram of the third-order spectrum and the fourth-order spectrum in one embodiment of the present invention. DETAILED DESCRIPTION

[0029] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. All other embodiments obtained by ordinary technicians in this field based on the embodiments of the present invention without making any creative efforts shall fall within the scope of protection of the present invention.

[0030] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations, or descriptions. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be construed as being preferred or advantageous over other embodiments or designs. Rather, the use of words such as "exemplary" or "for example" is intended to present the relevant concepts in a concrete manner.

[0031] The present invention provides the following preferred embodiments:

[0032] Example 1: In order to solve the problem of low accuracy in detecting small imbalances in wheel hub dynamic balancing, this example proposes a wheel hub dynamic balancing detection method based on vibration analysis. Figure 1 As shown, the steps of the detection method are:

[0033] S100. Install at least one vibration sensor on the wheel hub to be inspected, so as to collect vibration signals during the rotation of the wheel hub.

[0034] S200. Collect vibration signals of the wheel hub at different rotation speeds through a vibration sensor and convert the vibration signals into digital signals.

[0035] S300. Process the digital signal using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics.

[0036] S400. Based on the target vibration characteristics, a multi-dimensional space mapping algorithm is used to determine the unbalance position and unbalance mass of the wheel hub, and obtain a detection result.

[0037] S500. Transmit the detection result to the control unit, and the control unit generates a correction instruction according to the unbalanced position and unbalanced mass.

[0038] S600. Perform wheel hub dynamic balancing correction according to the correction instruction through the execution unit.

[0039] Specifically, in this embodiment, multiple vibration sensors are installed at different positions of the wheel hub to fully cover the vibration conditions of the wheel hub. These vibration sensors can be selected from high-sensitivity acceleration sensors, such as ADXL355, which have excellent noise performance and wide-band response, ensuring that vibration signals can be accurately collected at different rotation speeds. Figure 2 Figure 2 shows the vibration signal waveforms collected at different speeds. Multiple sensors are installed in the radial and axial directions of the wheel hub to capture multi-directional vibration information, thereby improving the comprehensiveness and accuracy of detection.

[0040] Furthermore, the vibration signals collected by these vibration sensors undergo analog-to-digital conversion using a data acquisition card, such as the NIUSB-6259, which converts analog signals into digital signals. The selection of a data acquisition card requires consideration of its sampling rate, resolution, and stability to ensure the integrity and accuracy of the vibration signals. Furthermore, this embodiment employs synchronous sampling technology to ensure that signals collected at different rotational speeds have the same sampling rate, facilitating subsequent data processing and analysis.

[0041] Furthermore, in the signal processing stage, this embodiment uses a denoising algorithm based on high-order spectrum analysis. High-order spectrum analysis is a non-Gaussian signal analysis method that can effectively remove noise from the signal, such as Figures 3 to 6 As shown, the vibration characteristics representing wheel hub imbalance are extracted. Specifically, in this embodiment, the third-order spectrum and the fourth-order spectrum are calculated. By calculating these high-order spectra, the nonlinear components in the vibration signal can be captured, thereby more accurately identifying the imbalance position and imbalance mass.

[0042] It should be understood that the calculation process of high-order spectrum analysis is relatively complex, but through reasonable parameter selection and optimization, good results can be achieved in practical applications. For example, when calculating the third-order spectrum B3(x,f1,f2) and the fourth-order spectrum B4(x,f1,f2,f3), appropriate frequency resolution and time window length can be selected to balance calculation accuracy and efficiency. In addition, the calculation of the denoising coefficient γ(k) is the key to accurately extracting the target vibration characteristics. By adjusting the order of the high-order spectrum and the weight parameters α(k-1) and β(k-1), noise can be effectively removed and important vibration characteristic information can be retained, such as Figure 6 shown.

[0043] Furthermore, based on the extracted target vibration characteristics, this embodiment uses a multi-dimensional space mapping algorithm to determine the unbalanced position and unbalanced mass of the wheel hub. The multi-dimensional space mapping algorithm maps the vibration feature vector to a high-dimensional space. By calculating the mapping value U of the unbalanced position and unbalanced mass, the unbalanced state of the wheel hub can be determined more accurately, such as Figure 5 In this embodiment, the multi-dimensional space mapping algorithm is specifically implemented by optimizing the mapping matrix A through the training data set, thereby mapping the target vibration feature F to the unbalanced position and unbalanced mass U.

[0044] It can be understood that the optimization process of the multidimensional space mapping algorithm is achieved by minimizing the sum of squared errors. Through optimization, the accuracy of the mapping results can be ensured, thereby improving the accuracy of imbalance detection.

[0045] Furthermore, after the detection results are generated, this embodiment transmits them to the control unit. The control unit generates correction instructions based on the detected imbalance position and mass. The core function of the control unit is to calculate the required correction torque vector M, which determines the corrective torque that the actuator must apply during the dynamic balancing correction operation. The calculation of the correction torque vector takes into account the distribution of the imbalance position and mass, and an optimization algorithm is used to ensure the accuracy of the correction instructions.

[0046] Furthermore, based on the correction instructions generated by the control unit, the execution unit performs dynamic balancing correction operations. The execution unit can include both electromagnetic and mechanical correction devices, with the specific choice depending on the actual application scenario. Electromagnetic correction devices dynamically adjust the intensity and direction of the electromagnetic field, while mechanical correction devices adjust the position and mass of the counterweights. The selection and design of these execution units must consider their response speed, accuracy, and stability to ensure efficient and accurate correction operations.

[0047] The benefits of this embodiment lie in the comprehensive coverage of wheel hub vibration conditions achieved by installing multiple high-performance vibration sensors on the wheel hub. High-order spectrum analysis and multidimensional spatial mapping algorithms are used for precise signal processing and feature extraction, improving the accuracy of wheel hub dynamic balancing at different rotational speeds. Furthermore, the coordinated operation of the control unit and the execution unit enables efficient and accurate dynamic balancing correction, enhancing vehicle driving safety.

[0048] Example 2: In order to solve the noise removal problem of wheel hub dynamic balancing detection at different rotation speeds, this example further proposes and optimizes a denoising algorithm based on high-order spectrum analysis. Specifically, by initializing the denoising parameters α(0)=1 and β(0)=0, and gradually updating these parameters during the processing process, the denoising effect is improved. At the same time, the third-order spectrum B3(x,f1,f2) and the fourth-order spectrum B4(x,f1,f2,f3) are calculated, and these high-order spectrum features are used to calculate the denoising coefficient γ(k). Finally, by updating the denoising parameters, high-precision denoising of the vibration signal is achieved.

[0049] First, this embodiment uses a method to initialize denoising parameters. The denoising parameters α(0) and β(0) are used to adjust the order and weight of the higher-order spectra, respectively. The initial values ​​α(0) = 1 and β(0) = 0 are chosen to give a higher weight to the third-order spectrum at the beginning of the algorithm, because the third-order spectrum is generally better able to capture linear features. As the algorithm progresses, these parameters will be gradually adjusted to adapt to the signal characteristics in different noise environments.

[0050] Furthermore, this embodiment explains the process of high-order spectrum analysis in detail. What needs to be understood in the calculation of the third-order spectrum B3 (x, f1, f2) and the fourth-order spectrum B4 (x, f1, f2, f3) is that these high-order spectra can effectively extract the nonlinear components in the signal. Specifically, the third-order spectrum B3 (x, f1, f2) can capture the third harmonic component in the signal, while the fourth-order spectrum B4 (x, f1, f2, f3) can capture the fourth harmonic component. By calculating these high-order spectra, the noise components in the vibration signal can be more accurately identified and removed, such as Figure 6 shown.

[0051] The calculation of the denoising coefficient γ(k) is based on the third-order spectrum and the fourth-order spectrum. The specific expression is:

[0052] ,

[0053] Where K is the number of frequency bands, B3 and B4 are the values ​​of the third-order and fourth-order spectra, respectively. The calculation of the denoising coefficient γ(k) needs to consider the high-order spectral characteristics of multiple frequency bands to ensure comprehensive and accurate denoising.

[0054] Furthermore, the steps of updating the denoising parameters α(k) and β(k) are implemented by the step size parameters η1 and η2. The specific update formula is:

[0055] α(k)=α(k-1)+η1γ(k), β(k)=β(k-1)+η2γ(k);

[0056] The value of η1 ranges from 0.01 to 0.1, and the value of η2 ranges from 0.001 to 0.01. By gradually adjusting these parameters, the denoising effect can be gradually optimized in multiple iterations to improve the detection accuracy.

[0057] The benefit of this embodiment is that by initializing and gradually updating denoising parameters, combined with the calculation of third-order and fourth-order spectra, it achieves high-precision denoising of vibration signals at different rotational speeds. This method performs well in complex noise environments and can effectively improve the accuracy of wheel hub dynamic balancing testing.

[0058] Example 3: In order to solve the application problem of high-order spectrum analysis in short-time Fourier transform noise reduction, this example further refines the specific steps of using high-order spectrum analysis for noise reduction. Specifically, by performing short-time Fourier transform (STFT) on the digital signal, the time-frequency spectrum matrix X(t,f) is obtained, and then the spectral density B of the high-order spectrum is calculated. n (x,f1,f2,…,f n-1 ) and use spectral density to remove noise to improve the clarity of vibration signals.

[0059] In this embodiment, the acquired digital signal is first processed using the short-time Fourier transform (STFT). The STFT converts a signal from the time domain to the time-frequency domain, better reflecting the signal's frequency characteristics within different time windows. Generating the time-frequency spectrum matrix X(t,f) requires selecting an appropriate window length and frequency resolution to ensure that the signal's time-frequency characteristics are accurately captured.

[0060] Furthermore, the spectral density B of the high-order spectrum is calculated n (x,f1,f2,…,f n-1 ). The specific expression is:

[0061] , where T is the number of time windows and n is the order of the high-order spectrum. It should be understood that by calculating the spectral density of the high-order spectrum, the nonlinear components in the signal can be amplified, thereby more effectively identifying and removing noise.

[0062] Furthermore, spectral density is used to remove noise. The specific expression is:

[0063] , where X clean(t,f) is the time-frequency spectrum matrix after denoising. The selection of the denoising coefficient γ(k) needs to be optimized according to the noise characteristics in the actual application scenario to ensure the maximum denoising effect.

[0064] This embodiment allows for more accurate extraction of target vibration features from vibration signals, improving detection accuracy. Furthermore, the application of short-time Fourier transforms allows for a clearer display of the signal's frequency characteristics over different time periods, providing a solid foundation for subsequent determination of imbalance location and quality.

[0065] Example 4: In order to solve the implementation details of the multi-dimensional space mapping algorithm in the wheel hub dynamic balance detection, this example further refines the specific steps of the multi-dimensional space mapping algorithm. Specifically, by performing multi-dimensional space mapping on the target vibration characteristics, a high-dimensional feature vector F=[F1,F2,…,F D ], and use the multi-dimensional space mapping algorithm to calculate the mapping value of the unbalanced position and unbalanced mass U=[U1,U2,…,U M ] to improve detection accuracy.

[0066] First, the extraction of target vibration features in this embodiment is based on the results of high-order spectrum analysis. The extracted vibration features may include the amplitudes of each frequency and time point of the time-frequency spectrum matrix, as well as the changing trends of these amplitudes. The generation of the high-dimensional feature vector F requires consideration of multiple feature points to ensure the comprehensiveness of the feature vector. The dimension D of the feature vector is selected according to the needs of the actual application. For example, 10 feature points can be selected to generate a 10-dimensional feature vector.

[0067] Furthermore, the mapping value U of the unbalanced position and unbalanced mass is calculated. The specific expression is:

[0068] , where A, B, and C are mapping matrices. These mapping matrices are optimized using a training dataset to ensure the accuracy and reliability of the mapping results. Mapping matrix A is used for mapping the linear portion, B for mapping the quadratic nonlinear portion, and C for mapping the cubic nonlinear portion. This multi-level mapping approach allows for a more comprehensive capture of wheel imbalance characteristics.

[0069] It is understandable that the optimization process of the multidimensional space mapping algorithm is achieved by minimizing the sum of squared errors of the training data set. Specifically, for each training sample F i and the corresponding unbalanced position and mass mapping value U iBy adjusting the mapping matrices A, B, and C, the difference between the mapping result and the actual imbalance position and mass is minimized. This optimization method can provide stable detection results in different application scenarios. Through this embodiment, the target vibration characteristics are processed using a multi-dimensional space mapping algorithm, which can more accurately determine the imbalance position and imbalance mass. The multi-dimensional space mapping algorithm performs multi-level mapping on the high-dimensional feature vector F, taking into account not only the linear part but also the quadratic and cubic nonlinear parts, thereby improving the comprehensiveness of the mapping results.

[0070] In practical applications, the above optimization problem can be solved using gradient descent or other optimization algorithms. During the optimization process, the initial values ​​of each matrix and the learning rate must be considered to ensure stability and convergence speed. For example, the initial values ​​of A, B, and C can be set to small random values, and these matrices can be gradually adjusted through multiple iterations to ultimately achieve the optimal mapping result.

[0071] The benefit of this embodiment lies in mapping the target vibration signature into the high-dimensional space of unbalanced position and mass through a multidimensional spatial mapping algorithm, enabling more comprehensive capture and analysis of vibration signal characteristics. This approach performs well in complex and changing environments, improving the accuracy and stability of wheel hub dynamic balancing testing and providing reliable data support for subsequent dynamic balancing corrections.

[0072] Example 5: To address the efficiency and accuracy issues of the multidimensional space mapping algorithm when optimizing the mapping matrix, this example further refines the specific steps for optimizing the mapping matrix using a training dataset. Specifically, by optimizing the high-dimensional feature vector F and the mapping values ​​U of the imbalance position and mass to minimize the sum of squared errors, the optimal values ​​of the mapping matrices A, B, and C are ensured, thereby improving the accuracy of the detection results.

[0073] Specifically, in this embodiment, a training data set is used to optimize the mapping matrix. The training data set includes a plurality of hub vibration signals of different imbalance states and their corresponding imbalance position and mass mapping values ​​U i The training data needs to be collected under different conditions and speeds to ensure data diversity and representativeness. For example, vibration signals can be collected at different rotational speeds, and the corresponding imbalance position and mass can be recorded to form a data set containing multiple samples.

[0074] Furthermore, the process of optimizing the mapping matrix A can be achieved by minimizing the sum of squared errors. The specific expression is:

[0075] , where N is the number of training samples, F i is the high-dimensional feature vector of the i-th sample, U iis the mapping value of the imbalance position and imbalance mass of the i-th sample. The optimization process can be implemented by gradient descent or other optimization algorithms to ensure that the value of A can minimize the sum of squared errors.

[0076] Furthermore, the process of optimizing the mapping matrix B is also achieved by minimizing the sum of squared errors. The specific expression is:

[0077] , where B is used to capture the mapping relationship of the quadratic nonlinear part. During the optimization process, the value of B needs to be gradually adjusted to minimize the difference between the mapping result and the actual imbalance position and quality.

[0078] Furthermore, the process of optimizing the mapping matrix C is also achieved by minimizing the sum of squared errors. The specific expression is:

[0079] , where C is used to capture the mapping relationship of the cubic nonlinear part. During the optimization process, the value of C needs to be gradually adjusted to minimize the difference between the mapping result and the actual imbalance position and quality.

[0080] It is understandable that by gradually optimizing the values ​​of A, B, and C, the adaptability of the multidimensional space mapping algorithm in different application scenarios can be ensured. For example, the optimization results can be evaluated through cross-validation techniques to ensure the generalization ability of the model on new data.

[0081] In addition, this embodiment also considers the preprocessing steps of the training dataset. Preprocessing steps include data normalization, noise removal, and feature selection to improve data quality and model training results. Data normalization can scale the values ​​of the eigenvectors to a fixed interval, such as [0, 1], thereby preventing the impact of differences in the magnitude of the eigenvalues ​​on the training results. Noise removal can be achieved using a denoising algorithm based on high-order spectral analysis to ensure a clean signal input to the mapping algorithm. Feature selection can be achieved using methods such as principal component analysis (PCA) to select feature points that have the greatest impact on the imbalance location and quality, thereby improving the efficiency and accuracy of the mapping algorithm.

[0082] This embodiment utilizes a training dataset to optimize the mapping matrices A, B, and C in the multidimensional space mapping algorithm, enabling more accurate determination of the wheel hub's imbalance position and mass. This approach not only improves the accuracy of detection results but also ensures the stability and reliability of the mapping algorithm under various conditions, thus providing support for wheel hub dynamic balancing correction.

[0083] Example 6: In order to solve the accuracy and real-time problem of generating correction instructions during the wheel hub dynamic balancing test, this embodiment further optimizes the correction instruction generation step of the control unit. The required correction torque vector M=[M1,M2,…,M P ] and uses the optimization algorithm to optimize the correction torque vector, thereby generating accurate correction instructions and transmitting them to the execution unit.

[0084] Specifically, in this embodiment, the control unit determines the unbalance position and unbalance mass U=[U1,U2,…,U M ] to calculate the required correction torque vector M. The dimension P of the correction torque vector is determined by the number of torque points requiring correction in the actual application scenario. For example, if correction is required at four locations on the wheel hub, then P is 4. The specific calculation method can be based on a physical model, taking into account the wheel hub structure and imbalance distribution, to determine the required correction torque at each location.

[0085] Furthermore, the optimized correction torque vector M opt The step is achieved by minimizing the sum of squared errors. The specific expression is:

[0086] , where U represents the mapping between the unbalanced position and unbalanced mass, and G represents the influence matrix of the correction torque on the unbalance. It is important to understand that the correction torque influence matrix G must be determined through experimental data or derivation from a physical model. This matrix describes the relationship between the influence of each correction torque point on the unbalanced position and mass of the wheel hub. By minimizing the sum of squared errors, the optimal correction torque vector can be found.

[0087] Furthermore, in this embodiment, the gradient descent method is used to optimize the correction torque vector M opt Gradient descent is a commonly used optimization algorithm that gradually adjusts the value of the moment vector to reduce the sum of squared errors, ultimately reaching a minimum. Specifically, the optimization process can be divided into multiple iterative steps, with the gradient calculated and the moment vector updated in each step. It is understood that choosing an appropriate initial value and learning rate is crucial to the convergence speed of the optimization process and the accuracy of the results.

[0088] Furthermore, the optimized correction torque vector M opt These instructions are converted into specific correction commands and transmitted to the execution unit. These instructions can include specific correction parameters for each torque point, such as the current intensity and direction of the electromagnetic correction device and the position and mass of the counterweight for the mechanical correction device. Upon receiving these instructions, the execution unit can quickly perform dynamic balancing correction operations, thereby improving overall detection and correction efficiency.

[0089] This embodiment utilizes an optimization algorithm to calculate and adjust the correction torque vector, generating precise correction instructions and ensuring effective wheel hub dynamic balance correction under varying imbalance conditions. This method has demonstrated excellent performance in practical applications, improving the accuracy and real-time nature of detection and correction.

[0090] Example 7: To address the diversity and adaptability of the actuator unit in wheel hub dynamic balancing testing, this example further refines the design and implementation of the actuator unit. The actuator unit includes an electromagnetic correction device and a mechanical correction device. These two devices use different correction methods to ensure effective dynamic balancing correction under various imbalance conditions.

[0091] Specifically, the electromagnetic correction device in this embodiment performs corrections by dynamically adjusting the strength and direction of the electromagnetic field. The core components of the electromagnetic correction device include an electromagnetic coil and a current controller. The electromagnetic coil is mounted at a specific location around the wheel hub. The current controller adjusts the current in the coil, thereby changing the strength and direction of the electromagnetic field. It should be understood that the electromagnetic correction device can be adjusted quickly, making it suitable for addressing transient and dynamic imbalances.

[0092] Furthermore, the mechanical correction device performs correction by adjusting the position and mass of the installed counterweight. The components of the mechanical correction device include the counterweight, a fine-tuning mechanism, and a mass controller. The counterweight is mounted on or within the wheel hub. The fine-tuning mechanism allows for precise adjustment of the counterweight's position, while the mass controller regulates the counterweight's mass. It is understood that the mechanical correction device can provide more stable correction when addressing static and long-term imbalance issues.

[0093] Furthermore, this embodiment considers the coordinated operation of electromagnetic and mechanical correction devices. Specifically, the most appropriate correction method can be selected based on the location and quality of the imbalance. For example, for transient imbalances, electromagnetic correction can be prioritized, while for chronic imbalances, mechanical correction can be prioritized. These two devices can be coordinated through a control unit to ensure effective dynamic balance correction of the wheel hub in all situations.

[0094] Furthermore, this embodiment also considers the optimized design of the correction device. For example, the electromagnetic coils of the electromagnetic correction device can be optimized in layout to reduce interference and improve correction efficiency. The counterweights of the mechanical correction device can be made of lightweight materials and optimized in structure to reduce the impact on wheel hub performance. These optimized designs enable the actuator to demonstrate higher reliability and efficiency in practical applications.

[0095] This embodiment, utilizing a combination of electromagnetic and mechanical correction devices, can better adapt to different types of imbalance problems, increasing the flexibility and adaptability of wheel hub dynamic balancing detection and correction. This approach excels in dealing with complex and changing imbalance conditions, providing a more comprehensive solution.

[0096] Example 8: To verify the effectiveness of dynamic balancing correction, this example further optimizes the detection method and adds a feedback step. Specifically, after dynamic balancing correction is completed, the wheel hub vibration signal is re-collected and processed to determine the corrected imbalance position and imbalance mass. These values ​​are then compared with the initially determined imbalance position and mass to ensure that the correction meets the requirements.

[0097] First, in this embodiment, after dynamic balancing is complete, a vibration sensor is used to collect the wheel hub's vibration signal. When selecting a vibration sensor, consider its accuracy and stability. For example, the ADXL355 vibration sensor offers excellent high resolution and low noise. The re-collected vibration signal should be collected under the same conditions as the initial acquisition to ensure data consistency and comparability.

[0098] Furthermore, the re-collected vibration signal is processed to extract the target vibration characteristics. Specifically, the signal processing steps may include digital filtering, short-time Fourier transform, and high-order spectrum analysis. Digital filtering is used to remove high-frequency noise from the signal, short-time Fourier transform is used to convert the signal from the time domain to the time-frequency domain, and high-order spectrum analysis is used to extract nonlinear characteristics from the signal. It is important to understand that these processing steps can effectively remove noise and extract key vibration characteristics, providing accurate data support for subsequent imbalance detection.

[0099] Furthermore, the multi-dimensional space mapping algorithm is used to determine the corrected imbalance position and imbalance quality. The specific expression is:

[0100] , where U corr is the mapping value of the corrected unbalanced position and unbalanced mass, F corr is the high-dimensional feature vector of the re-collected vibration signal. The mapping matrices A, B, and C are optimized using the training dataset to ensure the accuracy of the mapping results.

[0101] Furthermore, the corrected unbalanced position and unbalanced mass U corrThe initial unbalance position and unbalance mass U are compared. Specifically, this comparison can be performed by calculating the difference between the two values ​​and checking whether the difference is within a preset range. If the difference exceeds the preset range, for example, exceeding 1% of the initial value, it indicates that the correction effect is unsatisfactory, and a correction instruction needs to be generated and executed again. It can be understood that this feedback mechanism ensures closed-loop control of the correction operation and improves the accuracy of dynamic balancing.

[0102] Through this embodiment, the feedback step is used to verify the effect of the dynamic balance correction, ensuring that the correction operation can achieve the expected goal. This method can effectively avoid under-correction or over-correction in practical applications and improve the overall reliability of detection and correction.

[0103] Example 9: To address the collaborative operation of various units in a wheel hub dynamic balancing detection system, this example proposes a wheel hub dynamic balancing detection system based on vibration analysis, which is used to implement a wheel hub dynamic balancing detection method. By integrating multiple functional units, including a vibration sensor, a signal processing unit, an analysis unit, a control unit, an execution unit, and a feedback unit, the system ensures efficient and reliable operation.

[0104] Specifically, the vibration sensor in this embodiment is used to collect vibration signals from the wheel hub during rotation. When selecting a vibration sensor, consider its accuracy and response speed. For example, the ADXL355 vibration sensor offers excellent high resolution and low noise. The vibration sensor is installed at predetermined locations on the wheel hub, such as the center and edge, to ensure comprehensive detection of the hub's vibration information.

[0105] Furthermore, the signal processing unit converts the collected vibration signal into a digital signal and processes the digital signal using a denoising algorithm based on high-order spectrum analysis. Specifically, the denoising algorithm can initialize denoising parameters α(0) = 1 and β(0) = 0 and gradually update the denoising parameters by calculating the third-order spectrum and the fourth-order spectrum, thereby removing noise and extracting the target vibration characteristics. It can be understood that the signal processing unit ensures the accuracy of subsequent analysis and correction during the denoising and feature extraction process.

[0106] Furthermore, the analysis unit uses a multi-dimensional spatial mapping algorithm to determine the imbalance position and imbalance mass based on the target vibration characteristics. The optimized design of the analysis unit can improve the speed and accuracy of imbalance detection.

[0107] Furthermore, the control unit generates a correction instruction based on the unbalanced position and unbalanced mass determined by the analysis unit. The specific steps include calculating the required correction torque vector M and optimizing the correction torque vector through an optimization algorithm. The optimized correction torque vector M optThe control unit is converted into a specific correction instruction and transmitted to the execution unit. It should be understood that the optimized design of the control unit can ensure that the generated correction instructions are accurate and timely, thereby improving the efficiency of dynamic balancing correction.

[0108] Furthermore, the execution unit performs dynamic balancing correction operations based on the correction instructions generated by the control unit. Specifically, the execution unit may include an electromagnetic correction device and a mechanical correction device. The electromagnetic correction device performs correction by dynamically adjusting the intensity and direction of the electromagnetic field, while the mechanical correction device performs correction by adjusting the position and mass of the counterweight. The coordinated operation of these two devices can better adapt to different types of imbalance problems and improve the adaptability of the correction.

[0109] Furthermore, after the dynamic balancing correction operation is completed, the feedback unit re-collects the vibration signal of the hub and converts the corrected imbalance position and imbalance mass U corr The unbalance position and mass U are compared with the initially determined unbalance position. If the difference exceeds a preset range, such as 1% of the initial value, the feedback unit generates a correction instruction and executes the correction. This closed-loop feedback mechanism ensures that the correction effect meets the requirements and improves the overall stability of the system.

[0110] The benefit of this embodiment is that by integrating multiple functional units, it ensures efficient, accurate and reliable operation of the wheel hub dynamic balancing detection system. In practical applications, this method can better adapt to different types of imbalance problems and improve the overall performance of detection and correction.

[0111] Example 10: To address the optimization issue after dynamic balancing correction, this example further optimizes the detection system by adding a correction optimization unit. The correction optimization unit calculates the correction effect of the execution unit and uses a particle swarm optimization algorithm to adjust the parameters of the correction instruction to minimize the remaining imbalance.

[0112] Specifically, after the control unit generates the correction instruction, the correction optimization unit calculates the correction effect of the execution unit. Specifically, by re-collecting the vibration signal after dynamic balance correction and using the multi-dimensional space mapping algorithm to determine the corrected imbalance position and imbalance mass U corr It is understandable that the calculation process of the correction optimization unit needs to consider multiple factors, including the initial unbalanced position and mass U, the correction parameters of the execution unit and the actual collected vibration signal.

[0113] Furthermore, the specific steps of particle swarm optimization (PSO) are:

[0114] Initialize the particle swarm, each particle represents a possible correction instruction parameter combination p=[p1,p2,…,p Q ], where Q is the dimension of the parameter;

[0115] Calculate the fitness value of each particle:

[0116] ,

[0117] Among them, U i (p) is the value of the ith unbalanced position and unbalanced mass;

[0118] Update the velocity and position of each particle:

[0119] ,

[0120] where v i (t) is the velocity vector of the i-th particle, p i,best (t) is the optimal position of the i-th particle, p gbest (t) is the global optimal position, c1 and c2 are acceleration constants, and the value of c1 ranges from 1.5 to 2.0, the value of c2 ranges from 1.0 to 1.5, and r1 and r2 are random numbers between [0,1];

[0121] Repeat the above steps until the fitness value F(p) converges to the minimum value.

[0122] Furthermore, the optimization process requires consideration of various parameters, including the size of the particle swarm, the number of iterations, and the search space. For example, 50 particles can be selected for optimization, the number of iterations set to 100, and the search space set to a reasonable range for the correction torque vector. By properly setting these parameters, the accuracy of the optimization process can be ensured.

[0123] Furthermore, the optimization results need to be verified and adjusted. Specifically, cross-validation techniques can be incorporated into the optimization process to ensure the stability and consistency of the optimization results by verifying them on different datasets. If the optimization results are found to perform poorly on certain datasets, the optimization effect can be improved by adjusting optimization parameters such as the learning rate and inertia weight. This verification and adjustment mechanism ensures closed-loop control of the optimization process.

[0124] This embodiment uses a particle swarm optimization algorithm to optimize the parameters of the correction instructions, minimizing the residual imbalance and improving the accuracy and stability of dynamic balancing. This method can effectively avoid under- or over-correction in practical applications, providing a more reliable technical approach for wheel hub dynamic balancing testing.

Claims

1. A wheel hub dynamic balance detection method based on vibration analysis, characterized in that: The steps of the detection method include: Install at least one vibration sensor on the wheel hub to be tested to collect vibration signals during the rotation of the wheel hub; The vibration sensor collects vibration signals of the wheel hub at different rotation speeds and converts the vibration signals into digital signals; Processing the digital signal using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics; Based on the target vibration characteristics, a multi-dimensional space mapping algorithm is used to determine the unbalance position and unbalance mass of the wheel hub, and obtain a detection result; transmitting the detection result to a control unit, wherein the control unit generates a correction instruction according to the unbalanced position and the unbalanced mass; Performing wheel hub dynamic balance correction according to the correction instruction by an execution unit; The step of determining the unbalanced position and unbalanced mass of the hub by using a multi-dimensional space mapping algorithm includes: The target vibration characteristics are mapped into a multi-dimensional space to obtain a high-dimensional feature vector F=[F1,F2,…,F D ], where D is the dimension of the feature vector; Calculate the mapping value U=[U1,U2,…,U M ], where M is the dimension of the unbalanced position and unbalanced mass: , where A, B, and C are mapping matrices, which are optimized through the training data set; Determining the imbalance position and the imbalance mass using the mapping value U; The expression for optimizing the A, B, and C mapping matrices through the training data set is: ； ； ； Where N is the number of training samples, F i is the high-dimensional feature vector of the i-th sample, U i is the mapping value of the imbalance position and imbalance mass of the i-th sample.

2. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The step of processing the digital signal using a denoising algorithm based on high-order spectrum analysis comprises: Initialize the denoising parameters α(0)=1 and β(0)=0, where α and β are used to adjust the order and weight of the high-order spectrum; Performing high-order spectrum analysis on the digital signal to calculate the third-order spectrum B3(x, f1, f2) and the fourth-order spectrum B4(x, f1, f2, f3); Calculate the denoising coefficient γ(k): ， Where K is the number of frequency bands; Update the denoising parameters α(k)=α(k-1)+η1γ(k) and β(k)=β(k-1)+η2γ(k), where η1 and η2 are step size parameters, and the value range of η1 is 0.01 to 0.1, and the value range of η2 is 0.001 to 0.

01.

3. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The step of processing the digital signal using a denoising algorithm based on high-order spectrum analysis comprises: Performing a short-time Fourier transform on the digital signal to obtain a time-frequency spectrum matrix X(t,f); Calculate the spectral density B of the higher-order spectrum n (x,f1,f2,…,f n-1 ): , where T is the number of time windows and n is the order of the high-order spectrum; The spectral density is used to remove noise, and the expression is: , where X clean (t,f) is the time-frequency spectrum matrix after denoising, and γ(k) is the denoising coefficient.

4. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The control unit generates a correction instruction by the following steps: According to the unbalanced position and unbalanced mass, the required correction torque vector M=[M1,M2,…,M P ], where P is the dimension of the correction moment; Optimize the correction torque vector: , where U is the mapping value of unbalanced position and unbalanced mass, and G is the influence matrix of the correction torque on the unbalance; The optimized correction torque vector M opt The instructions are converted into correction instructions and transmitted to the execution unit.

5. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The execution unit includes an electromagnetic correction device and a mechanical correction device. The electromagnetic correction device performs correction by dynamically adjusting the intensity and direction of the electromagnetic field, and the mechanical correction device performs correction by adjusting the position and mass of the counterweight block.

6. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The detection method further comprises a feedback step: After the wheel hub dynamic balancing correction is completed, recollecting the vibration signal of the wheel hub; Process the re-collected vibration signal to determine the corrected unbalance position and corrected unbalance quality; The corrected unbalanced position and the corrected unbalanced mass are compared with the unbalanced position and the unbalanced mass, and if the difference is outside a preset range, a correction instruction is generated again and a correction operation is performed.

7. A wheel hub dynamic balance detection system based on vibration analysis, used to implement the wheel hub dynamic balance detection method based on vibration analysis according to any one of claims 1 to 6, characterized in that: The detection system comprises: At least one vibration sensor, used to collect vibration signals during the rotation of the wheel hub; a signal processing unit, configured to convert the vibration signal into a digital signal, and process the digital signal using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics; an analyzing unit, configured to determine an unbalanced position and an unbalanced mass based on the target vibration characteristics by using a multi-dimensional space mapping algorithm; a control unit, configured to generate a correction instruction according to the unbalanced position and the unbalanced mass; an execution unit, configured to perform a dynamic balancing correction operation according to the correction instruction; The feedback unit is used to re-collect the vibration signal of the wheel hub after the dynamic balancing correction operation is completed, and compare the corrected unbalance position and corrected unbalance mass with the unbalance position and unbalance mass determined by the analysis unit to confirm the correction effect.

8. The wheel hub dynamic balance detection system based on vibration analysis according to claim 7, characterized in that: The detection system further includes a correction optimization unit for optimizing dynamic balance correction, wherein the correction optimization unit includes the following processing steps: Calculating a correction effect of the execution unit according to the correction instruction generated by the control unit; The parameters of the correction instruction are adjusted based on a particle swarm optimization algorithm to minimize the remaining imbalance.

Images