Hub dynamic balance detection method and system based on vibration analysis

Abstract

The invention relates to the technical field of hub detection, in particular to a 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 a hub to be detected, wherein the vibration sensor is used for collecting a vibration signal in the rotation process of the hub; collecting vibration signals of the hub at different rotation speeds through the vibration sensor, and converting the vibration signals into digital signals; and processing the digital signal by using a denoising algorithm based on high-order spectrum analysis so as to remove noise and extract target vibration characteristics. According to the method, the limitation of a traditional denoising technology in a complex noise environment is overcome, the detection precision of the tiny unbalance problem is improved, and it is ensured that the hub can obtain accurate unbalance information in a high-speed state. A high-precision and high-efficiency dynamic balance detection and correction scheme is provided, tire wear is reduced, and vehicle driving safety is improved.

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] In order to ensure the performance of the vehicle at high speeds, the wheel hub needs to be dynamically balanced and effectively corrected before leaving the factory. Traditional methods usually rely on mechanical detection equipment, which has low detection accuracy for small imbalance problems, especially at high rotation speeds. Due to the influence of noise, traditional methods are difficult to accurately extract the characteristics of wheel hub imbalance vibration, resulting in deviations in dynamic balance correction.

[0003] By installing a vibration sensor on the wheel hub, the vibration signal during the rotation process can be collected in real time. Combined with signal processing technology, the imbalance characteristics can be analyzed more accurately in theory. However, existing vibration analysis mostly uses traditional denoising techniques, such as low-pass filters or spectral subtraction, which have limited effects when processing high-order nonlinear non-Gaussian noise, easily cause signal distortion, and cannot effectively improve the detection accuracy of small imbalance problems. 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 purpose is: On the one hand, a wheel hub dynamic balance detection method based on vibration analysis is provided, and the steps of the detection method include: Install at least one vibration sensor on the wheel hub to be tested, so as 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 unbalanced position and unbalanced mass of the hub, and a detection result is obtained; 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; The execution unit performs wheel hub dynamic balancing correction according to the correction instruction.

[0006] On the other hand, a wheel hub dynamic balance detection system based on vibration analysis is provided, which is used to implement the wheel hub dynamic balance detection method based on vibration analysis as described above, and 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, used 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 analysis 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, used for performing 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 unbalanced position and the corrected unbalanced mass with the unbalanced position and the unbalanced mass determined by the analysis unit to confirm the correction effect.

[0007] The beneficial effects of the present invention are: The wheel hub dynamic balancing 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 small 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, and can effectively map the vibration characteristics into a multi-dimensional space, amplify weak characteristic signals, and reduce processing errors, and provides a set of high-precision and high-efficiency dynamic balancing detection and correction solutions, which reduces tire wear and improves vehicle driving safety.

[0008] 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 in conjunction with the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0009] Figure 1 This is a flow chart of a wheel hub dynamic balance detection method according to an embodiment of the present invention; Figure 2 It is a vibration waveform diagram of the wheel hub at different speeds in one embodiment of the present invention; Figure 3 A schematic diagram of vibration waveform denoising in one embodiment of the present invention; Figure 4A denoising comparison diagram of a time spectrum diagram in one embodiment of the present invention; Figure 5 A schematic diagram of determining wheel hub imbalance using a mapping algorithm in one embodiment of the present invention; Figure 6 It 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

[0010] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0011] In the embodiments of the present invention, words such as "exemplary" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplary" or "for example" in the embodiments of the present invention should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary" or "for example" is intended to present related concepts in a specific way.

[0012] The present invention provides the following preferred embodiments: Embodiment 1: In order to solve the problem of low accuracy in detecting small imbalances in wheel hub dynamic balancing, this embodiment proposes a wheel hub dynamic balancing detection method based on vibration analysis. Figure 1 As shown, the steps of the detection method are: S100. Install at least one vibration sensor on the wheel hub to be tested, so as to collect vibration signals during the rotation of the wheel hub.

[0013] S200. Collect vibration signals of the wheel hub at different rotation speeds through a vibration sensor and convert the vibration signals into digital signals.

[0014] S300. Process the digital signal using a denoising algorithm based on high-order spectrum analysis to remove noise and extract target vibration characteristics.

[0015] S400. Based on the target vibration characteristics, the unbalanced position and unbalanced mass of the wheel hub are determined using a multi-dimensional space mapping algorithm to obtain a detection result.

[0016] S500. Transmit the detection result to the control unit, and the control unit generates a correction instruction according to the unbalanced position and the unbalanced mass.

[0017] S600. Perform wheel hub dynamic balancing correction according to the correction instruction through the execution unit.

[0018] 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 use high-sensitivity acceleration sensors, such as ADXL355, which has excellent noise performance and wide-band response, ensuring that vibration signals can be accurately collected at different rotation speeds. Figure 2 The following are waveform diagrams of vibration signals collected at different speeds. Multiple sensors are installed in the radial and axial directions of the hub to capture multi-directional vibration information, thereby improving the comprehensiveness and accuracy of detection.

[0019] Furthermore, the vibration signals collected by these vibration sensors are converted into digital signals by a data acquisition card, such as NIUSB-6259, which converts analog signals into digital signals. The selection of the data acquisition card needs to consider its sampling rate, resolution and stability to ensure the integrity and accuracy of the vibration signal. At the same time, synchronous sampling technology is also used in this embodiment to ensure that the signals collected at different rotation speeds have the same sampling rate, thereby facilitating subsequent data processing and analysis.

[0020] 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 the imbalance of the wheel hub are extracted. Specifically, the third-order spectrum and the fourth-order spectrum are calculated in this embodiment. Through the calculation of these high-order spectra, the nonlinear components in the vibration signal can be captured, so as to more accurately identify the imbalance position and imbalance mass.

[0021] It should be understood that the calculation process of high-order spectrum analysis is relatively complicated, 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), you can choose the appropriate frequency resolution and time window length to balance the 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), the noise can be effectively removed and important vibration characteristic information can be retained, such as Figure 6 shown.

[0022] 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, and 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 5In 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.

[0023] 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.

[0024] Furthermore, after the detection results are generated, the present embodiment transmits these results to the control unit. The control unit generates a correction instruction based on the detected unbalanced position and unbalanced mass. The core function of the control unit is to calculate the required correction torque vector M, which determines the correction torque that the execution unit needs to apply in the dynamic balancing correction operation. The calculation of the correction torque vector needs to take into account the distribution of the unbalanced position and unbalanced mass, and the accuracy of the correction instruction is ensured by the optimization algorithm.

[0025] Furthermore, the execution unit performs a dynamic balancing correction operation through the correction instructions generated by the control unit. The execution unit may include an electromagnetic correction device and a mechanical correction device, and the specific selection depends on the actual application scenario. The electromagnetic correction device performs correction by dynamically adjusting the strength and direction of the electromagnetic field, while the mechanical correction device performs correction by adjusting the position and mass of the counterweight. The selection and design of these execution units need to consider their response speed, accuracy and stability to ensure that the correction operation is efficient and accurate.

[0026] The benefit of this embodiment is that by installing multiple high-performance vibration sensors on the wheel hub, the vibration conditions of the wheel hub are fully covered, and high-order spectrum analysis and multi-dimensional space mapping algorithms are used to perform accurate signal processing and feature extraction, thereby improving the accuracy of wheel hub dynamic balance detection at different rotation speeds. In addition, through the coordinated work of the control unit and the execution unit, efficient and accurate dynamic balance correction is achieved, thereby improving the safety of vehicle driving.

[0027] Embodiment 2: In order to solve the problem of noise removal in wheel hub dynamic balancing detection at different rotation speeds, this embodiment 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), and finally the high-precision denoising of the vibration signal is achieved by updating the denoising parameters.

[0028] First, the method of initializing denoising parameters is used in this embodiment. The denoising parameters α(0) and β(0) are used to adjust the order and weight of the higher-order spectrum, respectively. The initial values ​​α(0)=1 and β(0)=0 are selected to give the third-order spectrum a higher weight at the beginning of the algorithm, because the third-order spectrum can usually better capture linear features. As the algorithm proceeds, these parameters will be gradually adjusted to adapt to the signal characteristics in different noise environments.

[0029] 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 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.

[0030] The calculation of the denoising coefficient γ(k) is based on the third-order spectrum and the fourth-order spectrum. The specific expression is: , Among them, K is the number of frequency bands, B3 and B4 are the values ​​of the third-order spectrum and the fourth-order spectrum respectively. The calculation process of the denoising coefficient γ(k) needs to consider the high-order spectrum characteristics of multiple frequency bands to ensure the comprehensive and accurate denoising effect.

[0031] Furthermore, the step of updating the denoising parameters α(k) and β(k) is implemented by the step size parameters η1 and η2. The specific updating formula is: α(k)=α(k-1)+η1γ(k), β(k)=β(k-1)+η2γ(k); Among them, the value range of η1 is 0.01 to 0.1, and the value range of η2 is 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.

[0032] The benefit of this embodiment is that by initializing and gradually updating the denoising parameters, combined with the calculation of the third-order spectrum and the fourth-order spectrum, high-precision denoising of vibration signals at different rotation speeds is achieved. This method performs well in the face of complex noise environments and can effectively improve the accuracy of wheel hub dynamic balancing detection.

[0033] Example 3: In order to solve the application problem of high-order spectrum analysis in short-time Fourier transform denoising, this example further refines the specific steps of using high-order spectrum analysis for denoising. 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.

[0034] In this embodiment, short-time Fourier transform (STFT) is first used to process the collected digital signal. Short-time Fourier transform is a method of converting a signal from the time domain to the time-frequency domain, which can better reflect the frequency characteristics of the signal in different time windows. The generation of the time-frequency matrix X(t,f) requires the selection of an appropriate window length and frequency resolution to ensure that the time-frequency characteristics of the signal can be accurately captured.

[0035] Further, calculate the spectral density B of the high-order spectrum n (x,f1,f2,…,f n-1 ). The specific expression is: , 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.

[0036] Furthermore, the spectral density is used to remove noise, and the specific expression is: , 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.

[0037] Through this embodiment, the target vibration characteristics in the vibration signal can be extracted more accurately, and the detection accuracy can be improved. In addition, the application of short-time Fourier transform enables the frequency characteristics of the signal in different time periods to be more clearly displayed, providing a solid foundation for the subsequent determination of the imbalance position and quality.

[0038] Embodiment 4: In order to solve the implementation details of the multi-dimensional space mapping algorithm in wheel hub dynamic balancing detection, this embodiment 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.

[0039] First, the extraction of the 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 needs to consider multiple feature points to ensure the comprehensiveness of the feature vector. The dimension D of the feature vector is selected according to the needs in the actual application. For example, 10 feature points can be selected to generate a 10-dimensional feature vector.

[0040] Furthermore, the mapping value U of the unbalanced position and unbalanced mass is calculated. The specific expression is: , where A, B and C are mapping matrices. The optimization of these mapping matrices is achieved through training data sets to ensure the accuracy and reliability of the mapping results. Mapping matrix A is used for mapping the linear part, B is used for mapping the quadratic nonlinear part, and C is used for mapping the cubic nonlinear part. Through this multi-level mapping method, the imbalance characteristics of the wheel hub can be captured more comprehensively.

[0041] It can be understood 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 i , by adjusting the mapping matrices A, B and C, the difference between the mapping result and the actual unbalanced 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 unbalanced position and unbalanced mass. The multi-dimensional space mapping algorithm not only considers the linear part, but also the quadratic and cubic nonlinear parts by performing multi-level mapping on the high-dimensional feature vector F, thereby improving the comprehensiveness of the mapping result.

[0042] In practical applications, the above optimization problem can be solved by gradient descent method or other optimization algorithms. During the optimization process, the initial values ​​and learning rates of each matrix need to be considered to ensure the stability and convergence speed of the optimization process. For example, the initial values ​​of A, B, and C can be set to random small values, and these matrices can be gradually adjusted through multiple iterations to finally obtain the optimal mapping result.

[0043] The benefit of this embodiment is that the target vibration characteristics are mapped to the high-dimensional space of unbalanced position and unbalanced mass through a multi-dimensional space mapping algorithm, which can more comprehensively capture and analyze the characteristics of the vibration signal. This method performs well in complex and changing environments, improves the accuracy and stability of wheel hub dynamic balancing detection, and thus provides reliable data support for subsequent dynamic balancing correction.

[0044] Embodiment 5: In order to solve the efficiency and accuracy problems of the multidimensional space mapping algorithm when optimizing the mapping matrix, this embodiment further refines the specific steps of optimizing the mapping matrix through the training data set. Specifically, by optimizing the high-dimensional feature vector F and the mapping value U of the imbalance position and quality 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.

[0045] 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 in different imbalance states and their corresponding imbalance positions and mass mapping values ​​U i The collection of these training data needs to be carried out under different conditions and speeds to ensure the diversity and representativeness of the data. For example, vibration signals can be collected at different rotation speeds, and the corresponding imbalance position and mass can be recorded to form a data set containing multiple samples.

[0046] Furthermore, the process of optimizing the mapping matrix A can be achieved by minimizing the sum of squared errors. The specific expression 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. 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.

[0047] Furthermore, the process of optimizing the mapping matrix B is also achieved by minimizing the sum of squared errors. The specific expression is: , 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.

[0048] Furthermore, the process of optimizing the mapping matrix C is also achieved by minimizing the sum of squared errors. The specific expression is: , 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.

[0049] It can be understood 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 by cross-validation techniques to ensure the generalization ability of the model on new data.

[0050] In addition, the preprocessing steps of the training data set are also considered in this embodiment. The preprocessing steps include data standardization, noise removal and feature selection to improve the quality of the data and the training effect of the model. Data standardization can scale the value of the eigenvector to a fixed interval, such as [0,1], thereby avoiding the influence of the magnitude difference of the eigenvalue on the training results. Noise removal can be achieved by a denoising algorithm based on high-order spectral analysis to ensure that the signal input to the mapping algorithm is clean. Feature selection can be achieved by methods such as principal component analysis (PCA) to select the feature points that have the greatest impact on the imbalance position and quality, thereby improving the efficiency and accuracy of the mapping algorithm.

[0051] Through this embodiment, the mapping matrices A, B and C in the multidimensional space mapping algorithm are optimized using the training data set, so that the unbalanced position and unbalanced mass of the wheel hub can be determined more accurately. This method not only improves the accuracy of the detection results, but also ensures the stability and reliability of the mapping algorithm under different conditions, thereby providing support for the dynamic balance correction of the wheel hub.

[0052] Embodiment 6: In order to solve the accuracy and real-time problem of generating correction instructions during the wheel hub dynamic balancing detection process, 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.

[0053] Specifically, in this embodiment, the control unit determines the unbalanced position and unbalanced mass U=[U1,U2,…,U M ] to calculate the required correction torque vector M. The dimension P of the correction torque vector is determined according to the number of torque points that need to be corrected in the actual application scenario. For example, if correction is required at four positions of the wheel hub, then P is 4. The specific calculation method can be based on the physical model, taking into account the structure and imbalance distribution of the wheel hub, to determine the correction torque required for each position.

[0054] Further, the optimized correction torque vector M opt The step is achieved by minimizing the sum of squared errors. The specific expression is: , where U is the mapping value of the unbalanced position and unbalanced mass, and G is the influence matrix of the correction torque on the unbalance. It should be understood that the influence matrix G of the correction torque on the unbalance needs to be determined by experimental data or the derivation of a physical model. This matrix describes the influence relationship of each correction torque point on the unbalanced position and mass of the hub. By minimizing the sum of squared errors, the optimal correction torque vector can be found.

[0055] Furthermore, in this embodiment, the gradient descent method is used to optimize the correction moment vector M opt . Gradient descent is a commonly used optimization algorithm that gradually adjusts the value of the moment vector so that the sum of squared errors gradually decreases and eventually reaches a minimum value. Specifically, the optimization process can be divided into multiple iterative steps, and the gradient is calculated and the value of the moment vector is updated in each step. It can be understood that choosing a suitable initial value and learning rate is of great significance to the convergence speed of the optimization process and the accuracy of the results.

[0056] Furthermore, the optimized correction torque vector M opt The correction instructions are converted into specific correction instructions and transmitted to the execution unit. The correction 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 of the mechanical correction device. After receiving these instructions, the execution unit can quickly perform dynamic balancing correction operations, thereby improving the overall efficiency of detection and correction.

[0057] Through this embodiment, the correction torque vector is calculated and adjusted using an optimization algorithm, and accurate correction instructions can be generated to ensure that the dynamic balance of the wheel hub can be effectively corrected under different imbalance states. This method performs well in practical applications and improves the accuracy and real-time performance of detection and correction.

[0058] Embodiment 7: In order to solve the diversity and adaptability of the execution unit in the wheel hub dynamic balancing detection, this embodiment further refines the design and implementation of the execution unit. The execution unit includes an electromagnetic correction device and a mechanical correction device. These two devices use different correction methods to ensure that dynamic balancing correction can be effectively performed in various unbalanced states.

[0059] Specifically, the electromagnetic correction device in this embodiment performs correction 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 installed at a specific position around the wheel hub, and the current in the coil is adjusted by the current controller to change the strength and direction of the electromagnetic field. It should be understood that the electromagnetic correction device can be quickly adjusted in a short time and is suitable for dealing with instantaneous and dynamic imbalance problems.

[0060] 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 a counterweight, a fine-tuning mechanism, and a mass controller. The counterweight is installed on the surface or inside of the wheel hub, and the position of the counterweight can be accurately adjusted by the fine-tuning mechanism, and the mass controller is used to adjust the mass of the counterweight. It can be understood that the mechanical correction device can provide a more stable correction effect when dealing with static and long-term imbalance problems.

[0061] Furthermore, the present embodiment also considers the coordinated work of the electromagnetic correction device and the mechanical correction device. Specifically, the most suitable correction method can be selected according to the characteristics of the unbalanced position and quality. For example, for instantaneous unbalance problems, the electromagnetic correction device can be used preferentially; for long-term unbalance problems, the mechanical correction device can be used preferentially. The two devices can be coordinated by the control unit to ensure that the dynamic balance of the wheel hub can be effectively corrected under different circumstances.

[0062] Furthermore, the present embodiment also considers the optimized design of the correction device. For example, the electromagnetic coil of the electromagnetic correction device can reduce interference and improve the correction efficiency by optimizing the layout. The counterweight of the mechanical correction device can reduce the impact on the performance of the wheel hub by using lightweight materials and optimizing the structure. These optimized designs enable the execution unit to show higher reliability and efficiency in practical applications.

[0063] Through this embodiment, the combination design of the electromagnetic correction device and the mechanical correction device can better adapt to different types of imbalance problems and improve the flexibility and adaptability of wheel hub dynamic balance detection and correction. This method performs well in the face of complex and changeable imbalance states and provides a more comprehensive solution.

[0064] Embodiment 8: In order to solve the problem of verifying the effect after dynamic balancing correction, this embodiment further optimizes the detection method and adds a feedback step. Specifically, by recollecting the vibration signal of the wheel hub after the dynamic balancing correction is completed, and processing the new signal, the corrected unbalanced position and unbalanced mass are determined, and then these values ​​are compared with the initially determined unbalanced position and mass to ensure that the correction effect meets the requirements.

[0065] First, in this embodiment, after the dynamic balancing correction is completed, the vibration sensor is reused to collect the vibration signal of the wheel hub. The selection of the vibration sensor needs to consider its accuracy and stability. For example, the ADXL355 vibration sensor can be selected, which performs well in terms of high resolution and low noise. The re-collected vibration signal needs to be carried out under the same conditions as the initial collection to ensure the consistency and comparability of the data.

[0066] 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 in 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 features in the signal. It should be understood that these processing steps can effectively remove noise and extract key vibration characteristics, providing accurate data support for subsequent imbalance detection.

[0067] Furthermore, the corrected imbalance position and imbalance quality are determined using a multi-dimensional space mapping algorithm. The specific expression is: , 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 through the training data set to ensure the accuracy of the mapping results.

[0068] Furthermore, the corrected unbalanced position and unbalanced mass U corr The initial unbalanced position and unbalanced mass U are compared. The specific comparison method can be to calculate the difference between the two values ​​and check whether the difference is within the preset range. If the difference exceeds the preset range, for example, exceeds 1% of the initial value, it indicates that the correction effect is not ideal, and it is necessary to generate a correction instruction again and perform the correction operation. It can be understood that this feedback mechanism can ensure the closed-loop control of the correction operation and improve the accuracy of dynamic balancing correction.

[0069] Through this embodiment, the feedback step is used to verify the effect of the dynamic balance correction to ensure 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.

[0070] Embodiment 9: In order to solve the problem of coordinated work of various units in the wheel hub dynamic balancing detection system, this embodiment proposes a wheel hub dynamic balancing detection system based on vibration analysis, which is used to implement the wheel hub dynamic balancing detection method. By integrating multiple functional units, including vibration sensors, signal processing units, analysis units, control units, execution units and feedback units, efficient and reliable operation of the system is ensured.

[0071] Specifically, the vibration sensor in this embodiment is used to collect vibration signals during the rotation of the wheel hub. The selection of the vibration sensor needs to consider its accuracy and response speed. For example, the ADXL355 vibration sensor can be selected, which is superior in high resolution and low noise. The vibration sensor is installed at a preset position of the wheel hub, such as the center and edge of the wheel hub, to ensure that the vibration information of the wheel hub can be fully captured.

[0072] 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 target vibration features. It can be understood that the signal processing unit ensures the accuracy of subsequent analysis and correction during the denoising and feature extraction process.

[0073] Furthermore, the analysis unit determines the unbalance position and unbalance mass based on the target vibration characteristics using a multi-dimensional space mapping algorithm. The optimized design of the analysis unit can improve the speed and accuracy of unbalance detection.

[0074] Furthermore, the control unit generates a correction instruction according to 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 opt Converted into specific correction instructions 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, and improve the efficiency of dynamic balancing correction.

[0075] Furthermore, the execution unit performs a dynamic balance correction operation according to the correction instruction 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 strength and direction of the electromagnetic field, while the mechanical correction device performs correction by adjusting the position and mass of the counterweight. The collaborative work of the two devices can better adapt to different types of imbalance problems and improve the adaptability of correction.

[0076] Furthermore, after the dynamic balancing correction operation is completed, the feedback unit re-collects the vibration signal of the wheel hub and sends the corrected imbalance position and imbalance mass U corr The feedback unit will generate a correction instruction and perform the correction operation again. This closed-loop feedback mechanism can ensure that the correction effect meets the requirements and improve the overall stability of the system.

[0077] The benefit of this embodiment is that by integrating multiple functional units, the efficient, accurate and reliable operation of the wheel hub dynamic balancing detection system is ensured. This method can better adapt to different types of imbalance problems in practical applications and improve the overall performance of detection and correction.

[0078] Embodiment 10: In order to solve the optimization problem after dynamic balancing correction, this embodiment further optimizes the detection system and adds a correction optimization unit. The correction optimization unit calculates the correction effect of the execution unit and adjusts the parameters of the correction instruction based on the particle swarm optimization algorithm to minimize the remaining imbalance.

[0079] 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 balancing correction, and using the multi-dimensional space mapping algorithm to determine the corrected unbalanced position and unbalanced mass U corrIt 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.

[0080] Furthermore, the specific steps of particle swarm optimization (PSO) are: 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; Calculate the fitness value of each particle: , Among them, U i (p) is the value of the ith unbalanced position and unbalanced mass; Update the velocity and position of each particle: , where v i (t) is the velocity vector of the ith particle, p i,best (t) is the optimal position of the ith particle, p gbest (t) is the global optimal position, c1 and c2 are acceleration constants, and the value range of c1 is 1.5 to 2.0, the value range of c2 is 1.0 to 1.5, r1 and r2 are random numbers between [0,1]; Repeat the above steps until the fitness value F(p) converges to the minimum value.

[0081] Furthermore, multiple parameters need to be considered during the optimization process, 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 can be set to 100, and the search space can be set to a reasonable range of the correction torque vector. By setting these parameters reasonably, the accuracy of the optimization process can be ensured.

[0082] Furthermore, the optimization results need to be verified and adjusted. Specifically, cross-validation technology can be introduced in the optimization process to ensure the stability and consistency of the optimization results by verifying them on different data sets. If it is found that the optimization results do not perform well on certain data sets, the optimization effect can be improved by adjusting the optimization parameters, such as the learning rate and inertia weight. It can be understood that this verification and adjustment mechanism can ensure closed-loop control of the optimization process.

[0083] Through this embodiment, the particle swarm optimization algorithm is used to optimize the parameters of the correction instruction, which can minimize the residual imbalance and improve the accuracy and stability of dynamic balance correction. This method can effectively avoid under-correction or over-correction in practical applications, and provides a more reliable technical means for wheel hub dynamic balance detection.

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, so as to collect vibration signals during the rotation of the wheel hub; The vibration sensor collects the vibration signal of the wheel hub at different rotation speeds and converts the vibration signal into a digital signal; 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 unbalanced position and unbalanced mass of the hub, and a detection result is obtained; 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; The execution unit performs wheel hub dynamic balancing correction according to the correction instruction.

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 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.

4. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The step of determining the unbalanced position and unbalanced mass of the hub by using a multi-dimensional space mapping algorithm comprises: 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; The mapping value U is used to determine the imbalance position and the imbalance mass.

5. The wheel hub dynamic balance detection method based on vibration analysis according to claim 4, characterized in that: The expression of optimizing the A, B, C mapping matrix 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 quality of the i-th sample.

6. 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 correction torque on unbalance; The optimized correction torque vector M opt The instructions are converted into correction instructions and transmitted to the execution unit.

7. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The execution unit comprises 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.

8. The wheel hub dynamic balance detection method based on vibration analysis according to claim 1, characterized in that: The detection method also includes a feedback step: After the dynamic balancing correction of the wheel hub 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 mass; 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.

9. A wheel hub dynamic balancing detection system based on vibration analysis, used to implement the wheel hub dynamic balancing detection method based on vibration analysis as claimed in any one of claims 1 to 8, 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, used 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 analysis 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, used for performing 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 unbalanced position and the corrected unbalanced mass with the unbalanced position and the unbalanced mass determined by the analysis unit to confirm the correction effect.

10. The wheel hub dynamic balancing detection system based on vibration analysis according to claim 9, characterized in that: The detection system also includes a correction optimization unit for optimizing dynamic balance correction, and the correction optimization unit processing steps include: 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.

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