Method and system for detecting space pose error of moving part and medium

Abstract

The invention discloses a moving part space pose error detection method and system and a medium. The method comprises the steps of obtaining pose information of a machine tool moving part based on an IMU module and an electronic level meter, wherein the IMU module obtains an accelerometer signal and an angular velocity signal, and the electronic level meter obtains an angle error signal; performing data fusion on the pose information to obtain a line value error and an angle error; establishing an error prediction model by adopting a BP neural network based on the line value error, the angle error and the movement speed of the linear axis of the machine tool; obtaining the optimal initial weight and threshold value of the BP neural network by adopting a beetle antennae search algorithm, and substituting the optimal initial weight and threshold value into the error prediction model for model training to obtain a trained error prediction model; and based on the trained error prediction model, carrying out motion part space pose error detection to obtain a pose error detection result. According to the invention, equipment motion space pose error measurement can be realized, and the measurement precision is improved under the condition that the accuracy is ensured.

Description

Technical Field

[0001] The present invention relates to the field of sensor detection, and in particular to a method, system and medium for detecting spatial posture errors of moving parts. Background Art

[0002] With the continuous development of modern manufacturing industry, higher and higher requirements are put forward for the processing accuracy of manufacturing equipment such as CNC machine tools and robots. There are many factors that affect the processing accuracy of manufacturing equipment, among which the spatial posture error of key moving parts of manufacturing equipment is one of the important factors. Measuring and compensating the spatial posture error of equipment movement is of great significance to improving the performance of manufacturing equipment.

[0003] At present, there are many methods for detecting the spatial posture error of equipment motion, including physical reference measurement, laser ballbar, orthogonal grating measurement, laser interferometry, and laser tracker measurement. The above measurement methods can efficiently and accurately complete the measurement of the spatial posture error of manufacturing equipment motion. However, during the implementation of the above measurement methods, a large number of manual operations and adjustments are required, such as the adjustment of the laser interferometer optical path mirror group and the adjustment of related inspection tools. It is also difficult to apply to extreme environments such as radiation, confinement, and highly toxic environments, as well as application scenarios with limited space. In comparison, the inertial measurement method can meet the requirements for miniaturization and unmanned measurement equipment in extreme environments, but direct measurement by the inertial measurement unit (IMU) will produce large cumulative errors.

[0004] In view of this, this application is hereby filed. Summary of the invention

[0005] The technical problem to be solved by the present invention is the detection method of the existing equipment motion spatial posture error, which is difficult to test the spatial accuracy of intelligent manufacturing equipment in extreme environments such as radiation, confinement, and toxicity, and it is difficult to measure the equipment motion spatial posture error. The purpose of the present invention is to provide a method, system and medium for detecting the spatial posture error of moving parts, combining the IMU module and the electronic level to obtain the posture information of the moving parts of the machine tool for data fusion, combining the high dynamic response performance of the gyroscope with the high precision advantage of the electronic level; and establishing a corresponding error prediction model to provide theoretical guidance for the high-precision online measurement of the spatial posture of key moving parts, realize the measurement of the spatial posture error of equipment motion, and improve the measurement accuracy while ensuring accuracy.

[0006] The present invention is achieved through the following technical solutions:

[0007] In a first aspect, the present invention provides a method for detecting a spatial posture error of a moving component, the method comprising:

[0008] The position and posture information of the moving parts of the machine tool is obtained based on the IMU module (including an accelerometer and a gyroscope) and an electronic level; the position and posture information includes an accelerometer signal obtained by the accelerometer, an angular velocity signal obtained by the gyroscope, and an angle error signal obtained by the electronic level;

[0009] The posture information is fused to obtain three line errors and two angle errors;

[0010] Considering the two main influencing factors of spatial coordinate position and movement speed, namely, based on line value error, angle error and movement speed of the linear axis of the machine tool, a BP neural network is used to establish an error prediction model;

[0011] The optimal initial weights and thresholds of the BP neural network are obtained by using the beetle whisker search algorithm, and the optimal initial weights and thresholds are substituted into the error prediction model for model training to obtain a trained error prediction model.

[0012] Based on the trained error prediction model, the spatial posture error detection of the moving parts is performed to obtain the posture error detection result.

[0013] Furthermore, obtaining the position and posture information of the moving parts of the machine tool refers to obtaining the position and posture information of the moving parts of the machine tool when the linear axes of the machine tool move no-load at different constant moving speeds.

[0014] Furthermore, the IMU module is a high-precision MEMS three-axis inertial measurement unit, and the electronic level is an RL-A high-precision angle measurement module.

[0015] Furthermore, the pose information is fused to obtain three line errors and two angle errors, including:

[0016] The accelerometer signals (A) in the linear axes X, Y and Z directions in different time bands are x , A y and A z ) perform filtering and integration on the corresponding frequency bands respectively to obtain three line value errors; wherein the three line value errors are the three-degree-of-freedom line value errors of the moving parts at different speeds at a specific position of the machine tool;

[0017] According to the accelerometer signal, the initial value of the angle error iteration is determined; based on the Kalman filter algorithm, the angular acceleration signal (ω x ,ω xy ) and the angle error signal (θ x ,θ y ) to perform data fusion calculation and obtain two angle errors (θ X ,θ Y ).

[0018] Furthermore, the angular error includes:

[0019] If it is in the low frequency band, the angle error signal (θ x ,θ y ) as the angle error measurement value;

[0020] If it is within the medium and high frequency band, the angular acceleration signal (ω x ,ω xy ) and the angle error signal (θ x ,θ y ) to perform data fusion and obtain the final angle error measurement value after iterative calculation.

[0021] Furthermore, the angle error θ X The calculation is an example: Based on the Kalman filter algorithm, the angle error θ is obtained X The calculation steps are:

[0022] The calculation result after k iterations of the data fusion algorithm is recorded as θ k The measurement result of the electronic level is z, the measurement result of the gyroscope is u, and the bias belongs to the random error of the IMU angle; the measurement noise v is a Gaussian noise distribution with a variance of R; R is a fixed value, which is derived from the official technical indicators of the RL-A electronic level; the process noise caused by the drift of the gyroscope is represented by the matrix w, and F, B, and H are matrices constructed to unify the data dimension.

[0023] x k =Fx k-1 +Bu k-1 +w k-1

[0024] z k =Hx k-1 +v k-1

[0025] H = [1 0]

[0026] set up is the random error x in Kalman filtering k The optimal estimate of can be obtained by the optimal estimate at time k-1 Instead of the real value, to get the next moment x k Predicted value The formula is:

[0027]

[0028] Among them, u k-1 is the measurement result of the gyroscope at time k-1 in the IMU module;

[0029] First try to take a larger diagonal matrix Q as the covariance matrix of the process error w.

[0030]

[0031] If the angle estimate in the subsequent step Starts to drift, then increase Q ω If the angle estimate Compared to If the change is too small and the estimated trend is slowing down, reduce Q θ Value. Thus, the diagonal matrix Q is determined, and the predicted value can be obtained The first covariance matrix of the error

[0032]

[0033] According to the Kalman filter algorithm, we can get the first covariance matrix Get the Kalman gain K. Thus, the optimal estimate at time k is determined Medium prediction value With the observed value z k The optimal estimate can also be obtained. The second covariance matrix P k .

[0034]

[0035] The Kalman gain is:

[0036]

[0037] According to the second covariance matrix P k And the initial posture information, select the appropriate x k , P k The initial values ​​x0 and P0 are used to fuse the angular acceleration signal and the angle error signal to obtain the angle error θ X .

[0038] Furthermore, the method also includes: verifying the model accuracy, specifically:

[0039] When the spatial position and running speed of the measurement system change, the maximum residual and mean square error of the posture error detection results measured by the verification model are verified.

[0040] During the design process of the present invention, three methods that can derive a specific whitening model, namely, BP neural network + beetle whisker search algorithm, multivariate regression, and BP neural network, were considered to establish an error prediction model, and the maximum residual and mean square error of the posture error detection results measured by each model were verified. It was concluded that the BP neural network + beetle whisker search algorithm had the best accuracy among the above models.

[0041] In a second aspect, the present invention further provides a system for detecting a spatial posture error of a moving component, the system comprising:

[0042] An acquisition unit, used for acquiring the position and posture information of the moving parts of the machine tool based on the IMU module and the electronic level; the position and posture information includes the accelerometer signal and angular velocity signal acquired by the IMU module and the angle error signal acquired by the electronic level;

[0043] A data fusion unit is used to fuse the position and posture information to obtain three line value errors and two angle errors;

[0044] A model building unit, used for building an error prediction model by using a BP neural network based on a line value error, an angle error and a motion speed of a linear axis of a machine tool;

[0045] A model training unit is used to obtain the optimal initial weight and threshold of the BP neural network by using a beetle whisker search algorithm, and substitute the optimal initial weight and threshold into the error prediction model for model training to obtain a trained error prediction model;

[0046] The error detection unit is used to perform spatial posture error detection of the moving parts based on the trained error prediction model to obtain the posture error detection result.

[0047] Furthermore, the data fusion unit includes:

[0048] The first fusion subunit is used to combine the accelerometer signals (A) in the linear axis X, Y and Z directions in different time bands. x , A y and A z ) perform filtering and integration on the corresponding frequency bands respectively to obtain three line value errors; wherein the three line value errors are the three-degree-of-freedom line value errors of the moving parts at different speeds at a specific position of the machine tool;

[0049] The second fusion subunit is used to determine the initial value of the angle error iteration according to the accelerometer signal; based on the Kalman filter algorithm, the angular acceleration signal (ω x ,ω xy ) and the angle error signal (θ x ,θ y ) to perform data fusion calculation and obtain two angle errors (θ X ,θ Y ).

[0050] In a third aspect, the present invention further provides a computer-readable storage medium, which stores a computer program, and is characterized in that when the computer program is executed by a processor, it implements the above-mentioned method for detecting the spatial posture error of a moving part.

[0051] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0052] The present invention provides a method, system and medium for detecting the spatial posture error of a moving part. The system combines an IMU module and an electronic level to obtain the posture information of the moving parts of a machine tool for data fusion, and combines the high dynamic response performance of a gyroscope with the high precision of an electronic level. A corresponding error prediction model is established to provide theoretical guidance for high-precision online measurement of the spatial posture of key moving parts, realize the measurement of the spatial posture error of equipment movement, and improve the measurement accuracy while ensuring accuracy, which can provide a reference for the compensation of the spatial posture measurement results of the IMU module-electronic level. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The drawings described herein are used to provide a further understanding of the embodiments of the present invention, constitute a part of this application, and do not constitute a limitation of the embodiments of the present invention. In the drawings:

[0054] Figure 1 This is a flow chart of a method for detecting spatial posture errors of moving parts of the present invention;

[0055] Figure 2 It is the curve of X-axis positioning error changing with spatial coordinate position and motion speed;

[0056] Figure 3 is the structure diagram of BP neural network, w1 and w2 are the weight matrices from input layer to hidden layer and from hidden layer to output layer respectively;

[0057] Figure 4 It is the logic flow chart of BP neural network + longicorn beard search algorithm;

[0058] Figure 5 It is a comparison diagram of the error prediction models of each degree of freedom at different positions at the same speed;

[0059] Figure 6 It is a comparison diagram of the error prediction models of each degree of freedom at the same position and different speeds;

[0060] Figure 7 This is a structural block diagram of a moving component spatial posture error detection system of the present invention. DETAILED DESCRIPTION

[0061] In order to make the objectives, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with embodiments and drawings. The exemplary embodiments of the present invention and their description are only used to explain the present invention and are not intended to limit the present invention.

[0062] The existing detection methods of equipment motion spatial posture errors require a lot of manual operations and adjustments, such as adjusting the laser interferometer optical path mirror group and the adjustment of related inspection fixtures, and are difficult to apply to extreme environments such as radiation, confined, and highly toxic environments, as well as application scenarios with limited space. In comparison, the inertial measurement method can meet the requirements for miniaturization and unmanned measurement equipment in extreme environments.

[0063] However, direct measurement by the inertial measurement unit (IMU) will produce a large cumulative error. It is very meaningful to use a data fusion algorithm and a high-precision electronic level to correct the angle measurement data to improve the measurement accuracy. At the same time, a prediction model for the single error of the spatial position and posture of the moving parts is established, which can compensate for the error of the measurement results and has great application value in monitoring the degradation of the motion axis of machine tools.

[0064] Therefore, the present invention combines the IMU module and the electronic level to obtain the posture information of the moving parts of the machine tool for data fusion, and establishes a corresponding error prediction model, so as to provide theoretical guidance for the high-precision online measurement of the spatial posture of key moving parts, realize the measurement of the spatial posture error of equipment movement, and improve the measurement accuracy while ensuring accuracy, which can provide a reference for the compensation of the spatial posture measurement results of the IMU module-electronic level.

[0065] Example 1

[0066] like Figure 1 As shown, the present invention provides a method for detecting spatial posture errors of moving parts, the method comprising:

[0067] Step S0: When the linear axis of the machine tool moves without load at different constant motion speeds, the position and posture information of the moving parts of the machine tool is obtained based on the IMU module (including accelerometer and gyroscope) and the electronic level; the position and posture information includes the accelerometer signal obtained by the accelerometer, the angular velocity signal obtained by the gyroscope, and the angle error signal obtained by the electronic level; then the error is evaluated through the test data obtained by the IMU module in the fixed cycle test during the test. Running at a constant speed can minimize the impact of instantaneous dynamics on the measurement data; and moving at different speeds (v1, v2, v3) is to obtain six-degree-of-freedom motion errors in different frequency bands. The obtained sensor measurement data in different frequency bands are subjected to bandpass filtering of the corresponding frequency bands for the change function over time, and finally the acceleration signal A is obtained. x,i , A y,i and A z,i; Angle error signal θ obtained by the electronic level x,i ,θ y,i And the angular velocity signal ω obtained by the gyroscope x,i ,ω y,i .

[0068] In this embodiment, the IMU module is a high-precision MEMS three-axis inertial measurement unit, and the electronic level is an RL-A high-precision angle measurement module.

[0069] Step S1: The accelerometer signals (A) in the linear axis X, Y and Z directions in different time bands are x , A y and A z ), the angular velocity signal obtained by the gyroscope and the angle error signal obtained by the electronic level are fused to obtain three line value errors and two angle errors;

[0070] For the angle error, in the low frequency band close to 0 Hz, the three accelerometer signals (A) in the linear axis X, Y and Z directions are x , A y and A z ) and angular error (θ X0 and θ Y0 ) can be expressed as:

[0071]

[0072] In the formula, g is the magnitude of gravitational acceleration;

[0073] In the low frequency band, a x 、a y and a z can be ignored, and the resulting θ X0 and θ Y0 Can be directly used as the measurement result of angle error θ X and θ Y Due to the slow response speed of the electronic level, the data is not fused with the measurement results of the electronic level.

[0074] In the mid-high frequency band, directly change θ X0 and θ Y0 The measurement result of the angle error is inaccurate and needs to be fused with the measurement result of the electronic level. X As an example, let θ X0 and will be used as x in the Kalman filter algorithm k The initial value of x0. k After a sufficient number of iterations of Kalman filtering, the final angle error measurement value is obtained.

[0075] For line value error, the error of using accelerometer signal to characterize the translational motion of linear axis comes from the inclination of linear axis. This acceleration error can be regarded as a function of its position on the travel path f(x). Each accelerometer will collect three signals A1, A2, A3, corresponding to three motion speeds v1, v2, v3. Each signal can be expressed as:

[0076] A n,i -f n (x) = a n,i (t)

[0077] In the formula, n can be X, Y, Z, indicating the accelerometer in three directions; i can be 1, 2, 3, indicating the motion signal at three motion speeds. n,i Perform filtering on the corresponding frequency band and then integrate over time to get V n,i At the same time, V i,n It is also a function of position V n,i (x). And f n The term (x) is independent of time and can be used to n,i -f n (x)=

[0078] a n,i (t) is transformed to eliminate f n (x) term, the net translational acceleration due to the linear axis geometric error can be separated.

[0079]

[0080] Combining the above formula, the net translation acceleration a can be calculated through IMU measurement data. n,1 (x), a n,2 (x). Then, through integration operation, the three-degree-of-freedom linear error d of the moving parts at different speeds at a specific position of the machine tool can be obtained. n,1 (x), d n,2 (x).

[0081] Step S2: Considering two main influencing factors, namely, the spatial coordinate position and the movement speed, an error prediction model is established using a BP neural network based on the line value error, the angle error and the movement speed of the linear axis of the machine tool;

[0082] Taking the X-direction line value error as an example, its curve of variation with spatial coordinate position and motion speed is as follows: Figure 2 As shown in FIG. 1 , it presents the characteristics of multiple influencing factors, non-periodic changes, and complex change patterns. The basic structure of the BP neural network used in the present invention is as follows: Figure 3As shown in the figure, the machine tool running speed and spatial position are two nodes for training input, the axis geometric error is one output node, the matrices w1 and w2 are the weights of the neural network, and b1 and b2 represent the thresholds. k is the input layer data, u k is the hidden layer data, and y is the output layer data.

[0083]

[0084] The X-axis positioning error measurement solution data d obtained in steps S0 and S1 X As the expected output d of the neural network.

[0085]

[0086] δ=(dy)y(1-y)

[0087] Determine the learning rate η, and get the weights from the hidden layer to the output layer and the threshold adjustment formula:

[0088]

[0089] b2 t+1 =b2 t +η(dy)y(1-y)

[0090] We can further obtain the weights of the input layer and hidden layer and the threshold adjustment formula (0<α<1):

[0091]

[0092] The number of hidden layer nodes n is given by the empirical formula Make sure. s is the number of nodes in the input layer, y s is the number of nodes in the output layer, and a is a constant between 1 and 10. Substituting different values ​​of p into the BP neural network training process, the effects of multiple values ​​are compared as shown in Table 1. It can be seen that when p = 6, the residual sum of squares is the smallest, and the BP neural network prediction accuracy is the highest. Table 1 shows the residual sum of squares corresponding to the BP neural network with different numbers of hidden layer nodes. The residual sum of squares is the smallest when the number of nodes p = 6.

[0093] Table 1

[0094]

[0095] Step S3: using a beetle whisker search algorithm to obtain the optimal initial weights and thresholds of the BP neural network, and substituting the optimal initial weights and thresholds into the error prediction model for model training to obtain a trained error prediction model;

[0096] The process of step S3 is as follows:

[0097] First, we need to set the maximum number of iterations maxgen, the step size factor eta, the initial step size step, the distance between the two whiskers of the longhorn beetle l, the optimized spatial dimension D, and create and normalize the random vector of the target direction of the longhorn beetle whiskers.

[0098]

[0099] Where D represents the spatial dimension and rands represents the random value.

[0100] Determine the spatial position of the left and right whiskers of the longicorn:

[0101]

[0102] Where, X r is the position of the right whisker at the tth iteration; X l is the position of the left antenna at the tth iteration; X t is the position of the centroid coordinates of the longhorn beetle at the tth iteration; l is the distance between the left and right antennae of the longhorn beetle.

[0103] The fitness function is used to identify the concentration of food odor on both sides of the left and right antennae of the longhorn beetle and compare the strength of the food odor of the two antennae. The fitness function is:

[0104]

[0105] Where N is the number of training samples; t sim(i) is the model prediction value for the i-th sample; y i is the actual value of the i-th sample.

[0106] The variable step length method is used to iteratively update the position of the longhorn beetle, and the following is obtained:

[0107] X t+1 =X t -eta t ×d×sign(Smell left -Smell right )

[0108] In the formula, eta t is the step size factor at the tth generation; sign() is the sign function.

[0109] Determine whether the algorithm termination condition is met: if it is met, the algorithm ends and outputs the optimal position of the longicorn, that is, the optimal initial weight and threshold of the BP neural network. Substitute it into the neural network to continue training and learning, and establish an on-machine measurement system error prediction whitening model; otherwise, return to step S2 to continue searching.

[0110] Neural network learning, the input data is normalized, and the function from the input layer to the hidden layer is:

[0111]

[0112] Function calculation from hidden layer to output layer:

[0113] purelin(n)=n

[0114] So the output function of the BAS-BP neural network is:

[0115] y=purelin(ω2×tansig(ω1×x+b1)+b2)

[0116] Where ω1 and ω2 are the neural network weights; b1 and b2 are the neural network thresholds; x is the input variable; and y is the expected output.

[0117] Input normalization and output denormalization, the value range of tansig(n) is between (-1,1). In order to maintain the identity of the training input data, normalization training is performed:

[0118]

[0119] In the formula, t is the input data; t1 is the normalized input; amax and amin are the maximum and minimum values ​​of the original input data respectively.

[0120] After the input data is normalized and trained, the desired output needs to be denormalized:

[0121]

[0122] The linear motion axis geometric error prediction model based on the beetle whisker search algorithm (i.e. the trained error prediction model) can be obtained. The overall process is as follows: Figure 4 shown.

[0123] Step S4: Based on the trained error prediction model, the spatial posture error detection and model verification of the moving parts are performed.

[0124] Taking the X-axis multi-item geometric errors as an example, three methods that can derive specific whitening models, namely, multiple regression, BP neural network, BP neural network + beetle whisker search algorithm (i.e., BAS-BP neural network or BAS-BP model), are used to establish an error prediction model. The units of the three line value errors of the on-machine geometric errors measured by the present invention are microns, and the units of the angle errors are arc seconds.

[0125] BP neural network and BP neural network + beetle whisker search algorithm are used to predict and model the single geometric error of the X-axis. The training times of the neural network algorithm parameters BP and BP neural network + beetle whisker search algorithm are determined to be 3000 times, the learning rate is 0.1, and the training target is 0.001. The maximum number of iterations of the BP neural network + beetle whisker search algorithm is maxgen=100, the step length and the initial distance ratio factor ratio=5, eta=0.8 is taken, and the initial step length step=5. Since the neural network used in the present invention is a three-layer structure of 2-6-1, the spatial dimension D=2×6+6×1+6+1=25. The spatial coordinate position and running speed of the X-axis are used as input variables, and the machine tool geometric error is used as output.

[0126] When comparing the impact of the spatial coordinate position of the machine tool on error modeling, 41 sample error data at different positions with a speed of v = 10mm / s and a machine tool stroke within (0-200)mm are used as the test set, and a total of 369 samples of geometric errors at the other 9 speeds are used as the training set. Similarly, when considering the impact of the machine tool's running speed, 10 samples with machine tool movement speeds of v = 1mm / s, 3mm / s, 5mm / s, 8mm / s, 10mm / s, 12mm / s, 14mm / s, 16mm / s, 18mm / s, and 20mm / s when X = 100mm are used as the test set, and 400 samples corresponding to 10 speeds at the other 40 positions are used as the test set. The modeling results are shown in the figure below. Figure 5 and Figure 6 As shown in Table 2 and Table 3, the mean square error and maximum residual of the prediction modeling are shown in Table 2 and Table 3. Table 2 and Table 3 respectively show the maximum residual and mean square error of the error model established by each algorithm at different speeds and different positions. In both cases, the data show that the error model established by the BAS-BP neural network algorithm has the highest accuracy. In the cases of different positions at the same speed and different speeds at the same position, the BP neural network + beetle whisker search algorithm method has the highest accuracy in modeling. At different spatial positions, the maximum mean square error is 0.7153μm; at different operating speeds, the maximum residual is only 1.3675μm, and the maximum mean square error is 0.4462μm.

[0127] Table 2

[0128]

[0129] Table 3

[0130]

[0131] The present invention uses an IMU module and an electronic level to jointly obtain the position and posture information of the moving parts of a machine tool; realizes data fusion of the measurement data of each sensor at different movement speeds, and solves the six-degree-of-freedom errors of the moving parts; trains a BP neural network based on the solved data, and establishes a prediction model for the errors of each degree of freedom with spatial coordinate position and movement speed as influencing factors; uses a beetle whisker search algorithm to optimize the initial weights and thresholds of the BP neural network; establishes a high-precision BAS-BP model and verifies it. The method is based on the cutting-edge MEMS three-axis IMU in the current commercial field, and the RL-A high-precision angle measurement module, and combines the high dynamic response performance of the gyroscope with the high-precision advantage of the electronic level through a data fusion algorithm. The BAS-BP neural network established by the method establishes an error model, which has the highest accuracy among the three commonly used methods for deriving a specific whitening model, can achieve relatively accurate error measurement, and can provide a reference for the compensation of the spatial position and posture measurement results of the IMU module-electronic level.

[0132] Example 2

[0133] like Figure 7 As shown, the difference between this embodiment and embodiment 1 is that this embodiment provides a moving component spatial posture error detection system, which corresponds one-to-one to a moving component spatial posture error detection method in embodiment 1; the system includes:

[0134] An acquisition unit, used for acquiring the position and posture information of the moving parts of the machine tool based on the IMU module and the electronic level; the position and posture information includes the accelerometer signal and angular velocity signal acquired by the IMU module and the angle error signal acquired by the electronic level;

[0135] A data fusion unit is used to fuse the position and posture information to obtain three line value errors and two angle errors;

[0136] A model building unit, used for building an error prediction model by using a BP neural network based on a line value error, an angle error and a motion speed of a linear axis of a machine tool;

[0137] A model training unit is used to obtain the optimal initial weight and threshold of the BP neural network by using a beetle whisker search algorithm, and substitute the optimal initial weight and threshold into the error prediction model for model training to obtain a trained error prediction model;

[0138] The error detection unit is used to perform spatial posture error detection of the moving parts based on the trained error prediction model to obtain the posture error detection result.

[0139] As a further implementation, the data fusion unit includes:

[0140] The first fusion subunit is used to combine the accelerometer signals (A) in the linear axis X, Y and Z directions in different time bands. x , A y and A z ) perform filtering and integration on the corresponding frequency bands respectively to obtain three line value errors; wherein the three line value errors are the three-degree-of-freedom line value errors of the moving parts at different speeds at a specific position of the machine tool;

[0141] The second fusion subunit is used to determine the initial value of the angle error iteration according to the accelerometer signal; based on the Kalman filter algorithm, the angular acceleration signal (ω x ,ω xy ) and the angle error signal (θ x ,θ y ) to perform data fusion calculation and obtain two angle errors (θ X ,θ Y ).

[0142] Among them, the execution process of each unit can be executed according to the process steps of a moving component spatial posture error detection method in Example 1, and will not be repeated one by one in this embodiment.

[0143] At the same time, the present invention also provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the above-mentioned method for detecting the spatial posture error of a moving part.

[0144] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0145] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0146] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0147] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0148] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for detecting spatial posture errors of moving parts, characterized in that: The method includes: Acquire the posture information of the moving parts of the machine tool based on the IMU module and the electronic level; the posture information includes the accelerometer signal and angular velocity signal acquired by the IMU module and the angle error signal acquired by the electronic level; Performing data fusion on the position and posture information to obtain line value error and angle error; Based on the line value error, the angle error and the motion speed of the linear axis of the machine tool, a BP neural network is used to establish an error prediction model; The optimal initial weight and threshold of the BP neural network are obtained by using a longicorn beard search algorithm, and the optimal initial weight and threshold are substituted into the error prediction model for model training to obtain a trained error prediction model; Based on the trained error prediction model, the spatial posture error detection of the moving parts is performed to obtain the posture error detection result.

2. A method for detecting spatial posture errors of moving parts according to claim 1, characterized in that: The acquisition of the position and posture information of the moving parts of the machine tool refers to the position and posture information of the moving parts of the machine tool acquired when the linear axis of the machine tool moves at different constant movement speeds without load.

3. A method for detecting spatial posture errors of moving parts according to claim 1, characterized in that: The IMU module is a high-precision MEMS three-axis inertial measurement unit, and the electronic level is an RL-A high-precision angle measurement module.

4. A method for detecting spatial posture errors of moving parts according to claim 1, characterized in that: The position and posture information is subjected to data fusion to obtain line value error and angle error, including: The accelerometer signals in the X, Y and Z directions of the linear axis in different time frequency bands are filtered and integrated in the corresponding frequency bands to obtain three line value errors; wherein the three line value errors are the three-degree-of-freedom line value errors of the moving parts at different speeds when they are at the predetermined position of the machine tool; According to the accelerometer signal, an initial value of the angle error iteration is determined; based on the Kalman filter algorithm, the angular acceleration signal and the angle error signal are subjected to data fusion calculation to obtain two angle errors.

5. A method for detecting spatial posture errors of moving parts according to claim 4, characterized in that: The angle error includes: If it is within the low frequency band, the angle error signal is directly used as the angle error measurement value; If it is within the medium and high frequency bands, the angular acceleration signal and the angle error signal are data fused, and the final angle error measurement value is obtained after iterative calculation.

6. A method for detecting spatial posture errors of moving parts according to claim 4, characterized in that: Based on the Kalman filter algorithm, the angle error θ is obtained X The calculation steps are: Step A, set is the random error x in Kalman filtering k The optimal estimate of the k-1 moment Replace the real value and get the next moment x k Predicted value Among them, F and B are matrices constructed to unify the data dimension; u k-1 is the measurement result of the gyroscope at time k-1 in the IMU module; Step B, determine the diagonal matrix Q as the covariance matrix of the process error w, and obtain the predicted value The first covariance matrix of the error Step C, based on the Kalman filter algorithm, according to the first covariance matrix Get the Kalman gain K; Step D: Determine the optimal estimate at time k based on the Kalman gain K Medium forecast value With the observed value z k The proportion of The second covariance matrix P k ; Step E, according to the second covariance matrix P k , the angular acceleration signal and the angle error signal are fused to obtain the angle error θ X .

7. A method for detecting spatial posture errors of moving parts according to claim 1, characterized in that: The method further includes: verifying the accuracy of the model, specifically: When the spatial position and running speed of the measurement system change, the maximum residual and mean square error of the posture error detection results measured by the verification model are verified.

8. A system for detecting spatial position error of a moving part, characterized in that: The system includes: An acquisition unit, used for acquiring the position and posture information of the moving parts of the machine tool based on the IMU module and the electronic level; the position and posture information includes the accelerometer signal and angular velocity signal acquired by the IMU module and the angle error signal acquired by the electronic level; A data fusion unit, used for fusing the position and posture information to obtain a line value error and an angle error; A model building unit, used to establish an error prediction model using a BP neural network based on the line value error, the angle error and the motion speed of the linear axis of the machine tool; A model training unit, used for obtaining the optimal initial weights and thresholds of the BP neural network by using a beetle whisker search algorithm, and substituting the optimal initial weights and thresholds into the error prediction model for model training to obtain a trained error prediction model; The error detection unit is used to perform spatial posture error detection of the moving parts based on the trained error prediction model to obtain the posture error detection result.

9. A moving component spatial posture error detection system according to claim 8, characterized in that: The data fusion unit comprises: The first fusion subunit is used to filter and integrate the accelerometer signals in the X, Y and Z directions of the linear axis in different time frequency bands in the corresponding frequency bands respectively to obtain three line value errors; wherein the three line value errors are three-degree-of-freedom line value errors of the moving parts at different speeds when they are at a predetermined position of the machine tool; The second fusion subunit is used to determine the initial value of the angle error iteration according to the accelerometer signal; based on the Kalman filter algorithm, the angular acceleration signal and the angle error signal are subjected to data fusion calculation to obtain two angle errors.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, a method for detecting a spatial posture error of a moving component as described in any one of claims 1 to 7 is implemented.

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