CN114004044A - Quick identification method for thermal error of machine tool spindle based on temperature sensitive point - Google Patents
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Abstract
The invention discloses a method for quickly identifying the thermal error of a machine tool spindle based on a temperature sensitive point, which comprises the steps of calculating a partial correlation coefficient; selecting an initial clustering center based on a K-Mean + + clustering algorithm and a partial correlation coefficient to obtain a clustering combination; selecting key temperature sensitive points from the cluster combinations based on the partial correlation coefficient to obtain temperature sensitive point combinations; sending data in the key temperature sensitive point combination into a BP neural network for training and selection; establishing an exponential machine tool temperature rise model based on data in the optimal temperature sensitive point combination, and performing self-adaptive adjustment based on a standard Kalman filtering algorithm; calculating to obtain delay time; calculating to obtain the identification time of each optimal temperature sensitive point of the machine tool spindle based on the time delay; and selecting the maximum value in the identification time to be unified as the identification time for predicting the temperature of each optimal temperature sensitive point, so as to obtain the unified identification time. The invention realizes the quick identification of the thermal error of the machine tool spindle.
Description
Technical Field
The invention relates to the field of identification of thermal errors of numerical control machine tools, in particular to a method for quickly identifying thermal errors of a machine tool spindle based on a temperature sensitive point.
Background
The generation of thermal errors of the machine tool is inevitable, and the thermal errors have a larger proportion in the total error source of the machine tool. The heat balance is an effective method for reducing the influence of thermal errors and improving the machining precision of the machine tool. However, it takes a long time for the machine tool to reach the thermal equilibrium state from the on state. The process is reasonably controlled, the time for the machine tool to reach a thermal equilibrium state is shortened, the machining efficiency of the machine tool is improved, and the method is a problem which needs to be solved urgently in the equipment manufacturing industry. The quick identification of the thermal error of the main shaft of the machine tool is the basis for shortening the thermal equilibrium time and is one of the prerequisites for improving the precision.
The existing method needs to be further improved in rapidity and high efficiency when identifying the thermal error of the main shaft of the machine tool. For the problem of spindle thermal error identification, most of the methods such as a genetic neural network, a grey theory, cluster fuzzy, linear regression and the like are adopted to establish thermal error models, and the models need a large amount of thermal error measurement data and need complex training. The whole process needs a large amount of calculation to obtain the complete machine tool spindle thermal error, and the time required by model training usually accounts for more than half of the total identification time. Therefore, the research on a method for rapidly identifying the thermal error of the spindle of the machine tool is an important part for shortening the thermal balance time of the machine tool.
Disclosure of Invention
Aiming at the defects in the prior art, the method for quickly identifying the thermal error of the machine tool spindle based on the temperature sensitive point solves the problem of long time for identifying the thermal error in the prior art.
In order to achieve the purpose of the invention, the invention adopts the technical scheme that:
the method for quickly identifying the thermal error of the spindle of the machine tool based on the temperature sensitive points comprises the following steps:
s1, calculating simple correlation coefficients between each temperature variable and each thermal error, and establishing a correlation coefficient matrix according to the simple correlation coefficients;
s2, calculating an inverse matrix of the correlation coefficient matrix based on the partial correlation analysis theory;
s3, calculating a partial correlation coefficient based on the inverse matrix of the correlation coefficient matrix;
s4, selecting an initial clustering center based on a K-Mean + + clustering algorithm and a partial correlation coefficient to obtain a clustering combination;
s5, selecting key temperature sensitive points from the cluster combinations based on the partial correlation coefficient to obtain temperature sensitive point combinations;
s6, sending data in the key temperature sensitive point combination into a BP neural network for training and selection to obtain an optimal temperature sensitive point combination;
s7, establishing an exponential machine tool temperature rise model based on a thermal model theory and a machine tool heat conduction theory, and obtaining a temperature rise state equation by combining a standard tasteless Kalman filtering algorithm;
s8, acquiring actual temperature measurement values of the optimal temperature sensitive points in the optimal temperature sensitive point combination within the initial time period, and performing self-adaptive adjustment based on a temperature rise state equation and a standard unscented Kalman filtering algorithm to obtain an adjusted optimal temperature predicted value;
s9, calculating the average absolute percentage error between the actual temperature measurement value and the optimal temperature prediction value to obtain the delay time;
s10, calculating the root mean square error of the actual temperature measurement value and the predicted temperature value based on the delay time to respectively obtain the identification time of each optimal temperature sensitive point;
s11, selecting the maximum value in the identification time of each optimal temperature sensitive point as the identification time of temperature prediction of each optimal temperature sensitive point uniformly to obtain uniform identification time;
s12, establishing a temperature-thermal error relation model, and respectively calculating thermal errors of the machine tool spindle in three directions based on the optimal temperature predicted value corresponding to the unified identification time to complete thermal error identification.
Further, the specific method for calculating the partial correlation coefficient in step S3 is as follows:
according to the formula:
obtaining a partial correlation coefficient c between the ith group temperature and the jth group thermal errorij(ii) a Wherein λijIs the ith row and the jth column parameter, lambda, in the inverse matrix of the matrix of correlation coefficientsiiIs the ith row and ith column parameter, lambda, in the inverse matrix of the matrix of correlation coefficientsjjIs the jth row and jth column parameter in the inverse matrix of the correlation coefficient matrix.
Further, the specific method for obtaining the temperature rise state equation in step S7 is as follows:
according to the formula:
yk＝Tk+vk
obtaining a temperature rise state equation; wherein the temperature state vector xkPredicted value of temperature yk，T∞,k-1Is the ambient temperature at time k-1, Tk-1Is the predicted temperature at time k-1, epsilonk-1Is a coefficient related to the physical property and the initial temperature of the system at the moment k-1, delta t is sampling time, e is the logarithm of a natural number, omegak-1The system noise at the time k-1; t iskPredicted temperature, v, for time kkTo measure noise.
Further, the specific method of step S8 is:
s8-1, acquiring the actual temperature measurement value and the predicted temperature value in the initial time period of each optimal temperature sensitive point, and calculating the residual r between the actual temperature measurement value and the predicted temperature valuet；
S8-2, determining residual error rtIf the absolute value of (a) is less than or equal to the positive threshold value, if so, the process is not executed and the process proceeds to step S9; otherwise, entering step S8-3;
s8-3, determining residual error rtIf the positive threshold is not greater than the positive threshold, the step S8-4 is executed; otherwise, entering step S8-7;
s8-4, acquiring a temperature prior estimated value, judging whether the actual measured value of the temperature is smaller than the temperature prior estimated value, and if so, entering the step S8-5; otherwise, entering step S8-6;
s8-5, increasing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, increasing a temperature state vector at the time t, increasing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, increasing a covariance of a temperature predicted value at the time t, reducing a Kalman gain at the time t, increasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-6, reducing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, reducing a temperature state vector at the time t, reducing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, reducing a covariance of a temperature predicted value at the time t, increasing a Kalman gain at the time t, increasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-7, acquiring a temperature prior estimated value, judging whether the actual measured value of the temperature is smaller than the temperature prior estimated value, and if so, entering the step S8-8; otherwise, entering step S8-9;
s8-8, reducing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, reducing a temperature state vector at the time t, reducing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, reducing a covariance of a temperature predicted value at the time t, increasing a Kalman gain at the time t, reducing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-9, increasing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, increasing a temperature state vector at the time t, increasing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, increasing a covariance of a temperature predicted value at the time t, decreasing a Kalman gain at the time t, decreasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9.
Further, the specific method for adjusting the Q covariance matrix and the R covariance matrix in step S8 is as follows:
the result of the Q covariance matrix adjustment in step S8 is Qupdate: when the Q covariance matrix is increased,
when the Q covariance matrix is reduced,
where k is the imaginary component, Q is the Q covariance before adjustment,
is the conjugate of the Q covariance before adjustment;
the result of the adjustment of the R covariance matrix in step S8 is Rupdate：
Rupdate＝hjR
Wherein h is an adjustment coefficient, j is an adjustment frequency, and R is a covariance of an actual temperature measurement value before the first adjustment; when the R covariance matrix is increased, h is larger than 1, and when the R covariance matrix is decreased, h is larger than 0 and smaller than 1.
Further, the specific method of step S9 is:
s9-1, acquiring the actual measurement value of the thermal error, and according to the formula:
obtain the average absolute valueFor the percentage error MAPE; wherein y isl(t) is the first adjusted predicted thermal error value at time t, al(t) is the actual thermal error measurement value of the ith at the time t, and n is a positive integer;
and S9-2, continuously calculating the average absolute percentage error until the average absolute percentage error is smaller than the percentage parameter threshold at a certain moment, and taking the moment as the delay moment.
Further, the specific method of step S10 is:
s10-1, starting from the delay time K and according to the formula:
calculating the root mean square error of the actual measured value of the thermal error and the predicted value of the thermal error to obtain a root mean square error beta; wherein y isl(t) is the first adjusted predicted thermal error value at time t, al(t) is the actual thermal error measurement value of the ith at the time t, and M is the total measurement value;
and S10-2, searching a certain moment to keep the root mean square error to the minimum value, and taking the moment as the identification time.
Further, the specific method for establishing the temperature-thermal error relationship model in step S12 is as follows:
according to the formula:
δ(t)＝δ(t-1)+αs·α·ΔT+κ
establishing a temperature-thermal error relation model; where δ (t) is the thermal error at time t, δ (t-1) is the thermal error at time t-1, αsThe linear expansion coefficient of the material is shown, delta T is the difference value between the optimal temperature predicted value corresponding to the unified identification time and the ambient temperature, and alpha and kappa are undetermined coefficients of an equation.
The invention has the beneficial effects that:
1. combining partial correlation analysis, a K-Mean + + clustering algorithm and a back propagation neural network (BP neural network), providing a comprehensive selection method of temperature sensitive points, and establishing a temperature-thermal error relation model based on the temperature of the sensitive points, so that the subsequent self-adaptive adjustment and rapid identification are facilitated;
2. aiming at the problem that the prediction divergence may occur in the standard Kalman filtering algorithm, a self-adaptive adjustment rule is added to enhance the robustness of the prediction;
3. designing a delay criterion: calculating to obtain delay time based on the average absolute percentage error, and using the delay time for delay processing, wherein a time judgment criterion is designed: and calculating the delayed system based on the root mean square error to obtain identification time, further calculating the identification time of the three directions of the main shaft of the machine tool, and selecting the maximum time as the unified identification time of the three directions to realize quick thermal error identification.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a diagram of the root mean square error of the present invention for finding the shortest identification time of a selected sensitive point;
FIG. 3 is a graph of the predicted temperature at a selected sensing point according to the present invention;
FIG. 4 is a diagram illustrating the result of the thermal error identification in the X-direction of the spindle according to the present invention;
FIG. 5 is a diagram illustrating the Y-direction thermal error identification result of the spindle of the present invention;
FIG. 6 is a diagram illustrating the Z-direction thermal error identification result of the spindle according to the present invention.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined in the appended claims, and all matters produced by the invention using the inventive concept are protected.
As shown in fig. 1, the method for quickly identifying the thermal error of the spindle of the machine tool based on the temperature sensitive point includes the following steps:
s1, calculating simple correlation coefficients between each temperature variable and each thermal error, and establishing a correlation coefficient matrix according to the simple correlation coefficients;
s2, calculating an inverse matrix of the correlation coefficient matrix based on the partial correlation analysis theory;
s3, calculating a partial correlation coefficient based on the inverse matrix of the correlation coefficient matrix;
s4, selecting an initial clustering center based on a K-Mean + + clustering algorithm and a partial correlation coefficient to obtain a clustering combination;
s5, selecting key temperature sensitive points from the cluster combinations based on the partial correlation coefficient to obtain temperature sensitive point combinations;
s6, sending data in the key temperature sensitive point combination into a BP neural network for training and selection to obtain an optimal temperature sensitive point combination;
s7, establishing an exponential machine tool temperature rise model based on data in the optimal temperature sensitive point combination and a standard tasteless Kalman filtering algorithm to obtain a temperature rise state equation;
s8, acquiring actual temperature measurement values of each optimal temperature sensitive point in the initial time period, and performing adaptive adjustment based on a temperature rise state equation and a standard unscented Kalman filtering algorithm to obtain an adjusted optimal temperature prediction value;
s9, calculating the average absolute percentage error between the actual temperature measurement value and the optimal temperature prediction value to obtain the delay time;
s10, calculating the root mean square error of the actual temperature measurement value and the predicted temperature value based on the delay time to respectively obtain the identification time of each optimal temperature sensitive point;
s11, selecting the maximum value in the identification time of each optimal temperature sensitive point as the identification time of temperature prediction of each optimal temperature sensitive point uniformly to obtain uniform identification time;
s12, establishing a temperature-thermal error relation model, and respectively calculating thermal errors of the machine tool spindle in three directions based on the optimal temperature predicted value corresponding to the unified identification time to complete thermal error identification.
The specific method for calculating the partial correlation coefficient in step S3 is as follows:
according to the formula:
obtaining a partial correlation coefficient c between the ith group temperature and the jth group thermal errorij(ii) a Wherein λijIs the ith row and the jth column parameter, lambda, in the inverse matrix of the matrix of correlation coefficientsiiIs the ith row and ith column parameter, lambda, in the inverse matrix of the matrix of correlation coefficientsjjIs the jth row and jth column parameter in the inverse matrix of the correlation coefficient matrix.
The specific method for obtaining the temperature rise state equation in the step S7 is as follows:
according to the formula:
yk＝Tk+vk
obtaining a temperature rise state equation; wherein the temperature state vector xkPredicted value of temperature yk，T∞,k-1Is the ambient temperature at time k-1, Tk-1Is the predicted temperature at time k-1, epsilonk-1Is a coefficient related to the physical property and the initial temperature of the system at the moment k-1, delta t is sampling time, e is the logarithm of a natural number, omegak-1The system noise at the time k-1; t iskPredicted temperature, v, for time kkTo measure noise.
The specific method of step S8 is:
s8-1, acquiring the actual temperature measurement value and the predicted temperature value in the initial time period of each optimal temperature sensitive point, and calculating the residual r between the actual temperature measurement value and the predicted temperature valuet；
S8-2, determining residual error rtIf the absolute value of (a) is less than or equal to the positive threshold value, if so, the process is not executed and the process proceeds to step S9; otherwise, entering step S8-3;
s8-3, determining residual error rtIf the positive threshold is not greater than the positive threshold, the step S8-4 is executed; otherwise, entering step S8-7;
s8-4, acquiring a temperature prior estimated value, judging whether the actual measured value of the temperature is smaller than the temperature prior estimated value, and if so, entering the step S8-5; otherwise, entering step S8-6;
s8-5, increasing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, increasing a temperature state vector at the time t, increasing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, increasing a covariance of a temperature predicted value at the time t, reducing a Kalman gain at the time t, increasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-6, reducing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, reducing a temperature state vector at the time t, reducing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, reducing a covariance of a temperature predicted value at the time t, increasing a Kalman gain at the time t, increasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-7, acquiring a temperature prior estimated value, judging whether the actual measured value of the temperature is smaller than the temperature prior estimated value, and if so, entering the step S8-8; otherwise, entering step S8-9;
s8-8, reducing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, reducing a temperature state vector at the time t, reducing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, reducing a covariance of a temperature predicted value at the time t, increasing a Kalman gain at the time t, reducing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-9, increasing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, increasing a temperature state vector at the time t, increasing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, increasing a covariance of a temperature predicted value at the time t, decreasing a Kalman gain at the time t, decreasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9.
The specific method for adjusting the Q covariance matrix and the R covariance matrix in step S8 is as follows:
the result of the Q covariance matrix adjustment in step S8 is Qupdate: when the Q covariance matrix is increased,
when the Q covariance matrix is reduced,
where k is the imaginary component, Q is the Q covariance before adjustment,
is the conjugate of the Q covariance before adjustment; the result of the adjustment of the R covariance matrix in step S8 is Rupdate：
Rupdate＝hjR
Wherein h is an adjustment coefficient, j is an adjustment frequency, and R is a covariance of an actual temperature measurement value before the first adjustment; when the R covariance matrix is increased, h is larger than 1, and when the R covariance matrix is decreased, h is larger than 0 and smaller than 1.
The specific method of step S9 is:
s9-1, acquiring the actual measurement value of the thermal error, and according to the formula:
obtaining a mean absolute percentage error MAPE; wherein y isl(t) is the first adjusted predicted thermal error value at time t, al(t) is the actual thermal error measurement value of the ith at the time t, and n is a positive integer;
and S9-2, continuously calculating the average absolute percentage error until the average absolute percentage error is smaller than the percentage parameter threshold at a certain moment, and taking the moment as the delay moment.
The specific method of step S10 is:
s10-1, starting from the delay time K and according to the formula:
calculating the root mean square error of the actual measured value of the thermal error and the predicted value of the thermal error to obtain a root mean square error beta; wherein y isl(t) is the first adjusted predicted thermal error value at time t, al(t) is the actual thermal error measurement value of the ith at the time t, and M is the total measurement value;
and S10-2, searching a certain moment to keep the root mean square error to the minimum value, and taking the moment as the identification time.
The specific method for establishing the temperature-thermal error relation model in the step S12 is as follows:
according to the formula:
δ(t)＝δ(t-1)+αs·α·ΔT+κ
establishing a temperature-thermal error relation model; where δ (t) is the thermal error at time t, δ (t-1) is the thermal error at time t-1, αsThe linear expansion coefficient of the material is shown, delta T is the difference value between the optimal temperature predicted value corresponding to the unified identification time and the ambient temperature, and alpha and kappa are undetermined coefficients of an equation.
As shown in fig. 2, the delay time is 18min, i.e. 18min, LT1 ═ 21min, LT15 ═ 26min, LT25 ═ 23min, and LT26 ═ 27min, and then 27min is selected as the thermal error identification time, which is better than the three to four hours of the conventional technology.
As shown in FIG. 3, the temperature is most gradual below the predicted value of T25.
As shown in fig. 4 to 6, the thermal error prediction results and the residual error results between the actual measurement values in the three directions of the machine tool main axes X, Y and Z at the recognition time are respectively shown.
The invention combines partial correlation analysis, a K-Mean + + clustering algorithm and a back propagation neural network (BP neural network), provides a comprehensive selection method of temperature sensitive points, establishes a temperature-thermal error relation model based on the temperature of the sensitive points, and is convenient for subsequent self-adaptive adjustment and rapid identification;
aiming at the problem that the prediction divergence may occur in the standard Kalman filtering algorithm, a self-adaptive adjustment rule is added to enhance the robustness of the prediction;
designing a delay criterion: calculating to obtain delay time based on the average absolute percentage error, and using the delay time for delay processing, wherein a time judgment criterion is designed: and calculating the delayed system based on the root mean square error to obtain identification time, further calculating the identification time of the three directions of the main shaft of the machine tool, and selecting the maximum time as the unified identification time of the three directions to realize quick thermal error identification.
Claims (8)
1. A method for quickly identifying a thermal error of a spindle of a machine tool based on a temperature sensitive point is characterized by comprising the following steps:
s1, calculating simple correlation coefficients between each temperature variable and each thermal error, and establishing a correlation coefficient matrix according to the simple correlation coefficients;
s2, calculating an inverse matrix of the correlation coefficient matrix based on the partial correlation analysis theory;
s3, calculating a partial correlation coefficient based on the inverse matrix of the correlation coefficient matrix;
s4, selecting an initial clustering center based on a K-Mean + + clustering algorithm and a partial correlation coefficient to obtain a clustering combination;
s5, selecting key temperature sensitive points from the cluster combinations based on the partial correlation coefficient to obtain temperature sensitive point combinations;
s6, sending data in the key temperature sensitive point combination into a BP neural network for training and selection to obtain an optimal temperature sensitive point combination;
s7, establishing an exponential machine tool temperature rise model based on a thermal model theory and a machine tool heat conduction theory, and obtaining a temperature rise state equation by combining a standard tasteless Kalman filtering algorithm;
s8, acquiring actual temperature measurement values of the optimal temperature sensitive points in the optimal temperature sensitive point combination within the initial time period, and performing self-adaptive adjustment based on a temperature rise state equation and a standard unscented Kalman filtering algorithm to obtain an adjusted optimal temperature predicted value;
s9, calculating the average absolute percentage error between the actual temperature measurement value and the optimal temperature prediction value to obtain the delay time;
s10, calculating the root mean square error of the actual temperature measurement value and the predicted temperature value based on the delay time to respectively obtain the identification time of each optimal temperature sensitive point;
s11, selecting the maximum value in the identification time of each optimal temperature sensitive point as the identification time of temperature prediction of each optimal temperature sensitive point uniformly to obtain uniform identification time;
s12, establishing a temperature-thermal error relation model, and respectively calculating thermal errors of the machine tool spindle in three directions based on the optimal temperature predicted value corresponding to the unified identification time to complete thermal error identification.
2. The method for rapidly identifying the thermal error of the spindle of the machine tool based on the temperature sensitive point according to claim 1, wherein the specific method for calculating the partial correlation coefficient in the step S3 is as follows:
according to the formula:
obtaining a partial correlation coefficient c between the ith group temperature and the jth group thermal errorij(ii) a Wherein λijIs the ith row and the jth column parameter, lambda, in the inverse matrix of the matrix of correlation coefficientsiiIs the ith row and ith column parameter, lambda, in the inverse matrix of the matrix of correlation coefficientsjjIs the jth row and jth column parameter in the inverse matrix of the correlation coefficient matrix.
3. The method for rapidly identifying the temperature of the spindle of the machine tool based on the temperature sensitive point according to claim 1, wherein the specific method for obtaining the temperature rise state equation in the step S7 is as follows:
according to the formula:
yk＝Tk+vk
obtaining a temperature rise state equation; wherein the temperature state vector xkPredicted value of temperature yk，T∞,k-1Is the ambient temperature at time k-1, Tk-1Is the predicted temperature at time k-1, epsilonk-1Is a coefficient related to the physical property and the initial temperature of the system at the moment k-1, delta t is sampling time, e is the logarithm of a natural number, omegak-1The system noise at the time k-1; t iskPredicted temperature, v, for time kkTo measure noise.
4. The method for rapidly identifying the thermal error of the spindle of the machine tool based on the temperature sensitive point according to claim 1, wherein the specific method of the step S8 is as follows:
s8-1, acquiring the actual temperature measurement value and the predicted temperature value in the initial time period of each optimal temperature sensitive point, and calculating the residual r between the actual temperature measurement value and the predicted temperature valuet；
S8-2, determining residual error rtIf the absolute value of (a) is less than or equal to the positive threshold value, if so, the process is not executed and the process proceeds to step S9; otherwise, entering step S8-3;
s8-3, determining residual error rtIf the positive threshold is not greater than the positive threshold, the step S8-4 is executed; otherwise, entering step S8-7;
s8-4, acquiring a temperature prior estimated value, judging whether the actual measured value of the temperature is smaller than the temperature prior estimated value, and if so, entering the step S8-5; otherwise, entering step S8-6;
s8-5, increasing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, increasing a temperature state vector at the time t, increasing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, increasing a covariance of a temperature predicted value at the time t, reducing a Kalman gain at the time t, increasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-6, reducing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, reducing a temperature state vector at the time t, reducing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, reducing a covariance of a temperature predicted value at the time t, increasing a Kalman gain at the time t, increasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-7, acquiring a temperature prior estimated value, judging whether the actual measured value of the temperature is smaller than the temperature prior estimated value, and if so, entering the step S8-8; otherwise, entering step S8-9;
s8-8, reducing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, reducing a temperature state vector at the time t, reducing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, reducing a covariance of a temperature predicted value at the time t, increasing a Kalman gain at the time t, reducing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9;
s8-9, increasing a standard unscented Kalman filtering algorithm Q covariance matrix at the time t, increasing a temperature state vector at the time t, increasing a standard unscented Kalman filtering algorithm R covariance matrix at the time t, increasing a covariance of a temperature predicted value at the time t, decreasing a Kalman gain at the time t, decreasing an optimal estimated value of the temperature state vector at the time t to obtain an optimal temperature predicted value, and entering the step S9.
5. The method for rapidly identifying the temperature of the spindle of the machine tool based on the temperature sensitive point according to claim 4, wherein the specific method for adjusting the Q covariance matrix and the R covariance matrix in the step S8 is as follows:
the result of the Q covariance matrix adjustment in step S8 is Qupdate: when the Q covariance matrix is increased,
when the Q covariance matrix is reduced,
where k is the imaginary component, Q is the Q covariance before adjustment,
is the conjugate of the Q covariance before adjustment;
the result of the adjustment of the R covariance matrix in step S8 is Rupdate：
Rupdate＝hjR
Wherein h is an adjustment coefficient, j is an adjustment frequency, and R is a covariance of an actual temperature measurement value before the first adjustment; when the R covariance matrix is increased, h is larger than 1, and when the R covariance matrix is decreased, h is larger than 0 and smaller than 1.
6. The method for rapidly identifying the thermal error of the spindle of the machine tool based on the unscented kalman filter algorithm according to claim 1, wherein the specific method of the step S9 is as follows:
s9-1, acquiring the actual measurement value of the thermal error, and according to the formula:
obtaining a mean absolute percentage error MAPE; wherein y isl(t) is the first adjusted predicted thermal error value at time t, al(t) is the actual thermal error measurement value of the ith at the time t, and n is a positive integer;
and S9-2, continuously calculating the average absolute percentage error until the average absolute percentage error is smaller than the percentage parameter threshold at a certain moment, and taking the moment as the delay moment.
7. The method for rapidly identifying the thermal error of the spindle of the machine tool based on the unscented kalman filter algorithm according to claim 1, wherein the specific method of the step S10 is as follows:
s10-1, starting from the delay time K and according to the formula:
calculating the root mean square error of the actual measured value of the thermal error and the predicted value of the thermal error to obtain a root mean square error beta; wherein y islWhen (t) is tThe first adjusted thermal error prediction value of the scale, al(t) is the actual thermal error measurement value of the ith at the time t, and M is the total measurement value;
and S10-2, searching a certain moment to keep the root mean square error to the minimum value, and taking the moment as the identification time.
8. The method for rapidly identifying the thermal error of the spindle of the machine tool based on the temperature sensitive point according to claim 1, wherein the specific method for establishing the temperature-thermal error relation model in the step S12 is as follows:
according to the formula:
δ(t)＝δ(t-1)+αs·α·ΔT+κ
establishing a temperature-thermal error relation model; where δ (t) is the thermal error at time t, δ (t-1) is the thermal error at time t-1, αsThe linear expansion coefficient of the material is shown, delta T is the difference value between the optimal temperature predicted value corresponding to the unified identification time and the ambient temperature, and alpha and kappa are undetermined coefficients of an equation.
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