CN116108335A - Efficient compression editing method for automobile part load spectrum based on Wigner-Ville transformation - Google Patents
Efficient compression editing method for automobile part load spectrum based on Wigner-Ville transformation Download PDFInfo
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Abstract
The efficient compression editing method for the load spectrum of the automobile part based on Wigner-Ville transformation comprises the following steps: 1) Preprocessing an original signal; 2) Performing Hilbert transform on the preprocessed signals to obtain analysis signals; 3) Performing Wigner-Ville transformation on the analysis signal to obtain Wigner-Ville distribution; 4) Integrating Wigner-Ville distribution to obtain an instantaneous energy spectrum; 5) Determining an optimal threshold value of the instantaneous energy spectrum by utilizing a genetic algorithm to obtain a reserved signal segment; 6) Splicing the reserved signal segments to obtain a compression load signal; 7) Determining whether the error of the relative damage reserved quantity and the statistical parameter meets the threshold requirement, judging whether the power spectral density and the penetration level counting curve distribution of the load signals before and after compression meet the requirement, and returning to the step 5) until the requirements are met if the power spectral density and the penetration level counting curve distribution do not meet the requirements; 8) And outputting a compression load signal. The invention can accurately identify and extract the high fatigue damage contribution part, quickly compress and edit the high fatigue damage contribution part to form an acceleration load spectrum, and realize the test effect identical to that of the original signal.
Description
Technical Field
The invention relates to the field of an indoor road simulation acceleration test of automobile parts, in particular to a method for efficiently compressing and editing a load spectrum of automobile parts based on Wigner-Ville transformation.
Background
The durability analysis of the automobile parts is an important link of the automobile enterprise in the product research and development process, and the load information acting on the parts is a key for analyzing the fatigue durability problem. For the actual measurement road load spectrum loading, a reasonable and effective load spectrum compiling method is adopted to accelerate editing, and on the basis of ensuring the load spectrum loading effect to be consistent, a load spectrum with shorter loading time is obtained, so that the research efficiency of the part fatigue durability test is remarkably improved. In order to ensure that the reduced load spectrum has the same loading effect on the part as the original load spectrum, the reduced load spectrum must be made to be substantially identical to the original signal in terms of damage contribution, statistical parameters (mean, root mean square value and peak coefficient), power spectral density, and penetration count.
At present, the load spectrum acceleration editing is mainly divided into a time domain editing method and a frequency domain editing method, and the basic principle is that the data length is shortened by deleting the load cycle with small damage contribution in the signal, and the damage contribution to the parts is ensured to be basically consistent with the original signal. The difference between editing methods is mainly characterized in that the identification and deletion of small damage contribution data comprise the steps of setting indexes such as strain amplitude, SWT threshold, damage reserved quantity and the like to identify and reject invalid signal fragments, and realizing load spectrum compression editing.
The method for accelerating programming of the load spectrum of the automobile part comprises the following steps: and setting damage reservation amount by utilizing fatigue analysis software such as Ncode and the like, and compressing a load spectrum based on time domain damage reservation. However, the compression effect of the method is limited, and the compression signal is easy to change greatly in the aspects of statistical characteristic parameters, frequency domain and amplitude domain distribution and the like compared with the original signal, and the difference is overlarge.
Disclosure of Invention
The invention aims to provide a vehicle part load spectrum compression editing method based on Wigner-Ville transformation. The method uses Wigner-Ville time-frequency processing technology to perform joint analysis on time-frequency domain characteristics of original load signals, can acquire instantaneous energy information corresponding to each time point of the load signals, optimizes an optimal threshold value according to the acquired instantaneous energy spectrum in combination with a genetic algorithm, can clearly and definitely identify and delete invalid damage contribution data of the original load signals, furthest compress the time length of the original load signals, ensures that the compressed signals and the original signals are basically consistent in terms of damage retention, statistical parameters (mean value, root mean square value and peak value coefficient), power spectrum density distribution, penetration count and the like, and realizes loading effects consistent with the original signals.
The technical scheme adopted for realizing the aim of the invention is that the efficient compression editing method for the load spectrum of the automobile part based on Wigner-Ville transformation comprises the following steps:
1) An original load signal is acquired.
2) And preprocessing the original load signal to obtain a preprocessed load signal.
3) And performing Hilbert transform on the preprocessed load signal to obtain an analysis signal.
4) And performing Wigner-Ville transformation on the analysis signal, and acquiring information of energy distribution along with time-frequency to obtain a two-dimensional time-frequency real number matrix and a Wigner-Ville spectrum.
5) And integrating the Wigner-Ville spectrum along the frequency axis at each time point of the two-dimensional time-frequency real number matrix to obtain the instantaneous energy spectrum of the original load signal.
6) Setting genetic algorithm parameters, determining an optimal threshold value of the instantaneous energy spectrum by utilizing the genetic algorithm, positioning the instantaneous energy spectrum fragments below the threshold value and time points of corresponding data according to the optimal threshold value, corresponding the time points to the time sequence of the original load signal, and deleting the original load signal fragments corresponding to the time points to obtain the reserved signal fragments.
7) And splicing the reserved signal segments to obtain the compression load signal.
8) And calculating the relative damage retention of the compression load signal and the original load signal and the error of the statistical parameter, and determining the frequency domain power spectral density and the amplitude domain level-through counting curve distribution of the compression load signal and the original load signal.
9) And determining whether the error of the relative injury retention and the statistical parameter meets the threshold requirement.
And judging whether the frequency domain power spectral density main distribution of the load signals before and after compression has consistency.
And judging whether the amplitude domain passing count distribution of the load signals before and after compression has consistency.
If the relative damage remaining quantity is lower than the threshold value, the error of the statistical parameter exceeds the threshold value, the frequency domain power spectrum density of the load signal before and after compression does not have consistency or the amplitude domain passing grade counting distribution curve trend of the load signal before and after compression does not have consistency, returning to the step 6), otherwise, entering the step 10).
10 Outputting a compression load signal.
Further, the preprocessing of the original load signal includes: filtering, deburring, drift correction and resampling.
Further, the analysis signal z (t) after hilbert transformation is as follows:
z(t)＝x(t)+jH[x(t)] (1)
where t represents a time vector, x (t) is an original load signal, and z (t) is an analysis signal of x (t).
Hilbert transform H [ x (t) ], as follows:
where PV represents the Cauchy principal value and τ is the integral variable.
Further, the Wigner-Ville distribution W after Wigner-Ville conversion z (nT, ω) is as follows:
in which W is z (nT, ω) is a Wigner-Ville distribution, nT, ω representing the index of the time vector and the frequency vector, respectively. L is the time data length, z, for Wigner-Ville transformation * (t) is the complex conjugate of the resolved signal z (t), z (nT+iT) z * (nT-iT) is the instantaneous correlation function of the resolved signal z (t). i denotes a time data sequence number.
The two-dimensional time-frequency real number matrix is as follows:
wherein TFR (t, f) is a two-dimensional time-frequency real number matrix, t is a time vector, f is a frequency, and a line vector a m ＝[a m1 ，a m2 ，...，a mn ]Corresponding to different time points, column vector a n ＝[a 1n ，a 2n ，...，a mn ] T Representing different frequency values. m and n are row and column numbers. The elements of the two-dimensional time-frequency real matrix represent the amplitude and phase angle information of the signal.
Further, the instantaneous energy spectrum of the original load signal is as follows:
in the formula, |Z (t) | 2 Is the instantaneous energy spectrum. W (W) z (t, f) is the Wigner-Ville spectrum.
Further, the step of determining an optimal threshold for the instantaneous energy spectrum using a genetic algorithm comprises:
the method comprises the steps of taking a threshold value in an instantaneous energy spectrum as a design variable, taking a minimum value min (L (y)) of a length compression ratio before and after load compression editing as an objective function, and taking a constraint condition that the damage ratio of a compressed signal to an original signal is not lower than a and a statistical parameter error is smaller than b as a constraint condition, and establishing a threshold value optimizing mathematical model. a. b is a preset error threshold.
And (3) carrying out threshold optimizing calculation by using a threshold optimizing mathematical model, and finding an optimal threshold value in the instantaneous energy spectrum numerical value interval [0, max P (n) ].
The threshold optimizing mathematical model is as follows:
wherein Δavr, Δku, and Δrms are the mean value, kurtosis coefficient, and root mean square value variation, respectively. L (y) is the length compression ratio before and after the load compression editing. L (L) y For the length after load compression and editing, L 0 The pre-edit length is compressed for the payload.
The relative damage reserve for the pre-and post-editing signals. d (x) 0 (t))、d(x y (t)) are pseudo-damage values of signals before and after editing, respectively.
Wherein the pseudo-impairment d (x) 0 (t))、d(x y (t)) is a theoretical value, the average stress correction is not carried out, the calculated damage value of each stress cycle is obtained, and the total pseudo damage of the load data is calculated as follows:
wherein D represents total pseudo-damage, n j The number of cycles for the j-th stress level. N (N) j For the number of cycles to failure at that stress level.
Further, in step 7), when the temporal sequence of the instantaneous energy spectrum segment corresponding to the original signal does not have an intersection, the remaining segments in the original signal can be directly spliced to obtain a compressed signal.
Further, the statistical parameters include: mean, root mean square, and kurtosis coefficient.
Further, the frequency domain characteristic of the load signal is represented as a power spectral density curve, and the amplitude domain characteristic is represented as a level-through count distribution.
The technical effect of the invention is undoubtedly that the automobile part load spectrum compression editing method based on Wigner-Ville transformation has the following beneficial effects:
the automobile part load spectrum compression editing method based on Wigner-Ville transformation can be used for carrying out joint analysis on random non-stationary signals from time-frequency domain distribution, can obtain energy information corresponding to each time point, has fundamental difference with the traditional editing method based on time domain damage reservation, can ensure consistency of characteristic parameters before and after signal compression in the time domain, and can keep consistency in frequency domain and amplitude domain characteristic distribution;
compared with the editing method based on time domain damage reservation and editing method, the editing method based on short-time Fourier transform and S transform, the method has better time compression effect on signals under the condition of the same damage reservation;
compared with an editing method based on time domain damage reservation, the automobile part load spectrum compression editing method based on Wigner-Ville transformation has the advantages that time data compression is more, data volume is smaller, and efficiency of a fatigue simulation test or a bench test can be effectively improved;
compared with the editing method based on time domain damage reservation, the method for compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation has the advantages that the frequency domain power spectrum density curve and the power spectrum density distribution trend of the original load are kept consistent in a low frequency region, and the power spectrum density distribution curve moves upwards in a high frequency region more than the latter because of the deletion of more small load amplitude values;
compared with an editing method based on time domain damage reservation, the method for compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation has the advantages that the distribution trend of the amplitude domain passing-level counting curve of the method is consistent with that of the original load passing-level counting curve, and the small amplitude cyclic deletion near the mean value is more, namely the compression effect is better;
compared with editing methods based on short-time Fourier transform and wavelet transform, the method for compressing and editing the load spectrum of the automobile part based on Wigner-Ville transform simplifies editing operation and avoids the steps of selecting and setting window functions or wavelet functions and the like under the condition of ensuring data precision;
the time-frequency analysis method based on Wigner-Ville distribution can accurately identify and extract the high fatigue damage contribution data part, rapidly compress the high fatigue damage contribution data part to form an acceleration load spectrum, maximally compress the original load signal, ensure that the obtained compressed signal is basically consistent with the original signal, and realize the same loading effect as the original signal.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2 is a Wigner-Ville spectrum showing the distribution of signal energy in the time-frequency domain;
FIG. 3 is an instantaneous energy spectrum of a load signal;
FIG. 4 is a schematic diagram of extracting signal time slices based on threshold settings of an instantaneous energy spectrum;
FIG. 5 is a schematic representation of time domain signal segment extraction for large impairment contributions;
fig. 6 is an edited acceleration spectrum of the payload signal.
Detailed Description
The present invention is further described below with reference to examples, but it should not be construed that the scope of the above subject matter of the present invention is limited to the following examples. Various substitutions and alterations are made according to the ordinary skill and familiar means of the art without departing from the technical spirit of the invention, and all such substitutions and alterations are intended to be included in the scope of the invention.
Example 1:
referring to fig. 1 to 6, the method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation comprises the following steps:
1) An original load signal is acquired. The original load signal is an actual measurement load signal.
2) And preprocessing the original load signal to obtain a preprocessed load signal.
3) And performing Hilbert transform on the preprocessed load signal to obtain an analysis signal.
4) And performing Wigner-Ville transformation on the analysis signal, and acquiring information of energy distribution along with time-frequency to obtain a two-dimensional time-frequency real number matrix and a Wigner-Ville spectrum.
The Wigner-Ville transform is defined as a function of energy over time and its frequency distribution.
5) And integrating the Wigner-Ville spectrum along the frequency axis at each time point of the two-dimensional time-frequency real number matrix to obtain the instantaneous energy spectrum of the original load signal.
6) Setting genetic algorithm parameters, determining an optimal threshold value of an instantaneous energy spectrum by utilizing the genetic algorithm, positioning the time points of instantaneous energy spectrum fragments and corresponding data which are lower than the threshold value according to the optimal threshold value, corresponding the time points to a time sequence of an original load signal, deleting original load signal fragments corresponding to the time points, wherein the deleted original load signal fragments are data fragments contributed by small damage, and after deletion, the accuracy of the load signal is not influenced, so that a reserved signal fragment is obtained.
7) And splicing the reserved signal segments to obtain the compression load signal.
8) And calculating the relative damage retention of the compression load signal and the original load signal and the error of the statistical parameter, and determining the frequency domain power spectral density and the amplitude domain level-through counting curve distribution of the compression load signal and the original load signal.
9) And determining whether the error of the relative injury retention and the statistical parameter meets the threshold requirement.
And judging whether the frequency domain power spectral density main distribution of the load signals before and after compression has consistency.
The method for judging whether the consistency exists is as follows: judging whether the distribution has obvious large difference or not, namely judging whether the distribution difference is larger than a preset threshold value or not.
And judging whether the amplitude domain passing count distribution of the load signals before and after compression has consistency.
If the relative damage remaining quantity is lower than the threshold value, the error of the statistical parameter exceeds the threshold value, the frequency domain power spectrum density of the load signal before and after compression does not have consistency or the amplitude domain passing grade counting distribution curve trend of the load signal before and after compression does not have consistency, returning to the step 6), otherwise, entering the step 10).
10 Outputting a compression load signal. The compression load signal has a short loading time and is used for analyzing the fatigue durability of the part.
By the signal, the fatigue durability test of the part can be accelerated, and the test efficiency is improved.
The preprocessing of the original load signal comprises: filtering, deburring, drift correction and resampling.
The hilbert transform is used for eliminating the influence of negative frequency components in the real signal and avoiding aliasing of a frequency domain, and the analysis signal z (t) after the hilbert transform is as follows:
z(t)＝x(t)+jH[x(t)] (1)
where t represents a time vector, x (t) is an original load signal, and z (t) is an analysis signal of x (t).
Hilbert transform H [ x (t) ], as follows:
where PV represents the Cauchy principal value and τ is the integral variable.
The Wigner-Ville distribution W after Wigner-Ville conversion z (nT, ω) is as follows:
in which W is z (nT, ω) is a Wigner-Ville distribution, nT, ω representing the indices of the time vector and the frequency vector, respectively; l is the time data length, z, for Wigner-Ville transformation * (t) is the complex conjugate of the resolved signal z (t), z (nT+iT) z * (nT-iT) is an instantaneous correlation function that resolves the signal z (t); i denotes a time data sequence number.
Wigner-Ville transformation is carried out on the discrete signals, so that time-frequency-energy three-dimensional distribution information of the signals can be obtained.
The two-dimensional time-frequency real number matrix is as follows:
wherein TFR (t, f) is a two-dimensional time-frequency real number matrix, t is a time vector, f is a frequency, and a line vector a m ＝[a m1 ，a m2 ，...，a mn ]Corresponding to different time points, column vector a n ＝[a 1n ，a 2n ，...，a mn ] T Representing different frequency values. m and n are row and column numbers. The elements of the two-dimensional time-frequency real matrix represent the amplitude and phase angle information of the signal.
The Wigner-Ville distribution has time edge property, and the integration along the frequency domain axis at each time point is equal to the instantaneous energy of the signal at the corresponding moment by a two-dimensional time-frequency real number matrix, so that the corresponding energy of the load signal at each time point is calculated to form an instantaneous energy spectrum. The integration of the Wigner-Ville spectrum is performed along the direction of the frequency axis at each time point, so that the corresponding instantaneous energy at each time point of the signal is obtained, and the cut-off threshold is closely related to the time history length of the compression load spectrum signal and the fatigue damage reserved quantity.
The instantaneous energy spectrum of the original load signal is as follows:
in the formula, |Z (t) | 2 Is a transient energy spectrum; w (W) z (t, f) is the Wigner-Ville spectrum.
The step of determining an optimal threshold for the instantaneous energy spectrum using a genetic algorithm comprises:
the method comprises the steps of taking a threshold value in an instantaneous energy spectrum as a design variable, taking a minimum value min (L (y)) of a length compression ratio before and after load compression editing as an objective function, and taking a constraint condition that the damage ratio of a compressed signal to an original signal is not lower than a and a statistical parameter error is smaller than b as a constraint condition, and establishing a threshold value optimizing mathematical model. a. b is a preset error threshold.
And (3) carrying out threshold optimizing calculation by using a threshold optimizing mathematical model, and finding an optimal threshold value in the instantaneous energy spectrum numerical value interval [0, max P (n) ].
The threshold optimizing mathematical model is as follows:
wherein Δavr, Δku, and Δrms are the mean value, kurtosis coefficient, and root mean square value variation, respectively. L (y) is the length compression ratio before and after the load compression editing. L (L) y For the length after load compression and editing, L 0 The pre-edit length is compressed for the payload.
The relative damage reserved quantity of signals before and after editing; d (x) 0 (t))、d(x y (t)) is a pseudo-damage value of the signal before and after editing, respectively;
wherein the pseudo-impairment d (x) 0 (t))、d(x y (t)) is a theoretical value, and is a method for reliably reflecting fatigue characteristics of a load spectrum by a simple value, notThe real damage is usually used for comparing the relations among different load histories, is mainly used in the field of structural endurance tests, and is particularly widely applied to the evaluation of editing effects in the stage of editing a rack driving spectrum. And (3) not carrying out average stress correction, calculating the damage value of each stress cycle, and calculating the total pseudo damage of the load data as follows:
wherein D represents total pseudo-damage, n j The number of cycles for the j-th stress level. N (N) j For the number of cycles to failure at that stress level.
In step 7), when the temporal sequence of the instantaneous energy spectrum segment corresponding to the original signal does not have intersection, the remaining segments in the original signal can be directly spliced to obtain the compressed signal.
The statistical parameters include: mean, root mean square, and kurtosis coefficient.
The frequency domain characteristic of the load signal is represented as a power spectrum density curve, and the amplitude domain characteristic is represented as a level-through counting distribution.
Example 2:
referring to fig. 1 to 6, the method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation comprises the following steps:
1) An original load signal is acquired. The original load signal is an actual measurement load signal.
2) And preprocessing the original load signal to obtain a preprocessed load signal.
3) And performing Hilbert transform on the preprocessed load signal to obtain an analysis signal.
4) And performing Wigner-Ville transformation on the analysis signal, and acquiring information of energy distribution along with time-frequency to obtain a two-dimensional time-frequency real number matrix and a Wigner-Ville spectrum.
The Wigner-Ville transform is defined as a function of energy over time and its frequency distribution.
5) And integrating the Wigner-Ville spectrum along the frequency axis at each time point of the two-dimensional time-frequency real number matrix to obtain the instantaneous energy spectrum of the original load signal.
6) Setting genetic algorithm parameters, determining an optimal threshold value of an instantaneous energy spectrum by utilizing the genetic algorithm, positioning the time points of instantaneous energy spectrum fragments and corresponding data which are lower than the threshold value according to the optimal threshold value, corresponding the time points to a time sequence of an original load signal, deleting original load signal fragments corresponding to the time points, wherein the deleted original load signal fragments are data fragments contributed by small damage, and after deletion, the accuracy of the load signal is not influenced, so that a reserved signal fragment is obtained.
7) And splicing the reserved signal segments to obtain the compression load signal.
8) And calculating the relative damage retention of the compression load signal and the original load signal and the error of the statistical parameter, and determining the frequency domain power spectral density and the amplitude domain level-through counting curve distribution of the compression load signal and the original load signal.
9) And determining whether the error of the relative injury retention and the statistical parameter meets the threshold requirement.
And judging whether the frequency domain power spectral density main distribution of the load signals before and after compression has consistency.
The method for judging whether the consistency exists is as follows: judging whether the distribution has obvious large difference or not, namely judging whether the distribution difference is larger than a preset threshold value or not.
And judging whether the amplitude domain passing count distribution of the load signals before and after compression has consistency.
If the relative damage remaining quantity is lower than the threshold value, the error of the statistical parameter exceeds the threshold value, the frequency domain power spectrum density of the load signal before and after compression does not have consistency or the amplitude domain passing grade counting distribution curve trend of the load signal before and after compression does not have consistency, returning to the step 6), otherwise, entering the step 10).
10 Outputting a compression load signal. The compression load signal has a short loading time and is used for analyzing the fatigue durability of the part.
By the signal, the fatigue durability test of the part can be accelerated, and the test efficiency is improved.
Example 3:
the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation mainly comprises the following steps of: filtering, deburring, drift correction and resampling.
Example 4:
the method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation mainly comprises the following steps of embodiment 2, wherein the Hilbert transformation is used for eliminating the influence of negative frequency components in real signals and avoiding aliasing of frequency domains, and an analysis signal z (t) after the Hilbert transformation is as follows:
z(t)＝x(t)+jH[x(t)] (1)
where t represents a time vector, x (t) is an original load signal, and z (t) is an analysis signal of x (t).
Hilbert transform H [ x (t) ], as follows:
where PV represents the Cauchy principal value and τ is the integral variable.
Example 5:
the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation mainly comprises the steps of example 2, wherein Wigner-Ville distribution W after Wigner-Ville transformation is obtained z (nT, ω) is as follows:
in which W is z (nT, ω) is a Wigner-Ville distribution, nT, ω representing the indices of the time vector and the frequency vector, respectively; l is the time data length, z, for Wigner-Ville transformation * (t) is the complex conjugate of the resolved signal z (t), z (nT+iT) z * (nT-iT) is an instantaneous correlation function that resolves the signal z (t); i represents a temporal data sequenceNumber (x).
Wigner-Ville transformation is carried out on the discrete signals, so that time-frequency-energy three-dimensional distribution information of the signals can be obtained.
The two-dimensional time-frequency real number matrix is as follows:
wherein TFR (t, f) is a two-dimensional time-frequency real number matrix, t is a time vector, f is a frequency, and a line vector a m ＝[a m1 ，a m2 ，...，a mn ]Corresponding to different time points, column vector a n ＝[a 1n ，a 2n ，...，a mn ] T Representing different frequency values. m and n are row and column numbers. The elements of the two-dimensional time-frequency real matrix represent the amplitude and phase angle information of the signal.
Example 6:
the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation mainly comprises the steps of in embodiment 2, wigner-Ville distribution has time edge property, and the integration along a frequency domain axis at each time point is equal to the instantaneous energy of the signal at the corresponding time point by a two-dimensional time-frequency real number matrix, so that the corresponding energy of the load signal at each time point is calculated and obtained to form an instantaneous energy spectrum. The integration of the Wigner-Ville spectrum is performed along the direction of the frequency axis at each time point, so that the corresponding instantaneous energy at each time point of the signal is obtained, and the cut-off threshold is closely related to the time history length of the compression load spectrum signal and the fatigue damage reserved quantity.
The instantaneous energy spectrum of the original load signal is as follows:
in the formula, |Z (t) | 2 Is a transient energy spectrum; w (W) z (t, f) is the Wigner-Ville spectrum.
Example 7:
the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation mainly comprises the following steps in embodiment 2:
the method comprises the steps of taking a threshold value in an instantaneous energy spectrum as a design variable, taking a minimum value min (L (y)) of a length compression ratio before and after load compression editing as an objective function, taking a damage ratio of a compressed signal to an original signal not lower than 97% and taking a statistical parameter error smaller than 15% as constraint conditions, and establishing a threshold value optimizing mathematical model.
And (3) carrying out threshold optimizing calculation by using a threshold optimizing mathematical model, and finding an optimal threshold value in the instantaneous energy spectrum numerical value interval [0, max P (n) ].
The threshold optimizing mathematical model is as follows:
wherein Δavr, Δku, and Δrms are the mean value, kurtosis coefficient, and root mean square value variation, respectively. L (y) is the length compression ratio before and after the load compression editing. L (L) y For the length after load compression and editing, L 0 The pre-edit length is compressed for the payload.
The relative damage reserved quantity of signals before and after editing; d (x) 0 (t))、d(x y (t)) is a pseudo-damage value of the signal before and after editing, respectively;
wherein the pseudo-impairment d (x) 0 (t))、d(x y (t)) is a theoretical value, is a method for reliably reflecting fatigue characteristics of a load spectrum by a simple value, is not true damage, is generally used for comparing relations among different load histories, is mainly used in the field of structural endurance tests, and is particularly widely applied to the evaluation of editing effects in the stage of editing a rack driving spectrum. And (3) not carrying out average stress correction, calculating the damage value of each stress cycle, and calculating the total pseudo damage of the load data as follows:
wherein D represents total pseudo-damage, n j The number of cycles for the j-th stress level. N (N) j For the number of cycles to failure at that stress level.
Example 8:
in the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation, the main steps are shown in the embodiment 2 and the step 7), when the temporal energy spectrum segment corresponds to the time sequence in the original signal and no intersection exists, the reserved segment in the original signal can be directly spliced, and the compressed signal is obtained.
Example 9:
the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation mainly comprises the following steps of example 2: mean, root mean square, and kurtosis coefficient.
Example 10:
the method for efficiently compressing and editing the load spectrum of the automobile part based on Wigner-Ville transformation mainly comprises the following steps of an embodiment 2, wherein the frequency domain characteristic of a load signal is represented as a power spectrum density curve, and the amplitude domain characteristic is represented as a level-through counting distribution.
Example 11:
referring to fig. 1 to 6, the method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation specifically comprises the following steps:
1) Acquiring and inputting an original load signal;
2) Preprocessing the load spectrum, including filtering, deburring, drift correction, resampling and the like;
3) Performing Hilbert transformation on the preprocessed signals to obtain analysis signal forms of real signals;
4) Performing Wigner-Ville transformation on the analysis signal, and acquiring information of energy distribution along with time-frequency to obtain a two-dimensional time-frequency real number matrix and a Wigner-Ville spectrum;
5) For a Wigner-Ville spectrum, integrating along the direction of a frequency axis at each time point to obtain the corresponding instantaneous energy at each time point of the signal, and obtaining the instantaneous energy spectrum of the load signal;
6) Optimizing threshold selection by combining a genetic algorithm, determining an optimal threshold by utilizing the genetic algorithm for the instantaneous energy spectrum, positioning instantaneous energy spectrum fragments lower than the threshold according to the threshold, positioning a time point of corresponding data, corresponding the time point to an original load time sequence, and deleting data fragments contributed by corresponding small damage;
7) Splicing the reserved signal segments to obtain a compressed signal;
8) Calculating the relative damage retention and statistical parameter errors of the compressed load signal and the original load signal, checking the distribution of the power spectrum density and the level-crossing count curve, determining whether the damage retention and the statistical parameter errors meet the requirements, determining whether the frequency domain power spectrum density of the signal before and after compression is reasonable, determining whether the trend of the level-crossing count distribution curve of the amplitude domain has consistency, if obvious difference exists, repeating the step 6) to reset the genetic algorithm parameters, solving the optimal threshold value until the engineering requirements are met, and jumping out of the cycle;
9) And (5) completing the compression editing work of the load spectrum of the automobile parts.
The hilbert transformation in the step 3) is shown in the formula (1), and is used for eliminating the influence of negative frequency components in the real signal and avoiding aliasing of a frequency domain.
Where x (t) is the original load signal, z (t) is the resolved signal form of x (t), H [ x (t) ] is the Hilbert transform, and PV represents the Cauchy principal value.
The Wigner-Ville transformation in the step 4) comprises the following steps: the Wigner-Ville transform is defined as a function of energy over time and its frequency distribution, as shown in equation (2), and the discrete Wigner-Ville transform form of the resolved signal is shown in equation (3). Wigner-Ville transformation is carried out on the discrete signals, so that time-frequency-energy three-dimensional distribution information of the signals can be obtained. Its two dimensionsThe real time frequency matrix is shown in formula (4), and the row vector a m ＝[a m1 ，a m2 ，...，a mn ]Corresponding to different time points, column vector a n ＝[a 1n ，a 2n ，...，a mn ] T Representing different frequency values, the matrix elements representing amplitude and phase angle information of the signal.
Where f is the frequency, τ is the integral variable, z * (t) is the complex conjugate of z (t),
is the instantaneous correlation function of the signal. W (W) z (T, ω) represents the Wigner-Ville distribution, T is the sampling period, L is the time data length for transformation, and T, ω represents the indices of the time vector and the frequency vector, respectively. TFR (t, f) is a two-dimensional time-frequency real-valued matrix.
The method for acquiring the instantaneous energy spectrum of the load signal in the step 5) is as follows: the Wigner-Ville distribution has a time-edge property, and is represented by a two-dimensional time-frequency real matrix, at each point in time, the integral along the frequency domain axis is equal to the instantaneous energy of the signal at the corresponding instant in time, as shown in equation (5). And then the corresponding energy of the load signal at each time point is calculated to form an instantaneous energy spectrum.
The step of setting a threshold for the instantaneous energy spectrum in the step 6) specifically includes: combining engineering requirements, using a genetic algorithm, taking an instantaneous energy spectrum threshold value as a design variable, taking a length compression ratio minimum min (L (y)) before and after load editing as an objective function, taking a damage ratio of a compressed signal to an original signal not lower than 97% and a statistical parameter error smaller than 15% as constraint conditions, carrying out threshold value optimizing calculation, and finding an optimal threshold value in an instantaneous energy spectrum numerical value interval [0, max P (n) ]. The cutoff threshold is closely related to the time history length of the compression load spectrum signal and the fatigue damage retention. The optimized mathematical model expression of the threshold is shown in formula (6).
Wherein,,
for the relative damage retention of the signals before and after editing, Δavr, Δku, and Δrms are the mean, kurtosis coefficient, and root mean square value variation, respectively. The pseudo damage value is calculated as shown in formula (7).
Wherein D represents total pseudo-injury, n i Number of cycles for a certain stress level; n (N) i For the number of cycles to failure at that stress level.
The step of splicing the reserved signal segments in the step 7) specifically includes: the instantaneous energy spectrum segments are corresponding to the time sequences in the original signals, and no intersection exists, so that the reserved segments in the original signals can be directly spliced to obtain compressed signals.
The step 8) includes: mean, root mean square value and kurtosis coefficient; the frequency domain features are represented as power spectral density curves and the amplitude domain features represent the through-level count distribution.
Claims (9)
1. The efficient compression editing method for the load spectrum of the automobile part based on Wigner-Ville transformation is characterized by comprising the following steps of:
1) Acquiring an original load signal;
2) Preprocessing an original load signal to obtain a preprocessed load signal;
3) Performing Hilbert transform on the preprocessed load signal to obtain an analysis signal;
4) And performing Wigner-Ville transformation on the analysis signal, and acquiring information of energy distribution along with time-frequency to obtain a two-dimensional time-frequency real number matrix and a Wigner-Ville spectrum.
5) Integrating the Wigner-Ville spectrum along a frequency axis at each time point of the two-dimensional time-frequency real number matrix to obtain an instantaneous energy spectrum of an original load signal;
6) Setting genetic algorithm parameters, determining an optimal threshold value of the instantaneous energy spectrum by utilizing the genetic algorithm, positioning the instantaneous energy spectrum fragments below the threshold value and time points of corresponding data according to the optimal threshold value, corresponding the time points to the time sequence of the original load signal, and deleting the original load signal fragments corresponding to the time points to obtain the reserved signal fragments.
7) Splicing the reserved signal segments to obtain a compression load signal;
8) Calculating the relative damage retention of the compression load signal and the original load signal and the error of the statistical parameter, and determining the frequency domain power spectral density and the amplitude domain level-passing count curve distribution of the compression load signal and the original load signal;
9) Determining whether the error of the relative injury retention and the statistical parameter meets a threshold requirement;
judging whether the distribution of the frequency domain power spectrum density main bodies of the load signals before and after compression has consistency;
judging whether the amplitude domain passing count distribution of the load signals before and after compression has consistency;
if the relative damage remaining quantity is lower than the threshold value, the error of the statistical parameter exceeds the threshold value, the frequency domain power spectrum density of the load signal before and after compression does not have consistency or the amplitude domain passing grade counting distribution curve trend of the load signal before and after compression does not have consistency, returning to the step 6), otherwise, entering the step 10);
10 Outputting a compression load signal.
2. The method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation according to claim 1, wherein the preprocessing of the original load signal comprises the following steps: filtering, deburring, drift correction and resampling.
3. The efficient compression editing method for the automobile part load spectrum based on the Wigner-Ville transformation according to claim 1, wherein the analysis signal z (t) after the Hilbert transformation is as follows:
z(t)＝x(t)+jH[x(t)] (1)
where t represents a time vector, x (t) is an original load signal, and z (t) is an analysis signal of x (t);
hilbert transform H [ x (t) ], as follows:
where PV represents the Cauchy principal value and τ is the integral variable.
4. The efficient compression editing method for the load spectrum of the automobile part based on the Wigner-Ville transformation of claim 1, wherein the Wigner-Ville transformed Wigner-Ville distribution W is characterized in that z (nT, ω) is as follows:
in which W is z (nT, ω) is a Wigner-Ville distribution, T, representing the index of the time vector and the frequency vector, respectively; l is the time data length, z, for Wigner-Ville transformation * (t) is the complex conjugate of the resolved signal z (t), z (nT+iT) z * (nT-iT) is the instantaneous phase of the resolved signal z (t)A closing function; i represents a time data sequence number;
the two-dimensional time-frequency real number matrix is as follows:
wherein TFR (t, f) is a two-dimensional time-frequency real number matrix, t is a time vector, f is a frequency, and a line vector a m ＝[a m1 ，a m2 ，…，a mn ]Corresponding to different time points, column vector a n ＝[a 1n ，a 2n ，…，a mn ] T Representing different frequency values; m and n are row and column numbers; the elements of the two-dimensional time-frequency real matrix represent the amplitude and phase angle information of the signal.
5. The method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation according to claim 1, wherein the instantaneous energy spectrum of the original load signal is as follows:
in the formula, |Z (t) 2 Is a transient energy spectrum; w (W) z (t, f) is the Wigner-Ville spectrum.
6. The method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transform according to claim 1, wherein the step of determining the optimal threshold value of the instantaneous energy spectrum by using a genetic algorithm comprises the steps of:
taking a threshold value in an instantaneous energy spectrum as a design variable, taking a minimum value min (y) of a length compression ratio before and after load compression editing as an objective function, and taking a constraint condition that the damage ratio of a compressed signal to an original signal is not lower than a and a statistical parameter error is smaller than b to establish a threshold value optimizing mathematical model; a. b is a preset error threshold;
performing threshold optimization calculation by using a threshold optimization mathematical model, and finding an optimal threshold value in an instantaneous energy spectrum numerical value interval [0, maxP (n) ];
the threshold optimizing mathematical model is as follows:
wherein Deltaavr, deltaku and Deltarms are respectively the mean value, kurtosis coefficient and root mean square value variation; l (y) is the length compression ratio before and after load compression editing; l (L) y For the length after load compression and editing, L 0 Compressing the length before editing for the load;
the relative damage reserved quantity of signals before and after editing; d (x) 0 (t))、d(x y (t)) is a pseudo-damage value of the signal before and after editing, respectively;
wherein the pseudo-impairment d (x) 0 (t))、d(x y (t)) is a theoretical value, the average stress correction is not carried out, the calculated damage value of each stress cycle is obtained, and the total pseudo damage of the load data is calculated as follows:
wherein D represents total pseudo-damage, n j Number of cycles for the j-th stress level; n (N) j For the number of cycles to failure at that stress level.
7. The method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation according to claim 1, wherein in the step 7), when the temporal energy spectrum segment corresponds to the time sequence in the original signal and no intersection exists, the reserved segment in the original signal can be directly spliced to obtain the compressed signal.
8. The method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation according to claim 1, wherein the statistical parameters comprise: mean, root mean square, and kurtosis coefficient.
9. The method for efficiently compressing and editing the load spectrum of the automobile part based on the Wigner-Ville transformation according to claim 1, wherein the frequency domain characteristic of the load signal is represented by a power spectrum density curve, and the amplitude domain characteristic is represented by a level-through count distribution.
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