CN116337492A - Vehicle-mounted vibration-based wheel non-circularization anti-interference detection method - Google Patents
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Abstract
The invention discloses a vehicle-mounted vibration-based wheel non-circularization anti-interference detection method, which comprises the following steps of: acquiring an axle box vibration acceleration signal and a wheel set rotating speed signal; carrying out equiangular resampling on the axle box vibration acceleration signal according to the wheel set rotating speed signal to obtain sampling data; calculating and calculating a weighted moving average according to the weight vector of the sampling data; carrying out frequency domain analysis processing on the weighted moving average value to obtain and judging whether the peak value exceeds a set value according to the frequency spectrum or the time frequency spectrum; if so, the periodic wheel non-circularization defect exists; otherwise, entering the next step; judging whether the peak value of the envelope spectrum or the spectrum during the envelope exceeds a set value; if yes, the local wheel non-circularization defect exists; otherwise, the wheel state is normal. The invention solves the problem of low-order resolution caused by cycle number loss in the traditional time synchronization average method; reducing the influence of abnormal impact; amplitude losses due to accumulated phase errors are avoided.
Description
Technical Field
The invention relates to the technical field of out-of-roundness detection, in particular to a vehicle-mounted vibration-based wheel non-roundness anti-interference detection method.
Background
The non-circularization problem of the wheel is the non-uniform abrasion and damage problem along the circumference and the longitudinal direction of the wheel. Comprising the following steps: periodic wear of the wheel circumference, i.e. the wheel polygon; there are also localized non-circularization problems typified by wheel flats, scratches, and the like. Severe wheel non-rounding can lead to severe vibration, noise, and structural safety problems, and the initial polygon, once formed, develops relatively quickly, so wheel non-rounding is a form of failure of high concern for railway operator doors.
For this reason, various means have been developed to examine it, which can be largely classified into trackside and car inspection technologies. The vehicle-mounted technology has been widely used in the bogie on-line monitoring system with the advantages of direct path, high signal-to-noise ratio, long-term tracking and the like. However, the current vehicle-mounted detection mainly aims at threshold clamping control under the condition of severe middle and late vibration. Considering that vibration and noise of non-circularization of the middle and late wheels can cause damage, the method has very important significance for on-line monitoring of the non-circularization of the wheels in an initial state.
In recent years, many scholars have studied various vibration-based methods of diagnosing non-circular wear of wheels. In terms of signal processing methods, empirical mode decomposition and its modification methods are widely used to consider the modulated nonlinear characteristics of the wheel response, including: EMD, ACMD, VMD methods, HHT, etc. In the aspect of quantitative identification of the non-circular wear of the wheel, the inertial integration method researches quantitative diagnosis of the non-circular wear of the wheel, but structural resonance which has great influence on the quantitative diagnosis is not considered.
As a direct acting object of wheel rail excitation, the diagnosis method based on axle box vibration acceleration is easy to be interfered by factors such as rail shortwave irregularity, wheel rotation speed fluctuation, structure self resonance and the like. In particular to rail wave grinding, which is a harmonic excitation on rails, the vibration characteristics of the rail wave grinding are very similar to the abrasion of the wheel polygon, so that misjudgment is very easy to occur with the wheel polygon. For the diagnosis of early wheel non-circular wear, the influence of these disturbing factors is more troublesome, which makes it difficult to accurately detect early wheel non-circular wear by the axle box vibration acceleration.
Disclosure of Invention
Aiming at the defects in the prior art, the vehicle-mounted vibration-based wheel non-circular anti-interference detection method provided by the invention solves the problems that the early wheel non-circular characteristic is weak, random noise is easy to submerge due to rail roughness, structural resonance and the like, interference frequency generated by track equal interval impact or other rotating equipment is easy to be mistakenly considered as wheel non-circular, fluctuation of rotating speed can cause non-linear modulation of wheel non-circular response, and frequency spectrum leakage and blurring in frequency spectrum characteristics are caused.
In order to achieve the aim of the invention, the invention adopts the following technical scheme: a vehicle-mounted vibration-based wheel non-rounding anti-interference detection method comprises the following steps:
s1, acquiring an axle box vibration acceleration signal x (t) n ) And wheel set rotational speed signal
S2, according to the rotating speed signal of the wheel set
For axle box vibration acceleration signal x (t) n ) Carrying out equal-angle resampling to obtain sampling data;
s3, calculating a weight vector of the sampling data;
s4, calculating a weighted moving average value according to the weight vector;
s5, carrying out frequency domain analysis processing on the weighted moving average to obtain a frequency spectrum or a time frequency spectrum containing the periodical non-circular order and amplitude characteristics of the wheel and an envelope spectrum or an envelope time frequency spectrum containing the local non-circular abrasion information of the wheel;
s6, judging whether the peak value of the frequency spectrum containing the periodical non-circular order and amplitude characteristics of the wheel exceeds a set value; if so, a periodical wheel non-circularization defect exists, namely a wheel polygon defect, otherwise, the step S7 is entered;
s7, judging whether the peak value of the envelope spectrum or the spectrum at the time of envelope containing the local non-circular abrasion information of the wheel exceeds a set value; if so, the local wheel is not round, namely flat and scratched; otherwise, the wheel state is normal.
Further, the specific manner of step S2 is as follows:
according to the formula:
n＝0,1,…,N-1,l＝0,1,…,L-1
θ l ＝lφ w (t N-1 )/(L-1)
obtaining axle box vibration acceleration x (theta) after equal angle resampling l ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein θ l A uniform discrete sample representing an angular variable; l is the total number of angle samples; x (t) n ) The vibration acceleration signal is an axle box vibration acceleration signal; n is N equal-time sampling points in total; t is t n For equal sampling time intervals; l is the first angle sample; interpolate (·) is the interpolation function; phi (phi) w (t N-1 ) As a function of phase.
Further, the specific implementation manner of step S3 is as follows:
s3-1, according to the formula:
b ij ＝|x(θ+2πi)-x(θ+2πk)|
obtaining element B of the ith row and jth column of the bias matrix B ij The method comprises the steps of carrying out a first treatment on the surface of the Where x (θ+2pi i) is the ith data point in the moving window of equiangular resampling in one sampling interval time, x (θ+2pi i) =x (θ) l ) The method comprises the steps of carrying out a first treatment on the surface of the x (θ+2πk) is the kth data point in the same window as x (θ+2πi), where i+.k; θ represents the coordinates of the equiangular resampled data points; pi represents the circumference ratio;
s3-2, according to the formula:
obtaining the element of the c-th row and the d-th column of the weight matrix WElement w cd ；
S3-3, obtaining a corresponding weight vector p (m) according to the weight matrix W.
Further, the specific implementation manner of step S4 is as follows:
according to the formula:
obtaining a weighted moving average of sampling points within a sampling interval time
Wherein Q is the number of turns of the wheel, M w The number of turns of the wheel rotation of the movable window which is resampled at equal angles in the time of one sampling interval; x (θ+2πm) is the mth data point in the moving window of equiangular resampling for one sampling interval time.
Further, the specific implementation manner of step S5 is as follows:
s5-1, according to the formula:
obtaining a spectral FFT (f) comprising cyclic non-rounded order and amplitude features of the wheel; or (b)
According to the formula:
obtaining a time spectrum STFT (θ, f) comprising periodic non-rounded order and magnitude features of the wheel; wherein e is a natural constant;
a weighted moving average of the data in the angular domain; />
f represents frequency; j represents a unit imaginary number;
s5-2, according to the formula:
obtaining an analytic signal
Wherein H [. Cndot.]Representing a Hilbert transform;
s5-3, according to the formula:
obtaining the envelope
S5-4, according to the formula:
obtaining an envelope spectrum FFT (f) containing local non-circular abrasion information of the wheel;
s5-5, according to the formula:
an envelope time spectrum STFT (θ, f) containing the wheel local non-circular wear information is obtained.
The beneficial effects of the invention are as follows: an improved method of moving average is introduced, so that the problem of low order resolution caused by cycle number loss is solved, and most of cycle numbers are reserved; the weighted average is introduced, so that the part of the average result related to the non-circularization of the wheel is improved, and the influence of abnormal impact is reduced.
Drawings
FIG. 1 is a flow chart of the present invention;
FIG. 2 is a schematic block diagram of the present invention;
FIG. 3 is a schematic illustration of verification using vehicle dynamics simulation data; wherein, fig. 3 (a) is the data before the filtering of the present invention; FIG. 3 (b) is a STFT time-frequency representation of axlebox acceleration; FIG. 3 (c) is a time-frequency representation of the STFT method; FIG. 3 (d) is a time-frequency representation of a conventional order tracking method (COT); FIG. 3 (e) is a time-frequency representation of the WTMA method;
FIG. 4 is a schematic illustration of the present invention in an orbital wave mill; wherein fig. 4 (a) is data prior to WTSMA filtration; FIG. 4 (b) shows the measured roughness of the wheels and rails; FIG. 4 (c) shows data obtained by using the STFT method; FIG. 4 (d) is data obtained after using the order tracking method; FIG. 4 (e) is a schematic diagram of the time-frequency expression after WTMA is used; fig. 4 (f) is a spectrum plot after WTSMA use.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate 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 all the inventions which make use of the inventive concept are protected by the spirit and scope of the present invention as defined and defined in the appended claims to those skilled in the art.
As shown in fig. 1, the vehicle-mounted vibration-based wheel non-circular anti-interference detection method comprises the following steps:
s1, acquiring an axle box vibration acceleration signal x (t) n ) And wheel set rotational speed signal
S2, according to the rotating speed signal of the wheel set
For axle box vibration acceleration signal x (t) n ) Carrying out equal-angle resampling to obtain sampling data;
s3, calculating a weight vector of the sampling data;
s4, calculating a weighted moving average value according to the weight vector;
s5, carrying out frequency domain analysis processing on the weighted moving average to obtain a frequency spectrum or a time frequency spectrum containing the periodical non-circular order and amplitude characteristics of the wheel and an envelope spectrum or an envelope time frequency spectrum containing the local non-circular abrasion information of the wheel;
s6, judging whether the peak value of the frequency spectrum containing the periodical non-circular order and amplitude characteristics of the wheel exceeds a set value; if so, a periodical wheel non-circularization defect exists, namely a wheel polygon defect, otherwise, the step S7 is entered;
s7, judging whether the peak value of the envelope spectrum or the spectrum at the time of envelope containing the local non-circular abrasion information of the wheel exceeds a set value; if so, the local wheel is not round, namely flat and scratched; otherwise, the wheel state is normal.
The specific manner of step S2 is as follows:
according to the formula:
n＝0,1,…,N-1,l＝0,1,…,L-1
θ l ＝lφ w (t N-1 )/(L-1)
obtaining axle box vibration acceleration x (theta) after equal angle resampling l ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein θ l A uniform discrete sample representing an angular variable; l is the total number of angle samples; x (t) n ) The vibration acceleration signal is an axle box vibration acceleration signal; n is N equal-time sampling points in total; t is t n For equal sampling time intervals; l is the first angle sample; interpolate (·) is the interpolation function; phi (phi) w (t N-1 ) As a function of phase.
The specific implementation manner of the step S3 is as follows:
s3-1, according to the formula:
b ij ＝|x(θ+2πi)-x(θ+2πk)|
obtaining element B of the ith row and jth column of the bias matrix B ij The method comprises the steps of carrying out a first treatment on the surface of the Where x (θ+2pi i) is the ith data point in the moving window of equiangular resampling in one sampling interval time, x (θ+2pi i) =x (θ) l ) The method comprises the steps of carrying out a first treatment on the surface of the x (θ+2πk) is the kth data point in the same window as x (θ+2πi), where i+.k; θ represents the coordinates of the equiangular resampled data points; pi represents the circumference ratio;
s3-2, according to the formula:
obtaining the element W of the c-th row and the d-th column of the weight matrix W cd ；
S3-3, obtaining a corresponding weight vector p (m) according to the weight matrix W.
The specific implementation manner of the step S4 is as follows:
according to the formula:
obtaining a weighted moving average of sampling points within a sampling interval time
Wherein Q is the number of turns of the wheel, M w The number of turns of the wheel rotation of the movable window which is resampled at equal angles in the time of one sampling interval; x (θ+2πm) is the mth data point in the moving window of equiangular resampling for one sampling interval time.
The specific implementation manner of the step S5 is as follows:
s5-1, according to the formula:
obtaining a spectral FFT (f) comprising cyclic non-rounded order and amplitude features of the wheel; or (b)
According to the formula:
obtaining a time spectrum STFT (θ, f) comprising periodic non-rounded order and magnitude features of the wheel; wherein e is a natural constant;
a weighted moving average of the data in the angular domain; />
f represents frequency; j represents a unit imaginary number;
s5-2, according to the formula:
obtaining an analytic signal
Wherein H [. Cndot.]Representing a Hilbert transform;
s5-3, according to the formula:
obtaining the envelope
S5-4, according to the formula:
obtaining an envelope spectrum FFT (f) containing local non-circular abrasion information of the wheel;
s5-5, according to the formula:
an envelope time spectrum STFT (θ, f) containing the wheel local non-circular wear information is obtained.
As shown in fig. 2, the average of the periods of the measurement signal is calculated, and the signal is not averaged from start to end. Assigning a weight to the degree of outliers of each data point for each revolution compared to other synchronized data points for each revolution; the greater the degree of outliers, the lower the weight.
In one embodiment of the present invention, as shown in fig. 3, fig. 3 (a) is data before being filtered by the present invention; FIG. 3 (b) is a STFT time-frequency representation of axlebox acceleration; the outliers are given lower weight to the abnormal impact, reducing its impact profile; the time-frequency representation of the WTSMA method of the invention is shown in fig. 3 (e), which can be seen to have significant advantages over the time-frequency representation of the STFT method as shown in fig. 3 (c) and the time-frequency representation of the conventional order tracking method (COT) as shown in fig. 3 (d). COT is a classical technique, where the order analysis is performed directly after equiangular resampling, without the need for averaging filtering. The time-frequency expression shows that in the time-frequency expression of STFT and COT, interference components such as impact at the track connection, sleeper spacing, structural resonance and the like are obvious, and the interference components can interfere with the diagnosis of non-circularity of the wheels. In the present invention, these components are suppressed to a varying degree, and only the components associated with non-rounding of the wheel are clearly visible. Wherein Angle represents an Angle; representation Inherent vibration represents natural vibration; gear shifting means gear engagement; frequency represents Frequency; rail welding joint rail welded joints; the Sleeper pass represents the Sleeper pass; rouchness level represents the Roughness level; WPW-related represents polygon dependent; order represents the order; ABA represents axle box vibration acceleration.
From the above, the periodic asynchronous component, the structural resonance component, the random track irregularity and other interferences can be effectively reduced by using the WTSMA method, so as to improve the robustness and the discrimination capability of the non-circular characteristic of the wheel under diversified interferences.
In ABA-based non-round identification frames for wheels, rail waving wear is considered a special defect and is easily misdiagnosed as a wheel polygon. To verify WTSMA resistance to rail waviness, we analyzed ABA data with such rail defects, as shown in fig. 4. The results indicate that in STFT and COT, rail wave milling is likely to be misdiagnosed as wheel non-circularity, as shown in fig. 4; wherein fig. 4 (a) is data prior to WTSMA filtration; FIG. 4 (b) shows the measured roughness of the wheels and rails; FIG. 4 (c) shows data obtained by using the STFT method; FIG. 4 (d) is data obtained after using the order tracking method; FIG. 4 (e) is a schematic diagram of the time-frequency expression after WTMA is used; fig. 4 (f) is a spectrum plot after WTSMA use. The reaction of the track wave mill is obvious and is similar to the non-circular characteristic of the wheel; both are observed as horizontal bright lines in the time-frequency representation. As shown in fig. 4 (e) and 4 (f), the WTSMA method effectively suppresses the track wave-grinding component. Rail grinding represents Rail wave grinding; wheel roughness represents wheel roughness; wheel roughness represents rail roughness.
The invention introduces an improved method of moving average, is not easy to be influenced by typical interference, is not easy to generate misdiagnosis, and solves the problem of low order resolution caused by cycle number loss; the influence of abnormal impact is reduced, and the steel rail wave mill interference is inhibited to a certain extent; calculating an accurate speed from the gear mesh vibration using an automatic algorithm to avoid amplitude loss due to accumulated phase errors; the method is suitable for non-circular anti-interference detection of various types of wheels including wheel polygons, wheel scratches, flat scars and the like, and has strong adaptability.
Claims (5)
1. The vehicle-mounted vibration-based wheel non-rounding anti-interference detection method is characterized by comprising the following steps of:
s1, acquiring an axle box vibration acceleration signal x (t) n ) And wheel set rotational speed signal
S2, according to the rotating speed signal of the wheel set
For axle box vibration acceleration signal x (t) n ) Carrying out equal-angle resampling to obtain sampling data;
s3, calculating a weight vector of the sampling data;
s4, calculating a weighted moving average value according to the weight vector;
s5, carrying out frequency domain analysis processing on the weighted moving average to obtain a frequency spectrum or a time frequency spectrum containing the periodical non-circular order and amplitude characteristics of the wheel and an envelope spectrum or an envelope time frequency spectrum containing the local non-circular abrasion information of the wheel;
s6, judging whether the peak value of the frequency spectrum containing the periodical non-circular order and amplitude characteristics of the wheel exceeds a set value; if so, a periodical wheel non-circularization defect exists, namely a wheel polygon defect, otherwise, the step S7 is entered;
s7, judging whether the peak value of the envelope spectrum or the spectrum at the time of envelope containing the local non-circular abrasion information of the wheel exceeds a set value; if so, the local wheel is not round, namely flat and scratched; otherwise, the wheel state is normal.
2. The vehicle-mounted vibration-based wheel non-circular anti-interference detection method according to claim 1, wherein the specific mode of the step S2 is as follows:
according to the formula:
θ l ＝lφ w (t N-1 )/(L-1)
obtaining axle box vibration acceleration x (theta) after equal angle resampling l ) The method comprises the steps of carrying out a first treatment on the surface of the Wherein θ l A uniform discrete sample representing an angular variable; l is the total number of angle samples; x (t) n ) The vibration acceleration signal is an axle box vibration acceleration signal; n is N equal-time sampling points in total; t is t n For equal sampling time intervals; l is the first angle sample; interpolate (·) is the interpolation function; phi (phi) w (t N-1 ) As a function of phase.
3. The vehicle-mounted vibration-based wheel non-circular anti-interference detection method according to claim 2, wherein the specific implementation manner of the step S3 is as follows:
s3-1, according to the formula:
b ij ＝|x(θ+2πi)-x(θ+2πk)|
obtaining element B of the ith row and jth column of the bias matrix B ij The method comprises the steps of carrying out a first treatment on the surface of the Where x (θ+2pi i) is the ith data point in the moving window of equiangular resampling in one sampling interval time, x (θ+2pi i) =x (θ) l ) The method comprises the steps of carrying out a first treatment on the surface of the x (θ+2πk) is the kth data point in the same window as x (θ+2πi), where i+.k; θ represents the coordinates of the equiangular resampled data points; pi represents the circumference ratio;
s3-2, according to the formula:
obtaining the element W of the c-th row and the d-th column of the weight matrix W cd ；
S3-3, obtaining a corresponding weight vector p (m) according to the weight matrix W.
4. The vehicle-mounted vibration-based wheel non-circular anti-interference detection method according to claim 3, wherein the specific implementation manner of the step S4 is as follows:
according to the formula:
obtaining a weighted moving average of sampling points within a sampling interval time
Wherein Q is the number of turns of the wheel, M w Moving window vehicle for equal-angle resampling in one sampling interval timeThe number of turns of the cycle; x (θ+2πm) is the mth data point in the moving window of equiangular resampling for one sampling interval time.
5. The vehicle-mounted vibration-based wheel non-circular anti-interference detection method according to claim 4, wherein the specific implementation manner of the step S5 is as follows:
s5-1, according to the formula:
obtaining a spectral FFT (f) comprising cyclic non-rounded order and amplitude features of the wheel; or (b)
According to the formula:
obtaining a time spectrum STFT (θ, f) comprising periodic non-rounded order and magnitude features of the wheel; wherein e is a natural constant;
a weighted moving average of the data in the angular domain; />
f represents frequency; j represents a unit imaginary number;
s5-2, according to the formula:
obtaining an analytic signal
Wherein H [. Cndot.]Representing a Hilbert transform;
s5-3, according to the formula:
obtaining the envelope
S5-4, according to the formula:
obtaining an envelope spectrum FFT (f) containing local non-circular abrasion information of the wheel;
s5-5, according to the formula:
an envelope time spectrum STFT (θ, f) containing the wheel local non-circular wear information is obtained.
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