CN115790499A - Magnetic suspension vehicle, wheel polygon detection and active suppression method and related system - Google Patents
Magnetic suspension vehicle, wheel polygon detection and active suppression method and related system Download PDFInfo
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- CN115790499A CN115790499A CN202211526444.XA CN202211526444A CN115790499A CN 115790499 A CN115790499 A CN 115790499A CN 202211526444 A CN202211526444 A CN 202211526444A CN 115790499 A CN115790499 A CN 115790499A
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Abstract
The invention discloses a magnetic suspension vehicle, a wheel polygon detection and active inhibition method and a related system, which are used for collecting data of a wheel out-of-roundness signal, a vibration acceleration signal and a noise signal; importing the wheel out-of-roundness signal into a collaborative simulation model, and outputting a simulated vibration acceleration signal and a simulated noise signal; constructing a data set S by using the simulated vibration acceleration signal and the simulated noise signal; randomly selecting a part of samples in a data set S as a training sample set, taking the training sample set as the input of a support vector machine, training the support vector machine, and establishing a mapping model of polygonal wavelength and amplitude relative to vibration noise signal characteristics; inputting a sample to be detected into a mapping model to obtain a polygon amplitude prediction result; and if the polygon amplitude prediction result exceeds a critical threshold value, judging that the wheel polygon problem exists. The detection method can truly reflect the wheel polygon problem, simplifies the detection flow and improves the detection precision.
Description
Technical Field
The invention relates to the technical field of rail transit, in particular to a magnetic levitation vehicle, a wheel polygon detection and active inhibition method and a related system.
Background
Along with the improvement of the running speed and the axle load of the train, the problem of wheel polygon appears for many times in the running process of the electric locomotive, abnormal vibration and noise of the locomotive are caused, the riding comfort is seriously influenced, and further development of the condition of the wheel polygon can cause the crack of a vehicle part, so that the potential safety hazard of driving noise is greatly caused. Currently, the improved control of wheel polygons is mainly a passive measure, including: turning wheel cycle shortening, turning wheel quality improvement, variable speed operation, addition of grinding, etc., but these passive measures are only a posterior remedy and wheel polygon problems continue to plague locomotive operation. Therefore, the forward design idea is adopted to prevent and control the appearance of the wheel polygon in advance, and the method has important significance for improving the vehicle running quality.
In the prior art, a method for diagnosing the wheel polygon through the order and the depth of the wheel polygon is provided, but the method is complex in implementation process and limited in detection precision, and the prior art does not provide a scheme for preventing and controlling the wheel polygon problem in advance.
Disclosure of Invention
The invention aims to solve the technical problems that aiming at the defects of the prior art, the invention provides a magnetic suspension vehicle, a wheel polygon detection and active inhibition method and a related system, which simplify the wheel polygon detection process and improve the detection precision; and realizing active suppression of the wheel polygon.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows: a wheel polygon detection method comprises the following steps:
s1, acquiring data of a wheel out-of-roundness signal, a vibration acceleration signal and a noise signal; importing the wheel out-of-roundness signal into a collaborative simulation model, and outputting a simulated vibration acceleration signal and a simulated noise signal; constructing a data set S by using the simulated vibration acceleration signal and the simulated noise signal;
s2, randomly selecting a part of samples in the data set S as a training sample set, using the training sample set as the input of a support vector machine, training the support vector machine, and establishing a mapping model of polygonal wavelength and amplitude relative to vibration noise signal characteristics;
s3, inputting the sample to be detected into a mapping model to obtain a polygon amplitude prediction result; and if the polygon amplitude prediction result exceeds a critical threshold value, determining that the wheel polygon problem exists.
The method utilizes the wheel out-of-roundness signal to simulate the vibration acceleration signal and the noise signal, utilizes the simulated signal to establish a data set, divides partial samples from the data set to train a support vector machine to obtain a mapping model, and utilizes the mapping model to detect the wheel polygon. Because the real out-of-roundness signal is used for indirectly generating the data set, the detection method can truly reflect the problem of the wheel polygon, simplifies the detection process and improves the detection precision.
In the present invention, in order to minimize the simulation result and the test error and ensure that the simulation model can truly simulate the line operation condition to the maximum extent, after step S2 and before step S3, the method further comprises:
s21, taking the rest samples in the data set S as samples to be detected, and taking the samples to be detected as the input of the mapping model to obtain a prediction result corresponding to the samples to be detected;
s22, comparing the deviation between the prediction result corresponding to the training sample set and the prediction result corresponding to the sample to be detected, and if the deviation is smaller than a set error, entering the step S3; otherwise, the procedure returns to step S2.
In step S2, the vibration noise signal characteristics include a waveform index, a peak index, a pulse index, a margin index, a kurtosis index of the vibration acceleration signal, and a time domain average value, a time domain median, a time domain standard deviation, and a dominant frequency of the noise signal.
In order to facilitate the use of the detection result obtained by the detection method on the vehicle and further facilitate the prevention of the wheel polygon problem in advance, the method of the invention further comprises the following steps:
and S4, importing the mapping model into an active controller.
As an inventive concept, the present invention also provides a wheel polygon detection system comprising a memory, a processor, and a computer program stored on the memory; the processor executes the computer program to implement the steps of the above-mentioned detection method of the present invention.
As an inventive concept, the present invention also provides a wheel polygon active suppression method, which includes:
a1, obtaining the amplitude of a wheel polygon, and entering the step A2 if the amplitude exceeds a critical threshold;
a2, extracting collected vibration acceleration signals and noise signal data every time Tw, and predicting time domain signal waveforms within the time range of Ty1+ Ty2+ Tw by using the vibration acceleration signals and the noise signal data; wherein Ty1 is the time delay of acquisition and fault diagnosis; ty2 is delay time for applying active control response and generating active excitation;
a3, generating opposite-phase, equal-amplitude and equal-frequency active control excitation in a Ty1+ Ty2+ Tw time range;
a4, applying active control excitation to the magnetic suspension wheel set system until the polygonal amplitude is smaller than a critical threshold value;
wherein the amplitude of the wheel polygon and the time domain signal waveform are obtained according to the above method of the present invention.
The invention provides a polygon active control method, which applies active control excitation to offset superposed polygon vibration excitation, actively cuts off polygon induction conditions from an excitation source, blocks further development of wheel polygons, realizes overall control of wheel polygons, reduces maintenance cost, reduces wheel rail vibration and noise, and improves vehicle operation quality.
In order to collect signals conveniently, the vibration acceleration signals are collected by a vibration acceleration sensor and a wireless acceleration sensor, the vibration acceleration sensor is arranged at two ends of an axle stator and close to an electromagnet, and the wireless acceleration sensor is arranged on a wheel rotor.
As an inventive concept, the present invention also provides a wheel polygon active suppression system, comprising a memory, a processor, and a computer program stored on the memory; wherein the processor executes the computer program to implement the steps of the active suppression method of the present invention.
The invention further provides a rail transit vehicle which adopts the wheel polygon detection system and/or the active suppression system.
Compared with the prior art, the invention has the following beneficial effects:
1) According to the invention, the real out-of-roundness signal is used for indirectly generating the data set, so that the problem of the polygon of the wheel can be truly reflected, the detection flow is simplified, and the detection precision is improved;
2) The invention provides a polygonal active control method, which is used for applying active control excitation to offset superposed polygonal vibration excitation, actively cutting off polygonal induction conditions from an excitation source, blocking further development of wheel polygons, realizing comprehensive control of wheel polygons, reducing maintenance cost, reducing wheel rail vibration and noise and improving vehicle running quality.
Drawings
FIG. 1 is a flow chart of a wheel polygon detection and active suppression method according to an embodiment of the present invention;
fig. 2 is a schematic diagram of active wheel polygon suppression according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
In this document, the terms "comprise", "include" and other similar words are intended to denote logical interrelationships, but are not to be construed as representing spatial structural relationships. For example, "a includes B" is intended to mean that logically B belongs to a, and not that spatially B is located inside a. Furthermore, the terms "comprising," "including," and other similar words are to be construed as open-ended, rather than closed-ended. For example, "a includes B" is intended to mean that B belongs to a, but B does not necessarily constitute all of a, and a may also include other elements such as C, D, E, and the like.
The method of the embodiment of the present invention can be used on the wheel set system disclosed in CN114312129A and the wheel set systems of the rest magnetic levitation vehicles.
Example 1
As shown in fig. 1, the present embodiment provides a wheel polygon detection method, which includes the following steps:
step S1: a large amount of sample data of wheel out-of-roundness, vibration acceleration and noise signals are collected on a real operation line in advance.
In this embodiment, the testing instrument is attached to the wheel, then lets the wheel rotate a round, can measure the circumferencial direction out-of-roundness of wheel, and the vehicle is static during the test, needs the jack to jack up the axle box and makes the wheel unsettled can the free rotation.
Step S2: and establishing a multidisciplinary collaborative simulation model based on multi-body dynamics software and noise software.
In this embodiment, the multidisciplinary collaborative simulation model may use a multi-body dynamics software simpack to perform dynamics simulation (output vibration acceleration data) and a noise software vone to perform noise simulation (output noise data).
And step S3: the method comprises the steps of introducing a wheel out-of-roundness signal acquired by a line into a model, outputting a vibration acceleration signal and a noise signal in a simulation mode, comparing and verifying the noise signal and the vibration acceleration signal which are output in the simulation mode with a noise signal and a vibration acceleration signal which are tested (acquired) in a test mode, and correcting the simulation model to enable a simulation result and a test error to be minimum, so that the simulation model can be used for truly simulating the line operation condition to the maximum extent.
In the embodiment, when the simulation model is corrected, a least square method is adopted, and the simulation model is continuously corrected, so that the error between the simulation result and the experimental result is controlled to a reasonable range (refer to Wanghong mountain, zhang xing, yang Shuying and the like, and the asynchronous motor vector control simulation based on the least square method online parameter identification is [ J ]. The university of fertile industry, school news of Nature, 2009,32 (4): 5.).
Step S5: preprocessing and characteristic extraction are carried out on a vibration acceleration and noise signal data set S output by simulation, and waveform indexes, peak indexes, pulse indexes, margin indexes and kurtosis indexes of vertical vibration acceleration a are extracted through characteristics; and extracting the time domain average value, the time domain median value, the time domain standard deviation and the main frequency of the noise signal y. Wherein the waveform index
Peak index K 2 ＝a max /a rms Pulse index
Margin index K 4 ＝a max /a r The kurtosis index
In the above formula, the average value
Root mean square value
Maximum value a max ＝max(a i ) Root mean square amplitude
N is the vibration acceleration data length, a i Vertical vibration acceleration data corresponding to the ith data point; noise signal characteristic parameterTime domain average of
Time domain standard deviation
Fourier transform of noise signal y
y i (t) represents a noise signal corresponding to the ith data point at time t, and 6 main frequencies in the Fourier transform signal F (w) correspond to K 7 ～K 13 。
Randomly selecting 80% of samples in a data set S as training samples, and establishing a polygon wavelength lambda and an amplitude A and a vibration noise signal characteristic K based on a Support Vector Machine (SVM) algorithm 1 ～K 13 λ = p (K) as a mapping model of (c) 1 ,…,K 5 ,…,K 13 )，A＝g(K 1 ,…,K 5 ,…,K 13 ) (mapping model establishment procedure reference: [1]Luwencleng, cheng chan mussel, ye cheng zhou, et al]Computer and application chemistry, 2002,19 (6): 6), the mapping model is a fitted curve function.
S6, using the residual 20% of samples in the data set as samples to be detected, and calculating the polygonal wavelength and the amplitude of the samples to be detected according to the mapping model to calculate the prediction result lambda 2 ，A 2 Calculating the polygonal wavelength and amplitude lambda of the sample point to be detected 1 ，A 1 If the deviation is smaller than the set error, the mapping model is feasible, otherwise, the mapping model needs to be corrected.
Through the process, a real and reliable wheel polygon self-learning diagnosis model is established; and inputting the intelligent diagnosis model of the wheel polygon into an electromagnetic control module of the magnetic levitation vehicle, and bringing the intelligent diagnosis model of the wheel polygon into the active controller of the wheel polygon.
It should be noted that step S6 is optional, that is, if the mapping model obtained in step S5 meets the requirement, step S6 may not be executed again, and the wheel polygon may be identified by directly using the mapping model obtained in step S5.
Example 2
The embodiment provides a method for actively suppressing a wheel polygon, which comprises the following steps:
step T1: arranging vibration acceleration sensors and noise sensor noise microphones near electromagnets at two ends of a magnetic suspension inner ring axle stator, mounting wireless acceleration sensors and wheel speed sensors on an outer ring wheel rotor, and outputting acquired speed signals, vibration acceleration signals and noise signals to a signal acquisition module in real time;
step T2: the signal acquisition module 12 transmits the speed signal, the vibration acceleration and the noise signal acquired in real time to a wheel polygon active controller in the electromagnetic control module 13;
and step T3: the wheel polygon active controller calls a wheel polygon self-learning diagnosis model to quickly identify the wavelength, amplitude and frequency characteristics of a polygon;
and step T4: when the amplitude of the wheel polygon exceeds a critical threshold value, the controller sends a polygon active control instruction;
and step T5: the time delay Ty1 of the process of collecting and diagnosing faults of the statistical analysis system, the time delay Ty2 of the active control response exerted by the system and the active excitation time delay generated by the system are counted, and the length Tw of a control time window is set; in the embodiment, because the process of acquiring data in real time and performing fault diagnosis has time delay, if active control excitation is directly applied according to the characteristics of the acquired data during active control, the control error is overlarge due to time delay, and therefore, related delay time and a time window are set;
and step T6: after the controller receives the polygon active control instruction, the system extracts the amplitude, frequency and phase characteristics of the acquired polygon vibration and noise time domain signals every Tw time, and predicts the time domain signal waveform in the subsequent Ty1+ Ty2+ Tw time range of the signals based on the machine self-learning according to the polygon wavelength, amplitude and frequency characteristics diagnosed in the step T3;
step T7: the controller quickly generates active control excitation with opposite phase, equal amplitude and equal frequency within a Ty1+ Ty2+ Tw time range;
step T8: adjusting the input current of a polygonal controller, applying active control excitation to a magnetic suspension wheel pair system to realize offset superposition of polygonal vibration (as shown in figure 2), actively cutting off the polygonal induction conditions from the source and blocking further development of the wheel polygon;
and step T9: and the polygon controller tracks the polygon active control effect in real time, adaptively adjusts the control logic according to the effect, and stops active control until the monitoring amplitude control of the polygon is smaller than a critical threshold value, so that accurate active control of the wheel polygon is realized.
Example 3
Embodiment 3 of the present invention provides a wheel polygon detection system corresponding to embodiment 1, where the detection system of this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program on the memory to implement the steps of the method of embodiment 1 described above.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory, such as at least one disk Memory.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 4
Embodiment 4 of the present invention provides an active suppression system for a wheel polygon corresponding to embodiment 2, where the active suppression system of this embodiment includes a memory, a processor, and a computer program stored in the memory; the processor executes the computer program on the memory to implement the steps of the method of embodiment 2 described above.
In some implementations, the Memory may be a high-speed Random Access Memory (RAM), and may also include a non-volatile Memory, such as at least one disk Memory.
In other implementations, the processor may be various general-purpose processors such as a Central Processing Unit (CPU), a Digital Signal Processor (DSP), and the like, and is not limited herein.
Example 5
Embodiment 5 of the present invention provides a rail transit vehicle corresponding to embodiment 3 or 4, where the rail transit vehicle adopts the system of embodiment 3 and/or embodiment 4.
While the preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. Therefore, it is intended that the appended claims be interpreted as including preferred embodiments and all alterations and modifications as fall within the scope of the application.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present application without departing from the spirit and scope of the application. Thus, if such modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is intended to include such modifications and variations as well.
Claims (9)
1. A wheel polygon detection method is characterized by comprising the following steps:
s1, acquiring data of a wheel out-of-roundness signal, a vibration acceleration signal and a noise signal; importing the wheel out-of-roundness signal into a collaborative simulation model, and outputting a simulated vibration acceleration signal and a simulated noise signal; constructing a data set S by using the simulated vibration acceleration signal and the simulated noise signal;
s2, randomly selecting a part of samples in the data set S as a training sample set, using the training sample set as the input of a support vector machine, training the support vector machine, and establishing a mapping model of polygonal wavelength and amplitude relative to vibration noise signal characteristics;
s3, inputting the sample to be detected into a mapping model to obtain a polygon amplitude prediction result; and if the polygon amplitude prediction result exceeds a critical threshold value, judging that the wheel polygon problem exists.
2. The active suppression method for wheel polygon according to claim 1, after step S2 and before step S3, further comprising:
s21, taking the rest samples in the data set S as samples to be detected, and taking the samples to be detected as the input of the mapping model to obtain a prediction result corresponding to the samples to be detected;
s22, comparing the deviation between the prediction result corresponding to the training sample set and the prediction result corresponding to the sample to be detected, and if the deviation is smaller than a set error, entering the step S3; otherwise, return to step S2.
3. The wheel polygon detection method according to claim 1, wherein in step S2, the vibration noise signal characteristics include a waveform index, a peak index, a pulse index, a margin index, a kurtosis index of the vibration acceleration signal, and a time-domain average value, a time-domain median value, a time-domain standard deviation, and a dominant frequency of the noise signal.
4. A wheel polygon detection method according to any one of claims 1 to 3, further comprising:
and S4, importing the mapping model into an active controller.
5. A wheel polygon detection system comprising a memory, a processor, and a computer program stored on the memory; characterized in that said processor executes said computer program to implement the steps of the method according to one of claims 1 to 4.
6. A method for active suppression of wheel polygon, comprising:
a1, obtaining the amplitude of a wheel polygon, and if the amplitude exceeds a critical threshold, entering a step A2;
a2, extracting collected vibration acceleration signal and noise signal data every time Tw, and predicting a time domain signal waveform within a continuous Ty1+ Ty2+ Tw time range by using the vibration acceleration signal and the noise signal data; wherein Ty1 is the time delay of acquisition and fault diagnosis; ty2 is delay time for applying active control response and generating active excitation;
a3, generating active control excitation of an opposite phase, an equal amplitude and an equal frequency within a Ty1+ Ty2+ Tw time range;
a4, applying active control excitation to a magnetic suspension wheel set system until the polygonal amplitude is smaller than a critical threshold value; wherein the amplitude of the wheel polygon and the time domain signal waveform are obtained according to the method of any one of claims 1 to 4.
7. The active suppression method for wheel polygon according to claim 6, wherein the vibration acceleration signal is collected by a vibration acceleration sensor and a wireless acceleration sensor, the vibration acceleration sensor is arranged at two ends of the axle stator and close to the electromagnet, and the wireless acceleration sensor is mounted on the wheel rotor.
8. A wheel polygon active suppression system comprising a memory, a processor, and a computer program stored on the memory; characterized in that the processor executes the computer program to implement the steps of the method of claim 6.
9. A rail transit vehicle employing the wheel polygon detection system of claim 5 and/or employing the active suppression system of claim 8.
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