CN104990717B - A kind of magnetic-levitation train sensor signal processing method - Google Patents
A kind of magnetic-levitation train sensor signal processing method Download PDFInfo
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- CN104990717B CN104990717B CN201510444147.4A CN201510444147A CN104990717B CN 104990717 B CN104990717 B CN 104990717B CN 201510444147 A CN201510444147 A CN 201510444147A CN 104990717 B CN104990717 B CN 104990717B
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Abstract
A kind of magnetic-levitation train sensor signal processing method, the sampled value of this method continuous some sampling instants including acquisition sensor current time sampled value and before current time, is used as constraints to build virtual flexible beam sampled value, forms optimum fit curve；The sampled value of next sampling instant is predicted according to the extended position of virtual flexible beam；Next sampling instant sampled value is obtained, if the sampled value of next sampling instant and the difference of predicted value exceed the threshold value of setting, then it is assumed that spurious signal occur in sensor, it is necessary to be rejected to it, and sampled value is replaced with predicted value, it is on the contrary then retain sampled value.Effectively the burr that mixes in sensor signal, outlier can be rejected using this method, i.e., the spurious signal to sensor is corrected；The processing method provided using the present invention both can have been judged and corrected to spurious signal, the form fit sensing data of virtual flexible beam can also be utilized, so as to reach the effect of smooth noise reduction.
Description
Technical field
It is more particularly to a kind of suitable for medium-and low-speed maglev train the invention mainly relates to medium-and low-speed maglev train field
The processing method of sensor signal.
Background technology
Medium-and low-speed maglev train is the Vehicular system that a kind of dependence electromagnetic force realizes contactless suspension, and its core system is outstanding
Floating control system.Suspension control system is used for various sensors, and such as levitation gap sensor, electromagnet vertical acceleration are sensed
The signals such as device, current sensor are acquired, and suitable voltage is exported after certain computing to control the electric current of electromagnet.
However, due to working environment, the signal that these sensors are obtained there may come a time when there is deceptive information.For example, floating in magnetic
When the traction electric machine of train and the work of other high-power electrical equipments, the interference of electromagnetic field stronger in short-term may be produced, may
Gap sensor, current sensor etc. can be caused to export of short duration pulse signal, this wrong pulse can be produced to suspension system
Raw impact even results in levitating electromagnet and collided with track；For another example, when the gap sensor of magnetic-levitation train is by track seam, survey
Amount gap can be because track be discontinuous and exports larger value, and this spurious signal can equally disturb the normal value of clearance measurement.
Therefore, how to reduce influence of this spurious signal to suspension control system, to ensure that levitation gap is steady, turn into
The problem of those skilled in the art's urgent need to resolve.
The content of the invention
The technical problem to be solved in the present invention, which is to provide one kind, to be believed in real time sensor in magnetic-levitation train running
Number detected and judged, the spurious signal detected is rejected, so as to reduce sensor spurious signal to suspension control
The influence of system, it is ensured that train levitation gap is steady.
The present invention solve its technical problem use technical scheme be, a kind of magnetic-levitation train sensor signal processing method,
Comprise the following steps：
Step 1：Obtain sensor current time sampled value and before current time continuous some sampling instants sampling
Value, virtual flexible beam is built using sampled value as constraints, forms optimum fit curve；
Wherein, the sampled value is as the virtual flexible beam of constraints structure：
Sampled value is connected on the longitudinal axis between virtual flexible beam by virtual linear spring, and virtual linear spring is according to adopting
Sample value and the position difference of virtual flexible beam produce pulling force effect on virtual flexible beam so that virtual flexible beam be subjected to displacement and
Bending, multiple sampled values are acted on simultaneously, the stress and moment of flexure of beam is reached balance, are determined that deflection of beam is deformed, are built in flat
Virtual flexible equalizer bar under weighing apparatus state；
Step 2：The sampled value of next sampling instant is predicted according to the extended position of virtual flexible beam；
Step 3：Next sampling instant sampled value is obtained, if the sampled value of next sampling instant and the difference of predicted value exceed
The threshold value of setting, then it is assumed that occur spurious signal in sensor, it is on the contrary then think that sensor signal is normal.
It is preferred that, when spurious signal occurs in the sensor, sampled value, on the contrary then reservation sampling are replaced with predicted value
Value.
It is preferred that, the bending rigidity EI and virtual linear rigidity k s of virtual flexible beam can adjust, to be adapted to different frequencies
Sensor signal with scope.
It is preferred that, the sampling instant number can be determined according to the type and purposes of sensor.
It is preferred that, time interval is identical in once virtual flexible beam building process between the neighbouring sample moment.
It is preferred that, the time interval between the sampling instant can be determined according to the type and purposes of sensor.
It is preferred that, in the sampling interval of the next sampling instant of prediction, it can be more than or equal between the sampling of sampling instant
Every.
The magnetic-levitation train sensor signal processing method of the present invention can effectively to mix in sensor signal burr,
Outlier is rejected, i.e., the spurious signal to sensor is corrected；Sampled value is determined and virtual soft using virtual linear spring
Restraining force between property beam, makes these sampled values and virtual flexible beam reach the balance on mechanics, therefore, this balance can have
The sampled data of effect fitting sensor, obtains sampled data variation tendency more smooth credible；The bending rigidity of virtual flexible beam
Can arbitrarily it be adjusted with virtual linear spring rate, to be adapted to the sensor signal of different frequency bands scope.Using present invention processing
Method both can be judged and be corrected to spurious signal, can also utilize the form fit sensing data of virtual flexible beam,
So as to reach the effect of smooth noise reduction.
Brief description of the drawings
Fig. 1 is a kind of flow chart of embodiment of magnetic-levitation train sensor signal processing method of the present invention；
Fig. 2 is restriction relation coordinate schematic diagram between virtual flexible beam and sampled value；
Fig. 3 is the coordinate schematic diagram that spurious signal is judged and corrected with virtual flexible beam；
Fig. 4 is schematic diagram of the magnetic-levitation train gap sensor by track seam；
Fig. 5 is that gap sensor passes through spurious signal correction principle schematic diagram during track seam.
Embodiment
In order that those skilled in the art more fully understands technical scheme, below in conjunction with the accompanying drawings to the present invention
It is described in further detail.
Reference picture 1, magnetic-levitation train sensor signal processing method of the present invention, comprises the following steps：
Step 1：Obtain sensor current time sampled value and before current time continuous some sampling instants sampling
Value, virtual flexible beam is built using sampled value as constraints, forms optimum fit curve；
Obtain sensor current time sampled value and before current time some sampling instants sampled value, by sampled value
Virtual flexible beam is built as constraints, the position of virtual flexible beam is fixed by multiple sampled values, virtual flexible beam
Shape is the Smoothing fit of these sampled values, so as to form optimum fit curve.
It is preferred that, the sampling period at neighbouring sample moment is identical in once virtual flexible beam building process.
Step 2：The sampled value of next sampling instant is predicted according to the extended position of virtual flexible beam；
Virtual flexible beam change in shape trend represents the real change trend of signal, and overhanging section of virtual flexible beam can be with
As the Approximate prediction of future signal, thus the sampling of next sampling instant can be predicted according to the extended position of virtual flexible beam
Value.
Step 3：Next sampling instant sampled value is obtained, if the sampled value of next sampling instant and the difference of predicted value exceed
The threshold value of setting, then it is assumed that occur spurious signal in sensor, it is on the contrary then think that sensor signal is normal.
Due to the controlled device as magnetic-levitation train, the electromagnet and bogie inertia of magnetic-levitation train are larger, therefore suspend
The true measurement signal of system can not possibly have larger mutation.If next sampling instant sensor signal value and virtual flexible beam
The difference of prediction signal exceedes the threshold value of setting, then it is considered that the sensor signal collected is false.
It is preferred that, in the sampling interval of the next sampling instant of prediction, it can be more than or equal between the sampling of sampling instant
Every.
By above-mentioned embodiment, medium-and low-speed maglev train is obtained after sensor signal without using wave filter, can be to void
Glitch is judged and corrected, and the form fit sensing data of virtual flexible beam can also be utilized, effectively to sensor
The burr that mixes in signal, outlier are rejected, so as to reach the effect of smooth noise reduction.
Alternatively, the sampled value is built as constraints in the step 1 of above-mentioned embodiment empty
The step of intending flexible beam can further comprise：The sampled value passes through virtual linear spring on the longitudinal axis between virtual flexible beam
Connection, virtual linear spring produces pulling force effect on virtual flexible beam according to sampled value and the position difference of virtual flexible beam,
So that virtual flexible beam is subjected to displacement and bent.Multiple sampled values are acted on simultaneously, may finally reach the stress and moment of flexure of beam
Balance, deflection of beam deformation also can determine that, so as to build the virtual flexible equalizer bar under poised state.Using virtual
Hookean spring determines the restraining force between sampled data value and virtual flexible beam, reaches these sampled values and virtual flexible beam
Balance on mechanics, therefore, this balance can effectively be fitted the sampled data of sensor, obtain sampled data variation tendency more
Plus it is smooth credible.
In actual application, it is preferred that threshold value could be arranged to three times of average forecasting error, i.e., when next sampling
The error of the sampled value at quarter and virtual flexible beam predicted value is more than the three times of average forecasting error, then to illustrate sensor collection
Abnormal signal, now just answers discarded sampling signal.The average forecasting error can be by all samplings on virtual flexible beam
Error Absolute Value between the sampled value at moment and virtual flexible beam is averagely obtained.
Further, the step 3 of above-mentioned embodiment can also include, when spurious signal occurs in the sensor, use
The signal value of virtual flexible beam prediction replaces sampled value, on the contrary then retain sampled value.
In reference picture 2, Fig. 3, a kind of embodiment of the invention, constraints structure is used as according to several sampled values
Need to calculate displacement and the slope of virtual flexible beam when building virtual flexible beam.For purposes of illustration only, adopting with 4 sampling instants here
It is illustrated exemplified by sample value.
Due to needing the sampled value of continuous 3 sampling instants before using sensor current time sampled value, therefore in journey
In sequence firstly the need of open up a length be 3 queue, the sampled value for 3 moment before storage.In Fig. 2, p4It is current
The sampled value at moment, p1To p3The sampled value of continuous 3 sampling instants before being respectively current time sampled value.If in the presence of one
Virtual virtual flexible beam, its bending rigidity is EI, has a rigidity to be k in each sampling instantsVirtual linear spring
Sampled value and virtual flexible beam are connected, so beam can twist and flexural deformation in the presence of virtual linear spring, use y tables
Show.
In the state of the equilibrium, suffered shearing force is beam in the x-direction
(1)
Because the low order end of beam is free, therefore work as x>Shearing force when 3 on beam is zero, i.e.,
(2)
These active forces equally can produce moment of torsion to beam, therefore the moment of flexure that beam is subject to is
(3)
Because the rightmost side of beam is free, therefore in the position of x=3, the moment of flexure of beam should be zero, therefore can draw
(4)
Assuming that the bending rigidity of beam is EI, then the moment of flexure of beam and the relation of deformation are
（5）
Therefore the slope of beam can in the x-direction be integrated by the moment of flexure being distributed on beam obtains, i.e.,
(6)
Here c1It is a undetermined constant.The displacement of beam can be by obtaining to the further integration of above formula：
(7)
Here c2It is another undetermined constant.After the displacement for obtaining beam, the drawing that each virtual linear spring is produced to beam
Power can just be calculated：
(8)
With reference to (2) formula and (4) formula, below equation group can be obtained with simultaneous：
(9)
Wherein,,.。
Solve equation, you can obtain unknown parameter f1~f4, and c1And c2.It can be obtained respectively from there through formula (6) and formula (7)
The slope of each sampling instant and displacement on to beam.
For the virtual flexible beam that the sampled value based on more sampling instants is built as constraints, it can pass through
The mode of recursion builds equation group (9).By observing A matrixes it can be found that the element of the matrix may be roughly divided into four pieces, a left side
Upper 4 × 4 submatrix A11, the submatrix A of upper right 4 × 212, the submatrix A of lower-left 2 × 421, and the null matrix A of bottom right 2 × 222.For A11
For, its matrix element with 1/6 (k s /EI), 23/6(k s /EI), 33/6(k s /EI) ... form occur and arrange, A12With
A21In element also change significantly rule.Similarly, the element in X and B also changes significantly rule.For example, for
Sampling instant number be 5 as the virtual flexible beam of constraints for, A, X, B form directly can be write out by recursion：
,,。
In specific scheme, sampling instant number is adjusted according to the type and purposes of sensor signal.Sampling instant number
Mesh is more, and Lubricity is better, predicts more credible.But sampling instant is excessive, performance improves unobvious, can increase computing on the contrary
Amount.When sampling sensor is that the gap sensor that the current sensor and factor data communications errors polluted by impulsive noise triggers is
Example, the number of sampling instant can be taken between 4 ~ 15.
Solved by formula (9) after X, it is possible to which the oblique of each sampling instant on beam can be respectively obtained by formula (6) and formula (7)
Rate and displacement.So as to build virtual flexible beam as constraints according to several sampled values, optimum fit curve is formed.
Further, in the present invention virtual flexible beam bending rigidityEIWith virtual linear spring ratek s It is adjustable, with
It is adapted to the sensor signal of different frequency bands scope.EIWithk s Ratio it is bigger, the deformation of virtual flexible beam is smaller, and smoothing effect is also got over
It is good, now it is adapted to the signal of narrower bandwidth；Otherwise the signal of broader bandwidth can be adapted to.
After virtual flexible beam is in poised state, next sampling instant can be predicted according to the extended position of virtual flexible beam
Sampled value：For sampling instant is the virtual flexible beam of N number of constraints, the slope on beam at low order end sampling instant
It is respectively with displacement
, (10)
; (11)
For real system as magnetic-levitation train, the signal that sensor is collected has a continuity, therefore for
Future time instance h (h > N 1), the predicted value of signal is
(12)
For example, for 4 sampling instants shown in Fig. 3 are as the virtual flexible beam of constraints, in next sampling
Carve (x=4) predicted value be
(13)
The following possible value of a period of time sensor signal can be predicted with virtual flexible beam, if in follow-up sampling
In, the data of sensor collection and the difference of predicted value exceed the threshold value set, then may determine that the signal that sensor is collected
It is probably false, is now accomplished by giving up these sampled values, and sampled value is replaced with predicted value.
In reference picture 4 and Fig. 5, a kind of embodiment, adjusted according to the type of sensor signal and different purposes
Whole sampling time interval.Below using detection sensor as magnetic-levitation train gap sensor, when magnetic-levitation train gap sensor passes through
Exemplified by track seam, come the selection of the time interval that further illustrates sampling instant.Pass through seam for correcting gap sensor
When the spurious signal that produces for, the interval between sampling instant should accordingly increase.For example, for 20mm track seam and
30mm diameters gap sensor probe for, when magnetic-levitation train with 18km/h (5m/s) speed by the seam when, gap
The time span of sensor probe output spurious signal is (0.02+0.03 × 2)/5=0.016s, as shown in Figure 4.Now adopt
The time interval at sample moment can take 16 ~ 20ms, and this can be sampled by the sampled data to sensor and obtained.This
Under situation, because virtual flexible beam is continuous, its value extended in estimation range is also continuous, therefore in these scopes
Interior sampled value can be replaced by these predicted values.In general, " projection " being superimposed upon in normal signal is extended pre-
Survey signal " trimming " to fall, as shown in Figure 5.For the higher situation of speed, sensor is also shorter by the time of seam, now
Between be spaced and equally can effectively judge spurious signal and be corrected.By being connect to magnetic-levitation train gap sensor by track
Spurious signal correction during seam.It can make magnetic-levitation train more steady when by track seam, reduce electromagnet and collide with rail
The probability in road.
In another embodiment, when sensor is that the current sensor and factor data polluted by impulsive noise leads to
Believe the gap sensor that error code triggers, burr typically only lasts for 1 ~ 2 controlling cycle to both sensors in short-term, when now sampling
Interval between quarter can be taken as consistent with suspension controller controlling cycle.
A kind of magnetic-levitation train sensor signal processing method provided by the present invention is described in detail above.Herein
In apply specific case the principle and embodiment of the present invention be set forth, the explanation of above example is only intended to side
The core concept of the assistant solution present invention.It should be pointed out that for those skilled in the art, not departing from this hair
On the premise of bright principle, some improvement and modification can also be carried out to the present invention, these are improved and modification also falls into power of the present invention
In the protection domain that profit is required.
Claims (7)
1. a kind of magnetic-levitation train sensor signal processing method, it is characterised in that the described method comprises the following steps：
Step 1：Obtain sensor current time sampled value and before current time continuous some sampling instants sampled value, general
Sampled value builds virtual flexible beam as constraints, forms optimum fit curve；
Wherein, the sampled value is as the virtual flexible beam of constraints structure：
Sampled value is connected on the longitudinal axis between virtual flexible beam by virtual linear spring, and virtual linear spring is according to sampled value
Produce pulling force effect on virtual flexible beam with the position difference of virtual flexible beam so that virtual flexible beam be subjected to displacement with it is curved
Song, multiple sampled values are acted on simultaneously, the stress and moment of flexure of beam is reached balance, are determined that deflection of beam is deformed, are built in balance
Virtual flexible equalizer bar under state；
Step 2：The sampled value of next sampling instant is predicted according to the extended position of virtual flexible beam；
Step 3：Next sampling instant sampled value is obtained, if the sampled value of next sampling instant and the difference of predicted value exceed setting
Threshold value, then it is assumed that occur spurious signal in sensor, it is on the contrary then think that sensor signal is normal.
2. magnetic-levitation train sensor signal processing method according to claim 1, it is characterised in that when the sensor goes out
During existing spurious signal, sampled value is replaced with predicted value, it is on the contrary then retain sampled value.
3. magnetic-levitation train sensor signal processing method according to claim 1, it is characterised in that virtually flexible beam is anti-
Curved rigidityEIWith virtual linear spring ratek s It is adjustable, to be adapted to the sensor signal of different frequency bands scope.
4. magnetic-levitation train sensor signal processing method according to claim 1, it is characterised in that the sampling instant
Several types and purposes according to sensor is determined.
5. magnetic-levitation train sensor signal processing method according to claim 1, it is characterised in that the neighbouring sample moment it
Between time interval be identical in once virtual flexible beam building process.
6. magnetic-levitation train sensor signal processing method according to claim 4, it is characterised in that the sampling instant it
Between time interval according to the type and purposes of sensor determine.
7. magnetic-levitation train sensor signal processing method according to claim 1, it is characterised in that the prediction is next to adopt
The sampling interval at sample moment, more than or equal to the sampling interval of sampling instant.
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| Publication number | Priority date | Publication date | Assignee | Title |
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| CN105978848A (en) * | 2015-12-04 | 2016-09-28 | 乐视致新电子科技（天津）有限公司 | Processing method and device for collection of sensor data |
| CN108657014B (en) * | 2018-07-11 | 2019-12-31 | 中国人民解放军国防科技大学 | Method and device for processing position sensor signal of suspension system of magnetic-levitation train |
| CN113525098B (en) * | 2020-04-20 | 2023-02-03 | 株洲中车时代电气股份有限公司 | Suspension control method and device for magnetic suspension vehicle |
| CN113092143B (en) * | 2021-04-30 | 2023-01-31 | 中车青岛四方机车车辆股份有限公司 | Detection system for suspension system of maglev train |
| CN113819959B (en) * | 2021-11-24 | 2022-02-08 | 中国空气动力研究与发展中心设备设计与测试技术研究所 | Suspension system anomaly detection method based on Hailinge distance and correlation coefficient |
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| CN100430260C (en) * | 2006-08-28 | 2008-11-05 | 中国人民解放军国防科学技术大学 | Suspension method for controling module of electromagnetism type magnetic suspension train in normal conduction and low temperature |
| CN100497037C (en) * | 2007-10-17 | 2009-06-10 | 中国人民解放军国防科学技术大学 | Method and system for preventing magnet levitation train from adsorption for track |
| CN103522913B (en) * | 2013-10-17 | 2015-10-28 | 中国人民解放军国防科学技术大学 | For suspension control method and the device of EMS type low speed aerotrain |
| CN104477049B (en) * | 2014-11-28 | 2016-08-17 | 中国人民解放军国防科学技术大学 | Magnetic-levitation train based on virtual energy capture device-bridge self-excited vibration suppressing method |
| CN104477048B (en) * | 2014-11-28 | 2016-06-08 | 中国人民解放军国防科学技术大学 | A kind of electromagnetic type is often led the suspension control method of low-speed maglev train |
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