CN111044303B - Diagnosis method and device for abnormal vibration of passenger room of maglev train - Google Patents

Diagnosis method and device for abnormal vibration of passenger room of maglev train Download PDF

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CN111044303B
CN111044303B CN202010000625.3A CN202010000625A CN111044303B CN 111044303 B CN111044303 B CN 111044303B CN 202010000625 A CN202010000625 A CN 202010000625A CN 111044303 B CN111044303 B CN 111044303B
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passenger room
measuring point
frequency
time sequence
signals
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CN111044303A (en
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龙子凡
佟来生
罗华军
司恩
王明华
李笑言
王家恒
高行行
刘圣前
李皓
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CRRC Zhuzhou Locomotive Co Ltd
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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
    • G01M17/00—Testing of vehicles
    • G01M17/08—Railway vehicles
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B61—RAILWAYS
    • B61L—GUIDING RAILWAY TRAFFIC; ENSURING THE SAFETY OF RAILWAY TRAFFIC
    • B61L15/00—Indicators provided on the vehicle or train for signalling purposes
    • B61L15/0081—On-board diagnosis or maintenance
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H1/00—Measuring characteristics of vibrations in solids by using direct conduction to the detector
    • G01H1/04—Measuring characteristics of vibrations in solids by using direct conduction to the detector of vibrations which are transverse to direction of propagation
    • G01H1/06—Frequency
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
    • G01H11/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties
    • G01H11/06—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means
    • G01H11/08—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves by detecting changes in electric or magnetic properties by electric means using piezoelectric devices

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Abstract

The invention discloses a diagnosis method and a diagnosis device for abnormal vibration of a passenger room of a maglev train, and relates to a fault diagnosis technology of the maglev train. According to the diagnosis method and the diagnosis device, a plurality of points are selected on the same section or near the same section in the passenger room as internal measuring points and excitation source measuring points of the passenger room, vertical vibration signals of the measuring points are synchronously acquired, and then the vertical vibration signals of the measuring points in the passenger room are calculated and analyzed to determine whether abnormal vibration exists; when abnormal vibration exists, the correlation between the internal measuring point of the passenger room and the vertical vibration signal of the measuring point of the excitation source is determined by calculating the correlation coefficient of the two signals, so that the source and the frequency of the abnormal vibration are determined; the diagnosis method can effectively and quickly diagnose the abnormal state of the passenger room, determine the source and the vibration frequency of the abnormal vibration, provide a monitoring and diagnosis means for the abnormal vibration of the passenger room, and simultaneously provide a support for the investigation and the elimination of the abnormal vibration.

Description

Diagnosis method and device for abnormal vibration of passenger room of maglev train
Technical Field
The invention belongs to the technology of magnetic-levitation train fault diagnosis, and particularly relates to a method and a device for diagnosing abnormal vibration of a passenger room of a magnetic-levitation train.
Background
In the running process of the maglev train, the interior of the passenger room is influenced by the vibration of an external excitation source. When the amplitude of the interior of the passenger compartment is within a certain range, the comfort of the passenger sitting in the passenger compartment is not affected, but when the amplitude of the interior of the passenger compartment exceeds a certain range, the sitting in the passenger compartment becomes uncomfortable. If the problem vibration fault cannot be corrected in time, the suspension stability is even affected, and the train is hit to the rail and stops running.
At present, no reference documents or technical application cases exist for the research on abnormal vibration monitoring and fault diagnosis of a passenger room of a magnetic-levitation train. However, in actual operation, the problem of abnormal vibration of the passenger compartment of the maglev train exists.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a diagnosis method and a diagnosis device for abnormal vibration of a passenger room of a maglev train, which are used for judging and analyzing the abnormal vibration of the passenger room of the maglev train and rapidly determining the source and the vibration frequency of the abnormal vibration.
The invention solves the technical problems through the following technical scheme: a diagnosis method for abnormal vibration of a passenger room of a maglev train comprises the following steps:
step 1: selecting a plurality of points on or near the same section in the passenger room as a passenger room internal measuring point and an excitation source measuring point;
step 2: in the running process of the train, vertical vibration signals of the passenger room internal measuring point and the excitation source measuring point are synchronously acquired;
and step 3: calculating the root mean square of vertical vibration signals of a measuring point in the passenger room, and determining whether the passenger room of the train has abnormal vibration or not according to the root mean square;
and 4, step 4: and when abnormal vibration exists, calculating the time-frequency correlation coefficient of the vertical vibration signal of the measuring point in the passenger room and the vertical vibration signal of the exciting source measuring point so as to determine the source and the frequency of the abnormal vibration.
The diagnosis method comprises the steps of setting a plurality of passenger room internal measuring points and excitation source measuring points, synchronously acquiring vertical vibration signals of the measuring points, and then calculating and analyzing the vertical vibration signals of the passenger room internal measuring points to determine whether abnormal vibration exists; when abnormal vibration exists, the correlation between the internal measuring point of the passenger room and the vertical vibration signal of the measuring point of the excitation source is determined by calculating the correlation coefficient of the two signals, so that the source and the frequency of the abnormal vibration are determined; the passenger room internal measuring point and the excitation source measuring point are positioned on the same section or nearby the same section, so that the vibration attenuation of the excitation source is reduced, and the influence of the excitation source on abnormal vibration in the passenger room is better reflected; the diagnosis method of the invention provides a monitoring and diagnosis means for the abnormal vibration of the passenger room, not only can determine whether the abnormal vibration exists in the passenger room, but also can determine the source and the frequency of the generated abnormal vibration, thereby providing support for the examination and elimination of the abnormal vibration and saving manpower and material resources.
Further, in the step 1, the passenger room internal measuring points comprise an internal ceiling measuring point and a floor measuring point which are positioned on an internal ceiling of the passenger room; the excitation source measuring points comprise measuring points of a compressor on an air conditioner external unit mounting seat and an air compressor mounting seat and two electromagnet measuring points on a left polar plate and a right polar plate of a suspension electromagnet.
Further, in the step 2, a piezoelectric three-way acceleration sensor is adopted to synchronously acquire vertical vibration signals of a measuring point inside the passenger room and a measuring point of the excitation source.
Further, the specific operation of step 3 includes the following sub-steps:
step 3.1: vertical vibration signal sequence { x) of each passenger room internal measuring pointi(N), N ═ 1,2,3, …, N; i is 1,2,3, …, M segments, and the number of sampling points of each segment is S x fsObtaining K sections of time sequence signals, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure BDA0002353191960000021
Comprises the following steps:
Figure BDA0002353191960000031
wherein x isi(N) is the amplitude of the nth sampling point collected by the ith sensor, N is the number of each sensor sampling point, M is the number of the sensors, S is the sampling time, fsIn order to be able to sample the frequency,
Figure BDA0002353191960000032
the amplitude of the p-th sampling point acquired for the ith sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure BDA0002353191960000033
To round down;
step 3.2: calculating a time sequence signal matrix
Figure BDA0002353191960000034
Root mean square of rows and using vectors
Figure BDA0002353191960000035
To show that:
Figure BDA0002353191960000036
wherein the content of the first and second substances,
Figure BDA0002353191960000037
as a matrix of time-sequential signals
Figure BDA0002353191960000038
Root mean square of the k-th row;
step 3.3: judging time sequence signal matrix
Figure BDA0002353191960000039
And whether the root mean square of a certain middle row is larger than the riding comfort index or not, if so, indicating that abnormal vibration exists in the passenger room in the running process of the train.
Further, in step 3.3, the riding comfort index is 0.04.
Further, the specific operation of step 4 includes the following sub-steps:
step 4.1: vertical vibration signal sequence { y) of each excitation source measuring pointj(N), N ═ 1,2,3, …, N; j is 1,2,3, …, G segment, and the number of sampling points of each segment is S x fsObtaining K sections of time sequence signals, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure BDA00023531919600000310
Comprises the following steps:
Figure BDA0002353191960000041
wherein, yj(N) is the amplitude of the nth sampling point collected by the jth sensor, N is the number of sampling points of each sensor, G is the number of sensors, S is the sampling time, fsIn order to be able to sample the frequency,
Figure BDA0002353191960000042
the amplitude of the p-th sampling point acquired for the j-th sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure BDA0002353191960000043
Step 4.2: to the time sequence signal matrix
Figure BDA0002353191960000044
Each row of the time-frequency-amplitude matrix is subjected to Fourier transform to form a time-frequency-amplitude matrix
Figure BDA0002353191960000045
Figure BDA0002353191960000046
Wherein the content of the first and second substances,f＝1/S,2/S,…,0.5*fsthe unit: the frequency of the Hz,
Figure BDA0002353191960000047
amplitude corresponding to the Fourier transform of the kth section of time sequence signal acquired by the jth sensor;
step 4.3: computing matrices
Figure BDA0002353191960000048
Each column and vector in
Figure BDA0002353191960000049
To obtain a time-frequency correlation coefficient vector RijComprises the following steps:
Figure BDA00023531919600000410
wherein Cov () is covariance, D () is mean square error, i is 1,2,3, …, M, i is different measuring points in the passenger room, j is 1,2,3, …, G, j is different measuring points of the excitation source, r (f) is matrix
Figure BDA00023531919600000411
Corresponding column and vector when the medium frequency is f
Figure BDA00023531919600000412
The correlation coefficient of (a);
step 4.4: determining a vector RijIf the maximum correlation coefficient is larger than or equal to the strong correlation index, indicating that the abnormal vibration of the passenger room internal measuring point i is strongly correlated with the part corresponding to the excitation source measuring point j; if strongly correlated, then vector R is determined according to the maximum correlation coefficientijAnd determining the frequency f corresponding to the abnormal vibration of the passenger room internal measuring point i.
Further, in the step 4.4, the strong correlation index is 0.6.
The invention also provides a diagnosis device for abnormal vibration of a passenger room of a maglev train, which comprises the following components:
the sensors are respectively arranged on equipment or structures corresponding to the passenger room internal measuring points and the excitation source measuring points, the number of the sensors is equal to the total number of the passenger room internal measuring points and the excitation source measuring points, and the sensors are used for acquiring vertical vibration signals of the passenger room internal measuring points and the excitation source measuring points and synchronously transmitting the vertical vibration signals to the signal acquisition module;
the signal acquisition module is used for synchronously receiving the vertical vibration signals acquired by the sensors, performing analog-to-digital conversion and transmitting the signals to the signal processing module;
and the signal processing module is used for carrying out root mean square calculation on the signals transmitted by the signal acquisition module, determining whether abnormal vibration exists in the passenger room, and when the abnormal vibration exists, calculating a time-frequency correlation coefficient of the vertical vibration signals of the measuring points in the passenger room and the vertical vibration signals of the exciting source measuring points, and determining the source and frequency of the abnormal vibration.
Since the above-mentioned diagnostic method has the above-mentioned technical effects, a diagnostic apparatus corresponding to the diagnostic method should also have corresponding technical effects.
Advantageous effects
Compared with the prior art, the invention provides a diagnosis method and a device for abnormal vibration of a passenger room of a maglev train, wherein a plurality of points are selected on the same section or near the same section in the passenger room as internal measuring points and excitation source measuring points of the passenger room, vertical vibration signals of the measuring points are synchronously acquired, and then the vertical vibration signals of the internal measuring points of the passenger room are calculated and analyzed to determine whether abnormal vibration exists; when abnormal vibration exists, the correlation between the internal measuring point of the passenger room and the vertical vibration signal of the measuring point of the excitation source is determined by calculating the correlation coefficient of the two signals, so that the source and the frequency of the abnormal vibration are determined; the diagnosis method can effectively and quickly diagnose the abnormal state of the passenger room, determine the source and the vibration frequency of the abnormal vibration, provide a monitoring and diagnosis means for the abnormal vibration of the passenger room, and simultaneously provide a support for the investigation and the elimination of the abnormal vibration.
Drawings
In order to more clearly illustrate the technical solution of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only one embodiment of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on the drawings without creative efforts.
FIG. 1 is a flow chart of a method for diagnosing abnormal vibration of a passenger compartment of a magnetic-levitation train in an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of an abnormal vibration diagnosis device for a passenger compartment of a magnetic-levitation train in an embodiment of the invention;
FIG. 3 is a root mean square characteristic of vibration of a passenger compartment floor in an embodiment of the invention;
FIG. 4 is a time-frequency correlation coefficient characteristic curve of vibration of the passenger compartment floor and the left pole plate of the suspension electromagnet in the embodiment of the present invention;
the system comprises an air conditioner measuring point, an internal top plate measuring point, a floor measuring point, a compressor measuring point, an electromagnet measuring point, a sensor corresponding to the air conditioner measuring point, a sensor corresponding to the internal top plate measuring point, a sensor corresponding to the floor measuring point, a sensor corresponding to the compressor measuring point, a sensor corresponding to the electromagnet left plate measuring point, a sensor corresponding to the electromagnet right plate measuring point, a signal acquisition module and a signal processing module, wherein the air conditioner measuring point is 1, the internal top plate measuring point is 2, the floor measuring point is 3, the compressor measuring point is 4, the electromagnet measuring point is 5, the sensor corresponding to the air conditioner measuring point.
Detailed Description
The technical solutions in the present invention are 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 only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1, the method for diagnosing abnormal vibration of passenger compartment of a maglev train provided by the invention comprises the following steps:
1. and selecting a plurality of points on the same section or near the same section in the passenger room as a passenger room internal measuring point and an excitation source measuring point.
And selecting a plurality of points as a passenger room internal measuring point and an excitation source measuring point according to equipment or a structure which can generate vibration in the passenger room. In this embodiment, the passenger room internal measuring points include an internal ceiling measuring point 2 and a floor measuring point 3 (2) which are located on an internal ceiling of the passenger room (the passenger room internal measuring points can be selected according to requirements, such as handrail columns, passenger room side walls, and the like); the excitation source measuring points comprise an air conditioner measuring point 1 positioned on an air conditioner external unit mounting seat, a compressor measuring point 4 positioned on an air compressor mounting seat, and two electromagnet measuring points 5 (4) positioned on left and right polar plates of a suspension electromagnet, as shown in fig. 2. The number of the measuring points in the passenger room and the number of the measuring points of the excitation source can be equal or unequal, but the measuring points need to be located on the same section or nearby the same section as much as possible so as to reduce the vibration attenuation of the excitation source and better reflect the influence of the excitation source on the abnormal vibration in the passenger room.
2. And in the running process of the train, vertical vibration signals of the inside measuring point of the passenger room and the measuring point of the excitation source are synchronously acquired.
In this embodiment, vertical vibration signals of passenger room internal measuring points and excitation source measuring points are synchronously acquired by adopting piezoelectric three-way acceleration sensors, the number of the piezoelectric three-way acceleration sensors is equal to the total number of the passenger room internal measuring points and the excitation source measuring points, the mounting positions of the piezoelectric three-way acceleration sensors on the maglev train respectively correspond to the positions of the passenger room internal measuring points and the excitation source measuring points, as shown in fig. 2, a sensor 6 corresponding to an air conditioner measuring point is arranged on an air conditioner outer machine mounting seat, a sensor 7 corresponding to an internal top plate measuring point is arranged on an internal top plate, a sensor 8 corresponding to a floor measuring point is arranged on a floor, a sensor 9 corresponding to a compressor measuring point is arranged on a compressor mounting seat, and two sensors 10/11 corresponding to an electromagnet measuring point are respectively arranged on a left polar plate and a right polar plate of the electromagnet.
3. And calculating the root mean square of the vertical vibration signals of the internal measuring points of the passenger room, and determining whether the passenger room of the train has abnormal vibration or not according to the root mean square. The specific substeps are as follows:
3.1 the piezoelectric three-way acceleration sensor collects vertical vibration signals corresponding to the measuring points in the passenger room, and the vertical vibration signals form a vertical vibration signal sequence { x }i(n),n＝1,2,3,…,N；i＝1,2,3,…,M}，xi(n) is the ith sensorThe amplitude of the nth sampling point is collected, N is the number of the sampling points of each sensor, M is the number of the sensors, the vertical vibration signal sequence of the internal measuring point of each passenger room is segmented, and the number of the sampling points of each segment of the signal is SxfsS is the sampling time, fsObtaining K sections of time sequence signals for sampling frequency, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure BDA0002353191960000081
Comprises the following steps:
Figure BDA0002353191960000082
wherein the content of the first and second substances,
Figure BDA0002353191960000083
the amplitude of the p-th sampling point acquired for the ith sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure BDA0002353191960000084
To round down.
3.2 calculating the time sequence signal matrix
Figure BDA0002353191960000085
Root mean square of rows and using vectors
Figure BDA0002353191960000086
To show that:
Figure BDA0002353191960000087
wherein the content of the first and second substances,
Figure BDA0002353191960000088
as a matrix of time-sequential signals
Figure BDA00023531919600000811
Root mean square of the k-th row, and the time interval between two adjacent rows is S seconds.
3.3 judging time sequence signal matrix
Figure BDA0002353191960000089
Whether the root mean square of a row is greater than the ride comfort level, if any, in the time sequence signal matrix
Figure BDA00023531919600000810
If the root mean square of a certain middle row is larger than the riding comfort index, the abnormal vibration of the passenger room in the running process of the train is indicated, otherwise, the abnormal vibration of the passenger room of the maglev train is not indicated.
In this embodiment, the riding comfort index is 0.04. FIG. 3 is a root-mean-square characteristic value curve of passenger room floor vibration in the running process of a maglev train, a sensor 8 corresponding to a floor measuring point collects vertical vibration signals of the floor measuring point, then the root-mean-square of each row in a time sequence signal matrix of the floor measuring point is calculated according to the steps 3.1 to 3.2, a root-mean-square characteristic value curve (FIG. 3) is drawn, and as can be seen from FIG. 3, a part exceeding a riding comfort index of 0.04 indicates that abnormal vibration exists on the passenger room floor.
4. And when abnormal vibration exists, calculating the time-frequency correlation coefficient of the vertical vibration signal of the measuring point in the passenger room and the vertical vibration signal of the exciting source measuring point so as to determine the source and the frequency of the abnormal vibration. The specific substeps are as follows:
4.1 the piezoelectric three-way acceleration transducer collects the vertical vibration signal of the corresponding vibration source measuring point, and the vertical vibration signal sequence { y is formed by a plurality of vertical vibration signalsj(n),n＝1,2,3,…,N；j＝1,2,3,…,G}，yj(N) is the amplitude of the nth sampling point collected by the jth sensor, N is the number of each sensor sampling point, G is the number of the sensors, the vertical vibration signal sequence of each excitation source measuring point is segmented, and the number of each segment of signal sampling points is SxfsS is the sampling time, fsObtaining K sections of time sequence signals for sampling frequency, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure BDA0002353191960000091
Comprises the following steps:
Figure BDA0002353191960000092
wherein the content of the first and second substances,
Figure BDA0002353191960000093
the amplitude of the p-th sampling point acquired for the j-th sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure BDA0002353191960000094
The total number M + G of the sensors is equal to the number M + the number G of the excitation source measuring points, the number of the passenger room measuring points and the number of the excitation source measuring points can be different or the same, but the number of the sections of the passenger room measuring point vertical vibration signal sequence and the excitation source measuring point vertical vibration signal sequence is consistent, so that the calculation of the subsequent correlation coefficient is ensured.
4.2 time sequence signal matrix
Figure BDA0002353191960000095
Each row of the time-frequency-amplitude matrix is subjected to Fourier transform to form a time-frequency-amplitude matrix
Figure BDA0002353191960000096
Figure BDA0002353191960000101
Due to central symmetry of Fourier transform, e.g.
Figure BDA0002353191960000102
For simplicity, the matrix may be applied
Figure BDA0002353191960000103
Taking the first half part to obtain:
Figure BDA0002353191960000104
wherein, f is 1/S,2/S, …,0.5 fsThe unit: the frequency of the Hz,
Figure BDA0002353191960000105
amplitude corresponding to the Fourier transform of the kth section of time sequence signal acquired by the jth sensor; in a matrix
Figure BDA0002353191960000106
In the matrix, the frequency and time of each column of data are the same, the frequency interval between two adjacent rows is 1/SHz, the time interval is S seconds
Figure BDA0002353191960000107
Is a three-dimensional distribution matrix of time-frequency-amplitude.
4.3 calculation matrix
Figure BDA0002353191960000108
Each column and vector in
Figure BDA0002353191960000109
To obtain a time-frequency correlation coefficient vector RijComprises the following steps:
Figure BDA00023531919600001010
wherein Cov () is covariance, D () is mean square error, i is 1,2,3, …, M, i is different measuring points in the passenger room, j is 1,2,3, …, G, j is different measuring points of the excitation source, r (f) is matrix
Figure BDA00023531919600001011
Corresponding column and vector when the medium frequency is f
Figure BDA00023531919600001012
The correlation coefficient of (2).
4.4 determining the vector RijMaximum correlation coefficient r in (1)max(f) If the maximum correlation coefficient rmax(f) When the abnormal vibration of the passenger room internal measuring point i is greater than or equal to the strong correlation index, the abnormal vibration of the passenger room internal measuring point i is strongly correlated with a component corresponding to the excitation source measuring point j, otherwise, the abnormal vibration of the passenger room internal measuring point i is weakly correlated with the component corresponding to the excitation source measuring point j; if strongly correlated, according to the maximum correlation coefficient rmax(f) In the vector RijAnd determining the frequency f corresponding to the abnormal vibration of the passenger room internal measuring point i.
In this embodiment, the strong correlation index is 0.6, and the very strong correlation index is 0.8. Fig. 4 is a time-frequency correlation coefficient characteristic curve of the passenger room floor and the left pole plate vibration of the suspension electromagnet in the running process of the magnetic-levitation train, and it can be known from fig. 4 that the passenger room floor vibration and the left pole plate vibration of the suspension electromagnet have extremely strong correlation, and the corresponding frequency is 56.2Hz and the second harmonic thereof is 112.4 Hz. It can be seen that the method can accurately identify the source and frequency of the abnormal vibration.
The diagnosis method synchronously collects vertical vibration signals of an air conditioner, an internal top plate, a floor, an air compressor and a suspension electromagnet in the running process of the maglev train, extracts root mean square characteristic values of the vertical vibration signals of the internal top plate, the floor and the like of a passenger room by adopting a root mean square algorithm, compares the root mean square characteristic values with riding comfort indexes to judge whether abnormal vibration exists in the passenger room of the maglev train, extracts time-frequency correlation coefficient characteristic values of the vibration of the internal top plate, the floor, the air conditioner, the air compressor and the suspension electromagnet in the passenger room by adopting a short-time Fourier algorithm and a time-frequency correlation coefficient algorithm, compares the time-frequency correlation coefficient characteristic values with the correlation indexes to determine the source and fault frequency of the abnormal vibration of the passenger room of the maglev train.
As shown in fig. 2, the present invention further provides a diagnosis device for abnormal vibration of passenger compartment of a maglev train, comprising:
the 6 sensors are respectively arranged on equipment or structures corresponding to the passenger room internal measuring point and the excitation source measuring point, and are used for acquiring vertical vibration signals of the passenger room internal measuring point and the excitation source measuring point and synchronously transmitting the vertical vibration signals to the signal acquisition module;
the signal acquisition module is used for synchronously receiving the vertical vibration signals acquired by the 6 sensors, performing analog-to-digital conversion and transmitting the vertical vibration signals to the signal processing module;
and the signal processing module is used for carrying out root mean square calculation on the signals transmitted by the signal acquisition module, determining whether the passenger room has abnormal vibration, and when the abnormal vibration exists, calculating a time-frequency correlation coefficient of the vertical vibration signals of the measuring points in the passenger room and the vertical vibration signals of the exciting source measuring points, and determining the source and frequency of the abnormal vibration.
In the embodiment, the number of the passenger room internal measuring points is 2, the number of the excitation source measuring points is 4, the passenger room internal measuring points are used for determining the position of the passenger room where the abnormal vibration of the passenger room is located, and corresponding points are selected as the measuring points in the passenger room; the excitation source measuring point is used for determining the influence of which equipment or structure the passenger room abnormal vibration comes from, and corresponding points are selected as measuring points on or near the excitation source. The 2 passenger room internal measuring points are respectively an internal ceiling measuring point 2 and a floor measuring point 3 which are positioned on an internal ceiling of the passenger room. The 4 excitation source measuring points are respectively an air conditioner measuring point 1 positioned on an air conditioner outer machine mounting seat, a compressor measuring point 4 positioned on an air compressor mounting seat and two electromagnet measuring points 5 positioned on a left polar plate and a right polar plate of a suspension electromagnet. The 6 sensors are respectively a sensor 6 corresponding to an air conditioner measuring point, a sensor 7 corresponding to an inner installed top plate measuring point, a sensor 8 corresponding to a floor measuring point, a sensor 9 corresponding to a compressor measuring point, a sensor 10 corresponding to an electromagnet left polar plate measuring point and a sensor 11 corresponding to an electromagnet right polar plate measuring point; the 6 sensors are all piezoelectric type three-way acceleration sensors. The signal acquisition module 12 adopts a multi-channel data acquisition card to synchronously acquire vertical vibration signals of the internal measuring points of each passenger room and the measuring points of the excitation source, and the signal processing module 13 analyzes and processes the vertical vibration signals according to the vibration method to determine the source and the frequency of abnormal vibration.
The above disclosure is only for the specific embodiments of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art can easily conceive of changes or modifications within the technical scope of the present invention, and shall be covered by the scope of the present invention.

Claims (8)

1. A diagnosis method for abnormal vibration of a passenger room of a maglev train is characterized by comprising the following steps:
step 1: selecting a plurality of points on or near the same section in the passenger room as a passenger room internal measuring point and an excitation source measuring point;
step 2: in the running process of the train, vertical vibration signals of the passenger room internal measuring point and the excitation source measuring point are synchronously acquired;
and step 3: calculating the root mean square of vertical vibration signals of a measuring point in the passenger room, and determining whether the passenger room of the train has abnormal vibration or not according to the root mean square;
and 4, step 4: when abnormal vibration exists, calculating a time-frequency correlation coefficient of a vertical vibration signal of a measuring point in the passenger room and a vertical vibration signal of a measuring point of an excitation source so as to determine the source and frequency of the abnormal vibration;
the specific operation of the step 3 comprises the following substeps:
step 3.1: vertical vibration signal sequence { x) of each passenger room internal measuring pointi(N), N ═ 1,2,3, …, N; i is 1,2,3, …, M segments, and the number of sampling points of each segment is S x fsObtaining K sections of time sequence signals, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure FDA0003005262310000011
Comprises the following steps:
Figure FDA0003005262310000012
wherein x isi(N) is the amplitude of the nth sampling point collected by the ith sensor, N is the number of each sensor sampling point, M is the number of the sensors, S is the sampling time, fsIn order to be able to sample the frequency,
Figure FDA0003005262310000013
the amplitude of the p-th sampling point acquired for the ith sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure FDA0003005262310000014
Figure FDA0003005262310000015
To round down;
step 3.2: calculating a time sequence signal matrix
Figure FDA0003005262310000016
Root mean square of rows and using vectors
Figure FDA0003005262310000017
To show that:
Figure FDA0003005262310000018
wherein the content of the first and second substances,
Figure FDA0003005262310000019
as a matrix of time-sequential signals
Figure FDA00030052623100000110
Root mean square of the k-th row;
the specific operation of the step 4 comprises the following substeps:
step 4.1: vertical vibration signal sequence { y) of each excitation source measuring pointj(N), N ═ 1,2,3, …, N; j is 1,2,3, …, G segment, and the number of sampling points of each segment is S x fsObtaining K sections of time sequence signals, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure FDA0003005262310000021
Comprises the following steps:
Figure FDA0003005262310000022
wherein, yj(N) is the amplitude of the nth sampling point collected by the jth sensor, N is the number of sampling points of each sensor, G is the number of sensors, S is the sampling time, fsIn order to be able to sample the frequency,
Figure FDA0003005262310000023
the amplitude of the p-th sampling point acquired for the j-th sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure FDA0003005262310000024
Step 4.2: to the time sequence signal matrix
Figure FDA0003005262310000025
Each row of the time-frequency-amplitude matrix is subjected to Fourier transform to form a time-frequency-amplitude matrix
Figure FDA0003005262310000026
Figure FDA0003005262310000027
Wherein, f is 1/S,2/S, …,0.5 fsThe unit: the frequency of the Hz,
Figure FDA0003005262310000028
amplitude corresponding to the Fourier transform of the kth section of time sequence signal acquired by the jth sensor;
step 4.3: computing matrices
Figure FDA0003005262310000029
Each column and vector in
Figure FDA00030052623100000210
To obtain a time-frequency correlation coefficient vector RijComprises the following steps:
Figure FDA00030052623100000211
wherein Cov () is covariance, D () is mean square error, i is 1,2,3, …, M, i is different measuring points in the passenger room, j is 1,2,3, …, G, j is different measuring points of the excitation source, r (f) is matrix
Figure FDA00030052623100000212
Corresponding column and vector when the medium frequency is f
Figure FDA00030052623100000213
The correlation coefficient of (a);
step 4.4: determining a vector RijIf the maximum correlation coefficient is larger than or equal to the strong correlation index, indicating that the abnormal vibration of the passenger room internal measuring point i is strongly correlated with the part corresponding to the excitation source measuring point j; if strongly correlated, then vector R is determined according to the maximum correlation coefficientijAnd determining the frequency f corresponding to the abnormal vibration of the passenger room internal measuring point i.
2. The diagnostic method of claim 1, wherein: in the step 1, the passenger room internal measuring points comprise internal top plate measuring points and floor measuring points which are positioned on an internal top plate of the passenger room; the excitation source measuring points comprise air conditioner measuring points positioned on an air conditioner outer machine mounting seat, compressor measuring points positioned on an air compressor mounting seat and two electromagnet measuring points positioned on a left polar plate and a right polar plate of the suspension electromagnet.
3. The diagnostic method of claim 1, wherein: in the step 2, vertical vibration signals of the internal measuring point of the passenger room and the measuring point of the excitation source are synchronously acquired by adopting the piezoelectric three-way acceleration sensor.
4. The diagnostic method of claim 1, wherein the specific operations of step 3 further comprise the substeps of:
step 3.3: judging time sequence signal matrix
Figure FDA0003005262310000031
And whether the root mean square of a certain middle row is larger than the riding comfort index or not, if so, indicating that abnormal vibration exists in the passenger room in the running process of the train.
5. The diagnostic method of claim 4, wherein: in step 3.3, the riding comfort index is 0.04.
6. The diagnostic method of claim 1, wherein: in step 4.4, the strong correlation index is 0.6.
7. A diagnosis device for abnormal vibration of a passenger compartment of a maglev train is characterized by comprising the following components:
the sensors are respectively arranged on equipment or structures corresponding to the passenger room internal measuring points and the excitation source measuring points, the number of the sensors is equal to the total number of the passenger room internal measuring points and the excitation source measuring points, and the sensors are used for acquiring vertical vibration signals of the passenger room internal measuring points and the excitation source measuring points and synchronously transmitting the vertical vibration signals to the signal acquisition module;
the signal acquisition module is used for synchronously receiving the vertical vibration signals acquired by the sensors, performing analog-to-digital conversion and transmitting the signals to the signal processing module;
the signal processing module is used for carrying out root mean square calculation on the signals transmitted by the signal acquisition module, determining whether abnormal vibration exists in the passenger room, and when the abnormal vibration exists, calculating a time-frequency correlation coefficient of the vertical vibration signals of the measuring points in the passenger room and the vertical vibration signals of the exciting source measuring points, and determining the source and frequency of the abnormal vibration;
the signal processing module is specifically configured to:
vertical vibration signal sequence { x) of each passenger room internal measuring pointi(N), N ═ 1,2,3, …, N; i is 1,2,3, …, M segments, and the number of sampling points of each segment is S x fsObtaining K sections of time sequence signals, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure FDA0003005262310000032
Comprises the following steps:
Figure FDA0003005262310000041
wherein x isi(N) is the amplitude of the nth sampling point collected by the ith sensor, N is the number of each sensor sampling point, M is the number of the sensors, S is the sampling time, fsIn order to be able to sample the frequency,
Figure FDA0003005262310000042
the amplitude of the p-th sampling point acquired for the ith sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure FDA0003005262310000043
Figure FDA0003005262310000044
To round down;
calculating a time sequence signal matrix
Figure FDA0003005262310000045
Root mean square of rows and using vectors
Figure FDA0003005262310000046
To show that:
Figure FDA0003005262310000047
wherein the content of the first and second substances,
Figure FDA0003005262310000048
as a matrix of time-sequential signals
Figure FDA0003005262310000049
Root mean square of the k-th row;
vertical vibration signal sequence { y) of each excitation source measuring pointj(N), N ═ 1,2,3, …, N; j is 1,2,3, …, G segment, and the number of sampling points of each segment is S x fsObtaining K sections of time sequence signals, arranging each section of time sequence signals according to time sequence to construct a time sequence signal matrix
Figure FDA00030052623100000410
Comprises the following steps:
Figure FDA00030052623100000411
wherein, yj(N) is the amplitude of the nth sampling point collected by the jth sensor, N is the number of sampling points of each sensor, G is the number of sensors, S is the sampling time, fsIn order to be able to sample the frequency,
Figure FDA00030052623100000412
the amplitude of the p-th sampling point acquired for the j-th sensor, which is K1, 2,3, …, K, p 1,2,3s，
Figure FDA00030052623100000413
To the time sequence signal matrix
Figure FDA00030052623100000414
Each row of (a) is fourier transformed to form a time-frequency-amplitude matrix
Figure FDA00030052623100000415
Figure FDA0003005262310000051
Wherein, f is 1/S,2/S, …,0.5 fsThe unit: the frequency of the Hz,
Figure FDA0003005262310000052
amplitude corresponding to the Fourier transform of the kth section of time sequence signal acquired by the jth sensor;
computing matrices
Figure FDA0003005262310000053
Each column and vector in
Figure FDA0003005262310000054
To obtain a time-frequency correlation coefficient vector RijComprises the following steps:
Figure FDA0003005262310000055
wherein Cov () is covariance, D () is mean square error, i is 1,2,3, …, M, i is different measuring points in the passenger room, j is 1,2,3, …, G, j is different measuring points of the excitation source, r (f) is matrix
Figure FDA0003005262310000056
Corresponding column and vector when the medium frequency is f
Figure FDA0003005262310000057
The correlation coefficient of (a);
determining a vector RijIf the maximum correlation coefficient is larger than or equal to the strong correlation index, the abnormal vibration of the passenger room internal measuring point i and the abnormal vibration of the passenger room internal measuring point i are representedParts corresponding to the excitation source measuring point j are strongly correlated; if strongly correlated, then vector R is determined according to the maximum correlation coefficientijAnd determining the frequency f corresponding to the abnormal vibration of the passenger room internal measuring point i.
8. The diagnostic device of claim 7, wherein: the signal acquisition module adopts a multi-channel data acquisition card.
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