CN106842023B - The method for diagnosing faults of rotating electric machine - Google Patents
The method for diagnosing faults of rotating electric machine Download PDFInfo
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- CN106842023B CN106842023B CN201710047222.2A CN201710047222A CN106842023B CN 106842023 B CN106842023 B CN 106842023B CN 201710047222 A CN201710047222 A CN 201710047222A CN 106842023 B CN106842023 B CN 106842023B
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- frequency
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01R—MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES
- G01R31/00—Arrangements for testing electric properties; Arrangements for locating electric faults; Arrangements for electrical testing characterised by what is being tested not provided for elsewhere
- G01R31/34—Testing dynamo-electric machines
- G01R31/343—Testing dynamo-electric machines in operation
Abstract
The invention discloses a kind of method for diagnosing faults of rotating electric machine, including Time-Frequency Analysis Method and failure extracting method.The Time-Frequency Analysis Method includes, single-phase phase-locked loop estimates frequency of supply variation, then it is slided in monophase current data with a short time window, the frequency of supply at the center of time window is used to determine the best refinement frequency range of the window data, then the window data are by adaptive refinement, obtain new data, then the signal parameter contained with adaptive high-precision harmonic wave algorithm for estimating estimation new data packets.And so on, until time window sliding terminates, time-frequency figure is generated.The fault signature extracting method extracts the side frequency frequency and amplitude of fault signature using these Signal parameter estimation results as sample set.Entire method solves that high-precision harmonic wave estimation method calculation amount is huge, and in the defect of transient condition tracking fault signature.In addition, even if motor there is load in traction and the fluctuation of speed, the present invention still can Accurate Diagnosis fault signature and amplitudes.
Description
Technical field
The present invention relates to Diagnosing Faults of Electrical and state-detection field, provide a kind of fault diagnosis side of rotating electric machine
Method, the Time-Frequency Analysis Method including a kind of rotating electric machine fault diagnosis；The present invention relates to include by the diagnosis of monophase current signal
Rotor fault, bearing fault, drivetrain components failure etc..
Background technique
About the fault diagnosis of motor and electric system, cast aside inverter section and do not talk, the rotor fault of motor body and
The failure of electric system transmission shaft component is especially common.It is common if it is asynchronous machine by taking the rotor fault of motor body as an example
Failure mainly has: broken bar fault, air-gap eccentric fault, bearing fault；If it is permanent magnet synchronous motor, common failure is main
Have: demagnetization failure, air-gap eccentric fault, bearing fault.Method for diagnosing faults based on motor current signal analysis is current
Mainstream is in industrial application.
All method for diagnosing faults based on motor current signal analysis can substantially be divided into three classes: time-domain analysis side
Method, frequency-domain analysis method and Time-Frequency Analysis method.Time Domain Analysis is rare；Frequency-domain analysis method includes well known FFT,
MUSIC, Matrix Pencil, ESPRIT, Prony etc..Time-Frequency Analysis method has a well known Gabor transformation, wavelet transformation,
Short time FFT transform, Wigner-Ville transformation etc..Wherein, the resolution ratio with signal sampling frequencies and number of sampling points of FFT has
It closes, there are spectrum leakage, the leakage frequency spectrum of frequency of supply is easy to flood neighbouring sideband, such as rotor bar breaking fault, both
So in this way, we have to increase sampled data length, however motor can not in practical application when handling broken bar fault
The steady-state operation of certain time can be kept during detecting failure according to the will of fault diagnosis personnel, it is therefore desirable to when frequency division
Analysis method.Time-Frequency Analysis Method has tracking unstable signal, the advantage of jump signal.But according to the uncertain original of Heidelberg
Reason, selected basic function can not be less than 1/2 in the support width product of time domain and frequency domain；Moreover, Time Domain Analysis energy
Enough costs analyzed simultaneously in time domain and frequency domain are exactly to sacrifice frequency domain precision, therefore from precision, Time Domain Analysis can not
It can be more preferable than FFT.So, it would be desirable to the method that one kind can estimate high-precision harmonic parameters in short time data, this
Class modern signal processing method includes MUSIC, Matrix Pencil, ESPRIT, Prony etc., they are based on parameter Estimation
Angle, thus be not present spectrum leakage the problem of, the requirement to data length is very low, and noise resisting ability is strong, but the disadvantage is that
Calculation amount is huge, and antinoise signal fluctuation ability is poor, therefore compares suitable for electric system network voltage detection, is not suitable for examining
This occasion for being frequently present of load and the fluctuation of speed of power machine, especially traction electric machine.
Chinese Patent Application No. 201510791082.0 discloses a kind of adaptive M atrix for Diagnosing Faults of Electrical
The estimation for solving Matrix Pencil signal order under motor difference operating condition is claimed in Pencil method, the invention, together
When be able to suppress frequency of supply component, highlight fault signature harmonic wave.But the method for the invention is still without solving such algorithm
It is computationally intensive, it is only used for the stable state of different operating conditions, cannot be made well in the quasi-stability occasion in load and the fluctuation of speed
With three main problems.
Summary of the invention
It is computationally intensive that the present invention solves this kind of high-precision harmonic parameters estimation methods, is consequently not used for transient fault spy
Sign tracking accurately cannot estimate fault signature amplitude in the quasi-stability occasion of load and the fluctuation of speed well, these three problems.
A kind of method for diagnosing faults for rotating electric machine is provided, and including a kind of time frequency analysis for rotating electric machine fault diagnosis
Method.
Firstly, the present invention provides a kind of Time-Frequency Analysis Method of rotating electric machine fault diagnosis.The step of the method includes
A. the single-phase current data of acquisition rotating electric machine；B. it is bent the real-time frequency of supply of the current data to be calculated with single-phase phase-locked loop
Line；C. it is slided in collected current data with sliding window；D. to the current data covered by sliding window [0, Bp]
Frequency band in implement adaptive refinement process, the current data after being refined；Wherein BpIt is refinement frequency bandwidth, BpBy powering
Frequency determines；E. adaptive high-precision harmonic wave algorithm for estimating is implemented to the new current data, estimates new current data
The whole harmonic informations for being included, the harmonic information include the frequency and amplitude of harmonic wave；F. above-mentioned c-e step is repeated, until
The sliding window is moved to the end of collected current data, ultimately generates frequency patterns；G. occurred according to frequency patterns
Fault signature crestal line determines existing fault type.
Preferably, the length that the sliding window slides every time cannot be greater than the width of the sliding window；As preferred
, the refinement frequency bandwidth BpBy the frequency of supply f of sliding window centersIt determines；Further, Bp=2fs.As preferred
, the adaptive refinement process includes anti-aliasing filter and down-sampled, wherein the frequency overlapped-resistable filter of the anti-aliasing filter
Order NoWith the down-sampled down-sampled multiple D all with the refinement frequency bandwidth BpAdjust automatically；Preferably, institute
State the ripples FIR filters such as frequency overlapped-resistable filter is；The wherein order N of frequency overlapped-resistable filteroBy the refinement frequency bandwidth BpThrough
Parks-McClellan algorithm obtains；Preferably, the down-sampled multiple D of the decimator presses formulaIt calculates, wherein FsIt is the sample frequency of current data, ΩxIt is to consider low-pass filtering
The bandwidth of intermediate zone, η are greater than 0 and the nargin coefficient less than 1, symbolExpression is rounded downwards x, BpIt is the refinement frequency band
Width；η is preparatory determined constant value, ΩxWith BpDifference be preparatory determined constant value, the two can be according to technology people
Member it needs to be determined that.
In the specific implementation process, adaptive high-precision harmonic wave algorithm for estimating refers to the rank in high-precision harmonic wave algorithm for estimating
Number estimation procedure does not need to manually adjust, but is calculated by mathematical algorithm, such as Chinese Patent Application No.
201510791082.0 the signal Order- reduction based on maximal possibility estimation of invention, can not only be used to Matrix
Pencil can also be used for ESPRIT, the high-precision harmonic wave algorithm for estimating of this kind such as MUSIC, Prony.
On the other hand, the present invention discloses a kind of method for diagnosing faults of rotating electric machine, and the method includes being used for electric rotating
The Time-Frequency Analysis Method of machine fault diagnosis and a kind of failure extracting method.Firstly, being obtained by the Time-Frequency Analysis Method complete
Harmonic information needs in portion's are organized into { (fi,Ai)|i∈N+Aggregate form give failure extracting method processing, wherein fiTable
Show the frequency of i-th of data sample, AiIndicate the amplitude of i-th of data sample.
The step of failure extracting method includes: the frequency of occurrence for recording each Frequency point in the set, is made
Frequency distribution, horizontal axis dimension are frequencies, and longitudinal axis dimension is frequency of occurrence；When sample size is enough, all existing harmonic waves
There will be a spike in the frequency distribution；The fault characteristic frequency in spike is extracted as fault signature side frequency frequency
Rate；The magnitude extraction that all in the set and fault signature side frequency frequency matches is come out, the appearance of each amplitude is recorded
Number makes fault signature side frequency amplitude distribution figure, and horizontal axis dimension is decibel, and longitudinal axis dimension is frequency of occurrence；With distribution letter
Number is fitted the statistical distribution of the fault signature side frequency amplitude distribution figure, obtains amplitude expectation and the width of the fault signature side frequency
It is worth variance；The amplitude Expectation-based Representation for Concepts fault degree, the amplitude variance indicate fault degree deviation.
Method proposed by the present invention solves the problems, such as that the shared calculation amount of this kind of high-precision harmonic wave algorithm for estimating is huge,
Fault signature tracking of this kind of high-precision harmonic wave algorithm for estimating under instantaneous motor operating condition is realized, the last present invention mentions
Method out can be used in the fluctuation of speed, and the fault degree of the quasi-stability process of the fluctuation of load is estimated, diagnostic result deviation
It is small.
Detailed description of the invention
The preferred embodiment of the present invention is elaborated below with reference to the accompanying drawings.
Fig. 1 is the Time-Frequency Analysis Method flow chart of rotating electric machine fault diagnosis of the present invention.
Fig. 2 is the frequency patterns that Time-Frequency Analysis Method described in a specific embodiment of the invention generates.
Fig. 3 is a kind of method for diagnosing faults flow chart of rotating electric machine of the invention.
Fig. 4 is the frequency distribution that method for diagnosing faults described in a specific embodiment of the invention generates.
Fig. 5 is the broken bar fault feature side frequency width that method for diagnosing faults described in a specific embodiment of the invention generates
Distribution value and its fitted figure.
Specific embodiment
It should be appreciated that preferred embodiments are merely illustrative of the invention, rather than limiting the scope of protection of the present invention.
With the rotor fault that asynchronous machine is common, for fault of eccentricity and broken bar fault.Known fault of eccentricity is in mutually electricity
Existing characteristics frequency is [1 ± k (1-s)/p] f in streamsHarmonic wave, broken bar fault in phase current existing characteristics frequency be (1 ±
2ks)fsCharacteristic harmonics, wherein k is positive integer, fsIt is frequency of supply, frIt is rotor rotating machinery frequency, s is revolutional slip, and p is
Number of pole-pairs.
In the present embodiment, inverter output 35Hz voltage in a manner of VVVF open loop drive a 15-kW, the pole 6- it is different
Walk motor.Specific embodiment is as shown in Figure 1.Acquire the motor wherein any one phase current data, acquisition length 2 seconds,
Sample frequency is 10kHz.The real-time frequency of supply f of the current data is calculated with single-phase phase-locked loopsCurve；Existed with sliding window
It is slided in collected current data, the length that sliding window slides every time cannot be greater than the width of sliding window；Processing is by sliding window
The current data of covering；To [the 0, B of the data covered every time by sliding windowp] frequency band implement adaptive refinement process, obtain
New current data after refinement, wherein BpIt is refinement frequency bandwidth, Bp=2fs, wherein fsIt is the frequency of supply for sliding window center.
Adaptive high-precision harmonic wave algorithm for estimating is implemented to the new current data, estimates the harmonic wave that new current data is included
Information, the harmonic information include the frequency and amplitude of the whole harmonic waves occurred；In a preferred embodiment, it is so-called from
Adapting to high-precision harmonic wave algorithm is improved Matrix Pencil algorithm perhaps improved ESPRIT algorithm or improved
Prony algorithm etc., and the part of these algorithm improvements is, the signal order for needing to predict in advance in algorithm, is no longer by skill
Art personnel's assignment, but obtained by maximal possibility estimation.In a preferred embodiment, improved Matrix is selected
Pencil algorithm is as adaptive high-precision harmonic wave algorithm.
In a preferred embodiment, the adaptive refinement process includes anti-aliasing filter and down-sampled, wherein institute
The order No and down-sampled down-sampled multiple D of the frequency overlapped-resistable filter of anti-aliasing filter are stated all with the refinement frequency bandwidth Bp
Adjust automatically.The ripples FIR filters such as the frequency overlapped-resistable filter is, the refinement frequency bandwidth BpThrough Parks-
McClellan algorithm obtains the order No.The down-sampled multiple D presses formula
It calculates, wherein FsIt is the sample frequency of current data, ΩxIt is the bandwidth for considering low-pass filtering intermediate zone, η is greater than 0 and less than 1
Nargin coefficient, symbolExpression is rounded downwards x；η can be equal to 0.8, ΩxWith BpDifference remain constant.
Until sliding window is moved to the end of collected current data, the Time-Frequency Analysis Method ultimately generates time-frequency figure
Case, as shown in Figure 2.The fault signature crestal line occurred according to fig. 2 determines this in conjunction with previously described fault characteristic frequency formula
There are broken bar faults and rotor eccentricity failure for asynchronous motors.Figure it is seen that rotor eccentricity, and especially broken bar fault is special
Sign is being shaken always, illustrates that slip frequency s is changing always, this shake is that there are the fluctuations of speed to cause for motor open loop operation
, it can be seen that Time-Frequency Analysis Method proposed by the present invention can accurately reflect this shake, illustrate of the invention certain
High-precision fault signature tracking in short-term is showed.
At the same time, it should also be noted that arrive, since this asynchronous motors is in quasi-stability operation, only with primary or several
Secondary fault detection will not obtain consistent as a result, can perplex technical staff.Therefore frequency division when the present invention further will be described
Analysis method invents the method for diagnosing faults of a complete rotating electric machine in conjunction with the failure extracting method.Frequency division when described
Whole harmonic informations needs that analysis method obtains are organized into { (fi,Ai)|i∈N+Aggregate form give the failure extraction side
Method is handled, wherein fiIndicate the frequency of i-th of data sample, AiIndicate the amplitude of i-th of data sample.
As shown in figure 3, the step of failure extracting method includes: the occurrence out for recording each Frequency point in the set
Number, makes frequency distribution, as shown in figure 4, extracting the fault characteristic frequency in spike as fault signature side frequency frequency.It will
The magnitude extraction that all and fault signature side frequency frequency matches in the set comes out, and records the frequency of occurrence of each amplitude,
Fault signature side frequency amplitude distribution figure is made, the statistics of the fault signature side frequency amplitude distribution figure is then fitted with distribution function
Distribution, as shown in figure 5, the amplitude expectation and amplitude variance of the fault signature side frequency can be obtained from Fig. 5.The amplitude expectation
Indicate fault degree, the amplitude variance indicates fault degree deviation.As it can be seen that variance is very small, it is good to illustrate that fault degree obtains
Good estimation.
Claims (2)
1. a kind of Time-Frequency Analysis Method of rotating electric machine fault diagnosis, which is characterized in that the described method includes:
A. the single-phase current data of acquisition rotating electric machine；
B. the real-time frequency of supply curve of the current data is calculated with single-phase phase-locked loop；
C. it is slided in collected current data with sliding window, the length that the sliding window slides every time cannot be greater than the cunning
The width of dynamic window；
D. to the current data covered by sliding window [0, Bp] frequency band in implement adaptive refinement process, refined
Current data afterwards；Wherein BpIt is refinement frequency bandwidth, BpIt is determined by frequency of supply；The refinement frequency bandwidth BpBy sliding window
The frequency of supply f at centersIt determines, Bp=2fs；The adaptive refinement process includes anti-aliasing filter and down-sampled, wherein described
The order N of the frequency overlapped-resistable filter of anti-aliasing filteroWith the down-sampled down-sampled multiple D all with the refinement frequency bandwidth
BpAdjust automatically；The down-sampled multiple D presses formulaIt calculates, wherein FsIt is electric current number
According to sample frequency, ΩxIt is the bandwidth for considering low-pass filtering intermediate zone, η is greater than 0 and the nargin coefficient less than 1, symbolTable
Show and x is rounded downwards；η is preparatory determined constant value, ΩxWith BpDifference be preparatory determined constant value；The anti-aliasing filter
The ripples FIR filters such as wave device is, the refinement frequency bandwidth BpThe order N is obtained through Parks-McClellan algorithmo；
E. adaptive high-precision harmonic wave algorithm for estimating is implemented to the new current data, estimating new current data is included
Whole harmonic informations, the harmonic information includes the frequency and amplitude of harmonic wave；The high-precision harmonic wave algorithm for estimating includes square
Battle array beam, gyrator space invariance, Prony；The adaptive finger maximal possibility estimation judges high-precision harmonic wave algorithm for estimating
Matrix order necessary to intermediate computations；
F. above-mentioned c-e step is repeated, until the sliding window is moved to the end of collected current data, ultimately generates time-frequency
Pattern；
G. the fault signature crestal line occurred according to frequency patterns determines existing fault type.
2. a kind of method for diagnosing faults of rotating electric machine, which is characterized in that the method includes it is described in claim 1 when frequency division
Analysis method and a kind of failure extracting method；The whole harmonic informations needs obtained from the Time-Frequency Analysis Method are organized into
{(fi,Ai)|i∈N+Aggregate form give failure extracting method processing, wherein fiIndicate the frequency of i-th of data sample,
AiIndicate the amplitude of i-th of data sample；The failure extracting method includes:
A. the frequency of occurrence for recording each Frequency point in the set makes frequency distribution, and horizontal axis dimension is frequency, indulges
Axis dimension is frequency of occurrence；When sample size is enough, all existing harmonic waves can all have a point in the frequency distribution
Peak；The fault characteristic frequency in spike is extracted as fault signature side frequency frequency；
B. the magnitude extraction that all in the set and fault signature side frequency frequency matches is come out, records going out for each amplitude
Occurrence number makes fault signature side frequency amplitude distribution figure, and horizontal axis dimension is decibel, and longitudinal axis dimension is frequency of occurrence；
C. it is fitted the statistical distribution of the fault signature side frequency amplitude distribution figure with distribution function, obtains the fault signature side frequency
Amplitude expectation with amplitude variance；The amplitude Expectation-based Representation for Concepts fault degree, the amplitude variance indicate fault degree deviation.
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Cited By (1)
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| CN108919120A (en) * | 2018-07-20 | 2018-11-30 | 南京怡咖电气科技有限公司 | A kind of energy internet fault diagnosis method for power converter |
| WO2020155147A1 (en) * | 2019-02-02 | 2020-08-06 | 深圳市大疆创新科技有限公司 | Motor operating state obtaining method and apparatus |
| CN112433151B (en) * | 2019-08-26 | 2024-01-16 | 上海汽车集团股份有限公司 | Oil pump motor fault diagnosis method and device |
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