CN106096530A - A kind of Modal Parameters Identification under strong background noise environment - Google Patents
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Abstract
A kind of Modal Parameters Identification under strong background noise environment, first pass through power hammer tap test and record one-level impulse response signal, then by spectrum-subtraction, one-level impulse response signal is carried out preliminary noise reduction, obtain secondary vein punching response signal, re-use Minimum Mean Square Error short time spectrum method and secondary vein punching response signal is carried out secondary noise reduction, obtain preferable impulse response signal, finally use Modal Parameter Identification algorithm that preferable impulse response letter is carried out Modal Parameter Identification, the present invention has self adaptation, calculating speed is fast, the Modal Parameter Identification advantage such as accurately under strong noise environment.
Description
Technical field
The present invention relates to mould measurement field, particularly to the Modal Parameter Identification side under a kind of strong background noise environment
Method.
Background technology
Modal Parameter Identification technology plays important in fields such as fault diagnosis, dynamic response analysis and Modifying model
Effect, identification parameter includes natural frequency, damping ratio and the vibration shape.Conventional Modal Parameters Identification is divided into frequency domain and time domain
Two big classes.Frequency domain and Time domain identification method all based on the impulse response signal that obtains of test data carry out modal parameter knowledge
Not, impulse response signal accurately and reliably is therefore obtained most important.Due to signal measuring, in transmitting procedure inevitably
By sound pollution in various degree, particularly in the mould measurement of large scale structure, around the parts such as motor, pump, air-conditioning produce
Raw very noisy is relatively big on impulse response signal impact, and the accuracy of Modal Parameter Identification is by extreme influence.
For the problems referred to above, currently mainly use the modes such as statistical average, wavelet de-noising and singular value decomposition to eliminate and make an uproar
The sound interference to impulse response signal, improves Modal Parameter Identification accuracy.Wherein, statistical average must assure that in measurement process
Middle parameter does not changes, and otherwise uses statistical average nonsensical.And wavelet de-noising and singularity value decomposition are required for root
Arranging different threshold parameter according to noise power, no survey noise reduction is impacted.Meanwhile, show currently without result of study,
When the periodic component in very noisy and natural frequency close to or overlapping time, existing method can accurately identify modal parameter.Cause
This, there is limitation in current Modal Parameters Identification, also room for improvement.
Summary of the invention
In order to overcome the shortcoming of above-mentioned prior art, it is an object of the invention to provide under a kind of strong background noise environment
Modal Parameters Identification, improves Precision of Estimating Modal Parameter, has self adaptation strong, calculates speed fast, solid under strong noise environment
The advantage such as have frequency identification accurately.
In order to achieve the above object, the technical scheme that the present invention takes is:
Modal Parameters Identification under a kind of strong background noise environment, comprises the following steps:
Step one, hammers tap test into shape by power, records one-level impulse response signal y (t), the one-level impulse response letter recorded
Number y (t) comprises preferable impulse response signalWith ambient noise signal n (t), t represents the time；
Step 2, uses Optimal Smoothing Algorithm, the noise Estimation Algorithm of minimum statistics, or minimum controlled recursive average method
In any one obtain one-level impulse response signal y (t) background noise estimated value
Step 3, estimates the background noise of one-level impulse response signal y (t) recorded He one-level impulse response signal y (t)
EvaluationCarry out framing, add Hanning window and FFT process, obtain y (t) amplitude spectrum Y (p, k) and phase spectrum φ (p, k), andAmplitude spectrum(p, k) represents the kth root spectral component amplitude of the pth frame signal of y (t) to Y, and (p k) represents y to φ
The kth root spectral component phase place of the pth frame signal of (t),RepresentThe kth root spectral component amplitude of pth frame signal,
During framing, every frame length is the sample frequency of 0.02 times, front and back uses the degree of overlapping of 50% between frame；
Step 4, by spectrum-subtraction, to the amplitude spectrum Y recording one-level impulse response signal y (t), (p, k) carries out noise reduction process, obtains
Compose to preliminary noise reduction amplitudeComputing formula is:
Wherein, spectrum subtracts factor alpha=9, β=0.05；
Step 5, the phase spectrum φ that step 3 is obtained (p, preliminary noise reduction amplitude spectrum k) and obtained in step 4In conjunction with, by inverse fourier transform and overlap-add, obtain secondary vein punching response signal y1(t)；
Step 6, uses Optimal Smoothing Algorithm, the noise Estimation Algorithm of minimum statistics, or minimum controlled recursive average method
In any one obtain secondary vein punching response signal y1The background noise estimated value of (t)
Step 7, to secondary vein punching response signal y1(t) and secondary vein punching response signal y1The background noise estimated value of (t)Carry out framing, add Hanning window and FFT process, obtain secondary vein punching response signal y1The amplitude spectrum Y of (t)1(p, k) and phase place
Spectrum φ1(p, k), andAmplitude spectrumY1(p k) represents y1The kth root spectral component width of the pth frame signal of (t)
Value, φ1(p k) represents y1The kth root spectral component phase place of the pth frame signal of (t),RepresentPth frame signal
Kth root spectral component amplitude, during framing, every frame length is the sample frequency of 0.2 times, front and back uses the degree of overlapping of 50% between frame；
Step 8, calculates secondary vein punching response signal y1Each frame posteriori SNR estimated value of (t), computing formula is:Wherein,RepresentVariance,With Y1(p, k) in step
It is calculated in rapid five；
Step 9, by Minimum Mean Square Error short time spectrum method to secondary vein punching response signal y1The amplitude spectrum Y of (t)1
(p, k) carries out secondary noise reduction process, obtains secondary noise reduction amplitude spectrumThis calculating process uses endless form to enter
OK, circulation starts to last frame to terminate from the first frame, carries out successively 1.～5. walking calculating below, and step is as follows:
1. use classical DD algorithm, calculate secondary vein punching response signal y1The pth frame a priori SNR estimation value of (t), meter
Calculation formula is:
Wherein, attenuation quotient β '
=0.98；It is calculated in step 8；Initial value be set to 0 vector, later each frame
The is used 5. to walk result of calculation；The posteriori SNR of the kth root spectral component representing pth+1 frame signal is estimated
Value, this value is calculated in step 8；
2. secondary vein punching response signal y is calculated1The noise suppression factor of the pth frame spectral component of (t), computing formula is:WhereinThe is used 1. to walk result of calculation；
3. use prior weight evaluation method, calculate secondary vein punching response signal y1T the pth frame prior weight of () is estimated
Evaluation, computing formula is:
Wherein, decline
Subtract factor beta '=0.98；Y1(p k) is calculated in step 7；It is calculated in step 8；GDD(p,
K) the is used 2. to walk result of calculation；It is calculated in step 8；
4. secondary vein punching response signal y is calculated1The noise suppression factor of the pth frame spectral component of (t), computing formula is:WhereinThe is used 3. to walk result of calculation；
5. by Minimum Mean Square Error short time spectrum method to Y1(p, pth frame spectral component k) carries out secondary noise reduction process,
Obtain secondary noise reduction amplitude spectrumComputing formula is:Wherein (p k) uses G
4. walk result of calculation；Y1(p k) is calculated in step 7；
Step 10, the phase spectrum φ that will obtain in step 71(p, k) and use after Minimum Mean Square Error short time spectrum method
The secondary noise reduction amplitude spectrum obtainedIn conjunction with, by inverse fourier transform and overlap-add, obtain preferable pulse and ring
Induction signal
Step 11, by Modal Parameters Identification to preferable impulse response signalCarry out modal parameter knowledge
Not.
Beneficial effects of the present invention: research finds, the method can effectively eliminate the steady letter in test pulse response signal
Continuous part in number part and non-stationary signal, retains the transient portion thereof in non-stationary signal, i.e. preferably impulse response letter
Number, thus improve Precision of Estimating Modal Parameter.
Compared to existing method, it is strong that the method has self adaptation, calculates speed fast, natural frequency identification under strong noise environment
The advantage such as accurately.Even if the method is strong at test noise, in test noise, cyclic component and structural natural frequencies extremely connect
In the case of Jin, it is also possible to accurately identify structural natural frequencies.
Accompanying drawing explanation
Fig. 1 is the steam turbine generator body diagram for Modal Parameter Identification.
Fig. 2 is one-level impulse response signal y (t) time-domain diagram of vibration measuring point.
Fig. 3 is one-level impulse response signal y (t) frequency domain figure of vibration measuring point.
Fig. 4 be vibration measuring point one-level impulse response signal y (t) processed by the invention after time-domain diagram.
Fig. 5 be vibration measuring point one-level impulse response signal y (t) processed by the invention after frequency domain figure.
Detailed description of the invention
Describe the present invention with case study on implementation below in conjunction with the accompanying drawings.
There is generator housing mesomerism problem in the generating set of certain nuclear power plant in running, in order to solve resonance
Problem, demand accurately identifies the structural natural frequencies of generator housing, owing to site environment noise is strong, and in test noise
Cyclic component and generator housing natural frequency are very close to, and existing method can not accurately identify the intrinsic frequency of generator housing
Rate.This problem is solved below by the present invention.
Modal Parameters Identification under a kind of strong background noise environment, comprises the following steps:
Step one, hammers Knock test into shape by power, records the one-level impulse response signal y vibrating measuring point on generator housing
T (), sample frequency is 500Hz, is shown that steam turbine generator body diagram with reference to Fig. 1, Fig. 1, and the casing of electromotor 3 only depends on
Being placed on installation foundation 4 by gravity, the rotor 5 of electromotor 3 is connected on generator's cover 2 by sliding bearing, electromotor 3
Casing top connect and have 2 hydrogen-cooled devices of electromotor 1, vibration point position is arranged on 1 hydrogen-cooled device of electromotor 1；Fig. 2 and Tu
3 is one-level impulse response signal time domain waveform and the frequency domain vibrating measuring point on the generator housing by obtaining after this step respectively
Oscillogram, owing to the environment noise around steam turbine generator is relatively big, in Fig. 2, the one-level impulse response signal of display is by noise
Signal contamination, in Fig. 3, a prominent frequency (49.82Hz) of display is periodic noise；
Step 2, uses Optimal Smoothing Algorithm to obtain the background noise estimated value of one-level impulse response signal y (t)
Step 3, estimates the background noise of one-level impulse response signal y (t) recorded He one-level impulse response signal y (t)
EvaluationCarry out framing, add Hanning window and FFT process, obtain y (t) amplitude spectrum Y (p, k) and phase spectrum φ (p, k), andAmplitude spectrumDuring framing, every frame length is the sample frequency of 0.02 times, front and back uses the weight of 50% between frame
Folded degree；
Step 4, by spectrum-subtraction, to the amplitude spectrum Y of one-level impulse response signal y (t), (p, k) carries out noise reduction process, obtains
Compose to preliminary noise reduction amplitudeComputing formula is:
Wherein, spectrum subtracts factor alpha=9, β=0.05；
Step 5, the phase spectrum φ that step 3 is obtained (p, preliminary noise reduction amplitude spectrum k) and obtained in step 4In conjunction with, by inverse fourier transform and overlap-add, obtain secondary vein punching response signal y1(t)；
Step 6, uses Optimal Smoothing Algorithm to obtain secondary vein punching response signal y1The background noise estimated value of (t)
Step 7, to secondary vein punching response signal y1(t) and secondary vein punching response signal y1The background noise estimated value of (t)Carry out framing, add Hanning window and FFT process, obtain secondary vein punching response signal y1The amplitude spectrum Y of (t)1(p, k) and phase place
Spectrum φ1(p, k), andAmplitude spectrumDuring framing, every frame length is the sample frequency of 0.2 times, front and back frame it
Between use 50% degree of overlapping；
Step 8, calculates secondary vein punching response signal y1Each frame posteriori SNR estimated value of (t), computing formula is:Wherein,RepresentVariance,With Y1(p, k) in step
It is calculated in five；
Step 9, by Minimum Mean Square Error short time spectrum method to secondary vein punching response signal y1The amplitude spectrum Y of (t)1
(p, k) carries out secondary noise reduction process, obtains the amplitude spectrum after secondary noise reductionThis calculating process uses endless form to enter
OK, circulation starts to last frame to terminate from the first frame, carries out successively 1.～5. walking calculating below, and step is as follows:
1. use classical DD algorithm, calculate secondary vein punching response signal y1The pth frame a priori SNR estimation value of (t), meter
Calculation formula is:
Wherein, attenuation quotient β '
=0.98；It is calculated in step 8；Initial value be set to 0 vector, later each frame
The is used 5. to walk result of calculation；The posteriori SNR of the kth root spectral component representing pth+1 frame signal is estimated
Value, this value is calculated in step 8；
2. secondary vein punching response signal y is calculated1The noise suppression factor of the pth frame spectral component of (t), computing formula is:WhereinThe is used 1. to walk result of calculation；
3. use the prior weight evaluation method of improvement, calculate secondary vein punching response signal y1The pth frame priori letter of (t)
Making an uproar compared estimate value, computing formula is:
Wherein,
Attenuation quotient β '=0.98；Y1(p k) is calculated in step 7；It is calculated in step 8；GDD
(p k) uses 2. to walk result of calculation；It is calculated in step 8；
4. secondary vein punching response signal y is calculated1The noise suppression factor of the pth frame spectral component of (t), computing formula is:WhereinThe is used 3. to walk result of calculation；
5. by Minimum Mean Square Error short time spectrum method to Y1(p, pth frame spectral component k) carries out secondary noise reduction process,
Obtain secondary noise reduction amplitude spectrumComputing formula is:Wherein (p k) uses G
4. walk result of calculation；Y1(p k) is calculated in step 7；
Step 10, the phase spectrum φ that will obtain in step 71(p, k) and use after Minimum Mean Square Error short time spectrum method
The secondary noise reduction amplitude spectrum obtainedIn conjunction with, by inverse fourier transform and overlap-add, obtain preferable pulse and ring
Induction signalIt is preferable impulse response signal with reference to Fig. 4, Fig. 4Time domain beamformer；
Step 11, to preferable impulse response signalCarry out Fourier transform, identify from corresponding frequency domain figure
Generator housing natural frequency, Fig. 4 shows that the environment noise in one-level impulse response signal y (t) is removed；Fig. 5 is preferable
Impulse response signalFrequency domain figure, the intrinsic of 48.99Hz can be there is with generator housing visible in detail from Fig. 5
Frequency, and then can determine whether that generator housing mesomerism problem is the natural frequency (48.99Hz) due to generator housing and work
Make to turn frequency (50Hz) and get too close to caused.
Above content is to combine concrete preferred implementation further description made for the present invention, it is impossible to assert
The detailed description of the invention of the present invention is only limitted to this, for general technical staff of the technical field of the invention, is not taking off
On the premise of present inventive concept, it is also possible to make some simple deduction or replace, all should be considered as belonging to the present invention by institute
The claims submitted to determine scope of patent protection.
Claims (1)
1. the Modal Parameters Identification under a strong background noise environment, it is characterised in that comprise the following steps:
Step one, hammers tap test into shape by power, records one-level impulse response signal y (t), the one-level impulse response signal y recorded
T () comprises preferable impulse response signalWith ambient noise signal n (t), t represents the time；
Step 2, uses in Optimal Smoothing Algorithm, the noise Estimation Algorithm of minimum statistics, or minimum controlled recursive average method and appoints
Meaning one obtains the background noise estimated value of one-level impulse response signal y (t)
Step 3, the background noise estimated value to one-level impulse response signal y (t) recorded He one-level impulse response signal y (t)Carry out framing, add Hanning window and FFT process, obtain y (t) amplitude spectrum Y (p, k) and phase spectrum φ (p, k), and
Amplitude spectrum(p, k) represents the kth root spectral component amplitude of the pth frame signal of y (t) to Y, and (p k) represents y's (t) to φ
The kth root spectral component phase place of pth frame signal,RepresentThe kth root spectral component amplitude of pth frame signal, framing
Time, every frame length is the sample frequency of 0.02 times, front and back uses the degree of overlapping of 50% between frame；
Step 4, by spectrum-subtraction, to the amplitude spectrum Y recording one-level impulse response signal y (t), (p, k) carries out noise reduction process, obtains
Compose to preliminary noise reduction amplitudeComputing formula is:
Wherein, spectrum subtracts factor alpha=9, β=0.05；
Step 5, the phase spectrum φ that step 3 is obtained (p, preliminary noise reduction amplitude spectrum k) and obtained in step 4
In conjunction with, by inverse fourier transform and overlap-add, obtain secondary vein punching response signal y1(t)；
Step 6, uses in Optimal Smoothing Algorithm, the noise Estimation Algorithm of minimum statistics, or minimum controlled recursive average method and appoints
Meaning one obtains secondary vein punching response signal y1The background noise estimated value of (t)
Step 7, to secondary vein punching response signal y1(t) and secondary vein punching response signal y1The background noise estimated value of (t)
Carry out framing, add Hanning window and FFT process, obtain secondary vein punching response signal y1The amplitude spectrum Y of (t)1(p, k) with phase spectrum φ1
(p, k), andAmplitude spectrumY1(p k) represents y1The kth root spectral component amplitude of the pth frame signal of (t),
φ1(p k) represents y1The kth root spectral component phase place of the pth frame signal of (t),RepresentPth frame signal
K root spectral component amplitude, during framing, every frame length is the sample frequency of 0.2 times, front and back uses the degree of overlapping of 50% between frame；
Step 8, calculates secondary vein punching response signal y1Each frame posteriori SNR estimated value of (t), computing formula is:Wherein,RepresentVariance,With Y1(p, k) in step
It is calculated in five；
Step 9, by Minimum Mean Square Error short time spectrum method to secondary vein punching response signal y1The amplitude spectrum Y of (t)1(p,k)
Carry out secondary noise reduction process, obtain secondary noise reduction amplitude spectrumThis calculating process uses endless form to carry out, and follows
Ring starts to last frame to terminate from the first frame, carries out successively 1.～5. walking calculating below, and step is as follows:
1. use classical DD algorithm, calculate secondary vein punching response signal y1The pth frame a priori SNR estimation value of (t), computing formula
For:
Wherein, attenuation quotient β '=
0.98；It is calculated in step 8；Initial value be set to 0 vector, later each frame makes
5. result of calculation is walked with；Represent the posteriori SNR estimated value of the kth root spectral component of pth+1 frame signal,
This value is calculated in step 8；
2. secondary vein punching response signal y is calculated1The noise suppression factor of the pth frame spectral component of (t), computing formula is:WhereinThe is used 1. to walk result of calculation；
3. use prior weight evaluation method, calculate secondary vein punching response signal y1The pth frame a priori SNR estimation value of (t),
Computing formula is:
Wherein, decay system
Number β '=0.98；Y1(p k) is calculated in step 7；It is calculated in step 8；GDD(p k) makes
2. result of calculation is walked with；It is calculated in step 8；
4. secondary vein punching response signal y is calculated1The noise suppression factor of the pth frame spectral component of (t), computing formula is:WhereinThe is used 3. to walk result of calculation；
5. by Minimum Mean Square Error short time spectrum method to Y1(p, pth frame spectral component k) carries out secondary noise reduction process, obtains
Secondary noise reduction amplitude is composedComputing formula is:Wherein (p k) uses to G
4. result of calculation is walked；Y1(p k) is calculated in step 7；
Step 10, the phase spectrum φ that will obtain in step 71(p k) and obtains after using Minimum Mean Square Error short time spectrum method
Secondary noise reduction amplitude spectrumIn conjunction with, by inverse fourier transform and overlap-add, obtain preferable impulse response letter
Number
Step 11, by Modal Parameters Identification to preferable impulse response signalCarry out Modal Parameter Identification.
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| CN112067116A (en) * | 2020-07-13 | 2020-12-11 | 东南大学 | Method for testing and analyzing impact vibration of medium and small bridges with noise resistance |
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