CN110160642B - Propeller noise line spectrum reconstruction method under small sample condition - Google Patents
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Abstract
The invention discloses a propeller noise line spectrum reconstruction method under a small sample condition, which comprises the following steps: (1) collecting a noise signal of the propeller; (2) detecting the collected noise signals according to the spectrum correlation function to obtain a circular modulation spectrum CMS; (3) carrying out compressed sensing observation on the cyclic modulation spectrum by using the measurement matrix to obtain a measurement result of a small sample, and then reconstructing to obtain a reconstructed cyclic modulation spectrum CMS 1; (4) obtaining a corresponding reinforced envelope spectrum EES according to the circular modulation spectrum CMS, and obtaining a corresponding reinforced envelope spectrum EES1 according to the reconstructed circular modulation spectrum CMS 1; (5) and performing line spectrum feature comparison through the feature information and the correlation coefficient. The invention can demodulate the interference of carrier signals such as fluid noise, environmental noise and the like, and accurately reconstruct the original signal characteristics of the propeller, thereby identifying the operating characteristics of the propeller and having strong practicability.
Description
Technical Field
The invention belongs to the field of signal processing, and particularly relates to a propeller noise line spectrum reconstruction method under a small sample condition.
Background
Propellers are important power components of a variety of vehicles and propulsion equipment. Because the method usually works in the working conditions of high-load operation and complex environment, the signal components of the method often contain complex non-stationary signals consisting of environmental noise (carrier waves) and modulation signals (shaft frequency, blade frequency and the like) of a propeller, the complex coupling relation among various signals causes that a plurality of traditional signal analysis methods are not applicable any more, and a characteristic line spectrum can not be extracted, so that the signal performance of a plurality of fault initial stages can be buried.
At present, the propeller noise signal line spectrum reconstruction methods commonly used in the field of signal processing mainly include Fourier transform, short-time Fourier transform, wavelet transform and the like, and all the methods aim to process signals by applying a basis function matched with a target signal and extract signal characteristics. However, the methods are all established on the basis that the target signal is a stationary signal, most signals in the actual industrial environment are non-stationary signals, for rotating machines such as propellers and the like, the noise signal is a special non-stationary signal, and the implicit periodic characteristics in the noise signal need to be reflected by high-order statistics, so that the traditional signal processing method is not suitable; meanwhile, the characteristics of the signals are weak at the initial stage of the fault, and are difficult to extract due to the modulation effect of various signals.
The cyclostationary analysis and the line spectrum reconstruction usually adopt second-order or higher-order statistics for feature extraction, and because the accurate higher-order cyclic statistics depends on a longer time sequence and a higher sampling rate, and massive data brings huge burden for data storage and transmission, the efficiency of data processing is also reduced, a method for obtaining a signal feature line spectrum under the condition of a small sample is necessarily sought, and the timeliness and the accuracy of signal analysis and processing are improved.
Disclosure of Invention
The invention provides a propeller noise line spectrum reconstruction method under the condition of a small sample, which can demodulate the interference of carrier signals such as fluid noise, environmental noise and the like and accurately reconstruct the original signal characteristics of the propeller, thereby identifying the operating characteristics of the propeller and having strong practicability.
The technical scheme of the invention is as follows:
a propeller noise line spectrum reconstruction method under a small sample condition comprises the following steps:
(1) acquiring a noise signal of the propeller by using a hydrophone;
(2) detecting the collected noise signals according to the spectrum correlation function to obtain a circular modulation spectrum CMS;
(3) carrying out compressed sensing observation on the cyclic modulation spectrum by using the measurement matrix to obtain a measurement result of a small sample, and then reconstructing to obtain a reconstructed cyclic modulation spectrum CMS 1;
(4) obtaining a corresponding reinforced envelope spectrum EES according to the circular modulation spectrum CMS, and obtaining a corresponding reinforced envelope spectrum EES1 according to the reconstructed circular modulation spectrum CMS 1;
(5) and performing line spectrum feature comparison through the feature information and the correlation coefficient.
In the step (2), the definition of the spectrum correlation function is as follows:
wherein T is time, and T is the total duration of the sequence to be processed; f. of1And f2Representing two frequencies calculated, the correlation between the two being reflective of modulation information; x is the number ofΔf(t,f1) Representing the signal x (t) at a central frequency f1Frequency range of [ f ]1-Δf1/2,f1+Δf1/2]A filtered time domain waveform result;
denotes xΔf(t,f2) The conjugate complex number of (a); j denotes an imaginary unit.
The above formula is a definition formula of the cyclostationary analysis spectrum correlation function, f1And f2After traversing the entire frequency domain range, a two-dimensional cyclic modulation spectrum CMS (f, α) can be derived with respect to the spectral frequency f and the cyclic frequency α.
In step (3), the process of compressed sensing observation is represented as:
y＝Φx
wherein, x ∈ RNIs an N × 1-dimensional original signal, here represented as each column of a two-dimensional cyclic modulation spectrum;Φ∈RM×Nis an M × N dimensional measurement matrix, y ∈ RMIs an observed value of M × 1 dimension, wherein M/N is a sampling rate, 0<M/N<1。
The reconstruction process is carried out based on a compressed sensing measurement result y and a measurement matrix phi, the reconstruction algorithm adopts a total variation regularization algorithm based on an alternating direction Lagrange multiplier, and the formula is as follows:
where β and μ are fault tolerance constants, viAnd λ is the optimum multiplier, Dix＝ωiRepresenting the discrete gradient of the signal x at the ith position, and solving for ω and x by iteration.
In the step (4), the formula for obtaining the corresponding emphasis envelope spectrum EES according to the cyclic modulation spectrum CMS is as follows:
EES(α)＝∫fCMS(f,α)df
where f is the spectral frequency and α is the cycle frequency.
In the step (5), the characteristic information includes an axis frequency, a leaf frequency and a relationship between the axis frequency and the leaf frequency of the signal: leaf frequency is axial frequency and leaf number; the correlation coefficient is expressed as:
wherein, EES is an enhanced envelope spectrum obtained by performing cyclostationary analysis on an original signal, EES1 is an enhanced envelope spectrum obtained by performing cyclostationary analysis on a reconstructed cyclic modulation spectrum, and D (EES) and D (EES1) respectively represent the variance of the two; cov (EES, EES1) represents the covariance (degree of dispersion of data) between them, and the specific calculation formula is:
Cov(EES,EES1)＝E(EES*EES1)-E(EES)*E(EES1)
where E (EES × EES1) is the mathematical expectation of the product of EES and EES1, E (EES) is the mathematical expectation of EES, and E (EES1) is the mathematical expectation of EES 1.
Compared with the prior art, the invention has the following beneficial effects:
1. according to the invention, the acquired noise signals are processed through the cyclostationary analysis based on the compressed sensing technology, and the modulation information under the complex working condition is accurately reconstructed under the condition of a small sample, so that the propeller in the running state is accurately identified and positioned, and the information processing efficiency is improved.
2. The method can break through the defects of the traditional spectrum analysis under the complex working condition, comprehensively utilizes the advantages of high-order statistics in the cyclostationary analysis and the advantages of a compressed sensing analysis small sample, can still accurately extract the characteristic line spectrum from the complex working condition on the basis that the traditional spectrum cannot extract useful information, and provides an efficient mode for the identification and diagnosis of the propeller.
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FIG. 1 is a schematic flow chart of a propeller noise line spectrum reconstruction method under a small sample condition according to the present invention;
FIG. 2 is a graph of a spectrum of a propeller noise signal after fast Fourier transform in an embodiment of the present invention;
FIG. 3 is a circular modulation spectrum CMS corresponding to the original noise signal of the propeller according to the embodiment of the present invention;
FIG. 4 is a CMS1 of a cyclic modulation spectrum reconstructed by a compressed sensing method according to an embodiment of the present invention;
FIG. 5 is a reinforced envelope spectrum EES corresponding to the circular modulation spectrum CMS according to the embodiment of the present invention;
FIG. 6 is a reinforced envelope spectrum EES1 corresponding to the circular modulation spectrum CMS1 according to an embodiment of the present invention.
Detailed Description
The invention will be described in further detail below with reference to the drawings and examples, which are intended to facilitate the understanding of the invention without limiting it in any way.
As shown in fig. 1, a method for reconstructing a propeller noise line spectrum under a small sample condition includes the following steps:
and S01, acquiring the noise signal of the underwater propeller by using the hydrophone.
S02, detecting the collected noise signals by using a spectrum correlation function to obtain a circular modulation spectrum CMS; wherein the spectral correlation function is defined as:
wherein T is time, and T is the total duration of the sequence to be processed; f. of1And f2Representing two frequencies calculated, the correlation between the two being reflective of modulation information; x is the number ofΔf(t,f1) Representing the signal x (t) at a central frequency f1Frequency range of [ f ]1-Δf1/2,f1+Δf1/2]A filtered time domain waveform result;
denotes xΔf(t,f2) The conjugate complex number of (a); j denotes an imaginary unit. The above formula is a definition formula of the cyclostationary analysis spectrum correlation function, f1And f2After traversing the entire frequency domain range, a two-dimensional cyclic modulation spectrum CMS (f, α) can be derived with respect to the spectral frequency f and the cyclic frequency α.
For the noise signal of the propeller, it can be simplified to the following model:
wherein v (t) is a random carrier signal, AiFor real numbers, representing the modulus of the cosine signal, αiThe modulation frequency to be detected.
Let α be f1-f2When is coming into contact with
Or
Or + - αiWhile corrx(f1,f2) Not zero, i.e. the cycle frequency to be detected is detected, and the frequency f and cycle can be calculatedThe loop frequency α two-dimensional cyclic modulation spectrum CMS (f, α).
S03, carrying out compressed sensing observation on the cyclic modulation spectrum by using the measurement matrix to obtain a measurement result of the small sample;
the resolution of the cyclic modulation spectrum obtained by the calculation of the spectrum correlation function is huge, and the spectrum in the whole range is not necessary to be compressed and sensed, so that only the limited low-frequency range containing useful information is extracted for compression and observation, the resolution of the finally obtained image to be processed is 10000 x 681, wherein 10000 is the number of points with continuous frequency, the value range of the number of points is about 0-2500 Hz, 681 is the number of points with discrete cyclic frequency, the value range of the number of points is about 0-15 Hz, and the cyclic modulation spectrum to be compressed and sensed is CMS 0.
The observation process can be expressed as: y ═ Φ x
Wherein, x ∈ RNIs an N × 1-dimensional original signal, which can be represented here as each column of a two-dimensional cyclic modulation spectrum CMS0, phi ∈ RM×NIs an M × N dimensional measurement matrix, here a random Gaussian measurement matrix, y ∈ RMIs an observed value of M × 1 dimension, wherein M/N is a sampling rate (0)<M/N<1) And taking M/N as 0.25.
S04, reconstructing the observation signal according to the observation result and the measurement matrix to obtain a reconstructed cyclic modulation spectrum CMS 1; the reconstruction algorithm adopts a total variation regularization algorithm based on an alternating direction Lagrange multiplier. The reconstruction process can be expressed as:
wherein y, phi and x have the same meanings, β and mu are fault-tolerant constants, viAnd λ is the optimum multiplier, Dix＝ωiRepresenting the discrete gradient of the signal x at the ith position, x can be found iteratively.
S05, performing an integration operation on the cyclic modulation spectrum CMS and the cyclic modulation spectrum 1(CMS1) at each cyclic frequency to obtain an enhanced envelope spectrum EES and an enhanced envelope spectrum 1(EES1) corresponding to the reconstructed cyclic modulation spectrum, and when the amplitude difference between the CMS and the CMS1 is large, performing a normalization operation on the cyclic modulation spectrum to obtain a cyclic correlation spectrum csc (cyclic spectrum), and then performing the integration operation to obtain the enhanced envelope spectrum. The process of deriving the emphasis envelope spectrum can be expressed as:
EES(α)＝∫fCMS(f,α)df
the process of normalization can be expressed as:
s06, comparing the line spectrum difference between the emphasized envelope spectrum EES and the emphasized envelope spectrum 1(EES1) using the correlation coefficient; meanwhile, the characteristic frequency in the line spectrum is judged and extracted according to the running state of the propeller. The correlation coefficient may be expressed as:
wherein, EES is an enhanced envelope spectrum obtained by performing cyclostationary analysis on an original signal, EES1 is an enhanced envelope spectrum obtained by performing cyclostationary analysis on a reconstructed cyclic modulation spectrum, and D (EES) and D (EES1) respectively represent the variance (the discrete degree of data) of the two; cov (EES, EES1) represents the covariance between them, and is calculated by the following formula:
Cov(EES,EES1)＝E(EES*EES1)-E(EES)*E(EES1)
where E (EES × EES1) is the mathematical expectation of the product of EES and EES1, E (EES) is the mathematical expectation of EES, and E (EES1) is the mathematical expectation of EES 1.
In order to show the advantages and characteristics of the method in the aspect of propeller noise line spectrum reconstruction, line spectrum reconstruction is carried out on propeller signals which are acquired by an actual hydrophone and have the underwater rotating speed of 72r/min and the number of 5 blades, and the acquired signals are accompanied by the modulation action of axial frequency and blade frequency. Fig. 2 is a result of performing fast fourier transform on the acquired signal, and the frequency spectrum has the highest amplitude at 1Hz and 6Hz, but this does not correspond to the axial frequency and the leaf frequency, and the correspondence based on the leaf number cannot be obtained, so that we cannot obtain useful information about the propeller from the frequency spectrum. Fig. 3 is a cyclic modulation spectrum to be processed, which has strong sparsity, and the result obtained by performing compressed sensing processing at a sampling rate of 0.25 and then performing reconstruction is shown in fig. 4, since the cyclic frequency characteristic shown in fig. 3 is not obvious enough, the enhanced envelope spectrum of fig. 4 is obtained by integration, which can represent more information and is easy to distinguish, and it is obvious that fig. 5 and 5 both demodulate the axial frequency and the leaf frequency from a complex noise signal, where the axial frequency is about 1.2Hz, the leaf frequency is about 6.0Hz, and the corresponding leaf number n is 6.0/1.2 is 5, which also satisfies the characteristic of the measured propeller. The correlation coefficient of the line spectrum of fig. 5 and 6 is about 0.98, and the local characteristic information and the overall information are very close.
The embodiment is based on the small sample condition, the cyclostationary analysis and the compressive sensing technology are applied to analyze the underwater propeller, the characteristic line spectrum which cannot be obtained by the traditional spectrum analysis can be accurately extracted under the condition of low sampling rate, and the practicability and the reliability of the method for detecting the noise characteristic of the propeller are proved.
The embodiments described above are intended to illustrate the technical solutions and advantages of the present invention, and it should be understood that the above-mentioned embodiments are only specific embodiments of the present invention, and are not intended to limit the present invention, and any modifications, additions and equivalents made within the scope of the principles of the present invention should be included in the scope of the present invention.
Claims (3)
1. A propeller noise line spectrum reconstruction method under a small sample condition is characterized by comprising the following steps:
(1) collecting a noise signal of the propeller;
(2) detecting the collected noise signals according to the spectrum correlation function to obtain a circular modulation spectrum CMS;
(3) carrying out compressed sensing observation on the cyclic modulation spectrum by using the measurement matrix to obtain a measurement result of a small sample, and then reconstructing to obtain a reconstructed cyclic modulation spectrum CMS 1; the process of compressed sensing observation is represented as:
y＝Φx
wherein, x ∈ RNIs the original signal of dimension N × 1, here represented as each column of the two-dimensional cyclic modulation spectrum；Φ∈RM×NIs an M × N dimensional measurement matrix, y ∈ RMIs an observed value of M × 1 dimension, wherein M/N is a sampling rate, 0<M/N<1；
The reconstruction process is carried out based on a compressed sensing measurement result y and a measurement matrix phi, the reconstruction algorithm adopts a total variation regularization algorithm based on an alternating direction Lagrange multiplier, and the formula is as follows:
where β and μ are fault tolerance constants, viAnd λ is the optimum multiplier, Dix＝ωiRepresenting the discrete gradient of the signal x at the ith position, and solving omega and x through iteration;
(4) obtaining a corresponding reinforced envelope spectrum EES according to the circular modulation spectrum CMS, and obtaining a corresponding reinforced envelope spectrum EES1 according to the reconstructed circular modulation spectrum CMS 1;
(5) performing line spectrum feature comparison through the feature information and the correlation coefficient; the characteristic information comprises the axial frequency, the leaf frequency and the relation between the axial frequency and the leaf frequency of the signal: leaf frequency is axial frequency and leaf number; the correlation coefficient is expressed as:
wherein, EES is an enhanced envelope spectrum obtained by performing cyclostationary analysis on an original signal, EES1 is an enhanced envelope spectrum obtained by performing cyclostationary analysis on a reconstructed cyclic modulation spectrum, and D (EES) and D (EES1) respectively represent the variance of the two; cov (EES, EES1) represents the covariance between them, and is calculated by the following formula:
Cov(EES,EES1)＝E(EES*EES1)-E(EES)*E(EES1)
where E (EES × EES1) is the mathematical expectation of the product of EES and EES1, E (EES) is the mathematical expectation of EES, and E (EES1) is the mathematical expectation of EES 1.
2. The method for reconstructing a propeller noise line spectrum under a small sample condition according to claim 1, wherein in the step (2), the spectral correlation function is defined as:
wherein T is time, and T is the total duration of the sequence to be processed; f. of1And f2Representing two frequencies calculated, the correlation between the two being reflective of modulation information; x is the number ofΔf(t,f1) Representing the signal x (t) at a central frequency f1Frequency range of [ f ]1-Δf1/2,f1+Δf1/2]A filtered time domain waveform result;
denotes xΔf(t,f2) The conjugate complex number of (a); j denotes an imaginary unit.
3. The method for reconstructing a propeller noise line spectrum under a small sample condition according to claim 1, wherein in the step (4), the formula for obtaining the corresponding emphasis envelope spectrum EES according to the cyclic modulation spectrum CMS is as follows:
EES(α)＝∫fCMS(f,α)df
where f is the spectral frequency and α is the cycle frequency.
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Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102611511A (en) * | 2011-12-31 | 2012-07-25 | 中国科学技术大学 | Wireless signal detection method based on enhanced frequency spectrum correlation function |
| CN106548780A (en) * | 2016-10-28 | 2017-03-29 | 南京邮电大学 | A kind of compressed sensing reconstructing method of voice signal |
| CN107045142A (en) * | 2017-07-04 | 2017-08-15 | 吉林大学 | Wavelet field geological data Real Time Compression and High precision reconstruction method based on compressed sensing |
| CN107329141A (en) * | 2017-08-03 | 2017-11-07 | 厦门大学 | A kind of boat body faint radiated noise signals detection method under marine environment |
| CN108233943A (en) * | 2018-01-23 | 2018-06-29 | 浙江大学 | A kind of compression sensing method based on minimum relatedness calculation matrix |
| CN108226934A (en) * | 2017-12-31 | 2018-06-29 | 厦门大学 | A kind of Underwater Moving Target detection method based on compressed sensing |
| CN108919240A (en) * | 2018-04-23 | 2018-11-30 | 东南大学 | A kind of underwater acoustic target radiated noise modulation spectrum reconstruction method based on group sparsity structure |
Family Cites Families (1)
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Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102611511A (en) * | 2011-12-31 | 2012-07-25 | 中国科学技术大学 | Wireless signal detection method based on enhanced frequency spectrum correlation function |
| CN106548780A (en) * | 2016-10-28 | 2017-03-29 | 南京邮电大学 | A kind of compressed sensing reconstructing method of voice signal |
| CN107045142A (en) * | 2017-07-04 | 2017-08-15 | 吉林大学 | Wavelet field geological data Real Time Compression and High precision reconstruction method based on compressed sensing |
| CN107329141A (en) * | 2017-08-03 | 2017-11-07 | 厦门大学 | A kind of boat body faint radiated noise signals detection method under marine environment |
| CN108226934A (en) * | 2017-12-31 | 2018-06-29 | 厦门大学 | A kind of Underwater Moving Target detection method based on compressed sensing |
| CN108233943A (en) * | 2018-01-23 | 2018-06-29 | 浙江大学 | A kind of compression sensing method based on minimum relatedness calculation matrix |
| CN108919240A (en) * | 2018-04-23 | 2018-11-30 | 东南大学 | A kind of underwater acoustic target radiated noise modulation spectrum reconstruction method based on group sparsity structure |
Non-Patent Citations (1)
| Title |
|---|
| 船舶实测辐射噪声信号的调制谱重构仿真;郑援 等;《声电技术》;20160831;第40卷(第8期);第40页-43页 * |
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