CN107843333B - A kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory - Google Patents
A kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory Download PDFInfo
- Publication number
- CN107843333B CN107843333B CN201710580385.7A CN201710580385A CN107843333B CN 107843333 B CN107843333 B CN 107843333B CN 201710580385 A CN201710580385 A CN 201710580385A CN 107843333 B CN107843333 B CN 107843333B
- Authority
- CN
- China
- Prior art keywords
- pipeline
- sensor
- radial
- frequency
- section
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Classifications
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01H—MEASUREMENT OF MECHANICAL VIBRATIONS OR ULTRASONIC, SONIC OR INFRASONIC WAVES
- G01H17/00—Measuring mechanical vibrations or ultrasonic, sonic or infrasonic waves, not provided for in the preceding groups
Landscapes
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory.The present invention is on a radius in measurement section, along the radially uniform N number of sensor arrangement position of setting of pipeline；H position is randomly choosed from N number of sensor arrangement position is respectively arranged sensor；Pipeline radial direction glottis neoplasms detection method based on compressive sensing theory breaches the limitation of Shannon-Nyquist sampling thheorem, by sensor array in large scale needed for script, it is reduced to acceptable quantitative range, and can still achieve the purpose that accurately to extract radial mode shape amplitude；Compared to traditional detection method, method proposed by the invention greatly reduces the complexity of hardware layout level and the cost of data acquisition, storage and processing, and process is succinct, it is easy to accomplish.
Description
Technical field
The present invention relates to pipeline glottis neoplasms detection techniques, and in particular to a kind of pipeline radial direction sound based on compressive sensing theory
Modal detection system and detection method.
Background technique
In noise testing field, the test for pipeline sound field is that far field is made an uproar outside sound source information and pipeline in determining pipeline
The main method that sound shadow is rung, has important application in related fieldss such as aerospace, automobile, household electrical appliances noise reductions.Glottis neoplasms and wind
The geometrical characteristic of the rotating mechanisms component such as fan, motor is directly related, is just enough to infer and analyze phase after determining glottis neoplasms structure
Shutting mechanism generates the mechanism of noise and the distribution situation of noise source, to provide guidance foundation, therefore acoustic mode for noise-reducing design
State detection method tests duct noise extremely important.
The detection of pipeline glottis neoplasms can be divided into circumferential Modal detection and radial mode shape detection, aim at and obtain each mode
Amplitude.Radial mode shape therein is detected, is needed using circumferential Modal detection as precondition, therefore complex.In order to
Circumferential mode is detected, is generally circumferentially mounted flush sensor array in pipeline wall surface, the data of acquisition are carried out based on space
Fourier decompose, the amplitude of each circumferential mode can be obtained.After determining circumferential master mode according to amplitude size, it can carry out
Radial mode shape detection.Actually can pipeline radially inside placement sensor array obtain sound field data, be then based on
Fourier-Bessel decomposes to obtain radial mode shape coefficient, the final radial mode shape spectrum for obtaining sound field.
It should be pointed out that above-mentioned circumferential direction Modal detection must comply with Shannon-Nyquist sampling thheorem, this means that
The array element sum that sensor array needs is twice of highest rank number of mode that can be detected.And in actual test, rotation section
The blade quantity of part is often more, by Taylor-Sofrin selection rate it is found that the circumferential rank number of mode of its generation is generally higher,
Circumferentially a large amount of sensor is needed thus.Similarly, it to obtain radial mode shape, generally requires in the multiple circumferential positions of pipeline
Radially arranged multiple measuring points are set, radial transducer also substantial amounts are thus caused.Above-mentioned factor is coupled in together, is easily led to
Conventional radial mode shape detection system complex, data acquisition, the cost of storage and processing are excessively high.At present in spite of rotation
The coping styles such as sensor array, but also above-mentioned contradiction is only alleviated to a certain extent, not from solving the problems, such as at all.
For this reason, it may be necessary to develop new Frequency extraction method from the angle of signal processing, biography while detecting larger range mode
The number control of sensor is within an acceptable range.
Summary of the invention
In order to solve the above problems of the prior art, the invention proposes a kind of pipes based on compressive sensing theory
Road radial direction glottis neoplasms detection system and detection method.
The pipeline radial direction glottis neoplasms detection system based on compressive sensing theory that an object of the present invention is to provide a kind of.
The shape of pipeline is that the cross-section that radius is R is round, using cylindrical-coordinate system (x, r, θ), generates high order mode sound
Section where the component of wave is sound source section, and sound source section is located at the section of x=0, sound wave in the duct along x forward-propagating,
Measurement section is located at x=x0Section.
Pipeline radial direction glottis neoplasms detection system based on compressive sensing theory of the invention includes: N number of sensor arrangement position
It sets and H sensor；Wherein, on a radius in measurement section, along the radially uniform N number of sensor arrangement position of arrangement of pipeline
It sets；H position is randomly choosed from N number of sensor arrangement position is respectively arranged sensor；The number N of sensor arrangement position
Radial mode shape order n can be propagated by pipeline highest to determine, n is natural number, and N > n；The number H of sensor meets: Cslog
(N/s) < H < N, wherein C is empirical, and s is mode degree of rarefication.
It is another object of the present invention to provide a kind of pipeline radial direction glottis neoplasms detection side based on compressive sensing theory
Method.
Pipeline radial direction glottis neoplasms detection method based on compressive sensing theory of the invention, comprising the following steps:
1) determine pipeline model: the shape of pipeline is that the cross-section that radius is R is round, using cylindrical-coordinate system (x, r, θ),
Section where generating the component of high order mode sound wave is sound source section, and sound source section is located at the section of x=0, and sound wave is in pipeline
It is middle along x forward-propagating, measure section and be located at x=x0Section；
2) highest frequency for determining detection, further according to the inside radius of pipeline, the pipeline highest detected needed for determining can be propagated
Radial mode shape order n can propagate radial mode shape order according to pipeline highest, determine on a radius in measurement section along pipe
The number N of the sensor arrangement position of the radially uniform arrangement in road, and H position is randomly selected from N number of sensor arrangement position
It sets and sensor is respectively set；
3) all the sensors synchronous acquisition signal obtains time domain data, time domain data is obtained by Fast Fourier Transform (FFT)
Measurement result vector is constructed from each sensor is extracted in frequency spectrum in the frequency domain data being concerned about under frequency to frequency spectrum；
4) according to the sensor arrangement position selected in step 2), H × N compressed sensing calculation matrix is constructed；
5) N × N space conversion matrices are constructed；
6) according to measurement result vector, compressed sensing calculation matrix and space conversion matrices, to radial frequency under care frequency
Rate signal is reconstructed, and obtains complete radial frequency signal；
7) according to the radial frequency signal extraction radial mode shape amplitude of reconstruct.
Wherein, in step 2), on a radius in measurement section, along the radially uniform N number of sensor cloth of setting of pipeline
Seated position；H position being randomly choosed from N number of sensor arrangement position, sensor being respectively set, the number H of sensor meets:
Cslog (N/s) < H < N, wherein C is empirical, and s is mode degree of rarefication.The value of H is bigger, then the original letter of Accurate Reconstruction
Number success rate it is higher, but can also lose the clear superiority of its method compared with the prior art simultaneously.
In step 3), time domain data is obtained into frequency spectrum by Fast Fourier Transform (FFT), determines and is concerned about frequency f0, from frequency spectrum
Middle extract is being concerned about frequency f0Under each sensor frequency domain data Ph(f0), h=1 ..., H construct measurement result vector y=
[P1(f0),P2(f0),...,PH(f0)]T, this is compressed sensing measurement result.
In step 4), using random 1-0 matrix as compressed sensing calculation matrix, thus constructs H × N compressed sensing and survey
Moment matrix:
Wherein, the matrix element value that r row i-th arranges is 1, remaining is 0, r=1 ..., H, i ∈ [1, N].
In step 5), since the frequency domain data itself obtained in step 3) is not sparse, action space is needed to convert square
Battle array is transformed to sparse Modal Space, to meet the requirement of compressed sensing.Detection for radial mode shape, mode point
Solution is converted based on Fourier-Bessel, therefore required space conversion matrices ψBWith following form:
Wherein, Jm() indicates that m rank the primal Bessel function, m are circumferential rank number of mode, riFor N number of biography of radial equipartition
I-th of radial position in sensor position, and have ri=i × R/N, i=1 ... N, and ki(i=1,2 ..., N) it is Bessel
Solution under the constraint of function second kind boundary condition, by J'm(kiR it)=0 acquires, wherein () ' indicates to differentiate；Meet this boundary
The k of conditioniFor multivalue, solves and obtain a series of k1、k2…kN, to finally determine space conversion matrices ψB。
In step 6), according to measurement result vector, compressed sensing calculation matrix and space conversion matrices, using l1Model
Number, which minimizes, is reconstructed radial frequency signal under care frequency, obtains the estimation signal in transformation spaceIt calculates
Obtain complete radial frequency signal
In step 7), the calculation method of the i-th rank radial mode shape amplitude is as follows:
Wherein,For the mould side of Bezier eigenfunction, under second kind boundary condition, and when m ≠ 0 its is defined as:
Thus each rank radial mode shape amplitude c is finally obtainedi。
Advantages of the present invention:
Shannon-Nyquist is breached the present invention is based on the pipeline radial direction glottis neoplasms detection method of compressive sensing theory to adopt
Sensor array in large scale needed for script is reduced to acceptable quantitative range by the limitation of sample theorem, and according to
It can so achieve the purpose that accurately to extract radial mode shape amplitude；Detection method compared with prior art, method proposed by the invention
The complexity of hardware layout level and the cost of data acquisition, storage and processing are greatly reduced, process is succinct, is easy to real
It is existing.
Detailed description of the invention
Fig. 1 is the illustraton of model of the pipeline radial direction glottis neoplasms detection system of the invention based on compressive sensing theory；
Fig. 2 is one embodiment of the pipeline radial direction glottis neoplasms detection method according to the present invention based on compressive sensing theory
The radial frequency signal contrast figure that obtained single-frequency radial frequency signal and the prior art obtains；
Fig. 3 is the detection knot that the present invention compares the radial frequency signal that 20 sensor of the prior art obtains using 5 sensors
Fruit figure.
Specific embodiment
With reference to the accompanying drawing, by specific embodiment, the present invention is further explained.
As shown in Figure 1, the shape of pipeline 1. is that the cross-section that radius is R is round, using cylindrical-coordinate system (x, r, θ), produce
4. the component sound source section of raw high order mode sound wave is located at the section of x=0, Acoustic Wave Propagation direction is 2. positive along x in the duct,
5. measurement section is located at x=x0Section.The pipeline radial direction glottis neoplasms detection system based on compressive sensing theory of the present embodiment
It include: H sensor；Radial mode shape order n can be propagated according to pipeline highest, determines natural number N, N > n；In measurement section
On one radius, 3. along the radially uniform N number of sensor arrangement position of setting of pipeline, selected at random from N number of sensor arrangement position
It selects H position and is respectively arranged H sensor；The number N of sensor arrangement position can propagate radial mode shape order by pipeline highest
N determines that n is natural number, and N > n；The number H of sensor needs to meet: Cslog (N/s) < H < N, and wherein C is empirical,
S is mode degree of rarefication.
In the present embodiment, the radius R=0.5m of pipeline, the circumferential mode m=3, radial mode shape n=of inside presence (3,
8) one group of sound wave (frequency f0=5000Hz), and two mode are in and cut logical state (cut-on).It may be noted that in reality
In test, the high order mode more than certain order can decay rapidly, and in the present embodiment, the sound wave of radial mode shape n > 14 will be fast
Speed decaying, can not in axial direction propagate in pipe.
The pipeline radial direction glottis neoplasms detection method based on compressive sensing theory of the present embodiment, comprising the following steps:
1) pipeline model is determined, the cross-section that pipeline is radius R=0.5m is round, using cylindrical-coordinate system (x, r, θ), produces
The component of raw high order mode sound wave is located at the sound source section of x=0, and sound wave along x forward-propagating, measures section and be located at x in the duct
=x0Section；
2) determine that the highest frequency of detection, circumferential mode m=3 determine the required pipe detected further according to the inside radius of pipeline
Road highest can propagate radial mode shape order n, in the present embodiment, determine the sensor arrangement position being arranged on measurement section
Number N=20, has covered all possible radial mode shape orders propagated, radially arranged 20 sensor arrangement positions
(detectable mode n=1~20), number is denoted as J since close to the center point respectivelyi(i=1 ..., 20), and from 20
5 positions are randomly selected in sensor arrangement position, sensor are respectively set, selected the 9th in this embodiment, 13,14,
15 and 17 sensor arrangement positions, i.e. J9、J13、J14、J15And J17；
3) 5 sensor synchronous acquisition signals obtain time domain data, time domain data are obtained by Fast Fourier Transform (FFT)
Frequency spectrum extracts from frequency spectrum and is concerned about frequency f0Amplitude P (the f of the frequency domain data of each sensor at=5000Hz0), building measurement
Result vector y=[P9(f0),P13(f0),P14(f0),P15(f0),P17(f0)]T；
4) according to the sensor arrangement position selected in step 2), 5 × 20 compressed sensing calculation matrix are constructed；
5) 20 × 20 space conversion matrices ψ are constructedB:
Wherein, Jm() indicates the primal Bessel function of m=3, ri=i × R/20, i=1 ... 20, and kiAccording to J'm
(kiR it)=0 solves, takes preceding 20 solutions for meeting this equation, as k1~k20；
6) according to measurement structure matrix, compressed sensing calculation matrix and space conversion matrices, convex optimization method solves l1Model
Number minimization problem:
It obtainsIt calculates againF on restructural original complete 20 sensor arrangement positions0The radial direction of=5000Hz
Frequency signal:
7) the i-th rank radial mode shape amplitude is calculated as follows according to the radial frequency signal extraction radial mode shape amplitude of reconstruct
ci:
Wherein,
Thus radial mode shape amplitude is finally obtained.
As shown in Fig. 2, the result and art methods result using the radial frequency signal of above method reconstruct carry out
To ratio error < 0.1%, show that method is only with 5 sensors (in the prior art the 1/4 of the number of sensor) in the present invention
Can all N number of sensor arrangement positions of accurate reconstruction signal.Using method in the present invention, obtained using 5 sensors
Each rank radial mode shape amplitude it is almost consistent compared with the Modal detection result of lower 20 sensors of technology, error is less than
0.1dB, as shown in figure 3, thus the provable pipeline radial direction glottis neoplasms detection method of the invention based on compressive sensing theory exists
In the case where substantially cutting down sensor scale, each rank modal amplitudes can be still efficiently extracted.
It is finally noted that the purpose for publicizing and implementing example is to help to further understand the present invention, but this field
Technical staff be understood that without departing from the spirit and scope of the invention and the appended claims, it is various replacement and repair
It is all possible for changing.Therefore, the present invention should not be limited to embodiment disclosure of that, the scope of protection of present invention with
Subject to the range that claims define.
Claims (8)
1. a kind of pipeline radial direction glottis neoplasms detection system based on compressive sensing theory, the shape of pipeline is that radius is that the equal of R cuts
Face is round, and using cylindrical-coordinate system (x, r, θ), the section where generating the component of high order mode sound wave is sound source section, sound source
Section is located at the section of x=0, and sound wave along x forward-propagating, measures section and be located at x=x in the duct0Section, feature exists
In the pipeline radial direction glottis neoplasms detection system includes: N number of sensor arrangement position and H sensor；Wherein, it is cut in measurement
On one radius in face, along the radially uniform N number of sensor arrangement position of arrangement of pipeline；It is random from N number of sensor arrangement position
H position of selection is respectively arranged sensor；The number N of sensor arrangement position can propagate radial mode shape order n by pipeline highest
It determines, n is natural number, and N > n；The quantity H of sensor meets: Cslog (N/s) < H < N, and wherein C is empirical, and s is
Mode degree of rarefication.
2. a kind of pipeline radial direction glottis neoplasms detection method based on compressive sensing theory, which is characterized in that the detection method packet
Include following steps:
1) determine pipeline model: the shape of pipeline is that the cross-section that radius is R is round, using cylindrical-coordinate system (x, r, θ), is generated
Section where the component of high order mode sound wave is sound source section, and sound source section is located at the section of x=0, and sound wave is in the duct along x
Forward-propagating, measurement section are located at x=x0Section；
2) highest frequency for determining detection, further according to the inside radius of pipeline, the pipeline highest detected needed for determining can propagate radial direction
Rank number of mode n can propagate radial mode shape order according to pipeline highest, determine on a radius in measurement section along pipeline radial direction
The number N for the sensor arrangement position being evenly arranged, and H position difference is randomly selected from N number of sensor arrangement position
Sensor is set；
3) all the sensors synchronous acquisition signal obtains time domain data, and time domain data is obtained frequency by Fast Fourier Transform (FFT)
Spectrum constructs measurement result vector from each sensor is extracted in frequency spectrum in the frequency domain data being concerned about under frequency；
4) according to the sensor arrangement position selected in step 2), H × N compressed sensing calculation matrix is constructed；
5) N × N space conversion matrices are constructed；
6) according to measurement result vector, compressed sensing calculation matrix and space conversion matrices, radial frequency under care frequency is believed
It number is reconstructed, obtains complete radial frequency signal；
7) according to the radial frequency signal extraction radial mode shape amplitude of reconstruct.
3. detection method as claimed in claim 2, which is characterized in that in step 2), on a radius in measurement section,
Along the radially uniform N number of sensor arrangement position of arrangement of pipeline；H position difference is randomly choosed from N number of sensor arrangement position
Sensor is arranged, the number H of sensor meets: Cslog (N/s) < H < N, wherein C is empirical, and s is mode degree of rarefication.
4. detection method as claimed in claim 2, which is characterized in that in step 3), by time domain data by quick Fu
Leaf transformation obtains frequency spectrum, determines and is concerned about frequency f0, extracted from frequency spectrum and be concerned about frequency f0Under each sensor frequency domain data Ph
(f0), h=1 ..., H construct measurement result vector y=[P1(f0),P2(f0),…PH(f0)]T, this is compressed sensing measurement knot
Fruit.
5. detection method as claimed in claim 2, which is characterized in that in step 4), using random 1-0 matrix as compression
Calculation matrix is perceived, H × N compressed sensing calculation matrix is thus constructed:
Wherein, the matrix element value that r row i-th arranges is 1, remaining is 0, r=1 ..., H, i ∈ [1, N].
6. detection method as claimed in claim 2, which is characterized in that in step 5), space conversion matrices ψBWith following shape
Formula:
Wherein, Jm() indicates that m rank the primal Bessel function, m are circumferential rank number of mode, riFor N number of sensor of radial equipartition
I-th of radial position in position, and have ri=i × R/N, i=1 ... N, and ki(i=1,2 ..., N) is Bessel function the
Solution under the constraint of two class boundary conditions, by J'm(kiR it)=0 acquires, wherein () ' indicates to differentiate；Meet this boundary condition
kiFor multivalue, solves and obtain a series of k1、k2…kN, to finally determine space conversion matrices ψB。
7. detection method as claimed in claim 2, which is characterized in that in step 6), according to measurement result vector, compressed sensing
Calculation matrix and space conversion matrices, using l1Radial frequency signal under care frequency is reconstructed in norm minimum, obtains
Estimation signal in transformation spaceIt calculatesObtain complete radial frequency signal
8. detection method as claimed in claim 7, which is characterized in that in step 7), the calculating of the i-th rank radial mode shape amplitude
Method is as follows:
Wherein,For the mould side of Bezier eigenfunction, under second kind boundary condition, and when m ≠ 0 its is defined as:
Thus each rank radial mode shape amplitude c is finally obtainedi。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710580385.7A CN107843333B (en) | 2017-07-17 | 2017-07-17 | A kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710580385.7A CN107843333B (en) | 2017-07-17 | 2017-07-17 | A kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107843333A CN107843333A (en) | 2018-03-27 |
| CN107843333B true CN107843333B (en) | 2019-06-07 |
Family
ID=61682874
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710580385.7A Active CN107843333B (en) | 2017-07-17 | 2017-07-17 | A kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107843333B (en) |
Families Citing this family (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109101769B (en) * | 2018-09-20 | 2021-09-03 | 中国人民解放军国防科技大学 | Leaf end timing sensor number determination method based on compressed sensing |
| US11521097B2 (en) | 2019-02-19 | 2022-12-06 | International Business Machines Corporation | Sparse modeling for optimizing sensor placement |
| CN111898317A (en) * | 2020-07-29 | 2020-11-06 | 上海交通大学 | Self-adaptive deviation pipeline modal analysis method based on arbitrary position compressed sensing |
| CN112525338B (en) * | 2020-11-30 | 2022-10-04 | 合肥工业大学 | Method for eliminating Doppler effect of rotary sound source based on compressed sensing theory |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103453980A (en) * | 2013-08-08 | 2013-12-18 | 大连理工大学 | Sound field parameter obtaining method based on compressed sensing |
| CN205507069U (en) * | 2016-02-02 | 2016-08-24 | 北京地平线机器人技术研发有限公司 | Sound positioner based on mike's array |
| CN106227962A (en) * | 2016-07-28 | 2016-12-14 | 中国船舶重工集团公司第七〇九研究所 | A kind of submarine pipeline circumference modal identification method using probability distribution to calculate |
-
2017
- 2017-07-17 CN CN201710580385.7A patent/CN107843333B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103453980A (en) * | 2013-08-08 | 2013-12-18 | 大连理工大学 | Sound field parameter obtaining method based on compressed sensing |
| CN205507069U (en) * | 2016-02-02 | 2016-08-24 | 北京地平线机器人技术研发有限公司 | Sound positioner based on mike's array |
| CN106227962A (en) * | 2016-07-28 | 2016-12-14 | 中国船舶重工集团公司第七〇九研究所 | A kind of submarine pipeline circumference modal identification method using probability distribution to calculate |
Non-Patent Citations (2)
| Title |
|---|
| Compressive sensing based spinning mode detections by in-duct microphone arrays;Wenjun Yu等;《Measurement Science and Technology》;20160411;第1-11页 |
| 基于压缩感知的管道声旋转模态检测方法;于文君等;《2016年度全国气动声学学术会议》;20161104;第1-5页 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107843333A (en) | 2018-03-27 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107843333B (en) | A kind of pipeline radial direction glottis neoplasms detection system and method based on compressive sensing theory | |
| Liu et al. | A new time-frequency analysis method based on single mode function decomposition for offshore wind turbines | |
| CN105240187A (en) | Method for realizing state monitoring and fault diagnosis of water turbine based on chaos theory | |
| CN109101768A (en) | Leaf end timing sensor layout optimization design method based on compressed sensing | |
| CN101592288A (en) | A kind of method for identifying pipeline leakage | |
| CN109140241B (en) | A kind of compressed sensing based pipeline leakage positioning method | |
| CN103091395B (en) | Method and device for three-dimensionally dynamically detecting fluid phase state in porous medium under reservoir conditions | |
| Sun et al. | Natural gas leak location with K–L divergence-based adaptive selection of Ensemble Local Mean Decomposition components and high-order ambiguity function | |
| CN110388570A (en) | A kind of self-adaptation noise reduction method based on VMD and its application in water pipeline leakage locating | |
| CN104374828A (en) | Ultrasonic tomography imaging method of detection on hidden defect | |
| CN105403878A (en) | Ocean acoustic tomography method based on time delay sensitive nucleus | |
| CN105637331A (en) | Abnormality detection device, abnormality detection method, and computer-readable storage medium | |
| CN104537251A (en) | Fan blade impulse load recognition method | |
| CN111896616A (en) | Gas-liquid two-phase flow pattern identification method based on acoustic emission-BP neural network | |
| CN105674065A (en) | Variational mode decomposition-based method for locating leakage point of pipeline by acoustic emission | |
| CN116429902A (en) | Fan blade multi-crack acoustic emission monitoring method and system | |
| CN110263762A (en) | A kind of semi-submersible offshore platform energy Transfer Path Analysis Method of Automobile based on output | |
| CN108107437B (en) | Marine environment monitoring method by using normal wave coupling interference | |
| Tang et al. | Leak detection of water pipeline using wavelet transform method | |
| Sabbatini et al. | Data acquisition and processing for tip timing and operational modal analysis of turbomachinery blades | |
| CN105822289B (en) | Frequency estimation method for well fluid level detection | |
| JP2004061361A (en) | Piping breakage investigating apparatus | |
| CN104236871A (en) | Crack failure recognition method for semi-open type impeller of centrifugal compressor | |
| NL2022689B1 (en) | Method and system for non-intrusively determining cross-sectional variation for a fluidic channel | |
| CN109855536B (en) | Oil and gas pipeline blockage detection method based on strain measurement |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |