CN113484417A - Pipeline corrosion detection method based on wavelet transformation - Google Patents

Pipeline corrosion detection method based on wavelet transformation Download PDF

Info

Publication number
CN113484417A
CN113484417A CN202110768578.1A CN202110768578A CN113484417A CN 113484417 A CN113484417 A CN 113484417A CN 202110768578 A CN202110768578 A CN 202110768578A CN 113484417 A CN113484417 A CN 113484417A
Authority
CN
China
Prior art keywords
wavelet
pipeline
time
frequency
detection method
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.)
Granted
Application number
CN202110768578.1A
Other languages
Chinese (zh)
Other versions
CN113484417B (en
Inventor
高飞
童彤
华佳东
张晗
林京
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Beihang University
Ningbo Institute of Innovation of Beihang University
Original Assignee
Beihang University
Ningbo Institute of Innovation of Beihang University
Priority date (The priority date 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 date listed.)
Filing date
Publication date
Application filed by Beihang University, Ningbo Institute of Innovation of Beihang University filed Critical Beihang University
Priority to CN202110768578.1A priority Critical patent/CN113484417B/en
Publication of CN113484417A publication Critical patent/CN113484417A/en
Application granted granted Critical
Publication of CN113484417B publication Critical patent/CN113484417B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/04—Analysing solids
    • G01N29/045—Analysing solids by imparting shocks to the workpiece and detecting the vibrations or the acoustic waves caused by the shocks
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N29/00—Investigating or analysing materials by the use of ultrasonic, sonic or infrasonic waves; Visualisation of the interior of objects by transmitting ultrasonic or sonic waves through the object
    • G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
    • G01N29/4472—Mathematical theories or simulation

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Pathology (AREA)
  • Health & Medical Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Biochemistry (AREA)
  • General Health & Medical Sciences (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Immunology (AREA)
  • Algebra (AREA)
  • Acoustics & Sound (AREA)
  • Mathematical Analysis (AREA)
  • Mathematical Optimization (AREA)
  • Mathematical Physics (AREA)
  • Pure & Applied Mathematics (AREA)
  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Testing Resistance To Weather, Investigating Materials By Mechanical Methods (AREA)

Abstract

The invention provides a pipeline corrosion detection method based on wavelet transformation, which comprises the following steps: s1, knocking the pipeline by using a force hammer, and collecting signals of an acceleration sensor; s2, selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain time-frequency domain expression of x (t) wavelet transformation; s3, filtering the time-frequency domain expression to obtain WTfilter(t, f); s4, calculating WT in time rangefilter(t, f) double integration with respect to frequency and time; s5, setting a judgment index y of the corrosion degree of the pipeline1And y2And judging the mass of the pipeline according to the integral value. After threshold value screening is creatively extracted, the integral of the characteristic range in the time-frequency plane is used as the pipeline quality distinguishing characteristic, and a new thought is provided for a characteristic extraction algorithm based on a time-frequency domain; the testing method is simple and convenient, avoids the limitation of manually comparing and searching damage characteristics, and realizes the automatic judgment of the corrosion quality of the pipeline。

Description

Pipeline corrosion detection method based on wavelet transformation
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to a pipeline corrosion detection method based on wavelet transformation.
Background
Pipeline transportation is an important mode of modern fluid transportation, and pipelines are used as transportation tools to transport gas or liquid such as oil, natural gas, water, slurry, gas and chemical gas for a long distance. The pipeline transportation has the advantages of large transportation amount, low cost, safety, reliability, small occupied area, capability of continuous transportation without weather influence and the like, and is widely applied to the fields of oil and gas industry and the like. However, because the pipeline transportation work environment is harsh, the metal pipeline is easily corroded and damaged by dark, humid and cold air outside and liquid or gas conveyed inside, and the pipeline leakage accident may be caused, so that serious consequences such as environmental pollution, personnel and property loss are caused. Therefore, in order to ensure the safe transportation of the pipeline and prevent serious accidents, it is necessary to perform quality inspection on the metal pipeline and evaluate the corrosion degree of the metal pipeline.
With the development of sound vibration detection technology, the force hammer equipment is widely applied to vibration analysis and quality detection of engineering structures. The sound vibration knocking detection generates a vibration signal by applying force hammer knocking to a test piece, and judges the physical state of an object to be detected by acquiring the vibration signal and analyzing information such as frequency, energy, amplitude and the like. Compared with other acoustic detection technologies such as ultrasonic detection, acoustic emission detection and the like, the acoustic vibration and knocking detection has the advantages of easiness in operation and capability of acquiring vibration information at any time, so that the acoustic vibration and knocking detection is widely researched and applied to the field of detection of internal defects of some products. To the pipeline corrosion detection problem that this patent relates to, the sound shakes to strike that detection operation is simple and convenient, adaptable complicated external field environment, can realize the short-term test of pipeline quality.
The sound vibration signal obtained by the force hammer knocking contains the physical state information of the test piece, but the effective pipeline quality characteristic index is difficult to extract from the time domain analysis. In general, the original time domain signal can be converted to the frequency domain or the time-frequency domain to highlight the impairment feature. Wavelet Transform (WT) is a time-frequency localization analysis method with a fixed window size but a variable shape, and with variable time and frequency resolutions, and has a higher frequency resolution and a lower time resolution in the low frequency part and a higher time resolution and a lower frequency resolution in the high frequency part, and has better adaptivity to signals. The wavelet transformation can convert the original time domain signal into a time-frequency domain, and is beneficial to searching for damage characteristics. However, the way of extracting the damage features is different for different application scenarios and objects. Therefore, with the aid of the force hammer detection means and the wavelet transformation method, a detection method and a characteristic index extraction means specially designed for metal pipeline corrosion degree evaluation are required to be designed.
Disclosure of Invention
The invention aims to provide a detection method capable of realizing rapid evaluation of the corrosion degree of a metal pipeline in a complex outfield working environment, which can accurately acquire the quality characteristics of the pipeline to be detected, greatly reduce the complexity of a test flow and conveniently and rapidly realize quality detection of a test piece.
The invention provides a pipeline corrosion detection method based on wavelet transformation, which comprises the following steps:
the method comprises the following steps: adsorbing an acceleration sensor with a magnetic seat on the surface of a pipeline to be tested, knocking the pipeline by using a force hammer, and reading an acceleration sensor signal x (t) by using a data acquisition board card;
step two: selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation;
step three: setting a threshold value, and performing screening filtering on the wavelet transform WT (t, f) to obtain WTfilter(t, f), the specific operation is as follows:
Figure RE-GDA0003192749890000021
wherein, the lambda is a screening and filtering threshold value;
step four: selecting a time range (t)1,t2) Calculate WT in the time rangefilter(t, f) double integral Y with respect to frequency and time. The specific calculation method is as follows:
Figure RE-GDA0003192749890000022
wherein f issIs the sampling frequency, t, of the data acquisition board card1And t2Two endpoints of the selected time range;
step five: setting a pipeline corrosion degree judgment index y1And y2And y is1<y2When Y > Y2Judging the quality of the pipeline to be good; when y is1≤Y≤y2Judging the quality of the pipeline to be 'medium'; when Y is less than Y1And judging that the quality of the pipeline is poor.
Preferably, in the second step, a cmor wavelet is selected as a mother wavelet, and continuous wavelet transformation and mapping are performed on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation; the method comprises the following specific steps:
selecting cmor wavelet as mother wavelet, and making continuous wavelet transformation on input signal x (t) to obtain Wf(a, b), the continuous wavelet transform formula is specifically as follows:
Figure RE-GDA0003192749890000031
wherein a and b are respectively a scale factor and a displacement factor, a and b belong to R, and a is more than 0; phi (t) represents mother wavelet, which is subjected to scale expansion and time shift to obtain wavelet basis phia,b(t), as specified by
Figure RE-GDA0003192749890000032
t is time;
known wavelet basis psia,b(t) has a center frequency of faAccording to the scale factor a and the frequency faWill be Wf(a, b) mapping to time-frequencyDomain, obtaining the time-frequency domain expression WT (t, f) of x (t) wavelet transform;
preferably, the first step further comprises: the distance between the knocking position and the acceleration sensor position is 10-40 cm.
Preferably, in the third step, λ is 0.001.
Preferably, in the fourth step, t1＝0.5s，t2＝0.8s，fs＝100kHz。
Preferably, in said step five, y1＝2.7×106And y2＝3.5×106。
Compared with the prior art, the invention has the following beneficial effects:
1. the invention takes the force hammer test signal as the analysis basis, the force hammer knocking test is simple and convenient, and the pipeline test is favorably carried out in the complex outfield environment.
2. The method is based on a mature wavelet transform algorithm, has high operation rate, is simple and efficient, and can realize real-time and rapid detection of the pipeline.
3. The method avoids the limitation of manually searching for the damage characteristics by contrast, gets rid of the dependence on engineering experience, and can realize the automatic judgment of the pipeline corrosion quality.
4. The invention creatively extracts the integral of the characteristic range in the time-frequency plane after the threshold value is screened as the pipeline quality distinguishing characteristic, and provides a new idea for a characteristic extraction algorithm based on the time-frequency domain.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2(a) is an acceleration signal of a pipeline with better quality acquired in the embodiment;
FIG. 2(b) is the acceleration signal of the medium-quality pipeline collected in the embodiment;
FIG. 2(c) is an acceleration signal of a poor-quality pipeline collected in the embodiment;
FIG. 3(a) is the result of threshold value screening of wavelet transform of acceleration signal of pipeline with better quality in the embodiment;
FIG. 3(b) is the result of threshold value screening of the wavelet transform of the acceleration signal of the medium-quality pipeline in the embodiment;
FIG. 3(c) is the result of threshold value screening of the wavelet transform of the acceleration signal of the pipeline with poor quality in the embodiment;
fig. 4 shows the final extracted index and the classification effect.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings and examples. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The whole testing system comprises a force hammer device, an acceleration sensor with a magnetic seat, a data acquisition board card, an industrial personal computer and a metal pipeline to be tested. The acceleration sensor is adsorbed on the surface of the metal pipeline through the magnetic seat, and when a force hammer is used for knocking the pipeline, a sound vibration signal of the metal pipeline is acquired through the data acquisition board card on the industrial personal computer.
As shown in fig. 1, a pipeline corrosion detection method based on wavelet transform includes the following steps:
the method comprises the following steps: adsorbing an acceleration sensor with a magnetic seat on the surface of a pipeline to be tested, knocking the pipeline by using a force hammer, wherein the distance between the knocking position and the acceleration sensor is about 10-40 cm, and reading an acceleration sensor signal x (t) by using a data acquisition board card.
To prove the effectiveness of the present invention, three tubes are used in this embodiment, the three tubes are respectively good, medium and poor quality tubes, the acceleration signals of the three tubes are respectively collected, and the acceleration signals are correspondingly recorded as x1(t)、x2(t) and x3(t) as shown in FIGS. 2(a) - (c).
Step two: selecting cmor wavelet as mother wavelet, and making continuous wavelet transformation on input signal x (t) to obtain Wf(a, b), the continuous wavelet transform formula is specifically as follows:
Figure RE-GDA0003192749890000041
wherein a and b are respectively a scale factor and a displacement factor, a and b belong to R, and a is more than 0; phi (t) represents mother wavelet, which is subjected to scale expansion and time shift to obtain wavelet basis phia,b(t), as specified by
Figure RE-GDA0003192749890000042
t is time.
Known wavelet basis psia,b(t) has a center frequency of faAccording to the scale factor a and the frequency faWill be Wf(a, b) mapping to the time-frequency domain to obtain the time-frequency domain expression WT (t, f) of the x (t) wavelet transform. Wavelet transformation of the signal x (t) into the time-frequency domain to express WT (t, f) is prior art and the implementation steps are not expanded here.
In this embodiment, the sampling frequency fs is 100000; setting minimum fmin as 200 as wavelet base center frequency; the maximum wavelet base center frequency is 50000 as fmax; the frequency step of the wavelet base is dF-200, and the central frequency of the mother wavelet is Fc; t is 1/fs; the wavelet base frequency requirement is converted to a corresponding a, a is Fc./((fmin: dF: fmax) × T), where "/" denotes dot division, fmin: dF: fmax denotes the increase of frequency step dF each time starting from fmin until fmax, Fc./((fmin: dF: fmax) × T) denotes the cycle starting from Fc divided by fmin, each cycle increasing the divisor by one step until the divisor exceeds fmax. b is set to x (t) at each instant, i.e.: b ═ T (1: length (x)); length (x) represents the length of the acceleration signal, and b is a vector, which is exactly every point of the time axis, such as: t (1:10), then 1:10 is the vector [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] which multiplied by the sampling interval becomes the time instant.
Acceleration signal x for three different quality pipelines1(t)、x2(t) and x3(t) performing the above operations to obtain WT respectively1(t,f)、WT2(t, f) and WT3(t,f)。
Step three: setting a threshold value, and performing wavelet transform WT (T, f) performs screening filtering to obtain WTfilter(t, f), the specific operation is as follows:
Figure RE-GDA0003192749890000051
in general, λ is about 0.1% of the maximum absolute value of WT (t, f), e.g. selected from 0.09% -0.11% of the maximum absolute value of WT (t, f), and in this embodiment, λ is set to 0.001, and wavelet transform results WT for three different quality channel signals are obtained1(t,f)、WT2(t, f) and WT3(t, f) performing the above operations to obtain WT respectively1filter(t,f)、 WT2filter(t, f) and WT3filter(t, f), as shown in FIGS. 3(a) - (c), the white region value is 1, and the black region value is 0.
Step four: selecting a time range (t)1,t2) Calculate WT in the time rangefilter(t, f) double integral Y with respect to frequency and time. The specific calculation method is as follows:
Figure RE-GDA0003192749890000052
wherein f issThe sampling frequency of the data acquisition board card. In this example, t1＝0.5s，t2＝0.8s，fs＝100kHz。
For three pipelines with different qualities, 50 groups of signals are respectively collected for each pipeline in order to verify the robustness of the method. The integration result Y obtained after all the signals are processed through the above steps is shown in fig. 4, wherein a circle represents the pipeline data with better quality, a square represents the pipeline data with medium quality, and a triangle represents the pipeline data with poor quality.
Step five: setting a pipeline corrosion degree judgment index y1And y2And y is1<y2When Y > Y2Judging the quality of the pipeline to be good; when y is1≤Y≤y2Judging the quality of the pipeline to be 'medium'; when Y is less than Y1In time, judge the pipelineThe quality was "poor". In this embodiment, a pipeline corrosion degree judgment index y is set1＝2.7×106And y2＝3.5×106As shown in fig. 4, the method can perfectly distinguish three types of pipelines with different qualities.
The invention takes the force hammer signal as the analysis basis, has simple operation and can adapt to the complicated and severe external field conditions. By means of a relatively mature wavelet transform algorithm, the operation rate is high, and real-time rapid detection can be achieved. Meanwhile, the integral of the characteristic range in the time-frequency plane after threshold screening is creatively extracted to serve as the pipeline quality distinguishing characteristic, so that the limitation of manually searching for the damage characteristic in a contrast manner is avoided, and the automatic distinguishing of the pipeline corrosion quality can be realized.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.

Claims (6)

1. A pipeline corrosion detection method based on wavelet transform is characterized in that: which comprises the following steps:
the method comprises the following steps: adsorbing an acceleration sensor with a magnetic seat on the surface of a pipeline to be tested, knocking the pipeline by using a force hammer, and reading an acceleration sensor signal x (t) by using a data acquisition board card;
step two: selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation;
step three: setting a threshold value, and performing screening filtering on the wavelet transform WT (t, f) to obtain WTfilter(t, f), the specific operation is as follows:
Figure FDA0003152868200000011
wherein, the lambda is a screening and filtering threshold value;
step four: selecting a time range (t)1,t2) Calculate WT in the time rangefilter(t, f) double integral Y with respect to frequency and time; the specific calculation method is as follows:
Figure FDA0003152868200000012
wherein f issIs the sampling frequency, t, of the data acquisition board card1And t2Two endpoints of the selected time range;
step five: setting a pipeline corrosion degree judgment index y1And y2And y is1<y2When Y > Y2Judging that the quality of the pipeline is good; when y is1≤Y≤y2Judging the quality of the pipeline to be medium; when Y is less than Y1And judging that the quality of the pipeline is poor.
2. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation; the method comprises the following specific steps:
selecting cmor wavelet as mother wavelet, and making continuous wavelet transformation on input signal x (t) to obtain Wf(a, b), the continuous wavelet transform formula is specifically as follows:
Figure FDA0003152868200000013
wherein a and b are respectively a scale factor and a displacement factor, a and b belong to R, and a is more than 0; phi (t) represents mother wavelet, which is subjected to scale expansion and time shift to obtain wavelet basis phia,b(t), as specified by
Figure FDA0003152868200000014
t is time;
known wavelet basis psia,b(t) has a center frequency of faAccording to the scale factor a and the frequency faWill be Wf(a, b) mapping to the time-frequency domain to obtain the time-frequency domain expression WT (t, f) of the x (t) wavelet transform.
3. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: the first step further comprises: the distance between the knocking position and the acceleration sensor position is 10-40 cm.
4. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: in the third step, the value of lambda is 0.001.
5. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: in the fourth step, t1＝0.5s，t2＝0.8s，fs＝100kHz。
6. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: in the fifth step, y1＝2.7×106And y2＝3.5×106。
CN202110768578.1A 2021-07-07 2021-07-07 Pipeline corrosion detection method based on wavelet transformation Active CN113484417B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202110768578.1A CN113484417B (en) 2021-07-07 2021-07-07 Pipeline corrosion detection method based on wavelet transformation

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202110768578.1A CN113484417B (en) 2021-07-07 2021-07-07 Pipeline corrosion detection method based on wavelet transformation

Publications (2)

Publication Number Publication Date
CN113484417A true CN113484417A (en) 2021-10-08
CN113484417B CN113484417B (en) 2022-09-20

Family

ID=77940884

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202110768578.1A Active CN113484417B (en) 2021-07-07 2021-07-07 Pipeline corrosion detection method based on wavelet transformation

Country Status (1)

Country Link
CN (1) CN113484417B (en)

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114110443A (en) * 2021-12-07 2022-03-01 郑州大学 Intelligent detection method for singular point characteristics of fluid transmission pipeline

Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004163250A (en) * 2002-11-13 2004-06-10 Shinryo Corp Method for diagnosing degradation of piping by ultrasonic wave
CN104458170A (en) * 2014-11-07 2015-03-25 桂林电子科技大学 Time-frequency diagram processing method and system for mechanical equipment monitoring vibration signals

Patent Citations (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP2004163250A (en) * 2002-11-13 2004-06-10 Shinryo Corp Method for diagnosing degradation of piping by ultrasonic wave
CN104458170A (en) * 2014-11-07 2015-03-25 桂林电子科技大学 Time-frequency diagram processing method and system for mechanical equipment monitoring vibration signals

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
张勇，段运达，王臣，姚岱男，姜鑫蕾: "基于小波阈值去噪的管道泄漏检测研究", 《化工自动化及仪表》 *
钱骥，陈鑫，杨金川: "小波时-频变换的高强钢丝弹性波传播模态分析", 《应用声学》 *
马骏骋等: "基于连续小波变换的热力管道裂纹识别研究", 《山东理工大学学报(自然科学版)》 *

Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN114110443A (en) * 2021-12-07 2022-03-01 郑州大学 Intelligent detection method for singular point characteristics of fluid transmission pipeline

Also Published As

Publication number Publication date
CN113484417B (en) 2022-09-20

Similar Documents

Publication Publication Date Title
RU2750516C1 (en) Method for multi-positional determination of leaks position in pipeline based on improved amd
CN109084186B (en) Pipeline leakage signal identification method based on improved ELMD (ensemble empirical mode decomposition) multi-scale entropy
CN106287240B (en) A kind of pipeline leakage testing device and single-sensor localization method based on sound emission
CN104391039B (en) Storage tank bottom plate corrosion non-contact ultrasonic detection method based on dynamic wavelet fingerprint technology
CN110823356B (en) Distributed optical fiber intrusion detection method based on Mel frequency spectrum
CN106815552B (en) Digital signal post-processing method based on time-frequency analysis
CN113484417B (en) Pipeline corrosion detection method based on wavelet transformation
CN114799610B (en) Welding quality real-time detection method and system based on inverse Fourier transform and self-encoder
CN114062490A (en) Rail weld Lamb wave modal decomposition and crack damage monitoring method based on GAN
CN102269814A (en) Method for intelligent detection on quality of foundation pile
CN103472141A (en) Signal demodulation method for recognizing vibrant noise modulation mechanism
CN114137079A (en) Ultrasonic guided wave nondestructive testing method based on combination of deep learning and Duffing system
CN104777224A (en) Defect detecting method for junction surface of metal alloy
CN110953488B (en) Gas-liquid two-phase flow pipeline leakage acoustic emission detection method based on stack self-coding
CN110057918B (en) Method and system for quantitatively identifying damage of composite material under strong noise background
CN105909979B (en) Leakage acoustic characteristic extracting method based on Wavelet Transform Fusion blind source separation algorithm
CN106679911B (en) Beam type structure damage identification method based on multi-scale data fusion theory
CN102680080B (en) Unsteady-state signal detection method based on improved self-adaptive morphological filtering
CN105928666A (en) Leakage acoustic characteristic extraction method based on Hilbert-Huang transform and blind source separation
CN108195932B (en) Ultrasonic guided wave quantitative assessment method for aircraft pipeline damage
Gao et al. A boosted wavelet improvement thresholding algorithm based on birgé-massart strategy for pipeline leakage signal noise reduction processing
CN116244631A (en) Pile foundation multi-defect identification method and system based on double-channel convolutional neural network
CN103630604A (en) Identification method for crack faults of semi-open impeller of centrifugal compressor
CN105927861B (en) Leakage acoustic characteristic extracting method based on Wavelet Transform Fusion blind source separation algorithm
CN107677717A (en) A kind of outer corrosion testing device of submarine pipeline and detection method

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