CN114487124A - Crane structure damage diagnosis method based on acoustic emission signal characteristic parameters - Google Patents
Crane structure damage diagnosis method based on acoustic emission signal characteristic parameters Download PDFInfo
- Publication number
- CN114487124A CN114487124A CN202210035333.2A CN202210035333A CN114487124A CN 114487124 A CN114487124 A CN 114487124A CN 202210035333 A CN202210035333 A CN 202210035333A CN 114487124 A CN114487124 A CN 114487124A
- Authority
- CN
- China
- Prior art keywords
- acoustic emission
- crane
- signal
- straight line
- upper boundary
- 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
Links
- 238000000034 method Methods 0.000 title claims abstract description 33
- 238000003745 diagnosis Methods 0.000 title claims abstract description 12
- 238000010586 diagram Methods 0.000 claims description 25
- 238000004364 calculation method Methods 0.000 claims description 3
- 238000012545 processing Methods 0.000 abstract description 8
- 238000004458 analytical method Methods 0.000 abstract description 7
- 238000005516 engineering process Methods 0.000 abstract description 2
- 230000000007 visual effect Effects 0.000 abstract description 2
- 238000001514 detection method Methods 0.000 description 10
- 239000000463 material Substances 0.000 description 5
- 238000009659 non-destructive testing Methods 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- 230000003044 adaptive effect Effects 0.000 description 2
- 230000007547 defect Effects 0.000 description 2
- 206010016256 fatigue Diseases 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000005457 optimization Methods 0.000 description 2
- 238000003672 processing method Methods 0.000 description 2
- 241000219000 Populus Species 0.000 description 1
- 229910000831 Steel Inorganic materials 0.000 description 1
- 238000005452 bending Methods 0.000 description 1
- 239000002131 composite material Substances 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 238000009826 distribution Methods 0.000 description 1
- 238000001914 filtration Methods 0.000 description 1
- 230000005484 gravity Effects 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 239000002184 metal Substances 0.000 description 1
- 238000005272 metallurgy Methods 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 239000011435 rock Substances 0.000 description 1
- 238000010183 spectrum analysis Methods 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 239000000126 substance Substances 0.000 description 1
- 230000001052 transient effect Effects 0.000 description 1
- 238000003466 welding Methods 0.000 description 1
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
- G01N29/11—Analysing solids by measuring attenuation of acoustic waves
-
- 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
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/01—Indexing codes associated with the measuring variable
- G01N2291/015—Attenuation, scattering
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2291/00—Indexing codes associated with group G01N29/00
- G01N2291/02—Indexing codes associated with the analysed material
- G01N2291/028—Material parameters
- G01N2291/0289—Internal structure, e.g. defects, grain size, texture
Landscapes
- Physics & Mathematics (AREA)
- Biochemistry (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Chemical & Material Sciences (AREA)
- Analytical Chemistry (AREA)
- General Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Immunology (AREA)
- Pathology (AREA)
- Acoustics & Sound (AREA)
- Engineering & Computer Science (AREA)
- Signal Processing (AREA)
- Investigating Or Analyzing Materials By The Use Of Ultrasonic Waves (AREA)
Abstract
The invention discloses a crane structural damage diagnosis method based on acoustic emission signal characteristic parameters, which is characterized in that a plurality of acoustic emission sensors are arranged in a linear positioning mode along the direction of a crane main beam, a threshold value is set according to the background noise intensity, an acoustic emission acquisition system is used for acquiring acoustic emission signals, the signal intensity and the duration of the acoustic emission signals are used for constructing a characteristic parameter graph for the characteristic parameters, and the crane structural damage condition is judged according to the comparison of a closed region formed by connecting an upper boundary straight line and a lower boundary straight line which are fitted by the characteristic parameter graph and a threshold region, so that a complex signal processing process is effectively avoided, and the dependence of a signal waveform analysis method on prior knowledge such as a signal processing technology, fault diagnosis experience and the like is eliminated. The method has the advantages of simple, visual and rapid use, and the like, and can carry out initial diagnosis on the damage of the crane under the condition of not influencing the normal work of the crane.
Description
Technical Field
The invention belongs to the technical field of nondestructive testing, and particularly relates to a crane structure damage diagnosis method based on acoustic emission signal characteristic parameters.
Background
The crane is used as a large-scale mechanical device for loading, unloading and transporting materials, and is widely applied to the industries of construction, metallurgy, chemical industry, mechanical manufacturing, logistics transportation and the like. Because the working environment of the crane is generally severe and is acted by alternating load during working, the internal damage (welding defect, fatigue crack and the like) of the crane after long-term working easily causes the bending deformation and even the fracture of the structural member of the crane, thereby causing serious safety accidents. Therefore, the method has important significance in effectively detecting and diagnosing the damage of the crane structural member.
The acoustic emission is a phenomenon that a material is deformed or broken under the action of external force or internal force to generate transient elastic waves, so that an acoustic emission signal directly comes from a material crack. At present, the existing nondestructive detection method based on acoustic emission signals has the characteristics of real-time performance, realization of dynamic detection and continuous detection, wide detection range, insensitivity to the shape and structure of a component and the like compared with the conventional nondestructive detection method. The existing processing and analysis methods for acoustic emission signals mainly comprise the following two methods: one is a sound emission signal waveform analysis method, namely, the time domain waveform of the sound emission signal is processed and analyzed by adopting a traditional signal processing method (such as frequency domain analysis, wavelet analysis and the like) so as to further obtain sound emission source information, and the method is more complex and has higher requirements on prior knowledge and experience; the other method is a characteristic parameter analysis method and is also a more classical method for analyzing acoustic emission signals, the acquired acoustic emission signals are processed and converted into different characteristic parameters (such as ringing count, rise time, energy, amplitude and the like) to represent the information of the acoustic emission source, and the method has the advantages of intuition, simplicity, easiness in measurement and the like.
At present, the acoustic emission signal-based nondestructive testing method is mainly applied to gear bearings, pressure vessels, boilers, rock mass materials and the like at home and abroad, and researches on structural damage detection and structural integrity of the crane are few. The acoustic emission signals of a box beam of a Q345B steel bridge crane and a small sample in a same metal structure material laboratory in the fatigue process are analyzed by using a double-spectrum analysis method, and the acoustic emission signals of the two samples are found to be the same in the fatigue fracture process and the fatigue fracture damage state in each fatigue stage; luo hong cloud and the like disclose patents of an acoustic emission detection device and a damage detection method of a box beam of a bridge crane based on an EAF and LAP composite strategy, and a positioning area is obtained by denoising a preprocessed acoustic emission signal through an EAF filtering unit and then carrying out LAP two-dimensional different-surface positioning processing; plum poplars and the like disclose a patent of a crane girder damage acoustic emission nondestructive testing method based on adaptive optimization VMD, noise reduction is carried out on acoustic emission signals through the adaptive optimization VMD, and different damage stages of the crane girder are judged according to the gravity center frequency distribution range of the signals. The methods adopt a method for processing and analyzing the waveform of the acoustic emission signal, the processing process is complex, and a user is required to have certain signal processing knowledge and experience.
Disclosure of Invention
The invention aims to solve the technical problems in the prior art and provides a crane structure damage diagnosis method based on acoustic emission signal characteristic parameters.
The technical solution of the invention is as follows: a crane structure damage diagnosis method based on acoustic emission signal characteristic parameters is characterized by sequentially comprising the following steps:
step 1: collecting acoustic emission signals
Arranging a plurality of acoustic emission sensors in a linear positioning mode along the direction of a main beam of the crane, setting a threshold value according to the background noise intensity, and collecting acoustic emission signals by using an acoustic emission collection system;
step 2: obtaining a characteristic parameter diagram of an acoustic emission signal of a main beam of the crane
Selecting the signal intensity and duration of an acoustic emission signal as parameters to make a scatter correlation diagram, namely a characteristic parameter diagram, in a plane rectangular coordinate system, wherein the abscissa of the scatter correlation diagram is the signal intensity SS of the measured acoustic emission signal, and the unit is nVs, and the ordinate is the duration Dur of the measured acoustic emission signal, and the unit is mus;
and step 3: extracting upper and lower boundary points of scatter point correlation diagram
Dividing the scatter correlation diagram of the measured signal into n sections along the X-axis direction, wherein the interval of each section is 0.2nVs, and respectively selecting the point with the maximum ordinate in each section
And the point with the smallest ordinate
I =1, 2.. and n, respectively, resulting in a set of upper boundary points
And a set of lower boundary points
；
And 4, step 4: fitting the upper boundary line and the lower boundary line of the scatter diagram by using a least square method
Set the upper boundary points
Substituting into a least square method calculation formula to obtain the slope k of the upper boundary point fitting straight line1And intercept b1Respectively as follows:
in the formula:
the expression of the upper boundary line is
；
Similarly, the expression of the lower boundary line is
；
And 5: judging whether the crane structure is damaged or not
The closed area formed by connecting the upper boundary straight line with the slope of the lower boundary straight line is compared with a preset threshold area, if the closed area formed by connecting the upper boundary straight line with the lower boundary straight line is all located in the preset threshold area, the crane structure is damaged, and otherwise, the crane structure is not damaged.
According to the method, the signal intensity and the duration time of the acoustic emission signal in the direction of the main beam of the crane are used as characteristic parameters to construct a characteristic parameter graph, and the damage condition of the crane structure is judged according to the comparison of a closed region formed by connecting an upper boundary straight line and a lower boundary straight line which are fitted by the characteristic parameter graph and a threshold region, so that a complex signal processing process is effectively avoided, and the dependence of a signal waveform analysis method on prior knowledge such as a signal processing technology, fault diagnosis experience and the like is eliminated. The method has the advantages of simple, visual and rapid use, and the like, and can carry out initial diagnosis on the damage of the crane under the condition of not influencing the normal work of the crane.
Drawings
FIG. 1 is a characteristic parameter diagram of a main beam acoustic emission signal obtained by an embodiment of the present invention.
FIG. 2 is a scatter diagram obtained by extracting upper and lower boundary points from a feature parameter diagram of a main beam acoustic emission signal in the embodiment of the present invention.
Fig. 3 is a graph showing the result of fitting a straight line to the upper and lower boundary points in the embodiment of the present invention.
Detailed Description
The invention discloses a crane structure damage diagnosis method based on acoustic emission signal characteristic parameters, which is characterized by comprising the following steps in sequence:
step 1: collecting acoustic emission signals
Arranging a plurality of acoustic emission sensors in a linear positioning mode along the direction of a main beam of the crane, setting a threshold value according to the background noise intensity, and collecting acoustic emission signals by using an acoustic emission collection system; testing the attenuation of signals before arranging the sensors, determining the distance between the sensors according to the attenuation condition, determining the number of the required sensors according to the length of the main beam and the distance between the sensors, testing background noise after arranging the sensors, selecting a proper threshold value according to the intensity of the background noise to filter the background noise, wherein the threshold value is generally set to be 40 dB;
the acoustic emission acquisition system provided by the embodiment of the invention is an AMSY-6 type 10-channel acoustic emission acquisition instrument produced by Wallen, Germany and matched acoustic emission signal acquisition and analysis software thereof, the acoustic emission sensor is a VS150-RSC type front-emitting integrated acoustic emission sensor, the frequency range is 100kHz-450kHz, the central frequency is 150kHz, and the built-in front-emitting gain is 34 dB; the crane is a single-beam bridge crane, the rated load is 3t, the length of a main beam of the crane is 6500mm, acoustic emission signals are subjected to attenuation measurement, the distance between acoustic emission sensors is 2700mm, the number of the acoustic emission sensors is 3, and the acoustic emission sensors are uniformly distributed along the main beam direction. The background noise is tested before the crane runs, the detected background noise is about 35dB, and then the threshold value is set to be 40 dB.
The crane loads used in the detection of the embodiment of the invention are respectively 0t (no load), 0.5t, 1t, 1.5t, 2t, 2.5t and 3t (full load), and acoustic emission signals when a crane hook is lifted and dropped, a trolley is independently operated, a cart is independently operated, the hook is linked with the trolley, and the trolley is linked with the trolley are respectively collected in each detection.
Step 2: obtaining a characteristic parameter diagram of an acoustic emission signal of a main beam of the crane
Selecting the signal intensity and duration of the acoustic emission signal as parameters to make a scatter correlation diagram, namely a characteristic parameter diagram shown in fig. 1, wherein the abscissa of the scatter correlation diagram is the signal intensity SS of the measured acoustic emission signal and has the unit of nVs, and the ordinate is the duration Dur of the measured acoustic emission signal and has the unit of mus;
and step 3: extracting upper and lower boundary points of scatter point correlation diagram
Dividing the scatter correlation diagram of the measured signal into n sections along the X-axis direction, wherein the interval of each section is 0.2nVs, and respectively selecting the point with the maximum ordinate in each section
And the point with the smallest ordinate
I =1, 2.. and n, as shown in fig. 2, respectively result in a set of upper boundary points
And a set of lower boundary points
；
And 4, step 4: fitting the upper boundary line and the lower boundary line of the scatter diagram by using a least square method
Set the upper boundary points
Substituting into a least square method calculation formula to obtain the slope k of the upper boundary point fitting straight line1And intercept b1Respectively as follows:
in the formula:
the expression of the upper boundary line is
；
Similarly, the expression of the lower boundary line is
；
And 5: judging whether the crane structure is damaged or not
The closed area formed by connecting the upper boundary straight line with the slope of the lower boundary straight line is compared with a preset threshold area, if the closed area formed by connecting the upper boundary straight line with the lower boundary straight line is all located in the preset threshold area, the crane structure is damaged, and otherwise, the crane structure is not damaged.
Determining the preset threshold area: selecting a crane with the same type as that in the embodiment of the invention and containing a prefabricated crack defect as a test object, and according to the steps 1-4 in the embodiment of the invention, obtaining upper and lower boundary straight lines of a characteristic correlation diagram containing the signal intensity and the duration of an acoustic emission signal of the crane with structural damage, wherein a closed area formed by connecting the upper boundary straight line and the lower boundary straight line is a preset threshold area.
Claims (1)
1. A crane structure damage diagnosis method based on acoustic emission signal characteristic parameters is characterized by sequentially comprising the following steps:
step 1: collecting acoustic emission signals
Arranging a plurality of acoustic emission sensors in a linear positioning mode along the direction of a main beam of the crane, setting a threshold value according to the background noise intensity, and collecting acoustic emission signals by using an acoustic emission collection system;
step 2: obtaining a characteristic parameter diagram of an acoustic emission signal of a main beam of the crane
Selecting the signal intensity and duration of an acoustic emission signal as parameters to make a scatter correlation diagram, namely a characteristic parameter diagram, in a plane rectangular coordinate system, wherein the abscissa of the scatter correlation diagram is the signal intensity SS of the measured acoustic emission signal, and the unit is nVs, and the ordinate is the duration Dur of the measured acoustic emission signal, and the unit is mus;
and step 3: extracting upper and lower boundary points of scatter point correlation diagram
Dividing the scatter correlation diagram of the measured signal into n sections along the X-axis direction, wherein the interval of each section is 0.2nVs, and respectively selecting the point with the maximum ordinate in each section
And the point with the smallest ordinate
I =1, 2.. and n, respectively, resulting in a set of upper boundary points
And a set of lower boundary points
；
And 4, step 4: fitting the upper boundary line and the lower boundary line of the scatter diagram by using a least square method
Set the upper boundary points
Substituting into a least square method calculation formula to obtain the slope k of the upper boundary point fitting straight line1And intercept b1Respectively as follows:
in the formula:
the expression of the upper boundary line is
；
Similarly, the expression of the lower boundary line is
；
And 5: judging whether the crane structure is damaged or not
The closed area formed by connecting the upper boundary straight line with the slope of the lower boundary straight line is compared with a preset threshold area, if the closed area formed by connecting the upper boundary straight line with the lower boundary straight line is all located in the preset threshold area, the crane structure is damaged, and otherwise, the crane structure is not damaged.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210035333.2A CN114487124B (en) | 2022-01-13 | 2022-01-13 | Crane structure damage diagnosis method based on acoustic emission signal characteristic parameters |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210035333.2A CN114487124B (en) | 2022-01-13 | 2022-01-13 | Crane structure damage diagnosis method based on acoustic emission signal characteristic parameters |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114487124A true CN114487124A (en) | 2022-05-13 |
| CN114487124B CN114487124B (en) | 2023-12-12 |
Family
ID=81511628
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210035333.2A Active CN114487124B (en) | 2022-01-13 | 2022-01-13 | Crane structure damage diagnosis method based on acoustic emission signal characteristic parameters |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114487124B (en) |
Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090070048A1 (en) * | 2004-07-15 | 2009-03-12 | Stothers Ian Mcgregor | Acoustic structural integrity monitoring system and method |
| CN105606451A (en) * | 2016-01-05 | 2016-05-25 | 同济大学 | Evaluation method for self-repair healing effect of cement-based material cracks |
| CN110849968A (en) * | 2019-11-05 | 2020-02-28 | 东南大学 | Crane main beam damage acoustic emission nondestructive detection method based on self-adaptive optimization VMD |
| CN111812211A (en) * | 2020-07-09 | 2020-10-23 | 武汉理工大学 | RA-AF-E rock material brittle fracture crack classification method based on acoustic emission parameters |
| CN112098524A (en) * | 2020-09-22 | 2020-12-18 | 北京航空航天大学 | Method for identifying asphalt concrete fracture process and quantifying microcracks based on acoustic emission |
| CN112525534A (en) * | 2020-11-10 | 2021-03-19 | 北京物声科技有限公司 | Bearing and gear damage quantitative evaluation method based on acoustic emission technology |
| US20210372967A1 (en) * | 2020-05-28 | 2021-12-02 | China Special Equipment Inspection And Research Institute | Magnetoacoustic emission detection method for fatigue damage of ferromagnetic metal component |
| CN113776943A (en) * | 2021-11-05 | 2021-12-10 | 中国矿业大学(北京) | Rock compressive strength prediction method |
-
2022
- 2022-01-13 CN CN202210035333.2A patent/CN114487124B/en active Active
Patent Citations (8)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090070048A1 (en) * | 2004-07-15 | 2009-03-12 | Stothers Ian Mcgregor | Acoustic structural integrity monitoring system and method |
| CN105606451A (en) * | 2016-01-05 | 2016-05-25 | 同济大学 | Evaluation method for self-repair healing effect of cement-based material cracks |
| CN110849968A (en) * | 2019-11-05 | 2020-02-28 | 东南大学 | Crane main beam damage acoustic emission nondestructive detection method based on self-adaptive optimization VMD |
| US20210372967A1 (en) * | 2020-05-28 | 2021-12-02 | China Special Equipment Inspection And Research Institute | Magnetoacoustic emission detection method for fatigue damage of ferromagnetic metal component |
| CN111812211A (en) * | 2020-07-09 | 2020-10-23 | 武汉理工大学 | RA-AF-E rock material brittle fracture crack classification method based on acoustic emission parameters |
| CN112098524A (en) * | 2020-09-22 | 2020-12-18 | 北京航空航天大学 | Method for identifying asphalt concrete fracture process and quantifying microcracks based on acoustic emission |
| CN112525534A (en) * | 2020-11-10 | 2021-03-19 | 北京物声科技有限公司 | Bearing and gear damage quantitative evaluation method based on acoustic emission technology |
| CN113776943A (en) * | 2021-11-05 | 2021-12-10 | 中国矿业大学(北京) | Rock compressive strength prediction method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114487124B (en) | 2023-12-12 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN102854252B (en) | Method and system used for detecting metal material fatigue state | |
| CN205861255U (en) | Ferromagnetic material stress based on barkhausen detection device | |
| CN105067239B (en) | The beam crack fault detection means and method vibrated based on swept frequency excitation | |
| CN104965023B (en) | Multi-modal guided wave industrial pipeline diagnostic method | |
| CN101566541A (en) | System for evaluating tensile damage of in-service 16Mn steel force-bearing part by adopting catastrophic model | |
| CN101718748A (en) | Magnetic memory method and device for diagnosing ferromagnetic component damage | |
| CN106768967A (en) | A kind of flange fastening bolt loosens lossless detection method and its system | |
| WO2021248962A1 (en) | Non-destructive testing method and device for testing and distinguishing internal and external defects of steel wire rope | |
| CN111737848A (en) | Hoisting machinery main beam structure crack identification method based on time domain strain signal decomposition | |
| CN114487124B (en) | Crane structure damage diagnosis method based on acoustic emission signal characteristic parameters | |
| CN101299034A (en) | Acoustic emission system for monitoring construction collapse in fire and monitoring method thereof | |
| Evseev et al. | Acoustic emission approach to determining survivability in fatigue tests | |
| Liu et al. | Fuzzy pattern recognition of impact acoustic signals for nondestructive evaluation | |
| Bashkov et al. | Damage evaluation criteria based on acoustic emission parameters for polymer composite materials | |
| CN110702801A (en) | Plate-shaped structure fatigue crack positioning system and method based on same-side ultrasonic frequency mixing wavelet | |
| CN110988138B (en) | Weld assembly quality detection device and method | |
| CN113640394A (en) | Fatigue crack identification method and system | |
| CN102590358A (en) | Rapid signal processing technology for vortex-excited acoustic emission | |
| CN102279222A (en) | 16Mn steel load carrying piece fatigue damage state identification system based on spot noise figure (SNF) strategy and direct stream digital (DSD) strategy | |
| CN111721510A (en) | Intelligent diagnosis method for steel crane beam based on real-time monitoring | |
| KR100561065B1 (en) | Method for detecting crack position of material with sensor | |
| CN110926770A (en) | Quantitative evaluation method for fatigue life of crane steel beam group | |
| Padzi et al. | Evaluating ultrasound signals of carbon steel fatigue testing using signal analysis approaches | |
| Zhang et al. | Quantitative study on cross-section damage of steel wire rope based on magnetic signal characteristics under weak magnetic excitation | |
| Qian et al. | Pattern recognition of fatigue damage acoustic emission signal |
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 |