CN110568083A - acoustic emission detection method for online monitoring of corrosion fatigue damage of steel - Google Patents
acoustic emission detection method for online monitoring of corrosion fatigue damage of steel Download PDFInfo
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- 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
- G01N29/14—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 using acoustic emission techniques
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- 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
- G01N2291/023—Solids
- G01N2291/0234—Metals, e.g. steel
Abstract
The invention discloses an acoustic emission detection method for online monitoring of steel corrosion fatigue damage, which comprises the following steps of: improving the Marr wavelet, removing side lobe components, adding control parameters p and m, wherein the p and m can self-adaptively select different parameter values to adjust the wavelet form according to acoustic emission signals in different fatigue damage states; processing the acoustic emission signal x (t) by using the improved Marr wavelet as the generalized S transform of a kernel function to obtain a time-frequency diagram of the acoustic emission signal in a time domain and a frequency domain; the information entropy quantization method is adopted to quantize the information entropy of the time-frequency graph with high resolution: the generalized S transformation in the invention has stronger time-frequency domain distinguishing capability on abundant signal components, can represent the characteristic change condition of the acoustic emission signal in the time domain and the frequency domain along with the increase of the fatigue cycle number, and provides more accurate time-frequency information change basis for the quantification and evaluation of the fatigue damage degree of the metal material.
Description
Technical Field
the invention relates to an acoustic emission detection method for online monitoring of steel corrosion fatigue damage, belongs to the field of nondestructive detection signal analysis of metal materials, and is applied to objective evaluation of steel corrosion fatigue damage degree.
Background
In industries, such as petrochemical industry, civil engineering, aerospace and aviation industry, bearing components in service are subjected to stress interaction for a long time, fatigue cracks can be initiated and formed, if the bearing components are exposed in a corrosion environment for a long time, the deterioration and the expansion of the fatigue cracks can be further accelerated, the anti-fatigue property and the service time of the material are obviously reduced, and the damage caused at the moment is corrosion fatigue damage. Unlike the damage of a single fatigue load, the corrosion fatigue damage is one of the most serious damage modes because the structural member is simultaneously affected by the fatigue stress and the corrosion environment, and if the damage is not found in time, the damage can cause the death of personnel and the great loss of property.
for a structure bearing a cyclic load under some special working environments, corrosion fatigue is difficult to completely avoid, from the aspect of detection technology, in order to prevent various accidents caused by corrosion fatigue, it is necessary to detect and monitor corrosion fatigue damage of a material or a structural member bearing the load, such as a metal material, quickly and effectively detect and monitor the corrosion fatigue damage in the service process, and effectively evaluate and diagnose the severity of the damage source and the location of the damage source. At present, two methods of destructive detection and nondestructive detection are mainly used for metal detection. The destructive detection is that when a material is sampled, a detected sample is melted, tableted and cut into small fragments to be uniformly mixed, and then a sample is weighed, so that destructive or damage detection is performed on the material. The nondestructive testing is a measuring or detecting method which does not damage or cause the deterioration of future use performance or normal use of the material or the structural part by using the physical properties of acoustics, optics, electromagnetism and the like of a testing party, is used for detecting the potential defects in the structural part or the material or the defects which cannot be observed by naked eyes on the surface of the material, and determining the integrity states of the internal composition, the organization, the structure, the mechanical property and the like, thereby timely finding out unqualified raw materials or semi-finished products, reducing the rejection rate, and reducing the probability and the cost of returning to the remanufacturing; the periodic nondestructive detection or monitoring is carried out in the using process, the micro defects or macro defects caused by the interaction of corrosion and fatigue and the material performance degradation can be found in time, and the production and life safety in the service process of the material is further ensured. Currently, the common nondestructive testing methods include: non-destructive inspection methods such as X/gamma ray inspection, computer-aided imaging inspection (CR), etc. in the radiation method, acoustic emission inspection, ultrasonic inspection, laser ultrasonic inspection, etc. in the acoustic method, eddy current inspection, magnetic flux leakage inspection, etc. in the electromagnetic method, bubble leakage inspection, ultrasonic leakage inspection, etc. in the leakage method, infrared thermal imaging inspection, etc. in the infrared method are used. The acoustic emission detection technology is an effective online unconventional online nondestructive detection technology for monitoring or detecting damage changes of structural members or materials in real time on line.
acoustic Emission (Acoustic Emission) refers to a phenomenon in which a transient elastic wave is locally emitted due to rapid release of energy under the action of external or internal force in a material. The frequency characteristics of the acoustic emission signals are from lower infrasonic frequency, audio frequency to higher ultrasonic frequency, the frequency range is from several Hz to several MHz, the frequency range is very wide, and the frequency range of the generated acoustic emission signals is not necessarily consistent due to the phenomena of crack nucleation, plastic deformation or crack rapid propagation, fracture and the like of different materials or structural parts due to the difference of the material structure, the material type and the like. If an acoustic emission sensor is used for collecting an acoustic emission signal generated by the material or the structural member in the period, the collected acoustic emission signal (analog signal) is converted into an electric signal which can be processed and displayed by a computer (analog-to-digital signal conversion), the amplitude range of the signal changes from about a few muV to hundreds of mV, and a higher amplitude value indicates that huge energy release occurs inside the material or the structure. Most metal materials have large and small acoustic emissions when damage, crack nucleation, plastic deformation, microcracking, crack propagation, fracture and the like occur inside the metal materials. The technology of exploring, collecting, storing and later processing acoustic emission signals by using a special acoustic emission instrument and evaluating the performance or structural integrity change condition of a material component according to certain characteristics of the acoustic emission signals is called acoustic emission technology, and relates to basic concepts such as an acoustic emission source, acoustic wave propagation, conversion from the acoustic signals to electric signals, acoustic emission signal data display and storage recording, acoustic emission signal processing, interpretation and evaluation of the acoustic emission source and the like.
compared with other nondestructive detection methods such as ultrasonic detection, ray detection and the like, the current acoustic emission detection method can detect the real-time expansion state of the internal or external defect of the material, and can dynamically detect or monitor the real-time damage state of the material or the structural part because the defect is not scanned by using the external physical characteristics such as acoustics, optics, electromagnetism and the like, and the defect can send out the information of the defect in real time dynamically. The acoustic emission signal processing method includes, in addition to analysis methods such as a frequency spectrum analysis method and a modal acoustic emission method, a pattern recognition method for acquiring acoustic emission source information and classifying and recognizing damage and fault features, a method combining generalized S-transform and information entropy is used for processing acoustic emission signals, in a variable Gaussian window function related to the scale of the S-transform, the Gaussian window function cannot be adjusted at will in terms of time or frequency width, and a kernel function w (t, f) is fixed, so that the application is limited, and the acoustic emission signal processing method cannot be applied to signal processing and analysis of different characteristics in multiple fields. The disadvantage that the window function is fixed in time or frequency is improved, so that the time domain window and the frequency domain window have the function of random regulation.
Therefore, in order to further reveal the relation between the acoustic emission and the damage degree, the invention uses the improved signal processing method of S transformation to the acoustic emission signal to objectively evaluate the corrosion fatigue damage degree, improves the Marr wavelet according to the characteristics of the acoustic emission signal generated by the corrosion fatigue of the metal material, removes the side lobe component which is suitable for analyzing the seismic wavelet in the Marr wavelet, adds the adjusting factors p and m, adjusts the values of the two factors to adaptively select different factors to adjust the shape of the kernel function, so as to quickly match the acoustic emission signal generated by the fatigue of the metal, then carries out the quantitative processing of the information entropy on the time-frequency diagram with rich time-frequency information, and obtains the result of representing the corrosion fatigue damage degree of the steel. The method provides a basis for acoustic emission detection and fatigue damage degree aggravation of common steel used for constructional engineering and bridge structural members in the fatigue process.
Disclosure of Invention
The invention provides an acoustic emission detection method for online monitoring of corrosion fatigue damage of steel, which utilizes generalized S transformation to process an acoustic emission signal of corrosion fatigue and can evaluate an acoustic emission detection technology of the corrosion fatigue of the steel.
the invention discloses an acoustic emission detection method for online monitoring of corrosion fatigue damage of steel, which comprises the following steps:
Step one, building a sound emission detection and fatigue test device system:
Before the corrosion fatigue test is started, an acoustic emission sensor, a preamplifier (gain: 40dB), a coaxial cable and an acoustic emission instrument are correctly connected;
the acoustic emission sensor which is evenly coated with the coupling agent on the contact surface of the acoustic emission sensor is tightly attached to the two ends of the sample, and the acoustic emission sensor is tightly wrapped by a plastic film;
Adding a linear defect sample with the unilateral depth of 3mm on the basis of a standard tensile sample, immersing the sample into a 3.5% NaCl solution at room temperature, and carrying out pre-corrosion for 10 days;
the sample was mounted in the corrosion apparatus, and after mounting, the airtightness of the bottom of the corrosion container was checked to prevent the leakage of the NaCl solution from the bottom of the container and further to corrode the lower chuck part of the fatigue testing machine. The detected corrosion device provided with the test sample is placed between the upper chuck and the lower chuck, so that the test sample is in full contact and symmetrical arrangement with the upper chuck and the lower chuck, the test sample is perpendicular to the horizontal plane, the test sample is stressed uniformly in the fatigue process, and other interferences are eliminated;
then preheating a fatigue testing machine, loading a sample with a loading stress of 0.2Hz and a stress ratio of 0.1, and then carrying out online corrosion on the sample in spray of 10% NaCl solution;
detecting acoustic emission and acquiring signals in a corrosion fatigue test to obtain a metal fatigue acoustic emission signal x (t);
Improving the Marr wavelet, removing side lobe components of the Marr wavelet, adding control parameters p and m, wherein the p and m can self-adaptively select different parameter values to adjust the wavelet form according to acoustic emission signals in different fatigue damage states;
The improved Marr wavelet time domain expression can be expressed as
where t is the time corresponding to the time domain information of the signal;
The expression in the frequency domain is:
Wherein f is the frequency of the signal;
The expression of the improved Marr wavelet generalized S transformation is as follows:
wherein t is a time corresponding to time domain information of the signal, τ is a time corresponding to frequency information;
wherein x (t) represents a metal fatigue acoustic emission signal, and p and m are control parameters;
Step three, processing the acoustic emission signal x (t) by using the generalized S transform with the improved Marr wavelet as a kernel function to obtain a time-frequency diagram of the acoustic emission signal in a time domain and a frequency domain;
Fourthly, quantizing the information entropy of the time-frequency graph with high resolution by adopting a quantization method of the information entropy:
segmenting the elevation information range in a certain time-frequency graph, setting the number of segments and calculating the interval value to be segmented, wherein the interval value is determined by the range value of the elevation information range in the time-frequency graph and the number of the segments, the interval value is J, the range value is r, and the number of the segments is d, then:
Wherein r ═ xmax-xmin，xmaxAnd xminthe maximum value and the minimum value of the elevation value in the time-frequency diagram are respectively. The k value is not suitable to be too large, and the too large value can cause the increase of the calculation amount of a computer, and the k value in the method is 100, so the calculation amount is more suitable.
counting each segment dithe number n of data of elevation information contained injAnd calculating the probability of each segment:
wherein l represents the maximum number of segments of the elevation information range in the time-frequency diagram
wherein the content of the first and second substances,
The information entropy of the time-frequency diagram is calculated by the following formula:
By quantifying the time-frequency components of the signals under different fatigue cycles, the average information content of the acoustic emission signals presents a lower level when cracks propagate by observing the fluctuation degree of the average information content along with the fatigue cycles. By carrying out generalized S transformation and information entropy processing on acoustic emission signals generated by corrosion fatigue under the depth of metal linear defects, comparing and analyzing, if the time-frequency components are richer, the average information content obtained after the information entropy processing is higher, and the average information content presents smaller characteristics in the crack propagation period. The deeper the depth of the linear defect, the fewer the number of fatigue cycles required to achieve the degree of corrosion fatigue damage.
the specific idea of the invention is to establish an online acoustic emission detection system for metal corrosion fatigue damage, collect acoustic emission signals under the metal material structure corrosion fatigue damage, and analyze the acoustic emission signals in time and frequency domains by generalized S transformation with improved Marr wavelets as kernel functions; and finally, quantizing the information entropy of the time-frequency graph with high resolution by adopting a quantization method of the information entropy. In the generalized S-transform time-frequency diagram taking the improved Marr wavelet as the kernel function, the form and the frequency resolution of the wavelet are adjusted by changing the values of the adjusting factors p and m, so that the theoretical wavelet which is close to the wave form of the acoustic emission signal generated by metal fatigue can be found, and compared with the generalized S-transform under standard S-transform and broadband Marr wavelet under a Gaussian window function, the generalized S-transform has stronger time-frequency analysis capability on the acoustic emission signal generated by metal fatigue, can distinguish different components of the signal in a time domain and a frequency domain, and has a fine corresponding relation with the real time and the frequency of the analysis signal. The generalized S-transform time-frequency graph is drawn by the elevation information of each contour line, and if the generalized S-transform time-frequency graph is taken as a random event consisting of a series of elevation values, the uncertainty degree of the random event can be represented by using the information entropy.
Compared with the prior art, the invention has the advantages that:
since the kernel function w (t, f) is fixed in the S transform, it is limited in application and is not suitable for analysis and processing of acoustic emission signals of metal materials. The improved Marr wavelet is generalized S transformation of a kernel function, and the shape of the wavelet can be arbitrarily changed by changing the values of p and m to match acoustic emission signals.
the improved Marr wavelet has no side lobe component, has the characteristics of tight support, tolerance and the like, and can better match acoustic emission signals generated by metal internal damage.
The resolution and the size of the wavelet are changed by searching for the proper control parameter p and the value m, so that the theoretical wavelet which is the best approximate to the wave spectrum of the acoustic emission signal generated by metal fatigue can be obtained, and compared with S transformation under a Gaussian window function and generalized S transformation under broadband Marr wavelets, the method has a stronger time-frequency information representation result.
The generalized S transformation in the invention has stronger time-frequency domain distinguishing capability on abundant signal components, can represent the characteristic change condition of the acoustic emission signal in the time domain and the frequency domain along with the increase of the fatigue cycle number, and provides more accurate time-frequency information change basis for the quantification and evaluation of the fatigue damage degree of the metal material.
Drawings
FIG. 1 is a schematic view of a system of an acoustic emission testing and corrosion fatigue testing apparatus.
FIG. 2 is a time domain diagram of a typical acoustic emission signal in a metal corrosion fatigue test
FIG. 3 is a generalized S-transform time-frequency diagram with improved Marr wavelets as kernel functions
FIG. 4 is a time-frequency diagram of 7470 fatigue cycles
FIG. 5 is a time-frequency diagram of 7570 cycles of fatigue
FIG. 6 is a time-frequency diagram of 4200 cycles of fatigue
FIG. 7 is a time-frequency diagram of 6510 cycles of fatigue
FIG. 8 is a statistical chart of entropy change of time-frequency diagram information of samples with increase of corrosion fatigue cycles
Detailed Description
the invention is described in further detail below with reference to the figures and specific examples.
firstly, a linear defect sample with the unilateral depth of 3mm is artificially added on the basis of a standard tensile sample, the sample is immersed into a 3.5% NaCl solution at room temperature for 10 days of pre-corrosion, the sample is loaded with a loading stress with the frequency of 0.2Hz and the stress ratio of 0.1, then the sample is subjected to online corrosion in the spray of the 10% NaCl solution, and an acoustic emission signal x (t) of metal corrosion fatigue is obtained by building an acoustic emission detection and corrosion fatigue test device system shown in figure 1, as shown in figure 2;
Step two, processing the acoustic emission signal x (t) according to the generalized S transform taking the improved Marr wavelet as a kernel function, and obtaining a time-frequency diagram of the acoustic emission signal in a time domain and a frequency domain, wherein the expression of the improved Marr wavelet generalized S transform is as follows:
Thirdly, quantizing the information entropy of the time-frequency graph with high resolution by adopting a quantization method of the information entropy:
Segmenting an elevation information range in a certain time-frequency graph, setting the number of segments and calculating an interval value to be segmented, wherein the interval value is determined by the range value of the information range and the number of the segments, the interval value is J, the range value is r, and the number of the segments is d, then:
wherein r ═ xmax-xmin，xmaxand xminThe maximum value and the minimum value of the elevation value in the time-frequency diagram are respectively. The k value is not suitable to be overlarge, and the overlarge value can cause the increase of the calculation amount of a computer, and the k value is 100 in the invention, so the calculation amount is more suitable.
The above description is only a preferred embodiment of the present invention, and it should be noted that it is possible for those skilled in the art to make various modifications and variations without departing from the technical principle of the present invention, and it is within the scope of the present invention without departing from the spirit of the present invention.
Claims (5)
1. an acoustic emission detection method for online monitoring of corrosion fatigue damage of steel is characterized in that: the method comprises the following steps of,
Step one, building a sound emission detection and fatigue test device system:
before the corrosion fatigue test is started, the acoustic emission sensor, the preamplifier, the coaxial cable and the acoustic emission instrument are correctly connected;
The acoustic emission sensor which is evenly coated with the coupling agent on the contact surface of the acoustic emission sensor is tightly attached to the two ends of the sample, and the acoustic emission sensor is tightly wrapped by a plastic film;
detecting acoustic emission and acquiring signals in a corrosion fatigue test to obtain a metal fatigue acoustic emission signal x (t);
improving the Marr wavelet, removing side lobe components of the Marr wavelet, adding control parameters p and m, and adaptively selecting different parameter values to adjust the wavelet form according to acoustic emission signals in different fatigue damage states by the p and the m;
Step three, processing the acoustic emission signal x (t) by using the generalized S transform with the improved Marr wavelet as a kernel function to obtain a time-frequency diagram of the acoustic emission signal in a time domain and a frequency domain;
Step four, quantizing the information entropy of the time-frequency graph with high resolution by adopting a quantization method of the information entropy;
By quantifying the time-frequency components of the signals under different fatigue cycles and observing the fluctuation degree of the average information content along with the fatigue cycles, the average information content of the acoustic emission signals presents a lower level when cracks are expanded; the acoustic emission signals generated by corrosion fatigue under the depth of the metal linear defect are subjected to generalized S transformation and information entropy processing and are compared and analyzed, if the time-frequency components are richer, the average information content obtained after the information entropy processing is higher, and the average information content presents smaller characteristics in the crack propagation period; the deeper the depth of the linear defect, the fewer the number of fatigue cycles required to achieve the degree of corrosion fatigue damage.
2. the acoustic emission detection method for online monitoring of corrosion fatigue damage of steel according to claim 1, characterized in that:
Adding a linear defect sample with the unilateral depth of 3mm on the basis of a standard tensile sample, immersing the sample into a 3.5% NaCl solution at room temperature, and carrying out pre-corrosion for 10 days;
installing the sample in a corrosion device, and after the sample is installed, checking the airtightness of the bottom of the corrosion container to prevent NaCl solution from seeping out of the bottom of the container so as to corrode a lower chuck part of a fatigue testing machine; the detected corrosion device provided with the test sample is placed between the upper chuck and the lower chuck, so that the test sample is in full contact and symmetrical arrangement with the upper chuck and the lower chuck, the test sample is perpendicular to the horizontal plane, the test sample is stressed uniformly in the fatigue process, and other interferences are eliminated;
then, the fatigue testing machine is preheated, the sample is loaded with a loading stress with the frequency of 0.2Hz and the stress ratio of 0.1, and then the sample is sprayed in 10 percent NaCl solution for online corrosion.
3. the acoustic emission detection method for online monitoring of corrosion fatigue damage of steel according to claim 1, characterized in that:
the improved Marr wavelet time domain expression is expressed as
where t is the time corresponding to the time domain information of the signal;
the expression in the frequency domain is:
Wherein f is the frequency of the signal;
The expression of the improved Marr wavelet generalized S transformation is as follows:
Wherein t is a time corresponding to time domain information of the signal, τ is a time corresponding to frequency information; x (t) represents a metal fatigue acoustic emission signal, and p and m are control parameters.
4. the acoustic emission detection method for online monitoring of corrosion fatigue damage of steel according to claim 1, characterized in that:
segmenting the elevation information range in a certain time-frequency graph, setting the number of segments and calculating the interval value to be segmented, wherein the interval value is determined by the range value of the elevation information range in the time-frequency graph and the number of the segments, the interval value is J, the range value is r, and the number of the segments is d, then:
Wherein r ═ xmax-xmin，xmaxAnd xminrespectively the maximum value and the minimum value of the elevation value in the time-frequency diagram, and the k value in the methodTaking 100;
counting each segment dithe number n of data of elevation information contained injand calculating the probability of each segment:
wherein l represents the maximum number of segments of the elevation information range in the time-frequency diagram
wherein the content of the first and second substances,
the information entropy of the time-frequency diagram is calculated by the following formula:
5. The acoustic emission detection method for online monitoring of corrosion fatigue damage of steel according to claim 1, characterized in that:
In the generalized S-transform time-frequency diagram taking the improved Marr wavelet as the kernel function, the form and the frequency resolution of the wavelet are adjusted by changing the values of the adjusting factors p and m, the theoretical wavelet which is close to the wave form of the acoustic emission signal generated by metal fatigue is found, and compared with the generalized S-transform under the standard S-transform and the broadband Marr wavelet under the Gaussian window function, the generalized S-transform has stronger time-frequency analysis capability on the acoustic emission signal generated by metal fatigue, can distinguish different components of the signal in a time domain and a frequency domain, and has a fine corresponding relation with the real time and the frequency of the analysis signal.
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Application publication date: 20191213 |
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| RJ01 | Rejection of invention patent application after publication |