CN117191952A - Fatigue damage identification and life prediction method based on acoustic emission signal wavelet packet decomposition frequency band energy spectrum - Google Patents
Fatigue damage identification and life prediction method based on acoustic emission signal wavelet packet decomposition frequency band energy spectrum Download PDFInfo
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Abstract
The invention discloses a fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum, which comprises the following steps: s10, acquiring acoustic emission signals of metal materials or welding structures under the action of fatigue load in the whole process of a fatigue loading test; s20, determining an optimal wavelet basis for decomposing a wavelet packet for fatigue damage identification; s30, performing multi-layer decomposition on the acoustic emission signal by using an optimal wavelet base, and decomposing the acoustic emission signal into frequency sub-bands; s40, calculating the energy of the wavelet packet decomposition coefficient; s50, fitting to obtain a fatigue damage evolution equation; s60, judging the fatigue life stage of the monitored object according to the fatigue damage evolution equation. According to the invention, the acoustic emission technology is adopted to capture the sound signal of the metal material or the welding structure when the internal change occurs, and fatigue damage identification and life prediction are carried out through the wavelet packet decomposition frequency band energy spectrum, so that the early fatigue damage identification capability is greatly improved, and a powerful guarantee is provided for the safe use of materials and structures.
Description
Technical Field
The invention relates to the field of civil engineering steel structure fatigue research, in particular to a fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum.
Background
The steel structure engineering of China has wide application field, and has a great deal of application in tower mast structures, ocean structures and other infrastructures of building structures, bridge engineering, power transmission tower telecommunication towers and the like. The steel structure engineering structure system, the load condition and the service environment are complex, the fatigue problem is very outstanding, and the fatigue cracking, the service performance is greatly reduced, and even the risk of causing disastrous accidents is very outstanding when the structure is far lower than the design life due to the insufficient performance. Therefore, the detection and life assessment of fatigue damage to important engineering structures are of great significance to safe service and risk early warning.
The traditional nondestructive testing method mainly comprises the technologies of liquid permeation, magnetic powder, vortex, ultrasonic and the like, and is an important means for evaluating the performance of engineering structural members in the manufacturing stage, the installation stage and the service stage. The fatigue damage detection of liquid penetration, magnetic powder and vortex is realized by adopting external means, so that fatigue cracks are seen in a macroscopic mode, then photographing is carried out for storage, and the damage size is expressed mainly by recording the crack length, so that the fatigue damage detection is very inaccurate. The ultrasonic wave is to transmit and receive sound signals to a continuous medium, and only when cracks appear, the signals can be changed, and the size of damage cannot be quantitatively expressed. In the aspect of fatigue damage detection, the methods have higher degree of dependence on the operation technology and experience judging capability of detection personnel, and lack of detection capability of micro-nano scale damage in early metal, when microscopic or macroscopic cracks are found, the structure is often at the end of fatigue life, and once the structure at the end of fatigue life is subjected to missed detection, serious deviation of service performance evaluation of the structure can be caused, so that serious safety accidents are caused.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum. The acoustic emission technology can sensitively capture the sound signals of the metal materials or the welding structure when the internal change occurs, and fatigue damage identification and life prediction are carried out through wavelet packet decomposition frequency band energy spectrum, so that the identification capability of early fatigue damage is greatly improved, the prediction range of fatigue life is improved, and more sufficient margin is provided for structural safety evaluation.
In order to achieve the above purpose, the invention is realized by the following technical scheme:
the invention provides a fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum, which comprises the following steps: s10, acquiring acoustic emission signals of metal materials or welding structures under the action of fatigue load in the whole process of a fatigue loading test; s20, carrying out wavelet packet decomposition on acoustic emission signal data, and determining an optimal wavelet base of the wavelet packet decomposition for fatigue damage identification; s30, performing multi-layer decomposition on the acoustic emission signal by using an optimal wavelet base, and decomposing the acoustic emission signal into frequency sub-bands; s40, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, and calculating the energy of wavelet packet decomposition coefficients of the characteristic frequency sub-bands; s50, carrying out normalization processing on the energy of the characteristic frequency sub-band wavelet packet decomposition coefficient and the fatigue life in the whole fatigue loading process, and fitting to obtain a fatigue damage evolution equation; s60, judging the fatigue life stage of the monitored object according to the fatigue damage evolution equation by combining the acoustic emission monitoring data of the monitored object.
In some embodiments, the step S10 includes: s101, arranging an acoustic emission signal acquisition sensor at a potential fatigue crack initiation position of a metal material or a welded structure; s102, in the whole process of the fatigue loading test, acquiring acoustic emission signals of the metal material or the welding structure, and obtaining acoustic emission data of fatigue damage evolution in the fatigue life.
In some embodiments, the step S20 includes: s201, carrying out wavelet packet decomposition on acoustic emission signal data by adopting different wavelet basis functions; s202, determining the optimal wavelet base of the wavelet packet decomposition identified by fatigue damage according to the time product of the decomposition effect parameter shannon entropy and the decomposition efficiency parameter shannon entropy.
In some embodiments, the wavelet basis functions selected satisfy: the selected wavelet basis functions have orthogonality so as to ensure that the energies of the sub-bands with different frequencies are not interfered with each other after the wavelet packet is decomposed; the selected wavelet basis function has tight support, so that the calculation efficiency of wavelet packet decomposition on a large number of acoustic emission signals is ensured; the selected wavelet basis functions enable discrete wavelet transforms.
In some embodiments, the step S30 includes: s301, performing multi-layer decomposition on the acoustic emission signal through wavelet packet transformation by applying an optimal wavelet base; s302, equally dividing the signal into frequency sub-bands within the frequency total bandwidth range.
In some embodiments, the signal is decomposed into 2 at the total frequency bandwidth F using the wavelet packet decomposition level n n The frequency sub-bands with equal widths correspond to the natural characteristic frequency of the cracking sound containing the metal material.
In some embodiments, the step S40 includes: and selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands according to the acoustic emission signal characteristics of the metal fracture.
In some embodiments, the step S50 includes: s501, normalizing the frequency sub-band energy and the fatigue life in the whole fatigue loading process; s502, obtaining a fatigue damage evolution curve based on damage mechanics principle fitting, and establishing a fatigue damage evolution equation.
In some embodiments, the frequency subband energy is normalized to yield the fatigue damage D, and the fatigue life is N/N f After normalization, according to fatigue damage evolution equationData fitting is performed, wherein alpha is a regulatory parameter related to the fatigue damage evolution speed, and beta is a material intrinsic parameter related to the material class.
In some embodiments, the step S60 includes: s601, performing wavelet packet decomposition on the acoustic emission signal obtained by monitoring by applying an optimal wavelet base to decompose the acoustic emission signal into frequency sub-bands; s602, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, and calculating the energy of wavelet packet decomposition coefficients of the characteristic frequency sub-bands; s603, normalizing the frequency sub-band energy to obtain fatigue damage D, introducing the fatigue damage D into a fatigue damage evolution equation, and calculating to obtain the fatigue life stage N/N of the monitored object f 。
Compared with the prior art, the invention has the beneficial effects that: according to the fatigue damage identification and life prediction method based on the acoustic emission signal wavelet packet decomposition frequency band energy spectrum, provided by the invention, the acoustic emission sensor is arranged for nondestructive detection, so that the fatigue damage of the engineering steel structure directly bearing the dynamic load is accurately and efficiently identified, and the influence of manual experience judgment in the traditional detection method is reduced. By predicting the fatigue damage stage and the residual life of the engineering metal material or the welded structure, the recognition capability of early fatigue damage is greatly improved, the prediction range of the fatigue life is improved, and more sufficient margin is provided for structural safety evaluation. Particular advantages of the invention include at least one or more of the following:
(1) The fatigue damage recognition technology based on the acoustic emission technology belongs to a nondestructive testing method, does not cause destructive influence on the original metal material and welding structure, and hardly affects the normal service of an actual engineering structure;
(2) The invention can be used for carrying out damage monitoring of the whole life cycle of a newly built structure, and can also be used for carrying out fatigue damage detection and performance evaluation of the existing structure under the service state;
(3) The sensor used by the invention has small volume and is convenient to install, the actual operation method is simple and quick, and the problem of difficult man-machine interaction such as high requirement on the operation space of the instrument in the traditional detection method is solved;
(4) The method can be used for quantitative fatigue damage evaluation, and solves the problems that the traditional detection method depends on visual observation, the performance evaluation depends on artificial experience judgment and the like;
(5) The acoustic emission technology adopted by the invention has effective identification capability on micro-nano scale fatigue damage (crystal dislocation, slip and the like) and evolution thereof, can identify damage change before microscopic or macroscopic cracks appear on a metal material or a welded structure, namely, the acoustic emission technology is used for processing an original signal, so that early-stage to late-stage fatigue damage is detected, and the identification capability on early-stage fatigue damage is greatly improved.
It should be understood that the implementation of any of the embodiments of the invention is not intended to simultaneously possess or achieve some or all of the above-described benefits.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
The structures, proportions, sizes, etc. shown in the present specification are shown only for the purposes of illustration and description, and are not intended to limit the scope of the invention, which is defined by the claims, but rather by the claims.
FIG. 1 is a flow chart of a fatigue damage detection and life prediction method of the present invention;
FIG. 2 is a schematic diagram of the wavelet packet decomposition method of the present invention for dividing the frequency band of a signal;
FIG. 3 is a graph of wavelet packet decomposition frequency subband energy spectrum according to the present invention;
FIG. 4 is a graph of the frequency subband energy evolution at different life phases according to the present invention;
FIG. 5 is a graph showing the evolution of fatigue damage according to the present invention;
FIG. 6 is a graph comparing fatigue damage evolution curves of the present invention and displacement monitoring method.
Like or corresponding reference characters indicate like or corresponding parts throughout the several views.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the embodiments of the present invention will be described in further detail with reference to the embodiments and the accompanying drawings. The exemplary embodiments of the present invention and their descriptions herein are for the purpose of explaining the present invention, but are not to be construed as limiting the invention.
In the present invention, unless explicitly specified and limited otherwise, the terms "mounted," "connected," "secured," and the like are to be construed broadly, and may be, for example, fixedly connected, detachably connected, or integrally formed; can be mechanically or electrically connected; can be directly connected or indirectly connected through an intermediate medium, and can be communicated with the inside of two elements or the interaction relationship of the two elements. The specific meaning of the above terms in the present invention can be understood by those of ordinary skill in the art according to the specific circumstances.
It should be understood that the terms "comprises/comprising," "consists of … …," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a product, apparatus, process, or method that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such product, apparatus, process, or method as desired. Without further limitation, an element defined by the phrases "comprising/including … …," "consisting of … …," and the like, does not exclude the presence of other like elements in a product, apparatus, process, or method that includes the element.
It is further understood that the terms "upper," "lower," "front," "rear," "left," "right," "top," "bottom," "inner," "outer," and the like are used in an orientation or positional relationship based on that shown in the drawings, merely to facilitate describing the present invention and to simplify the description, and do not indicate or imply that the devices, components, or structures referred to must have a particular orientation, be configured or operated in a particular orientation, and are not to be construed as limiting the present invention.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present invention, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.
The invention provides a fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum, which is a nondestructive testing method, is particularly suitable for fatigue damage detection residual life assessment of various engineering steel structures bearing fatigue loads, has almost no influence on normal use of the engineering structure, is simple and convenient to operate, and has higher accuracy in damage identification and life prediction.
In order to better understand the above technical solution, the following detailed description will refer to the accompanying drawings and specific embodiments.
The invention provides a fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum, which comprises the following steps: s10, acquiring acoustic emission signals of metal materials or welding structures under the action of fatigue load in the whole process of a fatigue loading test; s20, carrying out wavelet packet decomposition on acoustic emission signal data, and determining an optimal wavelet base of the wavelet packet decomposition for fatigue damage identification; s30, performing multi-layer decomposition on the acoustic emission signal by using an optimal wavelet base, and decomposing the acoustic emission signal into frequency sub-bands; s40, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, and calculating the energy of wavelet packet decomposition coefficients of the characteristic frequency sub-bands; s50, carrying out normalization processing on characteristic frequency sub-band energy and fatigue life in the whole fatigue loading process, and fitting to obtain a fatigue damage evolution equation; s60, judging the fatigue life stage of the monitored object according to the fatigue damage evolution equation by combining the acoustic emission monitoring data of the monitored object. According to the method provided by the invention, the acoustic emission signals of the metal material or the welding structure are obtained for nondestructive testing, so that the fatigue damage of the engineering steel structure directly bearing the power load is accurately and efficiently identified, and the influence of manual experience judgment in the traditional detection method is reduced. By predicting the fatigue damage stage and the residual life of the engineering metal material or the welded structure, the recognition capability of early fatigue damage is greatly improved, the prediction range of the fatigue life is improved, and more sufficient margin is provided for structural safety evaluation.
Referring to fig. 1, the proposed method comprises two stages. The first stage is a parameter calibration stage of a fatigue damage evolution equation, and model parameters are determined and the fatigue damage evolution equation is established through a modeling method; the second stage is a damage identification and life prediction stage, acoustic emission data of the monitoring object are obtained, and are substituted into a fatigue damage evolution equation established in the first stage to judge the fatigue life stage of the monitoring object.
In step S10, firstly, an acoustic emission signal of a metal material or a welded structure under the action of fatigue load in the whole process of the fatigue loading test is obtained, which comprises two sub-steps.
In S101, first, it is established that the monitoring object, typically a metallic material or structural welding detail, is provided with an acoustic emission signal acquisition sensor in the vicinity of its potential fatigue crack initiation location. In S102, acoustic emission signals of the metal material or the welded structure are acquired during the whole process of the fatigue loading test, so as to obtain acoustic emission data of fatigue damage evolution in the fatigue life.
The location of the onset of a potential fatigue crack for various metallic materials or structural weld details can be accurately determined from prior studies, and references are provided for the placement of the sensors and are not described in detail herein.
In step S20, wavelet packet decomposition is performed on the acoustic emission signal data, and an optimal wavelet base for the wavelet packet decomposition for fatigue damage recognition is determined, which includes two sub-steps.
In S201, wavelet packet decomposition is required for acoustic emission signal data using different wavelet basis functions. The method is characterized in that the broadband signal of the acoustic emission signal is divided into equal-width frequency sub-band signals by a wavelet packet decomposition method, and the signals of single or multiple frequency sub-bands are further processed, so that the selected wavelet base has the following characteristics: ensuring that the energy of sub-bands with different frequencies are not interfered with each other after the wavelet packet is decomposed, wherein the selected wavelet base has orthogonality; in order to ensure the calculation efficiency of wavelet packet decomposition on a large number of acoustic emission signals, the selected wavelet base has tight support; the wavelet basis is selected to be capable of discrete wavelet transformation.
For example, daubechies wavelets (db 1 to db 45) numbered 1 to 45, symlets wavelets (sym 1 to sym 45) numbered 1 to 45, and the like may be selected based on the above requirements.
In S202, an optimal wavelet basis function is further selected. In order to determine the decomposition effect of different wavelet basis functions on signals, the shannon entropy is applied to evaluate the wavelet packet decomposition effect, and the calculation formula is as followsWherein E is i Is the signal energy per frequency subband, and smaller shannon entropy under the same conditions represents better decomposition.
To determine the decomposition efficiency of different wavelet basis functions on signals, shannon entropy time product is appliedEvaluation of wavelet packet decomposition efficiency, where T trans Is the calculation time consumed by wavelet packet decomposition, and the smaller shannon entropy time product represents higher decomposition efficiency under the same condition. By the shannon entropy and shannon entropy time product selectionAnd taking out the optimal wavelet base.
In step S30, the acoustic emission signal is decomposed into frequency sub-bands by applying an optimal wavelet basis, including two sub-steps.
Referring to fig. 2, in S301, an acoustic emission signal is subjected to multi-layer decomposition by wavelet packet transformation using an optimal wavelet basis. In S302, the signal is equally-width-decomposed into frequency sub-bands over the total bandwidth of frequencies.
The invention adopts a wavelet transformation method to build a model, the wavelet transformation is a transformation analysis method, a 'time-frequency' window which changes along with frequency can be provided, and the method is an ideal tool for carrying out time-frequency analysis and processing of signals. Wavelet basis functions are a class of basis functions used for wavelet transforms. Wavelet basis functions are a set of functions used to analyze and process signals that can be decomposed and reconstructed at different times and frequencies.
Specifically, the number of wavelet packet decomposition layers n is adopted, and the original signal is the 0 th layer. And decomposing the signal into 2 sections of frequency sub-bands with the same width in the frequency total bandwidth F at the layer 1, wherein the bandwidth of each section of frequency sub-band is F/2. And decomposing each frequency sub-bandwidth F/2 of the upper layer into 2 sections of frequency sub-bands at the layer 2, wherein the bandwidth of each section of frequency sub-band is F/4. Continuing to decompose until the nth layer, and dividing each frequency sub-bandwidth F/2 of the upper layer n-1 Equally wide decomposition into 2 segments of frequency sub-bands, each segment of frequency sub-band having a bandwidth of F/2 n . Finally totally decompose into 2 n The frequency sub-bands with equal widths are required to be capable of corresponding to the natural characteristic frequency of the cracking sound containing the metal material. For example, the acoustic emission sampling frequency for fatigue test of steel is 3000kH, the total bandwidth of the acoustic emission signal is 1500kH, and 6 layers of wavelet packet decomposition is performed on the acoustic emission signal, namely, the bandwidth of 1500kH is divided into 64 (2) 6 ) The sub-band width is 23.4375 (1500/64) kH.
It should be noted that, the sub-band is to split the total bandwidth of the original signal into a plurality of different frequency bands which do not interfere with each other, and then find the frequency band corresponding to the natural characteristic frequency of the cracking sound of the metal material.
Referring to fig. 3, the wavelet packet decomposition performed with an example of a specific signal, the energy distribution of the 1 st to 16 th order frequency sub-bands after the 6-layer wavelet packet decomposition is plotted (since the energy of the frequency sub-bands of the 17 th to 64 th order high frequency band is almost 0, which is not plotted in the figure). As can be seen from the figure, after the decomposition of the 6-layer wavelet packet, the energy after the 10 th order is small, which means that in the normal case, the sound frequency generated by the load of the metal is mainly concentrated in the 1-9 th order, and the higher frequency is less. It is also suggested that in practice it is not necessary to collect data with a sampling frequency that is as high as possible, and that a sampling frequency of 1/3 or even 1/4 is sufficient. However, it is easy to understand that the higher the sampling frequency, the better, and in case of a higher frequency signal, the lower frequency sensor cannot capture, which may cause data acquisition errors or mistakes.
In step S40, according to the acoustic emission signal characteristics of the metal fracture, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, and calculating the energy of the wavelet packet decomposition coefficient thereof.
The corresponding frequency subband or subbands are so called because different metals will have their own specific sound ranges when broken. For example, the frequency of the acoustic emission signal of the crack propagation of the steel is about 90-180 kh, beyond which the energy of the acoustic emission is low or negligible, because the acoustic signal of the metal fracture does not have such high frequency energy, as shown in fig. 3, the frequency sub-band energy after the 10 th order is close to 0; sound components below this frequency range are mostly ambient noise, independent of internal damage and breakage of the metal. Whereas wavelet packet decomposition is a continuous range, according to the sub-bandwidth of 23.4375kH in this embodiment, the bandwidth of 93.75kH from the 1 st to 4 th steps can be disregarded, in this embodiment, the 5 th (93.75 kH to 117.1875 kH) to 9 th (164.0625 kH to 187.5 kH) frequency sub-bands are selected as characteristic frequency sub-bands, and energy is calculated.
In particular, for each frequency subband, a wavelet packet coefficient which is the same as the original length of the signal is obtained through wavelet packet decomposition, and the energy is the sum of squares of each element of the coefficient. For example, the sampling frequency of the acoustic emission test is 3000kH, the loading frequency of the fatigue test is 10Hz, that is, the acoustic emission signal length in each loading period is 0.1s, the wavelet packet coefficient of each frequency subband is a vector containing 30 ten thousand element numbers, and the energy is the square sum of 30 ten thousand element numbers.
As shown in fig. 4, the calculated frequency subband energy should have significant evolution characteristics with increasing fatigue loading times, and form incremental differences at different life stages, with relatively higher energy closer to the end of life.
In step S50, the characteristic frequency subband energy and the fatigue life in the whole fatigue loading process are normalized, and a fatigue damage evolution equation is obtained by fitting, which comprises two sub-steps.
In S501, normalizing the frequency subband energy to obtain fatigue damage D, and simultaneously prolonging the fatigue life by N/N f Normalization processing is also performed.
For example, a simple linear normalization method can range the frequency subband energy values from a maximum value E max To a minimum value E min Converting to 0-1, specifically: d= (E-E min )/(E max -E min ). Conversion of abscissa to N/N f N is from 0 to N f 。
In S502, according to fatigue damage evolution equationAnd (5) performing data fitting and establishing a fatigue damage evolution equation. Where α is a regulatory parameter related to the rate of fatigue damage evolution and β is a material intrinsic parameter related to the class of materials.
It should be noted that, since "one or more" frequency sub-bands are selected, the fatigue damage D and fatigue life obtained by normalizing the energy of each frequency sub-band can be fitted to form a curve, only one curve can be selected to generate a fatigue damage evolution equation, for example, only the curve fitted by the 5 th order frequency sub-band is selected; or selecting 5 steps to generate 5 curves, and then averaging the 5 curves to generate 1 curve; or further, 5 curves are enveloped into a strip. In summary, from the application point of view, fatigue evolution curves generated by different frequency sub-band energies can be selected.
It should be noted that the fatigue loading test is repeated until the test is broken. N is a period count, N f Is the final lifetime. For example, a test was loaded 125000 times, the subject was destroyed, and the test stopped. N is a variable of continuous counting, N f Is the final lifetime 125000, so N/N can be defined f Is a fatigue life stage of a metallic material or welded structure. In general, there are three stages of fatigue damage to metallic materials or welded structures: first stage, N/N f Less than or equal to 0.2, the metal material or the welded structure is deformed only in the initial stage; second stage, 0.2<N/N f <0.8, the metal material or the welding structure is damaged by micro-nano, mutual dislocation occurs between metal lattices, and no change exists in macroscopical sense; and a third stage: N/N f And not less than 0.8, and the metal material or the welded structure has macroscopic cracks.
During fatigue test, the energy of the acoustic emission signal is N/N f There is a significant change at a small time, by the time of the near failure (N/N f 80% or more) and the energy increase is more severe. Therefore, the energy intensity of the acoustic emission signal can be used as the damage index, N/N f Based on the characteristics, the invention can realize quantitative damage monitoring in the whole life stage by searching the connection between the two characteristics.
So-called quantitative damage monitoring, compared with the traditional method, the traditional fatigue damage detection method comprises the following steps: liquid penetration, magnetic powder, vortex, ultrasound, and the like. The fatigue damage detection of the liquid permeation, the magnetic powder and the vortex is realized by adopting external means, the fatigue crack is seen by naked eyes, then the picture is taken for storage, and the damage size is expressed mainly by recording the crack length, so that the method is very inaccurate. The ultrasonic wave is to transmit and receive sound signals to a continuous medium, and only when cracks appear, the signals can be changed, and the size of damage cannot be quantitatively expressed. These conventional methods can only detect the occurrence of a crack, N/N f At least over 80%, already at the end of fatigue life, once leakage occurs for structures at the end of fatigue lifeThe error detection and detection may cause serious deviation of structural service performance evaluation, and serious safety accidents are caused.
The acoustic emission technique is a technique of capturing a change in a recording material or structure itself to emit sound. N/N of acoustic emission signal in fatigue test process f There is a significant change in the very small time to the point of failure (N/N f =80% and later), so the energy intensity of the acoustic emission signal can be regarded as damage index (d=1 is considered as damage), the effect of quantitative damage detection of the whole life is achieved, which is a significant advantage different from the conventional detection method.
Referring to fig. 5 and 6, fig. 5 shows a fatigue damage evolution curve of the present invention, and fig. 6 is a graph comparing a fatigue damage evolution curve based on the method of the present invention and displacement monitoring. It is apparent that the acoustic emission technique of the present invention can identify early damage changes, while other conventional fatigue damage detection techniques based on macrocracks, such as conventional acoustic emission time domain processing methods, have short plates and lack N/N pairs f Early damage identification is particularly important, as the early fatigue damage identification capacity is 0.8. And the in-situ X-ray imaging equipment with high price is generally used for testing under laboratory conditions and cannot be applied to engineering practice.
It should be further noted that, during the process of adjusting the fatigue damage evolution equation, the confidence level of the fitting parameter α should be adjusted to enable the fatigue damage evolution curve to downwards envelop more test points, and obtain a larger N/N under the condition of the same damage degree D f The value is used for ensuring that more conservative results are obtained in the follow-up fatigue damage degree judgment and residual life prediction, and further ensuring the requirements of safe use of materials and welding structures. The method provided by the invention can identify early damage changes of the metal material or the welded structure, which is difficult to realize in the traditional technical method.
In step S60, the fatigue life stage of the monitored object is determined according to the fatigue damage evolution equation by combining the acoustic emission monitoring data of the monitored object, and the method includes three sub-steps.
Firstly, fatigue damage identification is carried out, in S601, acoustic emission signal data of a monitoring object are collected, and wavelet packet decomposition is carried out by applying an optimal wavelet base to be decomposed into frequency sub-bands. In S602, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, calculating the energy of the wavelet packet decomposition coefficient, and normalizing the frequency sub-band energy to obtain fatigue damage D. Then, predicting the life of the metal material or the welded structure, in S603, substituting the fatigue damage D into the fatigue damage evolution equation established in the first stage S50, and calculating to obtain the fatigue life stage N/N of the monitored object f 。
In summary, the fatigue loss identification and life prediction of the detection object are finally realized through the acoustic emission technology and the wavelet transformation data processing mode, the early fatigue damage identification capability is greatly improved, the fatigue life prediction range is improved, more sufficient margin is provided for structural safety evaluation, and the safety use of materials and structures is ensured.
While several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the invention. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination.
The foregoing description of embodiments of the invention has been presented for purposes of illustration and description, and is not intended to be exhaustive or limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvements in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein.
Claims (10)
1. A fatigue damage identification and life prediction method based on an acoustic emission signal wavelet packet decomposition frequency band energy spectrum is characterized by comprising the following steps:
s10, acquiring acoustic emission signals of metal materials or welding structures under the action of fatigue load in the whole process of a fatigue loading test;
s20, carrying out wavelet packet decomposition on acoustic emission signal data, and determining an optimal wavelet base of the wavelet packet decomposition for fatigue damage identification;
s30, performing multi-layer decomposition on the acoustic emission signal by using an optimal wavelet base, and decomposing the acoustic emission signal into frequency sub-bands;
s40, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, and calculating the energy of wavelet packet decomposition coefficients of the characteristic frequency sub-bands;
s50, carrying out normalization processing on the energy of the characteristic frequency sub-band wavelet packet decomposition coefficient and the fatigue life in the whole fatigue loading process, and fitting to obtain a fatigue damage evolution equation;
s60, judging the fatigue life stage of the monitored object according to the fatigue damage evolution equation by combining the acoustic emission monitoring data of the monitored object.
2. The method for fatigue damage identification and life prediction according to claim 1, wherein the step S10 includes:
s101, arranging an acoustic emission signal acquisition sensor at a potential fatigue crack initiation position of a metal material or a welded structure;
s102, in the whole process of the fatigue loading test, acquiring acoustic emission signals of the metal material or the welding structure, and obtaining acoustic emission data of fatigue damage evolution in the fatigue life.
3. The method for fatigue damage identification and life prediction according to claim 1, wherein the step S20 includes:
s201, carrying out wavelet packet decomposition on acoustic emission signal data by adopting different wavelet basis functions;
s202, determining the optimal wavelet base of the wavelet packet decomposition identified by fatigue damage according to the time product of the decomposition effect parameter shannon entropy and the decomposition efficiency parameter shannon entropy.
4. A fatigue damage identification and life prediction method according to claim 3, wherein the wavelet basis function is selected to satisfy:
the selected wavelet basis functions have orthogonality so as to ensure that the energies of the sub-bands with different frequencies are not interfered with each other after the wavelet packet is decomposed;
the selected wavelet basis function has tight support, so that the calculation efficiency of wavelet packet decomposition on a large number of acoustic emission signals is ensured;
the selected wavelet basis functions enable discrete wavelet transforms.
5. The method for fatigue damage identification and life prediction according to claim 1, wherein the step S30 includes:
s301, performing multi-layer decomposition on the acoustic emission signal through wavelet packet transformation by applying an optimal wavelet base;
s302, equally dividing the signal into frequency sub-bands within the frequency total bandwidth range.
6. The method for fatigue damage identification and life prediction according to claim 5, wherein:
the wavelet packet decomposition layer number n is adopted to decompose the signal into 2 in the total frequency bandwidth F n The frequency sub-bands with equal widths correspond to the natural characteristic frequency of the cracking sound containing the metal material.
7. The method for fatigue damage identification and life prediction according to claim 1, wherein the step S40 includes:
and selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands according to the acoustic emission signal characteristics of the metal fracture.
8. The method for fatigue damage identification and life prediction according to claim 1, wherein the step S50 includes:
s501, normalizing the frequency sub-band energy and the fatigue life in the whole fatigue loading process;
s502, obtaining a fatigue damage evolution curve based on damage mechanics principle fitting, and establishing a fatigue damage evolution equation.
9. The fatigue damage identification and life prediction method according to claim 8, wherein:
normalizing the frequency sub-band energy to obtain fatigue damage D, and prolonging fatigue life by N/N f After normalization, according to fatigue damage evolution equationData fitting is performed, wherein alpha is a regulatory parameter related to the fatigue damage evolution speed, and beta is a material intrinsic parameter related to the material class.
10. The method for fatigue damage identification and life prediction according to claim 1, wherein the step S60 includes:
s601, performing wavelet packet decomposition on the acoustic emission signal obtained by monitoring by applying an optimal wavelet base to decompose the acoustic emission signal into frequency sub-bands;
s602, selecting one or more corresponding frequency sub-bands as characteristic frequency sub-bands, and calculating the energy of wavelet packet decomposition coefficients of the characteristic frequency sub-bands;
s603, normalizing the frequency sub-band energy to obtain fatigue damage D, introducing the fatigue damage D into a fatigue damage evolution equation, and calculating to obtain the fatigue life stage N/N of the monitored object f 。
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