CN118961881A - Metal damage monitoring method, device and storage medium - Google Patents
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- G01N29/44—Processing the detected response signal, e.g. electronic circuits specially adapted therefor
- G01N29/4409—Processing the detected response signal, e.g. electronic circuits specially adapted therefor by comparison
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Abstract
The invention relates to the field of metal monitoring, and discloses a metal damage monitoring method, equipment and a storage medium, wherein the method comprises the following steps: acquiring an acoustic emission signal of metal running in an industrial system by using an acoustic emission sensor; preprocessing the acoustic emission signal to obtain a discrete signal; performing short-time Fourier transform on the discrete signals to obtain a time-frequency spectrum; calculating a fractal set of the time-frequency spectrum; fitting the fractal set to obtain a fractal dimension; constructing a mapping model between the fractal dimension and the metal damage; constructing an evolution dynamic model of metal damage; and inputting the fractal dimension of the metal to be monitored into the mapping model, and completing metal damage detection based on an output result of the mapping model and an evolution dynamic model. The invention has the beneficial effects that: the fractal characteristics closely related to the damage are successfully extracted from the acoustic emission signals of the metal damage, and the fractal characteristics provide important basis for accurately evaluating the damage state of the metal material.
Description
Technical Field
The present invention relates to the field of metal monitoring, and in particular, to a method and apparatus for monitoring metal damage, and a storage medium.
Background
Along with the acceleration of the industrialization process, the metal materials and structures are increasingly widely applied in various fields, such as buildings, bridges, aerospace, nuclear power stations and the like. The health of these metal structures is directly related to the safe operation of the equipment and the life safety of personnel. Therefore, effective damage monitoring and early warning of these metal structures is an important requirement for industrial safety.
Conventional metal damage monitoring methods often rely on periodic visual inspection, ultrasonic inspection or X-ray inspection, and the like. Although these methods can detect damage to some extent, there are many limitations such as low detection efficiency, high cost, and limited ability to identify minute damage and early stages of damage. In addition, these methods are often off-line detection, and cannot realize real-time monitoring and early warning.
Disclosure of Invention
The invention aims at: the invention provides a metal damage monitoring method, equipment and a storage medium, which solve the technical problems that the existing traditional metal damage detection method is limited in micro damage identification and cannot early warn in advance.
The invention provides a metal damage monitoring method, which comprises the following steps:
s1: acquiring an acoustic emission signal of metal running in an industrial system by using an acoustic emission sensor;
s2: preprocessing the acoustic emission signal to obtain a discrete signal;
s3: performing short-time Fourier transform on the discrete signals to obtain a time-frequency spectrum;
s4: calculating a fractal set of the time-frequency spectrum;
s5: fitting the fractal set to obtain a fractal dimension;
s6: constructing a mapping model between the fractal dimension and the metal damage;
S7: constructing an evolution dynamic model of metal damage;
S8: and inputting the fractal dimension of the metal to be monitored into the mapping model, and completing metal damage detection based on an output result of the mapping model and an evolution dynamic model.
A storage medium storing instructions and data for implementing a metal damage monitoring method.
A metal damage monitoring device, comprising: a processor and the storage medium; the processor loads and executes the instructions and data in the storage medium for implementing a metal damage monitoring method.
The beneficial effects provided by the invention are as follows:
1. Innovation of theoretical framework: the fractal geometric theory is combined with the acoustic emission technology for the first time, and is applied to metal damage monitoring, quantitative evaluation of damage degree is realized through fractal dimension and other parameters, and a brand new visual angle and evaluation tool are provided for health monitoring of metal materials.
2. Construction of a dynamic evaluation model: the method has the advantages that a dynamic damage evaluation model based on acoustic emission signal fractal characteristics is established, the model can track and reflect the evolution of material damage in real time, and compared with traditional static snapshot evaluation, the method is more flexible and prospective, and is helpful for understanding damage mechanisms in depth and predicting damage development trend.
3. Feature extraction accuracy: the fractal characteristics closely related to the damage are successfully extracted from the acoustic emission signals of the metal damage, and the fractal characteristics provide important basis for accurately evaluating the damage state of the metal material.
4. And (3) introducing an intelligent early warning system: through self-adaptive threshold setting and intelligent early warning mechanisms, the method can dynamically adjust the early warning threshold according to real-time monitoring data, so that early signs of damage can be found and warned in time, the sensitivity and accuracy of monitoring are remarkably improved, and the risks of false alarm and missing alarm are reduced.
5. Wide application scenarios: the method is not only suitable for damage monitoring of conventional metal materials, but also can be widely applied to special metal materials used in the fields of nuclear power stations, aerospace, ocean engineering and the like, and shows high reliability and wide adaptability in extreme and complex environments.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention;
FIG. 2 is a schematic diagram of the operation of the hardware device of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be further described with reference to the accompanying drawings.
Before formally describing the invention, the scheme of the invention is first described in a generalized way, so that the understanding is convenient.
Referring to fig. 1, fig. 1 is a schematic flow chart of the method of the present invention;
the invention provides a metal damage monitoring method, which comprises the following steps:
s1: acquiring an acoustic emission signal of metal running in an industrial system by using an acoustic emission sensor;
s2: preprocessing the acoustic emission signal to obtain a discrete signal;
It should be noted that, the acoustic emission signal may be expressed as a time function x (t), and the acoustic emission signal is preprocessed, such as noise removal, smoothing, and the like, and then discretized, to obtain a corresponding discrete signal x [ n ].
S3: performing short-time Fourier transform on the discrete signals to obtain a time-frequency spectrum;
It should be noted that the time-frequency spectrum of the time-series acoustic emission signal is a graphical representation showing signal characteristics in both the time and frequency dimensions. A time series acoustic emission signal is a non-stationary signal whose characteristics and intensity may vary over time. The time-frequency spectrum can show the energy distribution of the signal at different times and frequencies at the same time.
The time-frequency spectrum is typically generated by short-time fourier transform (STFT), wavelet transform, or the like. In time-frequency spectra, the intensity or energy of a signal is typically represented by color or brightness.
For example, darker colors or higher brightness may indicate higher signal strength at a particular time and frequency. By observing the distribution and variation of colors in the spectrum, the characteristics of the signal at different times and frequencies can be analyzed. Time-frequency spectra are very useful for analyzing and processing non-stationary signals, especially complex signals like acoustic emission signals. It can reveal the periodic components in the signal, the instantaneous frequency variation and the time-frequency distribution of the signal energy.
Short-time fourier transform (STFT) is a commonly used signal processing technique for analyzing the time-frequency characteristics of non-stationary signals. The basic idea of STFT is to divide the signal into a number of small time periods and fourier transform each time period to obtain a representation of the signal at a different time and frequency.
As an example, in practical applications, STFT is typically implemented in discrete form, i.e. by transforming the discrete signal x n:
Wherein: ω is a discrete window function; r is the number of samples of the frame shift; n is the number of FFT points, which determines the frequency resolution; k is the frequency index; x (n) is a discrete signal; m is an integer representing an index of a time period; STFT represents the short-time Fourier transform.
By calculating the above formula, a complex matrix can be obtained, the rows of which represent different time periods (or frames) and the columns represent different frequency components. The modulus of this matrix represents the amplitude of the signal at different times and frequencies, while the amplitude angle represents the phase information. Typically, only the amplitude is of interest to form a time-frequency spectrum.
S4: calculating a fractal set of the time-frequency spectrum;
as an embodiment, step S4 is specifically as follows:
s41, taking a time-frequency spectrum as input of a fractal box, and selecting an initial box size epsilon;
the size epsilon may be understood as a grid size or resolution.
S42, calculating the minimum box number N (epsilon) capable of covering all phase space points for each size epsilon;
S43, if the box at least comprises one point, the box is considered to be occupied;
S44, covering the whole signal by a box with the size epsilon; counting the minimum number of boxes N (epsilon) required to completely cover the signal;
S45, gradually reducing the box size epsilon, and repeating S44 to obtain a series of box sizes and the corresponding box numbers to form a fractal set.
S5: fitting the fractal set to obtain a fractal dimension;
As an example, for a fractal set, plotting N (epsilon) versus epsilon in a double logarithmic coordinate system typically results in a straight line. The slope of the line is the fractal dimension D. The mathematical relationship can be expressed as:
log(N(ε))＝-Dlog(ε)+C
wherein C is a constant term.
And obtaining the fractal dimension D from the slope of the fitting straight line. Namely: d= -slope. And (3) carrying out linear fitting on the equation by a least square method to obtain the fractal dimension D.
As an example, in Python, the above calculation can be done using a library of numpy and scipy, etc. For example, numpy is used to calculate the number of boxes required for signal coverage, and then a linear regression function in scipy.
S6: constructing a mapping model between the fractal dimension and the metal damage;
It should be noted that the mapping model in step S6 includes: linear models and nonlinear models.
As an example, in practical applications, the relationship between the fractal dimension D and the metal damage (e.g. stress corrosion cracking SCC) may exhibit linear or non-linear characteristics. To determine this relationship, a parametric fit can be made through experimental data to verify whether the relationship is linear or non-linear.
If a linear relationship is assumed to exist between the fractal dimension D and the degree of impairment S, a linear regression method may be used to fit the experimental data, and if the experimental data indicate that the relationship between D and S is not linear, a nonlinear model may be considered for the fitting.
For parameter fitting, a series of experimental data points (D i,Si) needs to be collected, where D i is the measured fractal dimension and S i is the corresponding damage level indicator (e.g., crack length, area, etc.). The model parameters are then estimated using statistical software or fitting functions in a programming language.
After the fitting is completed, the goodness of fit of the model also needs to be evaluated. Common evaluation metrics include a decision coefficient (R-squared), a mean square error (Mean Squared Error, MSE), and the like. These metrics can help us determine if the model can better describe the experimental data.
The linear correlation model between fractal dimension and the extent of injury is as follows:
there is a linear relationship between the fractal dimension D and the severity S of the metal damage, then the relationship can be expressed mathematically as:
S＝aD+b
wherein: s represents a quantitative index of damage, which may be the length, area, volume of a crack or other physical quantity that reflects the extent of damage. a and b are model parameters determined by fitting experimental data.
The nonlinear correlation model between fractal dimension and the extent of injury is as follows:
In some cases, the relationship between the fractal dimension and the extent of the impairment may be nonlinear. At this point, the power law relationship may be used to describe this nonlinear association:
S＝cDd
Wherein: c and d are parameters obtained by fitting experimental data. d reflects the rate at which the extent of damage increases with the fractal dimension.
S7: constructing an evolution dynamic model of metal damage;
as an example, considering the evolution of metal damage over time t, the rate of change of the fractal dimension can be described by the following differential equation:
wherein, Representing the rate of change of the fractal dimension over time, k being the rate constant of lesion propagation; d max is the maximum fractal dimension.
S8: and inputting the fractal dimension of the metal to be monitored into the mapping model, and completing metal damage detection based on an output result of the mapping model and an evolution dynamic model.
The output result of the mapping model is compared with a preset threshold value, and after the output result of the dynamic model of the damage evolution is combined and comprehensively judged, metal damage early warning is carried out.
By outputting a nonlinear model or a linear model and based on a threshold setting the severity S of the metal damage, the fractal dimension change rate can be combinedTo alarm and the corresponding operating and maintenance policies of the equipment components.
The adaptive threshold update strategy is as follows:
A threshold update period is set. In each update period, the threshold (T) is updated according to the monitoring data. The update strategy can be based on the change trend of the damage index in the last period of time or based on the dynamic model prediction result of damage evolution. For example, if a sustained increase in the damage indicator is detected, the threshold may be raised appropriately, whereas the threshold is lowered.
The adaptive threshold update algorithm is as follows:
Initial threshold t=0: s 0
Adaptive threshold updating: Where k is a scaling factor, which can be determined experimentally. According to the fractal dimension rate of change To adjust the size of the threshold. Referring to fig. 2, fig. 2 is a schematic working diagram of a hardware device according to an embodiment of the present invention, where the hardware device specifically includes: a metal damage monitoring device 401, a processor 402 and a storage medium 403.
A metal damage monitoring device 401: the one metal damage monitoring device 401 implements the one metal damage monitoring method.
Processor 402: the processor 402 loads and executes instructions and data in the storage medium 403 for implementing the one metal damage monitoring method.
Storage medium 403: the storage medium 403 stores instructions and data; the storage medium 403 is used to implement the one metal damage monitoring method.
The beneficial effects of the invention are as follows:
1. Innovation of theoretical framework: the fractal geometric theory is combined with the acoustic emission technology for the first time, and is applied to metal damage monitoring, quantitative evaluation of damage degree is realized through fractal dimension and other parameters, and a brand new visual angle and evaluation tool are provided for health monitoring of metal materials.
2. Construction of a dynamic evaluation model: the method has the advantages that a dynamic damage evaluation model based on acoustic emission signal fractal characteristics is established, the model can track and reflect the evolution of material damage in real time, and compared with traditional static snapshot evaluation, the method is more flexible and prospective, and is helpful for understanding damage mechanisms in depth and predicting damage development trend.
3. Feature extraction accuracy: the fractal characteristics closely related to the damage are successfully extracted from the acoustic emission signals of the metal damage, and the fractal characteristics provide important basis for accurately evaluating the damage state of the metal material.
4. And (3) introducing an intelligent early warning system: through self-adaptive threshold setting and intelligent early warning mechanisms, the method can dynamically adjust the early warning threshold according to real-time monitoring data, so that early signs of damage can be found and warned in time, the sensitivity and accuracy of monitoring are remarkably improved, and the risks of false alarm and missing alarm are reduced.
5. Wide application scenarios: the method is not only suitable for damage monitoring of conventional metal materials, but also can be widely applied to special metal materials used in the fields of nuclear power stations, aerospace, ocean engineering and the like, and shows high reliability and wide adaptability in extreme and complex environments.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (10)
1. A method for monitoring metal damage, which is characterized in that: the method comprises the following steps:
s1: acquiring an acoustic emission signal of metal running in an industrial system by using an acoustic emission sensor;
s2: preprocessing the acoustic emission signal to obtain a discrete signal;
s3: performing short-time Fourier transform on the discrete signals to obtain a time-frequency spectrum;
s4: calculating a fractal set of the time-frequency spectrum;
s5: fitting the fractal set to obtain a fractal dimension;
s6: constructing a mapping model between the fractal dimension and the metal damage;
S7: constructing an evolution dynamic model of metal damage;
S8: and inputting the fractal dimension of the metal to be monitored into the mapping model, and completing metal damage detection based on an output result of the mapping model and an evolution dynamic model.
2. A method of monitoring metal damage as recited in claim 1, wherein: the preprocessing in step S2 includes: denoising, smoothing, and discretizing.
3.A method of monitoring metal damage as recited in claim 1, wherein: the step S3 is specifically as follows:
S31, performing short-time Fourier transform on the discrete signals, wherein the following formula is as follows:
Wherein: ω is a discrete window function; r is the number of samples of the frame shift; n is the number of FFT points, which determines the frequency resolution; k is the frequency index; x (n) is a discrete signal; m is an integer representing an index of a time period; STFT represents short-time Fourier transform;
s32, obtaining a complex matrix after short-time Fourier transformation, wherein the time-frequency spectrum comprises information consisting of the complex matrix, wherein rows of the matrix represent different time periods and columns represent different frequency components; the modes of the matrix represent the amplitude of the signal at different times and frequencies, and the amplitude angles represent the phase information.
4.A method of monitoring metal damage as recited in claim 1, wherein: the step S4 is specifically as follows:
s41, taking a time-frequency spectrum as input of a fractal box, and selecting an initial box size epsilon;
S42, calculating the minimum box number N (epsilon) capable of covering all phase space points for each size epsilon;
S43, if the box at least comprises one point, the box is considered to be occupied;
S44, covering the whole signal by a box with the size epsilon; counting the minimum number of boxes N (epsilon) required to completely cover the signal;
S45, gradually reducing the box size epsilon, and repeating S44 to obtain a series of box sizes and the corresponding box numbers to form a fractal set.
5. A metal damage monitoring method as recited in claim 4, the method is characterized in that: the step S5 is specifically as follows: drawing a relation diagram of N (epsilon) and epsilon in a double-logarithmic coordinate system to obtain a straight line for the fractal set; the slope of the straight line is the fractal dimension D; the mathematical relationship can be expressed as:
log(N(ε))＝-Dlog(ε)+C，
Wherein C is a constant term.
6. A method of monitoring metal damage as recited in claim 1, wherein: the mapping model in step S6 includes: linear models and nonlinear models.
7. A method of monitoring metal damage as recited in claim 1, wherein: the dynamic model of the lesion evolution is as follows:
wherein, Representing the rate of change of the fractal dimension over time, k being the rate constant of lesion propagation; d max is the maximum fractal dimension.
8. A method of monitoring metal damage as recited in claim 1, wherein: the step S8 is specifically as follows:
and comparing the output result of the mapping model with a preset threshold value, and carrying out metal damage early warning after jointly and comprehensively judging by combining the output result of the dynamic model of damage evolution.
9. A storage medium, characterized by: the storage medium stores instructions and data for implementing a metal damage monitoring method according to any one of claims 1 to 8.
10. A metal damage monitoring device, characterized in that: comprising the following steps: a processor and a storage medium; the processor loads and executes instructions and data in a storage medium for implementing a metal damage monitoring method according to any one of claims 1 to 8.
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