Adaptive window width based signal fluctuation fractal dimension calculation method and device

Abstract

The present application relates to signal processing and structure / material damage nondestructive testing field, especially to a kind of signal fluctuation fractal dimension calculation method and equipment based on adaptive window width.The method includes the following steps: obtaining the time series of acoustic emission signal;For the current calculation point in time series, take the sliding window data of preset length, and calculate the local standard deviation of sliding window data;According to local standard deviation, adopt adaptive adjustment factor to calculate the adaptive window width, and calculate the fluctuation value of corresponding signal according to this;Iterate multiple calculation points in time series, obtain a series of adaptive window width and the set of corresponding fluctuation value, and take logarithm and carry out linear fitting, obtain slope, and calculate fractal dimension according to this.The present application automatically reduces window width to capture microscopic damage details when signal fluctuation is violent, and automatically increases window width to analyze macro trend when fluctuation is gentle, which significantly improves the accuracy of crack development stage identification.

Description

Technical Field

[0001] This invention belongs to the field of signal processing and structural / material damage nondestructive testing technology, and particularly relates to a method and device for calculating the fractal dimension of signal fluctuation based on adaptive window width. Background Technology

[0002] Non-destructive testing (NDT) technology plays an irreplaceable role in ensuring the safety of steel and reinforced concrete structures such as bridges and buildings. Acoustic emission (AE) technology, as a non-destructive-dynamic monitoring method, captures stress wave signals released during material cracking, providing real-time diagnostic evidence for damage evolution. The Ib value, as a statistical parameter of acoustic emission, has a clear physical meaning: a decrease in Ib value indicates the sudden onset of macroscopic cracks dominated by high-energy events, while an increase or maintenance of a high Ib value reflects the accumulation of microscopic cracks due to the aggregation of low-energy events. By plotting time-Ib value curves, the development stage of structural cracks can be qualitatively determined, which has important applications in judging the progress of structural damage and evaluating the cracking performance of novel fiber-modified building materials.

[0003] However, the current limitations of this structural cracking damage assessment technique based on Ib value curves are as follows:

[0004] ① High reliance on subjective experience: Engineers need to manually observe the trend changes of the Ib curve (such as steep drop, gradual rise, plateau) and infer the "micro-to-macro crack" transition node based on experience, resulting in significant differences in conclusions among different analysts;

[0005] ② Lack of quantitative standards: It is impossible to provide objective numerical indicators to quantify the crack resistance of materials, making it difficult to apply in practice and transform into engineering standards;

[0006] ③ Insufficient capture of dynamic details: The Ib curve exhibits high-frequency fluctuations during the microcrack accumulation period and drastic jumps during the macrocrack outbreak period, making it difficult to determine the critical abrupt change characteristics. Signal filtering is required at the cost of losing minute details.

[0007] Fractal theory can be used to analyze the fluctuation characteristics of Ib value curves to assess structural damage, but existing methods (such as box counting and Higuchi methods) require a fixed window width. When curve analysis includes both microscopic accumulation (high fluctuation) and macroscopic bursts (low fluctuation), the fixed window width has significant drawbacks. A window width that is too large smooths out microscopic details, while a window width that is too small amplifies macroscopic noise, resulting in distorted fractal dimension quantification and an inability to accurately correlate with the physical nature of crack evolution. Therefore, designing a dynamic function for the window width that automatically adjusts with the intensity of local fluctuations, thereby effectively overcoming the adaptability limitations of fractal algorithms caused by a fixed window width, remains a pressing problem in this field. Summary of the Invention

[0008] In view of this, the present invention aims to provide a method and device for calculating the fractal dimension of acoustic emission signal fluctuations based on adaptive window width, so as to solve the problem that the fixed window width in the prior art leads to the distortion of fractal dimension quantification and the inability to accurately capture microscopic details and macroscopic trends.

[0009] To achieve the above objectives, the technical solution created by this invention is implemented as follows:

[0010] In a first aspect, the present invention provides a method for calculating the fractal dimension of signal volatility based on an adaptive window width, comprising the following steps:

[0011] S10. Obtain the time series of acoustic emission signals. ;

[0012] S20. For the current calculation point in the time series k Take the preset length L The sliding window data is used to calculate the local standard deviation of the sliding window data.

[0013] S30. Calculate the current calculation point based on the local standard deviation using an adaptive adjustment factor. k Adaptive window width And based on this, calculate the corresponding signal volatility value. :

[0014] S40. Traverse multiple calculation points in the time series to obtain a series of adaptive window widths. and their corresponding volatility values The set, respectively, for the adaptive window width in the set. and volatility value Taking the logarithm, we get and ;

[0015] S50, to and Perform linear fitting to obtain the slope And calculate the fractal dimension accordingly. D , .

[0016] Furthermore, the preset length L and satisfy ,in, This is the dynamic maximum window width.

[0017] Furthermore, in step S30, the adaptive window width is calculated using a monotonic mapping function. The monotonic mapping function is selected from any one of exponential mapping, power-law mapping, or logical mapping; preferably, it is an exponential mapping.

[0018]

[0019] in: The minimum window width is preset and should be ≥3; This is the preset initial maximum window width; For adaptive adjustment factors, 0.5 ≤ ≤1.5; It can make adaptive adjustments. The upper limit that can be achieved through adaptive adjustment is , This is the dynamic maximum window width.

[0020] Further ， and Maximize by pre-calibration and The linearity of the fit.

[0021] Furthermore, the local standard deviation is a robust scale, which is calculated by multiplying the absolute deviation of the median within the window by a constant factor.

[0022] Furthermore, the volatility value Calculate using any of the following methods:

[0023] Method 1:

[0024] Method 2: .

[0025] Furthermore, after step S50, the method further includes:

[0026] S60, obtained by independent fitting within a time period of fixed duration or fixed number of events. and Curve, calculation of the and The first-order difference or sliding slope of the curve is used as an indicator of the rate of change.

[0027] Furthermore, if the aforementioned When a value increases sharply within a set time window and co-occurs with the minimum value or peak event rate of the time series, a critical instability expansion warning is output.

[0028] In a second aspect, the present invention provides a computer device comprising:

[0029] At least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the signal fluctuation fractal dimension calculation method based on adaptive window width according to the present invention.

[0030] Thirdly, the present invention provides a non-transient computer-readable storage medium storing computer instructions, the computer instructions being used to cause the computer to execute the signal fluctuation fractal dimension calculation method based on adaptive window width described in the present invention.

[0031] Compared with existing technologies, this invention uses an adaptive window width mechanism to dynamically adjust the window width based on the intensity of local signal fluctuations. This allows for the precise capture of microscopic damage details and the clear presentation of macroscopic trend changes, effectively overcoming the information loss caused by a fixed window width. It significantly improves the accuracy and objectivity of identifying the evolution stages of structural cracks and can be effectively used for structural damage early warning, material crack resistance performance evaluation, and evaluation of the toughening effect of fiber materials. Attached Figure Description

[0032] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments and descriptions of the invention are used to explain the invention and do not constitute an undue limitation of the invention. In the drawings:

[0033] Figure 1 A flowchart illustrating the signal fluctuation fractal dimension calculation method based on adaptive window width as described in an embodiment of the present invention;

[0034] Figure 2 This is a schematic diagram of the structure of a computer device provided in an embodiment of the present invention.

[0035] Explanation of reference numerals in the attached figures:

[0036] 62. Computer equipment; 64. External devices; 66. Processing unit; 68. Bus; 70. Network adapter; 72. Input / output (I / O) interface; 74. Display; 78. System memory; 80. Random access memory (RAM); 82. Cache memory; 84. Storage system; 92. Program module; 90. Program / utility. Detailed Implementation

[0037] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only for explaining the invention and do not constitute a limitation thereof. Similar elements in different embodiments are referred to by associated similar element reference numerals. In the following embodiments, many details are described to facilitate a better understanding of the invention. However, those skilled in the art will readily recognize that some features may be omitted in different situations, or may be replaced by other elements, materials, or methods. In some cases, some operations related to the invention are not shown or described in the specification. This is to avoid obscuring the core parts of the invention with excessive description. For those skilled in the art, detailed description of these related operations is not necessary; they can fully understand the related operations based on the description in the specification and general technical knowledge in the art.

[0038] It should be noted that, unless otherwise specified, the embodiments and features described in this invention can be combined to form various implementations. Furthermore, the order of the steps or actions in the method description can be changed or adjusted in a manner readily apparent to those skilled in the art. Therefore, the various orders in the specification and drawings are merely for the clear description of a particular embodiment and do not imply a mandatory order, unless otherwise stated that a particular order must be followed.

[0039] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicating orientations or positional relationships based on the orientations or positional relationships shown in the accompanying drawings, are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation, and therefore should not be construed as a limitation on this invention. The term "based on" should be understood as "at least partially based on." Furthermore, the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first," "second," etc., may explicitly or implicitly include one or more of that feature. In the description of this invention, unless otherwise stated, "a plurality of" means two or more, and the term "including" means "including but not limited to." Various embodiments of the present invention may exist in the form of a range; it should be understood that the description in the form of a range is merely for convenience and brevity and should not be construed as a rigid limitation on the scope of the invention; therefore, it should be considered that the range description has specifically disclosed all possible sub-ranges and single numerical values ​​within that range; for example, it should be considered that the range description from 1 to 6 has specifically disclosed sub-ranges, such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6, etc., and single numbers within the range, such as 1, 2, 3, 4, 5, and 6, regardless of the range. Furthermore, whenever a numerical range is referred to herein, it means including any referenced number (fraction or integer) within the range referred to.

[0040] In the description of this invention, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art will understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0041] The invention will now be described in detail with reference to the accompanying drawings and embodiments.

[0042] Example 1

[0043] like Figure 1 As shown, this invention provides a method for calculating the fractal dimension of signal volatility based on an adaptive window width, comprising the following steps:

[0044] S10. Obtain the time series of acoustic emission signals. ;

[0045] The raw signals collected by the acoustic emission sensor are subjected to event identification and amplitude statistics. The time series can be obtained by rolling calculation according to the preset event window and step size.

[0046] In this step, the raw signals collected by the acoustic emission sensor are converted into a time series that can reflect the density or intensity of the event. The signal data of this time series is the acoustic emission signal data.

[0047] The raw signal from an acoustic emission sensor is a continuous waveform containing numerous oscillations. Direct analysis of the waveform is insensitive to fractal dimension calculations because the waveform primarily reflects the vibrational characteristics of the medium rather than the scale characteristics of the event sequence.

[0048] A series of Ib values ​​are calculated by sliding the event window according to the preset step size, forming a sequence. N is the sequence length. In this embodiment, The value is Ib (IrregularityIndex). For other embodiments, such as those using equal-step sampling, It can also be a statistic related to the intensity of an event's activity, such as counts, energy, or RMS.

[0049] S20. For the current calculation point in the time series k Take the preset length L The sliding window data is used to calculate the local standard deviation of the sliding window data.

[0050] In this embodiment, local standard deviation is used. Quantification at the current point k The degree of local stability or volatility in the vicinity. In practical implementation, the local standard deviation... For robust scaling, the local standard deviation can be approximated by multiplying the median absolute deviation (MAD) within the window by a constant factor. Furthermore, extreme values ​​can be truncated or winsorized.

[0051] Local standard deviation This is the core basis for subsequent adaptive adjustment; specifically, if the local standard deviation... A large standard deviation indicates that the event in that area is sudden and concentrated (such as the propagation of the main crack); if the local standard deviation is large... Small values ​​indicate that events occur evenly and smoothly in the area (e.g., uniform micro-damage).

[0052] Preset length L The constraints are satisfied: ,in, This is the dynamic maximum window width. This ensures that the base window used to evaluate local fluctuations is large enough to encompass areas larger than the dynamic maximum window width. Longer information ensures the stability of the evaluation and avoids misjudgment due to an excessively small window. For example, if the dynamic maximum window width (maximum scale) contains 50 points, then the preset length (evaluation window) should contain at least 100-250 points to ensure that a pattern longer than the dynamic maximum window width can be seen.

[0053] S30. Calculate the current calculation point based on the local standard deviation using an adaptive adjustment factor. k Adaptive window width And based on this, calculate the corresponding signal volatility value. :

[0054] Calculating adaptive window width A monotonic mapping function can be called, which can be selected from any one of exponential mapping, power-law mapping, or logical mapping; preferably, it is an exponential mapping.

[0055]

[0056] in: The minimum window width is preset and should be ≥3; The settings ensure that even in the most stable regions, there is a basic analysis window, ensuring that the minimum window width is statistically significant. The preset initial maximum window width defines the maximum scale that can be used when the signal is completely stationary. For adaptive adjustment factors, 0.5 ≤ ≤1.5; control For adaptive window width Sensitivity to the impact. The larger, Follow The larger it increases, the faster it decays. Furthermore, It can make adaptive adjustments. The upper limit that can be achieved through adaptive adjustment is , This is the dynamic maximum window width.

[0057] As can be seen, in this embodiment, for regions with stable signals ( Small), with larger allocation To explore its long-range correlation; for areas with drastic signal fluctuations ( Large), smaller allocation To avoid the mixing of different sudden events, the method focuses on analyzing short-range local features. This adaptability allows the method to better match the non-stationary characteristics of the signal.

[0058] In calculating volatility values When this is the case, any of the following methods can be used for calculation:

[0059] Method 1:

[0060] Method 2: .

[0061] And in calculation It is necessary to ensure that Boundary conditions; Rounding is required to the nearest whole number.

[0062] Volatility value The measurement is based on the time scale of The typical amplitude of signal fluctuations at that time. When using method one, i.e., the standard mean square deviation form, to calculate... While the first method is statistically efficient, it is sensitive to outliers. The second method, the robust median form, is used to calculate... Although the statistical efficiency is slightly lower, it is not sensitive to outliers in the sequence (such as extremely strong acoustic emission events), which can improve the ability to suppress large pulses and outliers and provide more stable estimates.

[0063] S40. Traverse multiple calculation points in the time series to obtain a series of adaptive window widths. and their corresponding volatility values The set, respectively, for the adaptive window width in the set. and volatility value Taking the logarithm, we get and ;

[0064] S50, to and Perform linear fitting to obtain the slope and And calculate the fractal dimension accordingly. D , .

[0065] right and Perform linear fitting to obtain the slope At the same time as Generally, it will be required at the same time. It needs to be greater than the preset value. If the conditions are not met, the output can be paused or the process can return to step S20 to retrieve the parameters again.

[0066] Furthermore, the adaptive adjustment factor in step S30 With dynamic maximum window width It needs to be obtained through pre-calibration, i.e., search. and To maximize and Linearity of fit (using coefficient of determination) (Or least squares residuals as an indicator). By automatically finding the optimal parameters, the reliability of fractal dimension estimation is ensured.

[0067] In practice, it can be systematically changed. and By taking the value of S30-S40 repeatedly, different results can be obtained. , ) point set. Find the point set that makes ( , The adaptive adjustment factor that best fits the linear relationship of the point set. With dynamic maximum window width Using the coefficient of determination The closest possible value to 1 or the least squares residual (minimum) is used as the criterion for "goodness of fit". The final output... D The value, calculated under optimal parameters, represents the most reliable estimate of the signal fractal characteristics under that parameter configuration. R² Guided global optimization can avoid adaptive adjustment factors. With dynamic maximum window width The subjective choice ensures that the final scaling relation is most significant, thereby increasing the fractal dimension. D The credibility of the estimate.

[0068] fractal dimension It can be used for characterization:

[0069] (i) Damage / crack development stage: The fractal dimension can reflect the complexity of crack propagation. A high fractal dimension corresponds to the stage of unstable crack propagation with multiple coupled mechanisms.

[0070] (ii) Quantitative toughening effect of fiber: After the fiber is incorporated, the fractal dimension value increases, reflecting the transformation of the crack system from "instability propagation" to "dissipative dynamic instability".

[0071] Example 2

[0072] Based on Example 1, the present invention provides a method for calculating the fractal dimension of signal fluctuation based on adaptive window width, including steps S10 to S55; wherein, steps S10 to S40 are the same as in Example 1, and will not be repeated here.

[0073] S10. Obtain the time series of acoustic emission signals. ;

[0074] S20. For the current calculation point in the time series k Take the preset length LThe sliding window data is used to calculate the local standard deviation of the sliding window data.

[0075] S30. Calculate the current calculation point based on the local standard deviation using an adaptive adjustment factor. k Adaptive window width And based on this, calculate the corresponding signal volatility value. :

[0076] S40. Traverse multiple calculation points in the time series to obtain a series of adaptive window widths. and their corresponding volatility values The set, respectively, for the adaptive window width in the set. and volatility value Taking the logarithm, we get and ;

[0077] S50, to and Perform linear fitting to obtain the slope and And calculate the fractal dimension accordingly. D , ;

[0078] S51, Judgment Is it greater than the preset value? If yes, proceed to step S55; otherwise, proceed to step S52.

[0079] S52, For the current calculation point in the time series j Take a preset fixed scale set And calculate each fixed scale separately. The fluctuation function value below F ;

[0080] S53. Traverse multiple calculation points in the time series to obtain a series of fixed scales. and its corresponding fluctuation function value The set, respectively, for the fixed scale in the set. and its corresponding fluctuation function value Taking the logarithm, we get and ;

[0081] S54, to and Perform linear fitting to obtain the slope and And calculate the fractal dimension accordingly. D , ;

[0082] S55. Output the final fractal dimension. D。

[0083] In this embodiment, when Less than or equal to the preset value At that time, the fixed window algorithm will be used again to calculate the new fractal dimension. That is, under adaptive window width ( , When the goodness of fit is insufficient, a fixed window width baseline algorithm can be enabled, instead of relying on local standard deviations. Generate adaptive window width Instead, it is based on a preset fixed scale. and its corresponding fluctuation function value ,get and ,right and Perform a fitting test and output it as a control.

[0084] Example 3

[0085] Based on any of the above embodiments, the present invention provides a method for calculating the fractal dimension of signal fluctuations based on adaptive window width, after obtaining the slope. and and fractal dimension D back ， It also includes the following steps:

[0086] S60, obtained by independent fitting within a time period of fixed duration or fixed number of events. and The curve, the and The first-order difference or sliding slope of the curve serves as a rate of change indicator, which can be used for mutation detection.

[0087] If the above When a value increases sharply within a set time window and co-occurs with the minimum value or peak event rate of the time series, a critical instability expansion warning is output.

[0088] This invention employs an adaptive window width mechanism, automatically reducing the window width to capture microscopic damage details when signal fluctuations are severe, and automatically increasing the window width to analyze macroscopic trends when fluctuations are mild. This significantly improves the objectivity and accuracy of identifying crack development stages (microscopic activity / macroscopic expansion) and enhances computational efficiency. It can be widely applied to crack detection in bridge structures, assessment of concrete crack resistance, and analysis of the toughening effect of fibers on concrete materials.

[0089] Example 4

[0090] Accordingly, according to embodiments of the present invention, the present invention also provides a computer device, a readable storage medium, and a computer program product.

[0091] Figure 2 This is a schematic diagram of the structure of a computer device 62 provided in an embodiment of the present invention. Figure 2 A block diagram of an exemplary computer device 62 suitable for implementing embodiments of the present invention is shown. Figure 2 The computer device 62 shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of the present invention.

[0092] like Figure 2 As shown, computer device 62 is represented in the form of a general-purpose computing device. Computer device 62 is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices may also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0093] The components of computer device 62 may include, but are not limited to: one or more processors or processing units 66, system memory 78, and bus 68 connecting different system components (including system memory 78 and processing unit 66).

[0094] Bus 68 represents one or more of several bus architectures, including a memory bus or memory controller, a peripheral bus, a graphics acceleration port, a processor, or a local bus using any of the various bus architectures. Examples of these architectures include, but are not limited to, the Industry Standard Architecture (ISA) bus, the Micro Channel Architecture (MAC) bus, the Enhanced ISA bus, the Video Electronics Standards Association (VESA) local bus, and the Peripheral Component Interconnect (PCI) bus.

[0095] Computer device 62 typically includes a variety of computer system readable media. These media can be any available media that can be accessed by computer device 62, including volatile and non-volatile media, removable and non-removable media.

[0096] System memory 78 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 80 and / or cache memory 82. Computer device 62 may further include other removable / non-removable, volatile / non-volatile computer system storage media. By way of example only, storage system 84 may be used to read and write non-removable, non-volatile magnetic media (…). Figure 2 Not shown; usually referred to as a "hard drive"). Although Figure 2 Not shown, a disk drive for reading and writing to a removable non-volatile disk (e.g., a "floppy disk") and an optical disk drive for reading and writing to a removable non-volatile optical disk (e.g., a CD-ROM, DVD-ROM, or other optical media) may be provided. In these cases, each drive may be connected to bus 68 via one or more data media interfaces. System memory 78 may include at least one program product having a set (e.g., at least one) of program modules configured to perform the functions of the embodiments of the present invention.

[0097] A program / utility 90 having a set (at least one) of program modules 92 may be stored, for example, in system memory 78. Such program modules 92 include, but are not limited to, an operating system, one or more application programs, other program modules, and program data. Each or some combination of these examples may include an implementation of a network environment. Program modules 92 typically perform the functions and / or methods described in the embodiments of the present invention.

[0098] Computer device 62 can also communicate with one or more external devices 64 (e.g., keyboard, pointing device, display 74, etc.), and with one or more devices that enable a user to interact with computer device 62, and / or with any device that enables computer device 62 to communicate with one or more other computing devices (e.g., network card, modem, etc.). This communication can be performed via input / output (I / O) interface 72. Furthermore, computer device 62 can also communicate with one or more networks (e.g., local area network (LAN), wide area network (WAN), and / or public networks, such as the Internet) via network adapter 70. As shown, network adapter 70 communicates with other modules of computer device 62 via bus 68. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with computer device 62, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems.

[0099] The processing unit 66 executes various functional applications and data processing by running programs stored in the system memory 78, such as implementing the signal fluctuation fractal dimension calculation method based on adaptive window width provided in the embodiments of the present invention.

[0100] This invention also provides a non-transient computer-readable storage medium storing computer instructions, on which a computer program is stored. When the program is executed by a processor, it implements the signal fluctuation fractal dimension calculation method based on adaptive window width provided in all embodiments of this invention.

[0101] The computer storage medium of this invention can be any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. More specific examples (a non-exhaustive list) of computer-readable storage media include: electrical connections having one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this document, a computer-readable storage medium can be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device.

[0102] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. Computer-readable signal media may also be any computer-readable medium other than computer-readable storage media, capable of sending, propagating, or transmitting programs for use by or in connection with an instruction execution system, apparatus, or device.

[0103] The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, RF, etc., or any suitable combination thereof. The computer program code for performing the operations of this invention can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, as well as conventional procedural programming languages—such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a stand-alone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer can be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0104] This invention also provides a computer program product, including a computer program that, when executed by a processor, implements the above-described method for calculating the fractal dimension of signal fluctuations based on adaptive window width.

[0105] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this invention disclosure can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution disclosed in this invention can be achieved, and this is not limited herein.

[0106] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for calculating the fractal dimension of signal fluctuations based on adaptive window width, characterized in that, Including the following steps: S10. Obtain the time series of acoustic emission signals. ; S20. For the current calculation point in the time series k Take the preset length L The sliding window data is used to calculate the local standard deviation of the sliding window data. S30. Calculate the current calculation point based on the local standard deviation using an adaptive adjustment factor. k Adaptive window width And based on this, calculate the corresponding signal volatility value. ; S40. Traverse multiple calculation points in the time series to obtain a series of adaptive window widths. and their corresponding volatility values The set, respectively, for the adaptive window width in the set. and volatility value Taking the logarithm, we get and ; S50, to and Perform linear fitting to obtain the slope, and calculate the fractal dimension accordingly; In step S30, the adaptive window width is calculated using a monotonic mapping function. The monotonic mapping function is an exponential mapping: ， in, The minimum window width is preset and should be ≥3; This is the preset initial maximum window width; For adaptive adjustment factors, 0.5 ≤ ≤1.5; It can make adaptive adjustments. The upper limit that can be achieved through adaptive adjustment is , Maximum dynamic window width; Adaptive adjustment factor With dynamic maximum window width Maximize by pre-calibration and The linearity of the fit; The volatility value Calculate using any of the following methods: Method 1: ; Method 2: .

2. The method for calculating the fractal dimension of signal fluctuation based on adaptive window width according to claim 1, characterized in that, The preset length L satisfy ,in, This is the dynamic maximum window width.

3. The method for calculating the fractal dimension of signal fluctuation based on adaptive window width according to claim 1, characterized in that, The local standard deviation is a robust scale, calculated by multiplying the absolute deviation of the median within the window by a constant factor.

4. The method for calculating the fractal dimension of signal fluctuation based on adaptive window width according to claim 1, characterized in that; After step S50, the method further includes: S60, obtained by independent fitting within a time period of fixed duration or fixed number of events. and Curve, calculation of the and The first-order difference or sliding slope of the curve is used as an indicator of the rate of change.

5. The method for calculating the fractal dimension of signal fluctuation based on adaptive window width according to claim 4, characterized in that, If the above When a value increases sharply within a set time window and co-occurs with the minimum value or peak event rate of the time series, a critical instability expansion warning is output.

6. A computer device, characterized in that, include: At least one processor; as well as A memory communicatively connected to the at least one processor; wherein, The memory stores instructions that can be executed by the at least one processor, which, when executed by the at least one processor, enables the at least one processor to perform the signal fluctuation fractal dimension calculation method based on adaptive window width as described in any one of claims 1 to 5.

7. A non-transitory computer-readable storage medium storing computer instructions, characterized in that, The computer instructions are used to cause the computer to execute the signal fluctuation fractal dimension calculation method based on adaptive window width as described in any one of claims 1 to 5.

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