Sand-stone separation internal spiral welding detection method and system

Abstract

The invention relates to the technical field of internal spiral crack detection, and discloses a gravel separation internal spiral welding detection method and system, and the method comprises the steps: collecting temperature distribution data; calculating the thermal resistance change of the welding area; extracting dynamic frequency shift characteristics of the cold cracks; calculating a crack type discrimination index; and setting a judgment threshold to distinguish hot cracks and cold cracks in the welding area. In the prior art, crack detection only depends on a single thermal field or a vibration field or depends on a neural network to obtain a wide defect detection result, and particularly under complex working conditions such as a high-temperature and high-dust environment, hot cracks and cold cracks cannot be accurately distinguished. According to the method, accurate detection and classification of hot cracks and cold cracks are realized through dynamic spectrum feature extraction and a multi-physics field coupling analysis model, so that the problem of equipment performance reduction or potential safety hazards caused by misjudgment or missed judgment is avoided, the detection precision and applicability are improved, and the detection efficiency is improved. And the high-efficiency nondestructive testing requirement on the welding quality under the complex working condition is met.

Description

Technical Field

[0001] The invention belongs to the technical field of internal spiral crack detection, and in particular relates to a sand and gravel separation internal spiral welding detection method and system. Background Art

[0002] At present, in the process of spiral welding for sand and gravel separation, due to the unevenness of welding heat input and the stress release in the cooling stage, two typical cracks are easily generated in the welding area, namely hot cracks and cold cracks. These cracks will lead to a decrease in the strength of the welding part, shorten the service life of the equipment, and may even cause serious safety hazards; however, there are deficiencies in the existing welding detection technology. For example, the traditional infrared thermal imaging detection method only relies on thermal field characteristics to identify the crack location, and it is difficult to effectively distinguish between hot cracks and cold cracks; while the detection method based on vibration signals usually only focuses on the micro-vibration characteristics of cold cracks, ignoring the thermal gradient characteristics generated by hot cracks; in addition, under complex working conditions such as high temperature and high dust environment, a single detection method is susceptible to environmental interference, resulting in reduced detection accuracy. The existing technology cannot fully meet the needs of efficient and accurate distinction between hot cracks and cold cracks in complex environments. Therefore, there is an urgent need for a detection method that can be combined with multi-physical field characteristics for analysis under complex working conditions such as high temperature and high dust, and can still accurately distinguish between hot cracks and cold cracks while ensuring detection accuracy, so as to improve the evaluation efficiency and reliability of equipment welding quality. Summary of the invention

[0003] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to propose a method for detecting internal spiral welding in sand and gravel separation, aiming to solve the technical problem that the prior art only relies on a single thermal field or vibration field for crack detection or relies on a neural network to obtain broad defect detection results, especially in complex working conditions such as high temperature and high dust environment, and cannot accurately distinguish between hot cracks and cold cracks.

[0004] In order to solve the above technical problems, the present invention adopts the following technical solutions: The present invention provides a method for detecting internal spiral welding of sand and gravel separation.

[0005] The sand and gravel separation internal spiral welding detection method comprises:

[0006] Step S10: Using an infrared thermal imaging sensor to collect data at an angle θ and a sampling frequency f s The thermal field data of the welding area of ​​the inner spiral of the sand and stone separation is collected to obtain the temperature distribution data T(x, y, t), and the background noise of the collected temperature distribution data T(x, y, t) is corrected to obtain the corrected temperature distribution data T corrected(x, y, t); x is the horizontal coordinate of the welding area, the unit is m, which is used to describe the position in the transverse direction of the welding; y is the vertical coordinate of the welding area, the unit is m, which is used to describe the position in the longitudinal direction of the welding; t is the time, the unit is s, which is used to describe the temperature change at different time points during the welding process;

[0007] Step S20: Correct the temperature distribution data T corrected (x, y, t) Calculate the thermal resistance change R′ of the welding area th (x,y,t), the formula is:

[0008]

[0009] Among them, R th (x, y, t) is the basic thermal resistance value, unit is K·m 2 / W; γ is the crack propagation correction factor, dimensionless, calibrated based on the welding material and crack extension characteristics experiment; is the second-order spatial derivative of the corrected temperature distribution data, in K / m 2 , used to describe the thermal gradient changes in the welding area;

[0010] Step S30: Using a vibration sensor with a frequency range f min ~f max The vibration signal of the welding area is collected to obtain the dynamic vibration signal V(x, t), and the spectrum analysis of the collected vibration signal V(x, t) is performed to extract the dynamic frequency shift characteristics f of the cold crack. cold ′;

[0011] Step S40: Combined thermal resistance change R′ th (x,y,t) and cold crack dynamic frequency shift f′ cold , the crack type discrimination index M is calculated by multi-physics field coupling model crack , crack type identification index M crack The calculation formula is:

[0012]

[0013] Among them, α, β and k are the weight coefficients of thermal resistance, frequency shift and thermal gradient, which are dimensionless and calibrated according to material properties and crack growth characteristics experiments; is the thermal gradient distribution in the welding area;

[0014] Step S50: Setting the crack type determination threshold M thresh , when the crack type discrimination index M crack Greater than the crack type determination threshold M thresh When the crack type identification index M crackLess than or equal to the crack type determination threshold M thresh When , it is judged as cold crack.

[0015] Preferably, in step S10, the acquisition angle θ is the angle between the optical axis of the infrared thermal imaging sensor and the surface of the welding area, and the value range is 30° to 60°; the sampling frequency f s It is the sampling frequency of the infrared thermal imaging sensor, in Hz, and its value range is 10~100Hz.

[0016] Preferably, in step S10, the background noise correction adopts the formula:

[0017] T corrected (x,y,t)=T(x,y,t)-T background ·e -λd

[0018] Where λ is the thermal radiation absorption coefficient, in m -1 , determined according to the characteristics of the welding material; d is the distance between the infrared thermal imaging sensor and the welding area, in meters, and e is an exponent.

[0019] Preferably, in step S20, the basic thermal resistance value R th (x,y,t) is calculated by the formula Calculate, where ΔT is the temperature difference on both sides of the crack area, in K, measured by thermal imaging equipment; q(t) is the transient heat flux density per unit area, in W / m 2 , determined according to the welding head power.

[0020] Preferably, in step S40, the calculation formula for the thermal gradient distribution in the welding area is:

[0021]

[0022] Preferably, the calculation formula of the transient heat flux per unit area q(t) is:

[0023]

[0024] Among them, P w is the welding head power, in W; A is the effective surface area of ​​the welding area, in m 2 ; η is the welding thermal efficiency, dimensionless, ranging from 0.6 to 0.9; λ is the thermal radiation absorption coefficient, unit is m -1 , determined according to the characteristics of the welding material.

[0025] Preferably, the dynamic frequency shift characteristic f cold The calculation formula of ′ is:

[0026]

[0027] Where f0 is the fundamental frequency in the crack-free state, in Hz, obtained through equipment calibration; δ is the stress frequency shift sensitivity coefficient, dimensionless; σ local is the local stress in the crack area, in MPa, measured by the preset strain sensor; σ yield It is the yield strength of welding material, in MPa.

[0028] The present invention also provides a sand and gravel separation internal spiral welding detection system comprising:

[0029] Thermal field data acquisition and correction module, used to use infrared thermal imaging sensor to collect angle θ and sampling frequency f s The thermal field data of the welding area of ​​the inner spiral of the sand and stone separation is collected to obtain the temperature distribution data T(x, y, t), and the background noise of the collected temperature distribution data T(x, y, t) is corrected to obtain the corrected temperature distribution data T corrected (x, y, t); x is the horizontal coordinate of the welding area, the unit is m, which is used to describe the position in the transverse direction of the welding; y is the vertical coordinate of the welding area, the unit is m, which is used to describe the position in the longitudinal direction of the welding; t is the time, the unit is s, which is used to describe the temperature change at different time points during the welding process;

[0030] Thermal resistance change calculation module, used to correct the temperature distribution data T corrected (x, y, t) Calculate the thermal resistance change R′ of the welding area th (x,y,t), the formula is:

[0031]

[0032] Among them, R th (x, y, t) is the basic thermal resistance value, unit is K·m 2 / W; γ is the crack propagation correction factor, dimensionless, calibrated based on the welding material and crack extension characteristics experiment; is the second-order spatial derivative of the corrected temperature distribution data, in K / m 2 , used to describe the thermal gradient changes in the welding area;

[0033] Vibration signal acquisition and spectrum analysis module, used to use vibration sensors in the frequency range f min ~f max The vibration signal of the welding area is collected to obtain the dynamic vibration signal V(x, t), and the spectrum analysis of the collected vibration signal V(x, t) is performed to extract the dynamic frequency shift characteristics f of the cold crack. cold ′;

[0034] Crack type discrimination calculation module, used to combine thermal resistance change R′ th(x,y,t) and cold crack dynamic frequency shift f′ cold , the crack type discrimination index M is calculated by multi-physics field coupling model crack , crack type identification index M crack The calculation formula is:

[0035]

[0036] Among them, α, β and k are the weight coefficients of thermal resistance, frequency shift and thermal gradient, which are dimensionless and calibrated according to material properties and crack growth characteristics experiments; is the thermal gradient distribution in the welding area;

[0037] Crack type determination module, used to set the crack type determination threshold M thresh , when the crack type discrimination index M crack Greater than the crack type determination threshold M thresh When the crack type identification index M crack Less than or equal to the crack type determination threshold M thresh When , it is judged as cold crack.

[0038] The present invention also provides a computer program product, including a sand and gravel separation internal spiral welding detection program, and when the sand and gravel separation internal spiral welding detection program is executed by a processor, the sand and gravel separation internal spiral welding detection method is implemented.

[0039] The beneficial effect of the present invention is that compared with the prior art that only relies on a single thermal field or vibration field for crack detection or relies on a neural network to obtain broad defect detection results, especially in complex working conditions such as high temperature and high dust environment, the technical problem of being unable to accurately distinguish between hot cracks and cold cracks, the present application realizes accurate detection and classification of hot cracks and cold cracks through dynamic spectrum feature extraction and multi-physical field coupling analysis model, thereby avoiding equipment performance degradation or safety hazards caused by misjudgment or missed judgment, improving the accuracy and applicability of detection, and meeting the requirements for efficient non-destructive testing of welding quality under complex working conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0041] Figure 1 It is a schematic flow chart of a first embodiment of a method for detecting internal spiral welding in sand and gravel separation according to the present invention.

[0042] Figure 2 It is a schematic diagram of equipment for a method for detecting internal spiral welding of sand and gravel separation according to the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] Embodiment 1: Figure 1 Detailed description is given of the process flow diagram of the first embodiment of the method for detecting internal spiral welding in sand and gravel separation according to the present invention, which provides the first embodiment of the method for detecting internal spiral welding in sand and gravel separation according to the present invention.

[0045] In the first embodiment, the sand and gravel separation internal spiral welding detection method includes:

[0046] Step S10: Using an infrared thermal imaging sensor to collect data at an angle θ and a sampling frequency f s The thermal field data of the welding area of ​​the inner spiral of the sand and stone separation is collected to obtain the temperature distribution data T(x, y, t), and the background noise of the collected temperature distribution data T(x, y, t) is corrected to obtain the corrected temperature distribution data T corrected (x, y, t); x is the horizontal coordinate of the welding area, the unit is m, which is used to describe the position in the transverse direction of the welding; y is the vertical coordinate of the welding area, the unit is m, which is used to describe the position in the longitudinal direction of the welding; t is the time, the unit is s, which is used to describe the temperature change at different time points during the welding process;

[0047] It should be noted that the formula used for background noise correction is:

[0048] T corrected (x,y,t)=T(x,y,t)-T background ·e -λd

[0049] Where λ is the thermal radiation absorption coefficient, in m -1 , determined according to the characteristics of the welding material; d is the distance between the infrared thermal imaging sensor and the welding area, in meters, and e is an exponent.

[0050] It can be understood that the corrected temperature distribution data more accurately reflects the thermal field distribution in the welding area, providing reliable basic data for subsequent thermal resistance calculation and crack detection. This method is particularly suitable for temperature data collection needs under complex working conditions such as high temperature and high dust environments.

[0051] It should be understood that in order to adapt to different working conditions, the acquisition angle, sampling frequency, and parameters in the background noise correction formula of the infrared thermal imaging sensor can be adjusted to improve the adaptability and accuracy of the temperature distribution data acquisition and correction in the welding area.

[0052] Step S20: Correct the temperature distribution data T corrected (x, y, t) Calculate the thermal resistance change R′ of the welding area th (x,y,t), the formula is:

[0053]

[0054] Among them, R th (x, y, t) is the basic thermal resistance value, unit is K·m 2 / W; γ is the crack propagation correction factor, dimensionless, calibrated based on the welding material and crack extension characteristics experiment; is the second-order spatial derivative of the corrected temperature distribution data, in K / m 2 , used to describe the thermal gradient changes in the welding area;

[0055] It should be noted that different welding materials and crack propagation characteristics will affect the range of γ. For example, in steel welding, the crack propagation rate is slow, with a typical value of 0.15; in aluminum alloy welding, the crack propagation rate is fast, with a typical value of 0.25; the second-order spatial derivative of the corrected temperature distribution data is used to characterize the local thermal gradient change in the welding area. The larger its absolute value, the more obvious the thermal conduction anomaly in the crack area.

[0056] It can be understood that by combining the basic thermal resistance value with the correction factor of the thermal gradient, the dynamic thermal resistance characteristics of the crack area can be more accurately described. This method effectively makes up for the deficiency of the traditional method that only considers the overall thermal resistance and ignores the local thermal gradient changes.

[0057] It should be understood that thermal cracks are usually accompanied by high temperature and high gradient changes, and the thermal resistance value increases significantly; the residual stress caused by the propagation of cold cracks causes a small gradient change, and the thermal resistance correction amplitude is relatively small; the more significant the thermal gradient change, the stronger the temperature distribution inhomogeneity in the welding area, which may correspond to a larger range of crack extension.

[0058] Step S30: Using a vibration sensor with a frequency range f min ~f max The vibration signal of the welding area is collected to obtain the dynamic vibration signal V(x, t), and the spectrum analysis of the collected vibration signal V(x, t) is performed to extract the dynamic frequency shift characteristics f of the cold crack. cold ′;

[0059] It should be understood that the present invention adopts a dynamic frequency shift feature extraction method, and the nonlinear stress correction term δ in the formula can effectively quantify the fundamental frequency offset caused by the cold crack extension process. The dynamic frequency shift feature f cokd The calculation result of ′ directly reflects the existence of cold cracks and the concentration of stress in the crack area, thereby realizing accurate detection of cold cracks.

[0060] Step S40: Combined thermal resistance change R′ th (x,y,t) and cold crack dynamic frequency shift f′ cold , the crack type discrimination index M is calculated by multi-physics field coupling model crack , crack type identification index M crack The calculation formula is:

[0061]

[0062] Among them, α, β and k are the weight coefficients of thermal resistance, frequency shift and thermal gradient, which are dimensionless and calibrated according to material properties and crack growth characteristics experiments; is the thermal gradient distribution in the welding area;

[0063] It should be noted that the present invention organically combines the thermal resistance change, cold crack frequency shift characteristics and thermal gradient distribution through a multi-physical field coupling model, making full use of the differences in physical field characteristics between hot cracks and cold cracks, thereby achieving accurate classification of crack types; the thermal resistance change R′ of the hot crack th (x,y,t) and thermal gradient distribution The contribution of M crack The value of cold crack dynamic frequency shift f′ cold The contribution of M is large, but the influence of thermal resistance change and thermal gradient is small, resulting in crack The value of is low; the specific values ​​of weight coefficients α, β, and k are determined through experimental optimization to ensure that the discrimination model under different materials and working conditions has high accuracy and adaptability.

[0064] It can be understood that the crack type discrimination index of the present invention can effectively distinguish hot cracks from cold cracks by comprehensively analyzing the thermal resistance changes, dynamic frequency shift characteristics and thermal gradient distribution in the welding area, especially under complex working conditions, combining multi-physical field characteristics to classify cracks, making the discrimination result more reliable.

[0065] Step S50: Setting the crack type determination threshold M thresh , when the crack type discrimination index M crack Greater than the crack type determination threshold M thresh When the crack type identification index M crack Less than or equal to the crack type determination threshold M threshWhen , it is judged as cold crack.

[0066] It should be noted that the crack type determination threshold M thresh It is a standard for distinguishing hot cracks from cold cracks. The optimal threshold value under different materials and welding conditions is calibrated through experimental data. The threshold value can be dynamically adjusted according to the actual application scenario. For example, under the condition of large welding heat input, the threshold value M thresh It can be appropriately increased to adapt to the significance of thermal crack characteristics; under conditions of faster welding speed, the threshold M thresh It can be appropriately reduced to adapt to the sensitivity of cold crack characteristics, and the crack type identification index M crack The multi-physics field characteristics including thermal resistance change, dynamic frequency shift and thermal gradient are comprehensively analyzed, and the threshold M thresh The setting of ensures a clear boundary between different crack characteristics, making the classification result more accurate; the judgment threshold M thresh The setting is based on the experimental data statistics of materials and processes, including selecting a variety of welding materials such as low carbon steel, stainless steel, etc., collecting the thermal resistance change R′ th , dynamic frequency shift f′ cold and thermal gradient The threshold M is optimized using statistical analysis methods such as linear regression or machine learning classification models. thresh To improve classification accuracy.

[0067] It is understandable that thermal cracks are usually accompanied by a large change in thermal resistance R′ th and thermal gradient distribution Its comprehensive discrimination index M crack The value is high, so M crack >M thresh It is judged as hot crack when it is 0.0000; cold crack is mainly manifested as dynamic frequency shift feature f′ cold The significance of M crack ≤M thresh It is judged as cold crack when the threshold value M thresh The dynamic adjustment enables the present invention to adapt to different welding conditions, especially to maintain high classification accuracy in complex environments such as high temperature or high dust.

[0068] In addition, the sand and gravel separation internal spiral welding detection system provided by the present invention adopts a sand and gravel separation internal spiral welding detection method in the above embodiment, which can solve the technical problem of sand and gravel separation internal spiral welding detection. Compared with the prior art, the beneficial effects of the sand and gravel separation internal spiral welding detection system provided by the present invention are the same as the beneficial effects of the sand and gravel separation internal spiral welding detection method provided by the above embodiment, and other technical features of the sand and gravel separation internal spiral welding detection system are the same as the features disclosed in the above embodiment method, which will not be repeated here.

[0069] The present invention provides a sand and gravel separation internal spiral welding detection equipment, please refer to Figure 2, a sand and gravel separation internal spiral welding detection device includes: at least one processor; and, a memory connected to the at least one processor in communication; wherein the memory stores instructions executable by at least one processor, and the instructions are executed by at least one processor so that at least one processor can execute a sand and gravel separation internal spiral welding detection method in the above-mentioned embodiment one. A sand and gravel separation internal spiral welding detection device in the embodiment of the present invention may include but is not limited to mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), vehicle-mounted terminals (such as vehicle-mounted navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. A sand and gravel separation internal spiral welding detection device is only an example and should not bring any limitations to the functions and scope of use of the embodiments of the present invention. A sand and gravel separation internal spiral welding detection device may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM: Read Only Memory) 1002 or a program loaded from a storage device 1003 to a random access memory (RAM: Random Access Memory) 1004. In RAM1004, various programs and data required for the operation of a sand and gravel separation internal spiral welding detection device are also stored. The processing device 1001, ROM1002 and RAM1004 are connected to each other via a bus 1005. An input / output (I / O) interface 1006 is also connected to the bus. Typically, the following systems can be connected to the I / O interface 1006: input devices 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; output devices 1008 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 1003 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 1009. The communication device 1009 can allow a sand and gravel separation internal spiral welding detection device to communicate wirelessly or wired with other devices to exchange data. Although a sand and gravel separation internal spiral welding detection device with various systems is shown in the figure, it should be understood that it is not required to implement or have all the systems shown. More or fewer systems may be implemented or provided alternatively.

[0070] The present invention also provides a computer program product, including a computer program, which implements the steps of the above-mentioned method for detecting internal spiral welding of sand and gravel separation when executed by a processor. The computer program product provided by the present invention can solve the technical problem of detecting internal spiral welding of sand and gravel separation. Compared with the prior art, the beneficial effects of the computer program product provided by the present invention are the same as the beneficial effects of the method for detecting internal spiral welding of sand and gravel separation provided by the above-mentioned embodiment, which will not be described in detail here.

[0071] In particular, according to the embodiments disclosed in the present invention, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present invention include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program code for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through a communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment disclosed in the present invention are executed.

[0072] It should be understood that the various parts disclosed in the present invention can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in any one or more embodiments or examples in a suitable manner.

[0073] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A sand and gravel separation internal spiral welding detection method, characterized in that: Methods include: Step S10: Using an infrared thermal imaging sensor to collect data at an angle θ and a sampling frequency f s The thermal field data of the welding area of ​​the inner spiral of the sand-stone separation is collected to obtain the temperature distribution data T(x, y, t), and the background noise of the collected temperature distribution data T(x, y, t) is corrected to obtain the corrected temperature distribution data T corrected (x, y, t); x is the horizontal coordinate of the welding area, the unit is m, used to describe the position in the transverse direction of the welding; y is the vertical coordinate of the welding area, the unit is m, used to describe the position in the longitudinal direction of the welding; t is time, the unit is s, used to describe the temperature change at different time points during the welding process; Step S20: Correct the temperature distribution data T corrected (x, y, t) Calculate the thermal resistance change R' of the welding area th (x, y, t), the formula is: Among them, R th (x, y, t) is the basic thermal resistance value, unit is K·m 2 / W; γ is the crack propagation correction factor, dimensionless, calibrated based on the welding material and crack extension characteristics experiment; is the second-order spatial derivative of the corrected temperature distribution data, in K / m 2 , used to describe the thermal gradient variation in the welding area; Step S30: Using a vibration sensor with a frequency range f min ~f max The vibration signal of the welding area is collected to obtain the dynamic vibration signal V(x, t), and the spectrum analysis of the collected vibration signal V(x, t) is performed to extract the dynamic frequency shift characteristics f of the cold crack. cold ′; Step S40: Combined thermal resistance change R′ th (x, y, t) and cold crack dynamic frequency shift f′ cold , the crack type discrimination index M is calculated by multi-physics field coupling model crack , crack type identification index M crack The calculation formula is: Among them, α, β and k are the weight coefficients of thermal resistance, frequency shift and thermal gradient, which are dimensionless and calibrated according to material properties and crack growth characteristics experiments; is the thermal gradient distribution in the welding area; Step S50: Setting the crack type determination threshold M thresh , when the crack type discrimination index M crack Greater than the crack type determination threshold M thresh When the crack type identification index M crack Less than or equal to the crack type determination threshold M thresh When , it is judged as cold crack.

2. A sand and gravel separation internal spiral welding detection method as claimed in claim 1, characterized in that: In step S10, the acquisition angle θ is the angle between the optical axis of the infrared thermal imaging sensor and the surface of the welding area, and the value range is 30° to 60°; the sampling frequency f s It is the sampling frequency of the infrared thermal imaging sensor, in Hz, and its value range is 10~100Hz.

3. A sand and gravel separation internal spiral welding detection method as claimed in claim 1, characterized in that: In step S10, the background noise correction formula is: T corrected (x,y,t)=T(x,y,t)-T background ·e -λd Where λ is the thermal radiation absorption coefficient, in m -1 , determined according to the characteristics of the welding material; d is the distance between the infrared thermal imaging sensor and the welding area, in meters, and e is an exponent.

4. A sand and gravel separation internal spiral welding detection method as claimed in claim 1, characterized in that: In step S20, the basic thermal resistance value R th (x, y, t) is calculated by the formula Calculate, where ΔT is the temperature difference on both sides of the crack area, in K, measured by thermal imaging equipment; q(t) is the transient heat flux density per unit area, in W / m 2 , determined according to the welding head power.

5. A sand and gravel separation internal spiral welding detection method as claimed in claim 1, characterized in that: In step S40, the calculation formula of the thermal gradient distribution in the welding area is:

6. A sand and gravel separation internal spiral welding detection method as claimed in claim 4, characterized in that: The calculation formula of transient heat flux density per unit area q(t) is: Among them, P w is the welding head power, in W; A is the effective surface area of ​​the welding area, in m 2 ; η is the welding thermal efficiency, dimensionless, ranging from 0.6 to 0.9; λ is the thermal radiation absorption coefficient, unit is m -1 , determined according to the characteristics of the welding material.

7. A sand and gravel separation internal spiral welding detection method as claimed in claim 1, characterized in that: Dynamic frequency shift characteristic f cold The calculation formula of ′ is: Where f0 is the fundamental frequency in the crack-free state, in Hz, obtained through equipment calibration; δ is the stress frequency shift sensitivity coefficient, dimensionless; σ 1ocal is the local stress in the crack area, in MPa, measured by the preset strain sensor; σ yield It is the yield strength of welding material, in MPa.

8. A sand and gravel separation internal spiral welding detection system, characterized in that: The sand and gravel separation internal spiral welding detection system comprises: Thermal field data acquisition and correction module, used to use infrared thermal imaging sensor to collect angle θ and sampling frequency f s The thermal field data of the welding area of ​​the inner spiral of the sand and stone separation is collected to obtain the temperature distribution data T(x, y, t), and the background noise of the collected temperature distribution data T(x, y, t) is corrected to obtain the corrected temperature distribution data T corrected (x, y, t); x is the horizontal coordinate of the welding area, the unit is m, used to describe the position in the transverse direction of the welding; y is the vertical coordinate of the welding area, the unit is m, used to describe the position in the longitudinal direction of the welding; t is time, the unit is s, used to describe the temperature change at different time points during the welding process; Thermal resistance change calculation module, used to correct the temperature distribution data T corrected (x, y, t) Calculate the thermal resistance change R' of the welding area th (x, y, t), the formula is: Among them, R th (x, y, t) is the basic thermal resistance value, unit is K·m 2 / W; γ is the crack propagation correction factor, dimensionless, calibrated based on the welding material and crack extension characteristics experiment; is the second-order spatial derivative of the corrected temperature distribution data, in K / m 2 , used to describe the thermal gradient variation in the welding area; Vibration signal acquisition and spectrum analysis module, used to use vibration sensors in the frequency range f min ~f max The vibration signal of the welding area is collected to obtain the dynamic vibration signal V(x, t), and the spectrum analysis of the collected vibration signal V(x, t) is performed to extract the dynamic frequency shift characteristics f of the cold crack. cold ′; Crack type discrimination calculation module, used to combine thermal resistance change R′ th (x, y, t) and cold crack dynamic frequency shift f′ cold , the crack type discrimination index M is calculated by multi-physics field coupling model crack , crack type identification index M crack The calculation formula is: Among them, α, β and k are the weight coefficients of thermal resistance, frequency shift and thermal gradient, which are dimensionless and calibrated according to material properties and crack growth characteristics experiments; is the thermal gradient distribution in the welding area; Crack type determination module, used to set the crack type determination threshold M thresh , when the crack type discrimination index M crack Greater than the crack type determination threshold M thresh When the crack type identification index M crack Less than or equal to the crack type determination threshold M thresh When , it is judged as cold crack.

9. A sand and gravel separation internal spiral welding detection equipment, characterized in that: The sand and gravel separation internal spiral welding detection equipment comprises: a memory, a processor and a sand and gravel separation internal spiral welding detection program stored in the memory and executable on the processor. When the sand and gravel separation internal spiral welding detection program is executed by the processor, the sand and gravel separation internal spiral welding detection method described in any one of claims 1 to 7 is implemented.

10. A computer program product, characterized in that The computer program product comprises a sand and gravel separation internal spiral welding detection program, and when the sand and gravel separation internal spiral welding detection program is executed by a processor, the sand and gravel separation internal spiral welding detection method described in any one of claims 1 to 7 is implemented.

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