Ultrasonic infrared thermography detection methods, systems, devices and storage media

Abstract

This invention relates to the field of nondestructive testing (NDT) technology, and discloses an ultrasonic infrared thermography method, system, device, and storage medium. The method includes: acquiring a simulated test block and a test block under test; collecting initial temperature and vibration signals from the surface of the simulated test block; exciting the simulated test block and collecting temperature and vibration signals corresponding to the ultrasonic infrared thermography of the simulated test block under different detection parameters; determining calibration detection parameters and optimal vibration state; determining the optimal quantitative range of chaos level; determining the quantitative value of the detected chaos of the test block based on the calibration detection parameters; and acquiring the current temperature signal corresponding to the ultrasonic infrared thermography of the test block when the quantitative value of the detected chaos is within the optimal quantitative range of chaos level, and determining the detection result of the test block. By incorporating vibration state monitoring and using detection parameters corresponding to the optimal detection effect to detect the test block, the reliability of ultrasonic infrared thermography is ensured, and the problem of missed detection of crack defects is avoided.

Description

Technical Field

[0001] This invention relates to the field of nondestructive testing technology, specifically to ultrasonic infrared thermography methods, systems, devices, and storage media. Background Technology

[0002] Ultrasonic infrared thermography is a technology that uses ultrasonic energy as a thermal excitation source to generate heat at surface or shallow surface defects in materials. An infrared imager is then used to acquire and process the infrared image of the object, allowing for the inversion and characterization of the defect structure, thus enabling defect identification and location. However, the effectiveness of ultrasonic infrared thermography is highly sensitive to factors such as the preload between the excitation device and the specimen, excitation parameters (including excitation frequency, amplitude, and duration), and contact state. Even slight changes in these parameters can significantly reduce the vibration-generating effect, greatly impacting the detection sensitivity and the reliability of the results.

[0003] In related technologies, ultrasonic infrared thermal imaging detection systems lack a closed-loop module for acquiring and integrating the specimen's vibration state into the main system. This prevents the system from sensing the specimen's vibration state and achieving real-time linkage and feedback between the specimen's vibration state and the main system. Therefore, in actual testing, slight changes in preload, excitation parameters, and contact conditions often lead to a decrease in heat generation at the specimen's crack defects. Consequently, the infrared image acquired by the thermal imager may not show hot spot signals, causing inspectors to mistakenly conclude that the inspected component has no crack defects, resulting in a significant error of missed crack defects. Summary of the Invention

[0004] In view of this, the present invention provides an ultrasonic infrared thermographic detection method, system, device and storage medium to solve the problem of missed detection of crack defects caused by changes in existing detection parameters.

[0005] In a first aspect, the present invention provides an ultrasonic infrared thermal imaging detection method, comprising:

[0006] Obtain a simulated test block and the test block to be tested. The simulated test block is a test block with cracks.

[0007] The initial temperature and initial vibration signals of the simulated test block surface were collected;

[0008] The simulated test block was excited, and the temperature and vibration signals corresponding to the ultrasonic infrared thermographs of the simulated test block under different detection parameters were collected.

[0009] Based on temperature and vibration signals, determine calibration and detection parameters and optimal vibration state;

[0010] Based on the optimal vibration state, determine the quantitative range of the optimal degree of chaos;

[0011] Based on the calibrated detection parameters, the test block is excited to determine the quantitative value of the chaos under test.

[0012] Determine whether the quantitative value of the detected chaos is within the optimal quantitative range of chaos.

[0013] When the quantitative value of the detected chaos is within the optimal quantitative range of chaos, the current temperature signal corresponding to the ultrasonic infrared thermograph of the tested block is acquired, and the test result of the tested block is determined based on the current temperature signal.

[0014] In this invention, by incorporating vibration state monitoring, the simulated test block is excited, and the temperature and vibration signals corresponding to the ultrasonic infrared thermography of the simulated test block under different detection parameters are collected. The detection parameters and vibration state corresponding to the optimal detection effect are determined. By using the detection parameters corresponding to the optimal detection effect to detect the test block, the reliability of the entire ultrasonic infrared thermography detection process is ensured, and the problem of missed crack defects due to reduced heat generation caused by parameter changes is avoided.

[0015] In one alternative implementation, the method further includes:

[0016] When the detected chaotic quantitative value is not within the optimal chaotic degree quantitative range, an alarm is triggered, and / or the calibration detection parameters are adjusted, and the process returns to the steps of exciting the test block based on the calibration detection parameters and determining the detected chaotic quantitative value of the test block until the detected chaotic quantitative value is within the optimal chaotic degree quantitative range.

[0017] In this method, by issuing timely warnings and correcting detection parameters in real time when the quantitative value of the detected chaos is not within the optimal quantitative range of chaos, the reliability of ultrasonic infrared thermography is further improved, and the problem of missed detection of crack defects by the inspectors is avoided.

[0018] In one optional implementation, the simulated test block is excited, and temperature and vibration signals of the simulated test block under different detection parameters are acquired, including:

[0019] Set the lower and upper limits for preload, ultrasonic amplitude, and ultrasonic frequency;

[0020] Adjust the preload and ultrasonic signal applied to the simulated test block, and collect temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude, and ultrasonic frequency.

[0021] In this method, by continuously adjusting the preload and ultrasonic signal, the temperature and vibration signals of the simulated test block under different preloads, ultrasonic amplitudes, and ultrasonic frequencies can be collected more comprehensively. This allows for a more accurate determination of the test parameters and vibration state of the simulated test block under optimal conditions.

[0022] In one optional implementation, the calibration detection parameters include: the preload value at the moment when the temperature difference between the crack and the non-crack area is the largest, the ultrasonic amplitude, the ultrasonic frequency, and the ultrasonic action time.

[0023] The optimal vibration state includes the vibration velocity waveform and vibration velocity spectrum at the moment when the temperature difference between the cracked and non-cracked areas is the largest.

[0024] Based on temperature and vibration signals, the calibration detection parameters and optimal vibration state are determined, including:

[0025] Based on the temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude and ultrasonic frequency, the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block is the maximum is determined.

[0026] Based on the moment when the temperature difference between the cracked and non-cracked areas is at its maximum, the preload value, ultrasonic amplitude, ultrasonic frequency, ultrasonic action time, vibration velocity waveform, and vibration velocity spectrum of the simulated test block at the moment when the temperature difference between the cracked and non-cracked areas is at its maximum are calibrated.

[0027] In this method, the optimal vibration state is determined by the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block is the largest. The calibration and testing parameters of the simulated test block under the optimal vibration state are recorded, which improves the accuracy of the test parameters of the test block and facilitates subsequent ultrasonic infrared thermography testing of the test block.

[0028] In one alternative implementation, determining the optimal quantitative range of chaos level based on the optimal vibration state includes:

[0029] Chaos identification was performed on the vibration velocity waveform and vibration velocity spectrum at the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block was the maximum, and the optimal quantitative range of chaos was determined.

[0030] In this method, by performing chaos identification on the vibration state of the simulated test block, the optimal quantitative range of chaos level is determined, which can then provide data support for the assessment of the chaos level of the test block under test, and thus enable timely alarm when the quantitative value of the tested chaos is not within the optimal quantitative range of chaos level.

[0031] Secondly, the present invention provides an ultrasonic infrared thermal imaging detection system, the system comprising: a control device, the control device comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform the ultrasonic infrared thermal imaging detection method of the first aspect or any corresponding embodiment described above.

[0032] In this invention, by determining the detection parameters corresponding to the optimal detection effect of the simulated test block, and analyzing and calibrating the vibration characteristics under this state, the vibration state of the actual component being inspected during the ultrasonic infrared thermography detection process is monitored and quantitatively evaluated in real time. When a change in the vibration chaos level occurs due to parameter changes, an early warning is issued and the detection parameters are corrected in real time, ensuring the reliability of the entire ultrasonic infrared thermography detection process and avoiding the problem of missed crack defects due to reduced heat generation caused by parameter changes and failure of inspectors to detect them in time.

[0033] In one alternative implementation, the system further includes:

[0034] Ultrasonic generator, ultrasonic excitation device, preload device, infrared thermal imager, vibration monitoring device;

[0035] One end of the ultrasonic generator is connected to the ultrasonic excitation device, and the other end is connected to the control device. It is used to adjust the excitation signal output by the ultrasonic excitation device based on the control command of the control device.

[0036] One end of the preload device is connected to the ultrasonic excitation device, and the other end is connected to the control device. It is used to apply preload to the ultrasonic excitation device based on the control command of the control device.

[0037] The ultrasonic excitation device is connected to the test block and is used to excite the test block;

[0038] One end of the vibration monitoring device is connected to the test block, and the other end is connected to the control device. It is used to collect the surface vibration state of the test block, obtain the vibration signal, and send the vibration signal to the control device.

[0039] One end of the infrared thermal imager is connected to the test block, and the other end is connected to the control device. It is used to collect the surface temperature status of the test block, obtain the temperature signal, and send the temperature signal to the control device.

[0040] In this approach, by setting up real-time access linkage and feedback functions, real-time feedback of detection results is achieved, which facilitates timely adjustment of excitation parameters and further improves the sensitivity and reliability of the detection results.

[0041] In one alternative embodiment, the preload device is a hydraulic or pneumatic pressure device, and the vibration monitoring device is a contact piezoelectric sensor or a non-contact laser vibration meter.

[0042] In this method, the preload device can adjust the pressure in real time, and the vibration monitoring device can monitor the vibration state of the test block in real time, which improves the real-time recording of the vibration state of the test block and the sensitivity of the test results.

[0043] Thirdly, the present invention provides an ultrasonic infrared thermal imaging detection device, the device comprising:

[0044] The test block acquisition module is used to acquire the simulated test block and the test block to be tested. The simulated test block is a test block with cracks.

[0045] The initial state acquisition module is used to acquire the initial temperature signal and initial vibration signal of the simulated test block surface;

[0046] The status acquisition module is used to excite the simulated test block and acquire the temperature and vibration signals corresponding to the ultrasonic infrared thermographs of the simulated test block under different detection parameters.

[0047] The calibration parameter determination module is used to determine the calibration detection parameters and optimal vibration state based on temperature and vibration signals.

[0048] The chaos level determination module is used to determine the optimal quantitative range of chaos level based on the optimal vibration state;

[0049] The detected chaos determination module is used to excite the test block based on the calibrated detection parameters and determine the quantitative value of the detected chaos of the test block.

[0050] The detected chaos judgment module is used to determine whether the detected chaos quantitative value is within the optimal chaos level quantitative range;

[0051] The detection result determination module is used to acquire the current temperature signal corresponding to the ultrasonic infrared thermogram of the test block when the quantitative value of the detected chaos is within the optimal quantitative range of chaos, and to determine the detection result of the test block based on the current temperature signal.

[0052] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the ultrasonic infrared thermal imaging detection method of the first aspect or any corresponding embodiment described above. Attached Figure Description

[0053] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0054] Figure 1 This is a schematic flowchart of an ultrasonic infrared thermographic detection method according to an embodiment of the present invention.

[0055] Figure 2This is a schematic flowchart of another ultrasonic infrared thermography detection method according to an embodiment of the present invention.

[0056] Figure 3 This is a schematic flowchart of another ultrasonic infrared thermographic detection method according to an embodiment of the present invention.

[0057] Figure 4 This is a schematic flowchart of another ultrasonic infrared thermographic detection method according to an embodiment of the present invention.

[0058] Figure 5 This is a flowchart of a wavelet neural network prediction method according to an embodiment of the present invention.

[0059] Figure 6 This is a schematic diagram of the hardware structure of the control device in an ultrasonic infrared thermal imaging detection system according to an embodiment of the present invention.

[0060] Figure 7 This is a schematic diagram of the structure of an ultrasonic infrared thermal imaging detection system according to an embodiment of the present invention.

[0061] Figure 8 This is a structural block diagram of an ultrasonic infrared thermal imaging detection device according to an embodiment of the present invention. Detailed Implementation

[0062] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0063] In related technologies, ultrasonic infrared thermal imaging detection systems lack a closed-loop module for acquiring and integrating the specimen's vibration state into the main system. This prevents the system from sensing the specimen's vibration state and achieving real-time linkage and feedback between the specimen's vibration state and the main system. Therefore, in actual testing, slight changes in preload, excitation parameters, and contact conditions often lead to a decrease in heat generation at the specimen's crack defects. Consequently, the infrared image acquired by the thermal imager may not show hot spot signals, causing inspectors to mistakenly conclude that the inspected component has no crack defects, resulting in a significant error of missed crack defects.

[0064] To address the aforementioned problems, this invention provides an ultrasonic infrared thermal imaging detection method for use in a computer device. It should be noted that the executing entity can be an ultrasonic infrared thermal imaging detection device, which can be implemented as part or all of the computer device through software, hardware, or a combination of both. The computer device can be a terminal, client, or server. The server can be a single server or a server cluster composed of multiple servers. In this embodiment, the terminal can be a smartphone, personal computer, tablet computer, or other intelligent hardware device. The following method embodiments will use a computer device as the executing entity for illustration.

[0065] The computer equipment in this embodiment is suitable for applications involving ultrasonic infrared thermography to detect cracks in test blocks. The ultrasonic infrared thermography method provided by this invention incorporates vibration monitoring to excite a simulated test block. It collects temperature and vibration signals corresponding to the ultrasonic infrared thermography of the simulated test block under different detection parameters, determines the optimal detection parameters and vibration state, and uses these optimal parameters to detect the test block. This ensures the reliability of the entire ultrasonic infrared thermography process and avoids missed cracks due to reduced heat generation caused by parameter changes.

[0066] According to an embodiment of the present invention, an embodiment of an ultrasonic infrared thermal imaging detection method is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0067] This embodiment provides an ultrasonic infrared thermal imaging detection method, which can be used in the aforementioned computer equipment. Figure 1 This is a flowchart of an ultrasonic infrared thermography detection method according to an embodiment of the present invention, such as... Figure 1 As shown, the process includes the following steps:

[0068] Step S101: Obtain the simulation test block and the test block to be tested.

[0069] In this embodiment of the invention, the simulated test block is a test block with cracks.

[0070] Step S102: Collect the initial temperature signal and initial vibration signal of the simulated test block surface.

[0071] In one example, the surface temperature of the simulated test block can be acquired by a thermal imager to obtain an initial temperature signal; the vibration monitoring device simultaneously acquires the surface vibration state of the simulated test block to obtain an initial vibration signal.

[0072] Step S103: Excite the simulated test block and collect the temperature and vibration signals corresponding to the ultrasonic infrared thermographs of the simulated test block under different detection parameters.

[0073] In one example, by changing the detection parameters to excite the simulated test block, the temperature and vibration signals corresponding to the ultrasonic infrared thermographs of the simulated test block under different detection parameters were collected.

[0074] Step S104: Based on the temperature signal and vibration signal, determine the calibration detection parameters and the optimal vibration state.

[0075] In one example, by analyzing the surface temperature information of all collected simulated test blocks, the detection parameters and vibration state corresponding to the best detection effect are determined as the calibration detection parameters and the optimal vibration state.

[0076] Step S105: Based on the optimal vibration state, determine the optimal quantitative range of chaos.

[0077] In one example, the optimal vibration state information is identified and analyzed by using methods such as periodic orbit detection, power spectrum calculation, learning vector quantization, wavelet neural network, symplectic geometric spectrum, Kalman filtering, and deep convolutional neural network, and the quantitative range of chaos in the optimal state is obtained.

[0078] Step S106: Based on the calibration detection parameters, the test block under test is excited to determine the quantitative value of the chaos under test.

[0079] In one example, by using calibrated detection parameters to excite the test block, the degree of chaos of the test block's vibration is identified and quantified, and the quantitative value of the tested chaos is determined.

[0080] Step S107: Determine whether the detected chaos quantitative value is within the optimal chaos degree quantitative range.

[0081] Step S108: When the quantitative value of the detected chaos is within the quantitative range of the optimal degree of chaos, acquire the current temperature signal corresponding to the ultrasonic infrared thermograph of the tested block, and determine the detection result of the tested block based on the current temperature signal.

[0082] In one example, the actual detection results during the ultrasonic infrared thermography process of the test block are evaluated when the quantitative value of the detected chaos is within the optimal quantitative range of chaos.

[0083] The ultrasonic infrared thermography detection method provided in this embodiment incorporates vibration state monitoring to excite the simulated test block. It collects the temperature and vibration signals corresponding to the ultrasonic infrared thermography of the simulated test block under different detection parameters, determines the detection parameters and vibration state corresponding to the optimal detection effect, and uses the detection parameters corresponding to the optimal detection effect to detect the test block. This ensures the reliability of the entire ultrasonic infrared thermography detection process and avoids the problem of missed crack defects due to reduced heat generation caused by parameter changes and failure of inspectors to detect them in time.

[0084] This embodiment provides an ultrasonic infrared thermal imaging detection method, which can be used in the aforementioned computer equipment. Figure 2 This is a flowchart of another ultrasonic infrared thermal imaging detection method according to an embodiment of the present invention, such as... Figure 2 As shown, the process includes the following steps:

[0085] Step S201: Obtain the simulated test block and the test block to be tested. For details, please refer to [link to relevant documentation]. Figure 1 Step S101 of the illustrated embodiment will not be described again here.

[0086] Step S202: Acquire the initial temperature signal and initial vibration signal of the simulated test block surface. For details, please refer to [link to relevant documentation]. Figure 1 Step S102 of the illustrated embodiment will not be described again here.

[0087] Step S203 involves exciting the simulated test block and acquiring temperature and vibration signals corresponding to the ultrasonic infrared thermograms of the simulated test block under different detection parameters. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0088] Step S204: Based on the temperature and vibration signals, determine the calibration detection parameters and optimal vibration state. For details, please refer to [link to relevant documentation]. Figure 1 Step S104 of the illustrated embodiment will not be described again here.

[0089] Step S205: Based on the optimal vibration state, determine the optimal quantitative range for the degree of chaos. For details, please refer to [link to relevant documentation]. Figure 1 Step S105 of the illustrated embodiment will not be described again here.

[0090] Step S206: Based on the calibrated detection parameters, the test block is excited to determine the quantitative value of the detected chaos of the test block. For details, please refer to [link to relevant documentation]. Figure 1 Step S106 of the illustrated embodiment will not be described again here.

[0091] Step S207: Determine whether the detected chaos quantitative value is within the optimal chaos level quantitative range. For details, please refer to [link to relevant documentation]. Figure 1 Step S107 of the illustrated embodiment will not be described again here.

[0092] Step S208: When the quantitative value of the detected chaos is within the optimal quantitative range of chaos, acquire the current temperature signal corresponding to the ultrasonic infrared thermographic image of the test block, and determine the detection result of the test block based on the current temperature signal. For details, please refer to [link to relevant documentation]. Figure 1 Step S108 of the illustrated embodiment will not be described again here.

[0093] Specifically, after step S208 above, the ultrasonic infrared thermography detection method further includes:

[0094] Step S209: When the detected chaotic quantitative value is not within the optimal chaotic degree quantitative range, an alarm is triggered, and / or the calibration detection parameters are adjusted, and the process returns to the step of exciting the test block based on the calibration detection parameters and determining the detected chaotic quantitative value of the test block until the detected chaotic quantitative value is within the optimal chaotic degree quantitative range.

[0095] In one example, the chaotic state of a simulated test block under optimal testing conditions is obtained through ultrasonic infrared thermography. The chaotic state of the simulated test block under optimal testing conditions is then graded, assuming that a chaos level of 60-80% guarantees testing effectiveness. On the actual test piece, if the real-time monitored and identified chaos level is outside the 60-80% range, an alarm is triggered, and the testing parameters need to be fine-tuned until the chaos level of the test block falls within the 60-80% range. Fine-tuning the testing parameters can include sequentially and slightly increasing or decreasing the preload, ultrasonic amplitude, ultrasonic frequency, and ultrasonic duration. For example, if the optimal preload is 10N, values ​​of 9.5, 9, 10.5, and 11N would be tried first for fine-tuning.

[0096] The ultrasonic infrared thermographic detection method provided in this embodiment improves the reliability of ultrasonic infrared thermographic detection by providing timely warnings and correcting detection parameters in real time when the quantitative value of the detected chaos is not within the optimal quantitative range of chaos. This avoids the problem of missed detection of crack defects by the inspectors.

[0097] This embodiment provides an ultrasonic infrared thermal imaging detection method, which can be used in the aforementioned computer equipment. Figure 3 This is a flowchart of another ultrasonic infrared thermal imaging detection method according to an embodiment of the present invention, such as... Figure 3 As shown, the process includes the following steps:

[0098] Step S301: Obtain the simulated test block and the test block to be tested. For details, please refer to [link to relevant documentation]. Figure 2 Step S201 of the illustrated embodiment will not be described again here.

[0099] Step S302: Acquire the initial temperature signal and initial vibration signal of the simulated test block surface. For details, please refer to [link to relevant documentation]. Figure 2 Step S202 of the illustrated embodiment will not be described again here.

[0100] Step S303: Excite the simulated test block and acquire the temperature signal and vibration signal corresponding to the ultrasonic infrared thermograph of the simulated test block under different detection parameters.

[0101] Specifically, step S303 includes:

[0102] Step S3031: Set the lower and upper limits of the preload, ultrasonic amplitude, and ultrasonic frequency.

[0103] Step S3032: Adjust the preload and ultrasonic signal applied to the simulated test block, and collect the temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude and ultrasonic frequency.

[0104] In one example, the temperature and vibration signals corresponding to the ultrasonic infrared thermographs of the simulated test block under different detection parameters were acquired through the following steps:

[0105] 1) The computer equipment sets the preload force and the amplitude of the ultrasonic signal to the lower limit, and increases the frequency of the ultrasonic signal from the lower limit to the upper limit in sequence, with each frequency lasting for 5 seconds, to excite the simulated test block and collect its temperature and vibration status.

[0106] 2) Keep the preload constant, increase the amplitude of the ultrasonic signal by 10μm, and increase the frequency of the ultrasonic signal from the lower limit to the upper limit in sequence, with each frequency lasting for 5s, to excite the simulated test block and collect the temperature and vibration status.

[0107] 3) Keep the preload value constant, and continue to increase the amplitude of the ultrasonic signal by 10μm to the upper limit value. Repeat steps 1 and 2 to excite the simulated test block and collect the temperature and vibration status.

[0108] 4) Increase the preload by 1N, repeat steps 1, 2, and 3 to excite the simulated test block and collect temperature and vibration data.

[0109] 5) Increase the preload value by 1N in sequence, and repeat steps 1, 2 and 3 to excite the simulated test block and collect temperature and vibration data.

[0110] Step S304: Based on the temperature and vibration signals, determine the calibration detection parameters and optimal vibration state. For details, please refer to [link to relevant documentation]. Figure 2 Step S204 of the illustrated embodiment will not be described again here.

[0111] Step S305: Based on the optimal vibration state, determine the quantitative range of the optimal degree of chaos. For details, please refer to [link to relevant documentation]. Figure 2 Step S205 of the illustrated embodiment will not be described again here.

[0112] Step S306: Based on the calibrated detection parameters, the test block is excited to determine the quantitative value of the detected chaos of the test block. For details, please refer to [link to relevant documentation]. Figure 2 Step S206 of the illustrated embodiment will not be described again here.

[0113] Step S307: Determine whether the detected chaos quantitative value is within the optimal chaos level quantitative range. For details, please refer to [link to relevant documentation]. Figure 2 Step S207 of the illustrated embodiment will not be described again here.

[0114] Step S308: When the quantitative value of the detected chaos is within the optimal quantitative range of chaos, acquire the current temperature signal corresponding to the ultrasonic infrared thermographic image of the test block, and determine the detection result of the test block based on the current temperature signal. For details, please refer to [link to relevant documentation]. Figure 2 Step S208 of the illustrated embodiment will not be described again here.

[0115] The ultrasonic infrared thermography detection method provided in this embodiment can more comprehensively acquire temperature and vibration signals of the simulated test block under different preloads, ultrasonic amplitudes, and ultrasonic frequencies by continuously adjusting the preload and ultrasonic signals. This allows for more accurate determination of the detection parameters and vibration state of the simulated test block under optimal conditions.

[0116] This embodiment provides an ultrasonic infrared thermal imaging detection method, which can be used in the aforementioned computer equipment. Figure 4 This is a flowchart of another ultrasonic infrared thermographic detection method according to an embodiment of the present invention, such as... Figure 4 As shown, the process includes the following steps:

[0117] Step S401: Obtain the simulated test block and the test block to be tested. For details, please refer to [link to relevant documentation]. Figure 3 Step S301 of the illustrated embodiment will not be described again here.

[0118] Step S402: Acquire the initial temperature signal and initial vibration signal of the simulated test block surface. For details, please refer to [link to relevant documentation]. Figure 3 Step S302 of the illustrated embodiment will not be described again here.

[0119] Step S403: Excite the simulated test block and acquire the temperature and vibration signals corresponding to the ultrasonic infrared thermographic images of the simulated test block under different detection parameters. For details, please refer to... Figure 3 Step S303 of the illustrated embodiment will not be described again here.

[0120] Step S404: Based on the temperature signal and vibration signal, determine the calibration detection parameters and the optimal vibration state.

[0121] In this embodiment of the invention, the calibration detection parameters include: the preload value, ultrasonic amplitude, ultrasonic frequency, and ultrasonic action time at the moment when the temperature difference between the crack and the non-crack is the largest; the optimal vibration state includes: the vibration velocity waveform and vibration velocity spectrum at the moment when the temperature difference between the crack and the non-crack is the largest.

[0122] Specifically, step S404 includes:

[0123] Step S4041: Based on the temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude, and ultrasonic frequency, determine the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block is the largest.

[0124] Step S4042: Based on the moment when the temperature difference between the cracked and non-cracked areas is at its maximum, the preload value, ultrasonic amplitude, ultrasonic frequency, ultrasonic action time, vibration velocity waveform, and vibration velocity spectrum of the simulated test block at the moment when the temperature difference between the cracked and non-cracked areas is at its maximum are calibrated.

[0125] In one example, by analyzing the surface temperature information of all collected simulated test blocks, parameters such as the preload value, ultrasonic amplitude, ultrasonic frequency, and ultrasonic action time at the point where the temperature difference between the crack and the non-crack is the largest are calibrated. At the same time, the surface vibration state of the simulated test block at that moment, including the vibration velocity waveform and vibration velocity spectrum, is recorded.

[0126] In this method, the optimal vibration state is determined by the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block is the largest. The calibration and testing parameters of the simulated test block under the optimal vibration state are recorded, which improves the accuracy of the test parameters of the test block and facilitates subsequent ultrasonic infrared thermography testing of the test block.

[0127] Step S405: Based on the optimal vibration state, determine the optimal quantitative range of chaos.

[0128] Specifically, step S405 includes:

[0129] Step S4051: Perform chaos identification on the vibration velocity waveform and vibration velocity spectrum at the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block is the largest, and determine the optimal quantitative range of chaos.

[0130] In one example, methods such as periodic orbit detection, power spectrum calculation, learning vector quantization, wavelet neural network, symplectic geometric spectrum, Kalman filtering, and deep convolutional neural network are used to identify and analyze the recorded vibration velocity waveform and vibration velocity spectrum at the calibration time, and to obtain a quantitative value of the degree of chaos of vibration under the optimal state.

[0131] Specifically, the power spectrum method decomposes a complex time series into a superposition of sinusoidal oscillations at different frequencies. The power spectrum value at a given frequency is proportional to the square of the sinusoidal coefficient of that frequency. A typical power spectrum consists of one or more peaks, corresponding to the dominant frequencies in the signal. In addition to these dominant peaks, other frequencies may also appear, but with lower amplitudes, and the power spectrum is usually distributed over a wide frequency band.

[0132] Periodic orbit detection method: Due to the existence of random perturbations and the constant change of initial conditions, physical chaotic attractors are unpredictable (i.e., unpredictable in the time domain). However, the regression characteristics of their periodic orbits can be characterized using statistical prediction; therefore, this statistical characteristic of physical chaos can be called statistical predictability. In summary, the predictability of physical chaos has two aspects (two-sidedness): unpredictability in the time domain and statistical predictability. The statistical predictability of chaos should simultaneously contain the basic information of both the determinism and unpredictability of chaotic attractors. According to the theory in the literature, the hierarchical structure of the periodic orbits of chaotic attractors contains the main deterministic properties of attractors, while the combination of thermal periodic orbits roughly represents the basic characteristics of the attractor structure (thermal periodic orbits refer to periodic orbits with high regression frequency). As for the representation of unpredictability, it should be consistent with the definition of unpredictability. Based on these concepts, the proposed statistical predictability detection method includes a histogram (or a set of data lists) of the thermal periodic orbits of a chaotic attractor and a set of thermal periodic orbit regression spectra (recurrence spectra). The latter is a chaotic system constructed based on the regression properties of thermal periodic orbits and the definition of unpredictability. The higher the regression frequency of the thermal periodic orbits, or the narrower and more non-uniform the periodic orbit regression spectrum when the regression frequency is constant, the stronger the statistical predictability of the chaotic attractor, that is, the stronger the predictability or the weaker the unpredictability (the latter refers to the case of comparing two unpredictable chaotic signals). The periodic orbit regression spectrum is the periodic orbit recurrence spectrum, which represents the frequency of the recurrence of periodic orbits in the phase diagram of the chaotic signal.

[0133] Wavelet Neural Network Method: The wavelet neural network method is suitable for handling nonlinear relationships in complex systems and does not require a large amount of data. It combines the excellent time-frequency localization and focusing characteristics of wavelet transform with the self-learning, adaptive, and fault-tolerant properties of neural networks, thus possessing strong approximation and fault-tolerant capabilities. A wavelet neural network is a type of neural network based on the BP neural network topology, using wavelet basis functions as transfer functions for hidden layer nodes, with signal forward propagation and error backward propagation. Figure 5 This is a flowchart of a wavelet neural network prediction method according to an embodiment of the present invention. Figure 5As shown, phase space reconstruction is performed based on the time delay and embedding dimension of the time series. Phase space reconstruction is a method for studying system dynamics by reconstructing attractors based on limited measured data. Phase space reconstruction of a one-dimensional time series yields a multi-dimensional phase space signal, which is then used to predict chaotic time series. Currently, the number of input layer nodes in neural networks is generally determined using a trial-and-error method, i.e., continuously changing the number of input layer nodes until the required model accuracy is achieved. However, this method leads to increased computational cost and is not conducive to establishing dynamic prediction models. The optimal number of input layers is the saturated embedding dimension of the reconstructed phase space of the chaotic time series.

[0134] In this method, by performing chaos identification on the vibration state of the simulated test block, the optimal quantitative range of chaos level is determined, which can then provide data support for the assessment of the chaos level of the test block under test, and thus enable timely alarm when the quantitative value of the tested chaos is not within the optimal quantitative range of chaos level.

[0135] Step S406: Based on the calibration detection parameters, the test block under test is excited to determine the quantitative value of the chaos under test.

[0136] In one example, the simulated test block is replaced with the test specimen. The test specimen is excited using calibrated parameters such as preload, ultrasonic amplitude, ultrasonic frequency, and ultrasonic action time. At the same time, the vibration state of the test specimen is monitored in real time, and the degree of chaos of the vibration is identified and quantified in real time.

[0137] Step S407: Determine whether the detected chaos quantitative value is within the optimal chaos level quantitative range. For details, please refer to [link to relevant documentation]. Figure 3 Step S307 of the illustrated embodiment will not be described again here.

[0138] Step S408: When the quantitative value of the detected chaos is within the quantitative range of the optimal degree of chaos, acquire the current temperature signal corresponding to the ultrasonic infrared thermograph of the tested block, and determine the detection result of the tested block based on the current temperature signal.

[0139] In one example, if the real-time identified quantitative value of vibration chaos changes during the detection process, an alarm is issued and fed back to the ultrasonic generator and pre-tightening device by the computer equipment. The parameters such as the pre-tightening force value, ultrasonic amplitude, ultrasonic frequency, and ultrasonic action time are finely adjusted until the optimal quantitative value of vibration chaos is reached.

[0140] The ultrasonic infrared thermography detection method provided in this embodiment determines the optimal vibration state by identifying the moment when the temperature difference between the cracked and non-cracked areas of the simulated test block is the largest. The calibration and detection parameters of the simulated test block under the optimal vibration state are recorded, improving the accuracy of the detection parameters and facilitating subsequent ultrasonic infrared thermography detection of the test block. By performing chaos identification on the vibration state of the simulated test block and determining the optimal quantitative range of chaos, data support can be provided for assessing the chaos level of the test block, enabling timely alarms when the quantitative value of the detected chaos is outside the optimal quantitative range of chaos.

[0141] This embodiment also provides an ultrasonic infrared thermal imaging detection system, which includes a control device comprising a memory and a processor. The memory and the processor are communicatively connected. The memory stores computer instructions, and the processor executes these computer instructions to perform the ultrasonic infrared thermal imaging detection method of any of the above embodiments. Please refer to [link to previous document]. Figure 6 , Figure 6 This is a schematic diagram of the structure of a control device provided in an optional embodiment of the present invention, such as... Figure 6 As shown, the control device includes one or more processors 10, a memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise as required. The processors can process instructions executed within the control device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple control devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 6 Take a processor 10 as an example.

[0142] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GDA), or any combination thereof.

[0143] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0144] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the control device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the control device via a network. Examples of such networks include, but are not limited to, the Internet, corporate intranets, local area networks, mobile communication networks, and combinations thereof.

[0145] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0146] The control device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 40 can be connected via a bus or other means. Figure 6 Taking the example of a connection between China and Israel via a bus.

[0147] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the control device, such as a touch screen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touch screen.

[0148] Figure 7 This is a schematic diagram of the structure of an ultrasonic infrared thermal imaging detection system according to an embodiment of the present invention. Figure 7 As shown, in addition to the control device 701, the system also includes: an ultrasonic generator 702, an ultrasonic excitation device 703, a pre-tightening device 704, an infrared thermal imager 705, and a vibration monitoring device 706.

[0149] One end of the ultrasonic generator 702 is connected to the ultrasonic excitation device 703, and the other end is connected to the control device 701. It is used to adjust the excitation signal output by the ultrasonic excitation device 703 based on the control command of the control device 701.

[0150] One end of the pre-tightening device 704 is connected to the ultrasonic excitation device 703, and the other end is connected to the control device 701, for applying pre-tightening force to the ultrasonic excitation device 703 based on the control command of the control device 701.

[0151] The ultrasonic excitation device 703 is connected to the test block and is used to excite the test block;

[0152] One end of the vibration monitoring device 706 is connected to the test block, and the other end is connected to the control device 701. It is used to collect the surface vibration state of the test block, obtain the vibration signal, and send the vibration signal to the control device 701.

[0153] One end of the infrared thermal imager 705 is connected to the test block, and the other end is connected to the control device 701. It is used to collect the surface temperature status of the test block, obtain the temperature signal, and send the temperature signal to the control device 701.

[0154] In one example, the ultrasonic generator 702 can perform frequency sweeping and fixed-frequency excitation, and the amplitude and phase can be adjusted; the ultrasonic excitation device 703 includes an excitation head, a transducer, an amplitude transformer, etc.

[0155] In this approach, by setting up real-time access linkage and feedback functions, real-time feedback of detection results is achieved, which facilitates timely adjustment of excitation parameters and further improves the sensitivity and reliability of the detection results.

[0156] In one alternative embodiment, the preload device 704 is a hydraulic or pneumatic pressure device, and the vibration monitoring device 706 is a contact piezoelectric sensor or a non-contact laser vibration meter.

[0157] In this method, the pre-tightening device 704 can adjust the pressure in real time, and the vibration monitoring device 706 can monitor the vibration state of the test block in real time, which improves the real-time recording of the vibration state of the test block and the sensitivity of the test results.

[0158] This embodiment also provides an ultrasonic infrared thermal imaging detection device for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the device described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0159] This embodiment provides an ultrasonic infrared thermal imaging detection device, such as... Figure 8 As shown, it includes:

[0160] The test block acquisition module 801 is used to acquire a simulated test block and a test block to be tested. The simulated test block is a test block with cracks.

[0161] The initial state acquisition module 802 is used to acquire the initial temperature signal and initial vibration signal of the simulated test block surface;

[0162] The status acquisition module 803 is used to excite the simulated test block and acquire the temperature signal and vibration signal corresponding to the ultrasonic infrared thermograph of the simulated test block under different detection parameters;

[0163] The calibration parameter determination module 804 is used to determine the calibration detection parameters and the optimal vibration state based on the temperature signal and vibration signal.

[0164] The chaos level determination module 805 is used to determine the optimal quantitative range of chaos level based on the optimal vibration state;

[0165] The detected chaos determination module 806 is used to excite the test block based on the calibrated detection parameters and determine the detected chaos quantitative value of the test block.

[0166] The detected chaos judgment module 807 is used to determine whether the detected chaos quantitative value is within the optimal chaos degree quantitative range.

[0167] The detection result determination module 808 is used to acquire the current temperature signal corresponding to the ultrasonic infrared thermograph of the test block when the quantitative value of the detected chaos is within the optimal quantitative range of chaos, and to determine the detection result of the test block based on the current temperature signal.

[0168] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0169] In this embodiment, the ultrasonic infrared thermal imaging detection device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above functions.

[0170] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0171] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. An ultrasonic infrared thermographic detection method, characterized in that, The method includes: Obtain a simulated test block and a test block to be tested, wherein the simulated test block is a test block with cracks; The initial temperature signal and initial vibration signal of the simulated test block surface were collected; The simulated test block was excited, and temperature and vibration signals corresponding to the ultrasonic infrared thermographs of the simulated test block under different detection parameters were collected. Based on the temperature and vibration signals, calibration detection parameters and optimal vibration state are determined. The calibration detection parameters include: the preload value, ultrasonic amplitude, ultrasonic frequency, and ultrasonic action time at the moment of maximum temperature difference between the crack and non-crack locations. The optimal vibration state includes: the vibration velocity waveform and vibration velocity spectrum at the moment of maximum temperature difference between the crack and non-crack locations. Determining the calibration detection parameters and optimal vibration state based on the temperature and vibration signals includes: determining the moment of maximum temperature difference between the crack and non-crack locations of the simulated test block based on the temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude, and ultrasonic frequency conditions; and calibrating the preload value, ultrasonic amplitude, ultrasonic frequency, ultrasonic action time, vibration velocity waveform, and vibration velocity spectrum at the moment of maximum temperature difference between the crack and non-crack locations of the simulated test block based on the moment of maximum temperature difference between the crack and non-crack locations. Based on the optimal vibration state, determine the quantitative range of the optimal degree of chaos; Based on the calibration detection parameters, the test block is excited to determine the quantitative value of the chaos detected by the test block. Determine whether the detected quantitative value of chaos is within the optimal quantitative range of chaos. When the quantitative value of the detected chaos is within the quantitative range of the optimal degree of chaos, the current temperature signal corresponding to the ultrasonic infrared thermogram of the tested block is acquired, and the detection result of the tested block is determined based on the current temperature signal.

2. The method according to claim 1, characterized in that, The method further includes: When the detected chaotic quantitative value is not within the optimal chaotic degree quantitative range, an alarm is triggered, and / or the calibration detection parameters are adjusted, and the process returns to the step of exciting the test block based on the calibration detection parameters and determining the detected chaotic quantitative value of the test block until the detected chaotic quantitative value is within the optimal chaotic degree quantitative range.

3. The method according to claim 1, characterized in that, The process of exciting the simulated test block and acquiring temperature and vibration signals of the simulated test block under different detection parameters includes: Set the lower and upper limits for preload, ultrasonic amplitude, and ultrasonic frequency; Adjust the preload and ultrasonic signal applied to the simulated test block, and collect the temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude, and ultrasonic frequency.

4. The method according to claim 1, characterized in that, The determination of the optimal quantitative range of chaos based on the optimal vibration state includes: Chaos identification was performed on the vibration velocity waveform and vibration velocity spectrum at the moment when the temperature difference between the cracked and non-cracked parts of the simulated test block was the maximum, and the optimal quantitative range of chaos was determined.

5. An ultrasonic infrared thermal imaging detection system, characterized in that, The system includes a control device, which includes a memory and a processor. The memory and the processor are communicatively connected to each other. The memory stores computer instructions. The processor executes the computer instructions to perform the ultrasonic infrared thermal imaging detection method according to any one of claims 1 to 4.

6. The ultrasonic infrared thermal imaging detection system according to claim 5, characterized in that, The system also includes: Ultrasonic generator, ultrasonic excitation device, preload device, infrared thermal imager, vibration monitoring device; One end of the ultrasonic generator is connected to the ultrasonic excitation device, and the other end is connected to the control device, for adjusting the excitation signal output by the ultrasonic excitation device based on the control command of the control device. One end of the preload device is connected to the ultrasonic excitation device, and the other end is connected to the control device, for applying preload to the ultrasonic excitation device based on the control command of the control device; The ultrasonic excitation device is connected to the test block and is used to excite the test block. One end of the vibration monitoring device is connected to the test block, and the other end is connected to the control device. It is used to collect the surface vibration state of the test block, obtain the vibration signal, and send the vibration signal to the control device. One end of the infrared thermal imager is connected to the test block, and the other end is connected to the control device. It is used to collect the surface temperature status of the test block, obtain a temperature signal, and send the temperature signal to the control device.

7. The system according to claim 6, characterized in that, The preload device is a hydraulic or pneumatic pressure device, and the vibration monitoring device is a contact piezoelectric sensor or a non-contact laser vibration meter.

8. An ultrasonic infrared thermal imaging detection device, characterized in that, The device includes: The test block acquisition module is used to acquire a simulated test block and a test block to be tested, wherein the simulated test block is a test block with cracks; The initial state acquisition module is used to acquire the initial temperature signal and initial vibration signal of the surface of the simulated test block; The status acquisition module is used to excite the simulated test block and acquire the temperature signal and vibration signal corresponding to the ultrasonic infrared thermograph of the simulated test block under different detection parameters. The calibration parameter determination module is used to determine calibration detection parameters and optimal vibration state based on the temperature and vibration signals. The calibration detection parameters include: the preload value, ultrasonic amplitude, ultrasonic frequency, and ultrasonic action time at the moment of maximum temperature difference between the crack and non-crack locations. The optimal vibration state includes: the vibration velocity waveform and vibration velocity spectrum at the moment of maximum temperature difference between the crack and non-crack locations. Determining the calibration detection parameters and optimal vibration state based on the temperature and vibration signals includes: determining the moment of maximum temperature difference between the crack and non-crack locations of the simulated test block based on the temperature and vibration signals of the simulated test block under different preload, ultrasonic amplitude, and ultrasonic frequency conditions; and calibrating the preload value, ultrasonic amplitude, ultrasonic frequency, ultrasonic action time, vibration velocity waveform, and vibration velocity spectrum at the moment of maximum temperature difference between the crack and non-crack locations of the simulated test block based on the moment of maximum temperature difference between the crack and non-crack locations. The chaos level determination module is used to determine the optimal quantitative range of chaos level based on the optimal vibration state; The detected chaos determination module is used to excite the tested block based on the calibration detection parameters and determine the detected chaos quantitative value of the tested block. The detected chaos judgment module is used to determine whether the detected chaos quantitative value is within the optimal chaos degree quantitative range; The detection result determination module is used to acquire the current temperature signal corresponding to the ultrasonic infrared thermogram of the test block when the quantitative value of the detected chaos is within the quantitative range of the optimal degree of chaos, and to determine the detection result of the test block based on the current temperature signal.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the ultrasonic infrared thermal imaging detection method according to any one of claims 1 to 4.

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