Method for detecting microcracks on inner wall of wall enclosure superheater of boiler

Abstract

The invention relates to a method for detecting microcracks on the inner wall of a wall enclosure superheater of a boiler. The method comprises the following steps: macroscopically identifying and preliminarily screening a high-risk area; creating an endoscope channel based on the primary suspicious area list and performing primary peeping on the inner wall; carrying out detection surface preparation and acoustic coupling optimization; data acquisition is carried out through accurate arrangement of the small-sound-velocity self-coupling phased array ultrasonic probe; extracting defect signal features and carrying out three-dimensional digital modeling; performing crack depth accurate quantization based on a time of flight diffraction mode; carrying out comprehensive data analysis and evaluating a security level; and integrating detection results, and generating a digital file based on the results. According to the invention, standardization of the detection process and systematization of data management can be realized, the detection efficiency and accuracy are greatly improved, a scientific basis is provided for equipment life prediction and maintenance decision, and the safe operation level of the boiler is effectively improved.

Description

Technical Field

[0001] This invention relates to the field of boiler equipment testing technology, and in particular to a method for detecting microcracks in the inner wall of a boiler superheater. Background Technology

[0002] The boiler superheater is a crucial component of a power plant boiler. Its inner wall refers to the inner surface of the pipes that form the boiler furnace and flue walls. High-temperature, high-pressure steam or water-vapor mixtures flow inside these pipes, while the outside is subjected to the scouring of high-temperature flue gas and thermal radiation. The integrity of the inner wall directly affects the safe operation and thermal efficiency of the entire boiler system. Therefore, crack detection of the boiler superheater's inner wall is essential. Because the inner wall operates under harsh conditions of high temperature, high pressure, and alternating thermal stress for extended periods, it is highly susceptible to microscopic defects such as thermal fatigue cracks and stress corrosion cracks. If these microcracks are not detected and addressed promptly, they will gradually expand and deepen, eventually leading to pipe leaks or even pipe ruptures, causing unplanned shutdowns, significant economic losses, and safety hazards.

[0003] Currently, traditional methods for inspecting the inner wall of boiler superheaters mainly rely on conventional non-destructive testing techniques and experience-based judgment. They typically employ single endoscopic visual inspection or conventional ultrasonic testing. These methods have significant limitations. For example, endoscopic inspection can only detect surface-opening defects and the results depend on human experience. Conventional ultrasonic testing is insensitive to microcracks and cannot accurately quantify defect sizes. Furthermore, traditional methods lack systematic inspection processes and digital management tools, resulting in highly subjective inspection results that are difficult to compare with historical data and analyze trends. The entire inspection process is inefficient and prone to missed detections. Summary of the Invention

[0004] To address the aforementioned problems, the present invention aims to provide a method for detecting microcracks in the inner wall of a boiler superheater. By establishing a systematic detection process from macroscopic to microscopic levels, the method enables accurate identification and quantitative assessment of inner wall defects.

[0005] The technical solution adopted in this invention is as follows: The present invention proposes a method for detecting microcracks in the inner wall of a boiler superheater, comprising the following steps: S1, macroscopic identification and preliminary screening of high-risk areas; S2. Endoscopic channel creation and initial examination of the inner wall based on the preliminary list of suspicious areas; S3. Perform detection surface preparation and acoustic coupling optimization; S4. Data acquisition is performed through the precise deployment of low-velocity self-coupled phased array ultrasonic probes. S5. Extract defect signal features and perform three-dimensional digital modeling; S6. Accurate quantification of crack depth based on diffraction time-difference mode; S7. Conduct comprehensive data analysis and assess the security level; S8. Integrate test results and generate digital archives based on the results.

[0006] Furthermore, step S1 specifically includes: S1.1 First, develop a detailed visual inspection plan, determine the inspection path and key area division plan, and carry out the inspection in zones according to the boiler heating surface layout diagram; S1.2 When conducting macroscopic inspections, the overall color difference of the tube screen is recorded using a color comparison card, the surface condition of the fin weld area is inspected using a magnifying glass, and the surface morphology of the elbow is observed using oblique lighting. S1.3 When a suspicious area is discovered, it shall be marked immediately. The numbering rule shall be the area code plus the serial number. At the same time, use an explosion-proof digital camera to take overall and close-up photos of the parts. When recording, note the location information and the preliminary judgment conclusion. S1.4 After each inspection, organize all recorded data and generate a preliminary list of suspicious areas containing area number, location description, defect characteristics, and photo number. The list is arranged in order of area and marked with priority level.

[0007] Furthermore, step S2 specifically includes: S2.1. Based on the priority level marked in the preliminary suspicious area list, select the three areas with the highest degree of suspicion as the starting point for endoscopic examination, and measure to determine the location of the nearest handhole or manhole. S2.2 After installing the endoscope catheter sealing device, the flexible high-definition endoscope probe is introduced into the tube. The probe is controlled to move slowly, and the condition of the inner wall is observed in real time and the probe insertion depth is recorded. S2.3 After arriving at the predetermined inspection position, adjust the probe angle and focus, and conduct a full-coverage inspection of the suspicious area, paying special attention to the color change, corrosion pit shape and linear traces on the inner wall. If any abnormality is found, immediately acquire images and record videos. S2.4 During the inspection, the location of each suspicious point is recorded, including the pipe row number, pipe serial number and distance from the nearest weld. All image data are sorted out and an internal wall defect image report containing location information and defect description is generated. At the same time, a detailed list of suspicious points for specific pipe sections is output.

[0008] Furthermore, step S3 specifically includes: S3.1. Based on the specific location information provided by the detailed list of suspicious points, use a laser rangefinder to locate each point to be inspected on site, and mark the boundary line of the grinding area on the outer wall of the pipe accordingly. S3.2. Use an angle grinder for grinding. Grind in three directions until the metal substrate is exposed. The surface roughness should be controlled to be no more than 6.3μm. S3.3. Thoroughly clean the polished area with anhydrous ethanol and non-woven cloth to remove all oil and dust residue. After the ethanol has completely evaporated, use thin film test paper to test the surface cleanliness to ensure that the coupling standard required for acoustic wave testing is met. S3.4 Record the specific location and surface condition of each preparation point, and generate a set of coordinates of the prepared test points containing the tube row number, tube serial number and axial and circumferential coordinates, so as to provide a positioning basis for subsequent testing.

[0009] Furthermore, step S4 specifically includes: S4.1 Prepare the detection equipment based on the prepared set of detection point coordinates. Select a miniaturized phased array probe as the detection equipment. S4.2 Apply high-temperature coupling agent evenly to each preparation point, fix the phased array probe to the pipe surface with a magnetic clamp, adjust the angle of the phased array probe to keep the sound beam center line perpendicular to the pipe axis, and ensure that the coupling layer thickness is stable within 0.1mm. S4.3 Set the scanning parameters of the phased array detector, and adopt a combination of linear scanning and sector scanning; S4.4 Start the automatic scanning program to collect data. Save a complete data package for each detection point, including the original A-scan signal and S-scan image. The file name should be consistent with the coordinate set number. At the same time, record the ambient temperature and instrument setting parameters.

[0010] Furthermore, step S5 specifically includes: S5.1 Import the collected raw data into professional analysis software. First, perform data preprocessing, including signal filtering and gain compensation. Use wavelet transform algorithm to enhance the signal-to-noise ratio of weak signals and eliminate the influence of material noise and structural noise. S5.2 Set the defect identification threshold to 20% of the full screen height, and use a combination of automatic identification and manual interpretation to identify abnormal signals. Mark the time flight and amplitude characteristic parameters for each signal. S5.3 Utilize 3D imaging algorithms to reconstruct the spatial morphology of defects, calculate the approximate orientation and planar projection size of defects based on signal characteristics, and generate a 3D model image containing color depth information. S5.4 Output a 3D model file for each defect and support formats including STL and PLY. At the same time, generate a table of key dimension parameters including the defect length, maximum depth and orientation angle as the basis for quantitative depth analysis.

[0011] Furthermore, step S6 specifically includes: S6.1. Based on the key dimension parameter table, select the defects that need to be re-inspected, select the defects with a depth display exceeding 0.5mm, start the diffraction time difference scanning mode, adopt the dual probe arrangement method, and set the probe center distance to 8-12mm; S6.2 Adjust the instrument parameters and use time window control when acquiring diffraction signals. Set the window width to the μs level to ensure that the complete diffraction waveform is captured. S6.3 Measure the time difference of the diffraction wave packet, calculate the time difference to an accuracy of 0.1 ns using the cross-correlation method, calculate the crack depth of the defect based on the sound velocity value of the material, and repeat the measurement three times for each measurement point and take the average value. S6.4 Update the depth measurement results into the 3D model to generate a corrected model that includes depth dimensions. The output depth measurement report includes the depth value at the measurement location and the measurement uncertainty, providing accurate data for safety assessment.

[0012] Furthermore, step S7 specifically includes: S7.1 Establish a defect assessment database; S7.2. Safety assessment is carried out using failure assessment diagram technology. The equivalent size and driving force parameters of each defect are calculated. The safety factor is determined according to the standard specifications, and the safety level is divided into three levels: supervised operation, grinding and repair required, and replacement required. S7.3. For defects that can be monitored, provide a suggestion for the next inspection cycle; for defects that need to be ground and repaired, provide the maximum allowable grinding depth; for defects that must be replaced, indicate the urgency and temporary handling measures. S7.4 Generate a complete assessment report containing recommendations and justifications for handling each defect at its safety level. The report format should conform to industry standards and provide comparative analysis of assessment results under different assumptions.

[0013] Furthermore, in step S7.2, the safety assessment is based on the BS7910:2019 standard.

[0014] Furthermore, step S8 specifically includes: S8.1 Integrate all test data, standardize the data according to a unified format, and establish data correlation relationships; S8.2 Generate a digital inspection report, the main content of which includes an overview of the inspection, a detailed description of the defects in the inspection method, a safety assessment and handling recommendations, and an appendix containing all raw data and a detailed analysis process; S8.3 Compare and analyze the results of this inspection with historical inspection data, update the parameters in the boiler life prediction model, calculate the remaining life distribution curve, and propose the key areas to focus on and the recommended inspection time for the next inspection. S8.4 All data is uploaded to the boiler's lifelong digital twin archive system to establish the correlation between this inspection and historical data, update the equipment status rating, and provide data support for full life cycle management.

[0015] Compared with the prior art, the present invention has the following advantages: This invention establishes a systematic inspection process from macro to micro to achieve accurate identification and quantitative assessment of internal wall defects. By combining endoscopy with phased array ultrasonic technology, it ensures comprehensive inspection while improving the quantitative accuracy of defects. At the same time, the introduction of digital modeling and safety assessment systems makes the inspection results more objective and reliable, achieving standardization of the inspection process and systematization of data management. This significantly improves inspection efficiency and accuracy, provides a scientific basis for equipment life prediction and maintenance decisions, and effectively enhances the safe operation level of boilers. Attached Figure Description

[0016] Figure 1 This is a schematic flowchart of a method for detecting microcracks in the inner wall of a boiler superheater proposed in this invention. Detailed Implementation

[0017] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the 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 based on these drawings without creative effort.

[0018] The present invention proposes a method for detecting microcracks in the inner wall of a boiler superheater, comprising the following steps: S1. Conduct macroscopic identification and preliminary screening of high-risk areas. The specific implementation process is as follows: S1.1 First, develop a detailed visual inspection plan, determine the inspection path and key area division plan, and carry out the inspection in zones according to the boiler heating surface layout diagram; S1.2 When conducting macroscopic inspections, keep the distance between your eyes and the pipe wall surface no more than 1.5m. Record the overall color difference changes of the pipe screen using a color comparison card. Use a 5x magnifying glass to inspect the surface condition of the fin weld area. Use oblique lighting to observe the surface morphology of the bend. S1.3 When a suspicious area is discovered, immediately mark it with a high-temperature resistant yellow marker, using a combination of area code and serial number. At the same time, use an explosion-proof digital camera to take overall and close-up photos, noting the location information and preliminary judgment conclusions in the records. S1.4 After each inspection, organize all recorded data and generate a preliminary list of suspicious areas containing area number, location description, defect characteristics, and photo number. The list is arranged in order of area and marked with priority level. This step enables rapid and comprehensive initial defect localization. Through a systematic visual inspection plan and recording standards, it ensures efficient and thorough screening of large areas, forming a structured list of suspicious areas. This provides clear targets and priorities for subsequent refined testing, avoids blind testing, and saves overall time and costs.

[0019] S2. The endoscopic access channel creation and initial endoscopic examination based on the preliminary list of suspicious areas are implemented as follows: S2.1. Based on the priority level marked in the preliminary suspicious area list, select 3 areas with the highest degree of suspicion as the starting point for endoscopic examination, measure and determine the location of the nearest hand hole or manhole, and if necessary, use a magnetic drill to open a temporary detection hole with a diameter of 50mm on the adjacent fins. S2.2 After installing the endoscope catheter sealing device, insert the 8mm diameter flexible high-definition endoscope probe into the tube, control the probe travel speed to not exceed 0.2m / s, observe the inner wall condition in real time and record the probe insertion depth. S2.3 After arriving at the predetermined inspection position, adjust the probe angle and focus, and use the side-view rotation scanning method to conduct a full-coverage inspection of the suspicious area, paying special attention to the color change, corrosion pit shape and linear traces on the inner wall. If any abnormality is found, immediately acquire images and record videos. S2.4 During the inspection, the location of each suspicious point is recorded, including the pipe row number, pipe serial number and distance from the nearest weld. All image data are sorted out and an internal wall defect image report containing location information and defect description is generated. At the same time, a detailed list of suspicious points for specific pipe sections is output.

[0020] This step enables direct visual inspection of the internal surface condition of the pipeline. By creating minimally invasive techniques or utilizing existing channels, macroscopic defects (such as corrosion and crack initiation) on the inner wall can be intuitively discovered and confirmed without large-scale disassembly. External suspicious points are transformed into specific descriptions of internal defects, improving the intuitiveness and accuracy of the inspection.

[0021] S3. Perform detection surface preparation and acoustic coupling optimization. The specific implementation process is as follows: S3.1. Based on the specific location information provided by the detailed list of suspicious points, use a laser rangefinder to locate each point to be inspected on site, and use a white paint pen to mark the boundary line of a 20mm×30mm rectangular grinding area on the outer wall of the pipe. S3.2. Use an angle grinder equipped with an 80-grit abrasive wheel for grinding. When grinding, keep the grinder at a 15-degree angle to the pipe wall surface and grind in three directions until the metal substrate is exposed. The surface roughness should be controlled to be no more than 6.3μm. S3.3. Thoroughly clean the polished area with anhydrous ethanol and non-woven cloth to remove all oil and dust residue. After the ethanol has completely evaporated, use thin film test paper to test the surface cleanliness to ensure that the coupling standard required for acoustic wave testing is met. S3.4 Record the specific location and surface condition of each preparation point, and generate a set of coordinates of the prepared test points, including the tube row number, tube serial number and axial and circumferential coordinates, to provide a positioning basis for subsequent testing.

[0022] This step creates ideal acoustic wave transmission conditions for subsequent ultrasonic testing. Through precise grinding and cleaning processes, the interference and attenuation of acoustic waves caused by surface oxide layers and dirt are eliminated, ensuring that ultrasonic waves can be effectively transmitted to the workpiece and receive echoes, greatly improving the signal-to-noise ratio of subsequent ultrasonic testing signals and the reliability of testing results.

[0023] S4. Data acquisition is performed through precise deployment of low-velocity self-coupled phased array ultrasonic probes. The specific implementation process is as follows: S4.1. Prepare the detection equipment based on the prepared set of detection point coordinates. Select a dedicated miniaturized phased array probe with a sound velocity of 2700m / s, 32 probe chips, and a frequency range of 10-15MHz. S4.2 Apply high-temperature coupling agent evenly to each preparation point, fix the probe to the pipe surface with a magnetic clamp, adjust the probe angle to keep the sound beam center line perpendicular to the pipe axis, and ensure that the coupling layer thickness is stable within 0.1mm. S4.3 Set the scanning parameters of the phased array detector, using a combination of linear scanning and sector scanning, with focusing depths set to 3mm and 6mm respectively, scanning angle range of 0-60 degrees, and pulse repetition frequency set to 2kHz. S4.4 Start the automatic scanning program to collect data. Save a complete data package for each detection point, including the original amplitude scanning signal and the interface scanning image. The file name is consistent with the coordinate set number. At the same time, record the ambient temperature and instrument setting parameters.

[0024] This step enables high-quality, high-resolution ultrasound data acquisition. By employing a miniaturized, high-frequency phased array probe that adapts to complex structures and through optimized scanning strategies and stable coupling fixation methods, it is possible to acquire raw A-scan and S-scan data containing rich information, laying a data foundation for subsequent accurate identification and quantification of defects.

[0025] S5. Extract defect signal features and perform three-dimensional digital modeling. The specific implementation process is as follows: S5.1 Import the collected raw data into professional analysis software. First, perform data preprocessing, including signal filtering and gain compensation. Use wavelet transform algorithm to enhance the signal-to-noise ratio of weak signals and eliminate the influence of material noise and structural noise. S5.2 Set the critical amplitude level for automatic identification and alarm of the system to 20% of the maximum displayable amplitude on the screen. Use a combination of automatic identification and manual interpretation to identify abnormal signals and mark the time flight and amplitude characteristic parameters for each signal. S5.3 Utilize 3D imaging algorithms to reconstruct the spatial morphology of defects, calculate the approximate direction and planar projection size of defects based on signal characteristics, and generate a 3D model image containing color depth information. The digital model of defects reconstructed by 3D imaging algorithms has a size measurement accuracy of 0.1 mm. When representing the geometric dimensions of defects such as length, width, and depth, the error between the measured values ​​and the actual physical dimensions is within ±0.1 mm. S5.4 Output a 3D model file for each defect and support formats including STL and PLY. At the same time, generate a table of key dimension parameters including the defect length, maximum depth and orientation angle as the basis for quantitative depth analysis.

[0026] This step transforms abstract ultrasonic signals into intuitive and accurate three-dimensional defect models. Through advanced signal processing algorithms and three-dimensional imaging technology, the morphology, size, and orientation of defects are visualized, enabling inspectors to understand the spatial distribution of defects more intuitively and completing the conversion from signal to morphology.

[0027] S6. Accurate quantification of crack depth based on diffraction time-of-flight mode, the specific implementation process is as follows: S6.1. Based on the key dimension parameter table, select the defects that need to be re-inspected, select the defects with a depth display exceeding 0.5mm, start the diffraction time difference scanning mode, adopt the dual probe arrangement method, and set the probe center distance to 10mm; S6.2 Adjust the instrument parameters: set the pulse width to 100ns, the sampling frequency to 100MHz, and use time window control when acquiring diffraction signals, with the window width set to the μs level to ensure that the complete diffraction waveform is captured. S6.3 Measure the time difference of the diffraction wave packets. Calculate the time difference to an accuracy of 0.1 ns using the cross-correlation method. It should be noted that the calculation steps are as follows: acquire and store the ultrasonic diffraction waveform signals from the reference probe and the receiving probe, and then analyze these two digital signals... and Perform cross-correlation function Mathematical operations:

[0028] That is, one signal slides relative to another signal and the sum of the dot products at each position is calculated; the position where the cross-correlation function reaches its maximum value is found; the offset corresponding to this position is the precise time difference between the two signals; The crack depth of the defect is calculated based on the sound velocity value of the material, and the average value is taken after repeating the measurement three times at each measurement point. S6.4 Update the depth measurement results into the 3D model to generate a corrected model containing depth dimensions, and output a depth measurement report including the depth value at the measurement location and the measurement uncertainty, providing accurate data for safety assessment.

[0029] This step enables extremely high-precision measurement of the most critical dimension of crack defects—depth. By utilizing the sensitivity of diffraction waves to the defect tip, it overcomes the shortcomings of conventional ultrasonic normal reflection depth measurement, which has low accuracy. It provides the most critical and accurate quantitative data for assessing the degree of defect hazard and developing maintenance plans.

[0030] S7. Conduct comprehensive data analysis and assess the security level. The specific implementation process is as follows: S7.1 Establish a defect assessment database. Input the material grade as 12Cr1MoVG and the operating parameters as follows: temperature 540℃, pressure 17.5MPa. Call up the material's fracture toughness data and creep strength parameters. S7.2. Safety assessment is conducted using failure assessment chart technology in accordance with BS 7910:2019 standard. The equivalent size and driving force parameters of each defect are calculated. The safety factor is determined according to the standard specifications, and the safety level is divided into three levels: supervised operation requiring grinding and repair, and mandatory replacement. S7.3. For defects that can be monitored, provide a suggestion for the next inspection cycle; for defects that need to be ground and repaired, provide the maximum allowable grinding depth; for defects that must be replaced, indicate the urgency and temporary handling measures. S7.4 Generate a complete assessment report containing recommendations and justifications for handling each defect at its safety level. The report format should conform to industry standards and provide comparative analysis of assessment results under different assumptions.

[0031] This step transforms test data into direct engineering decision-making data. By combining defect quantification parameters with material properties and operating conditions, safety and remaining life assessments are conducted according to industry standards, resulting in clear safety levels and specific handling recommendations. This process realizes the value of the assessment from testing to evaluation, guiding maintenance decisions.

[0032] S8. Integrate the test results and generate digital archives based on the results. The specific implementation process is as follows: S8.1 Integrate all detection data, including preliminary suspicious area list, endoscopic image report, ultrasound detection data, 3D model and safety assessment report, and standardize the data according to a unified format to establish data correlation; S8.2 The main content of the generated digital inspection report includes an overview of the inspection, a detailed description of the defects in the inspection method, a safety assessment, and handling recommendations. The appendix contains all the raw data and detailed analysis process. The report is in a searchable PDF format. S8.3 The results of this inspection were compared and analyzed with historical inspection data. The parameters in the boiler life prediction model were updated, the remaining life distribution curve was calculated, and the key areas to focus on and the recommended inspection time for the next inspection were proposed. S8.4 All data is uploaded to the boiler's lifelong digital twin archive system to establish the correlation between this inspection and historical data, update the equipment status rating, and provide data support for full life cycle management.

[0033] This step enables the systematic and structured management and full lifecycle application of testing data. By creating standardized digital reports and archives, it not only serves the decision-making process for this maintenance but, more importantly, it links with historical data to update the digital twin model of the equipment. This provides strong data support for predictive maintenance, life assessment, and asset management, thereby improving the intelligence level and long-term benefits of equipment management.

[0034] The present invention will be further illustrated below through specific embodiments: Example 1 The specific implementation process of the method for detecting microcracks on the inner wall of a boiler superheater proposed in this embodiment is as follows: S1. Conduct macroscopic identification and preliminary screening of high-risk areas: S1.1 First, develop a detailed visual inspection plan, determine the inspection path and key area division plan, and carry out the inspection in zones according to the boiler heating surface layout diagram; S1.2 When conducting macroscopic inspections, keep the distance between your eyes and the pipe wall surface no more than 1.5m. Record the overall color difference changes of the pipe screen using a color comparison card. Use a 5x magnifying glass to inspect the surface condition of the fin weld area. Use oblique lighting to observe the surface morphology of the bend. S1.3 When a suspicious area is discovered, immediately mark it with a high-temperature resistant yellow marker, using a combination of area code and serial number. At the same time, use an explosion-proof digital camera to take overall and close-up photos, noting the location information and preliminary judgment conclusions in the records. S1.4 After the daily inspection is completed, organize all recorded data and generate a preliminary list of suspicious areas containing area number, location description, defect characteristics and photo number. The list is arranged in order of area and marked with priority level.

[0035] S2. Endoscopic channel creation and initial endoscopic examination based on the preliminary list of suspicious areas: S2.1. Based on the priority level marked in the preliminary suspicious area list, select 3 areas with the highest degree of suspicion as the starting point for endoscopic examination, measure and determine the location of the nearest hand hole or manhole, and if necessary, use a magnetic drill to open a temporary detection hole with a diameter of 50mm on the adjacent fins. S2.2 After installing the endoscope catheter sealing device, insert the 8mm diameter flexible high-definition endoscope probe into the tube, control the probe travel speed to not exceed 0.2m / s, observe the inner wall condition in real time and record the probe insertion depth. S2.3 After arriving at the predetermined inspection position, adjust the probe angle and focus, and use the side-view rotation scanning method to conduct a full-coverage inspection of the suspicious area, paying special attention to the color change, corrosion pit shape and linear traces on the inner wall. If any abnormality is found, immediately acquire images and record videos. S2.4 During the inspection, the location of each suspicious point is recorded, including the pipe row number, pipe serial number and distance from the nearest weld. All image data are sorted out and an internal wall defect image report containing location information and defect description is generated. At the same time, a detailed list of suspicious points for specific pipe sections is output.

[0036] S3. Perform detection surface preparation and acoustic coupling optimization: S3.1. Based on the specific location information provided by the detailed list of suspicious points, use a laser rangefinder to locate each point to be inspected on site, and use a white paint pen to mark the boundary line of a 20mm×30mm rectangular grinding area on the corresponding area of ​​the outer wall of the pipe. S3.2. Use an angle grinder equipped with an 80-grit abrasive wheel for grinding. When grinding, keep the grinder at a 15-degree angle to the pipe wall surface and grind in three directions until the metal substrate is exposed. The surface roughness should be controlled to be no more than 6.3μm. S3.3. Thoroughly clean the polished area with anhydrous ethanol and non-woven cloth to remove all oil and dust residue. After the ethanol has completely evaporated, use thin film test paper to test the surface cleanliness to ensure that the coupling standard required for acoustic wave testing is met. S3.4 Record the specific location and surface condition of each preparation point, and generate a set of coordinates of the prepared test points containing the tube row number, tube serial number and axial and circumferential coordinates, so as to provide a positioning basis for subsequent testing.

[0037] S4. Data acquisition is performed through precise deployment of low-velocity self-coupled phased array ultrasonic probes: S4.1. Prepare the detection equipment based on the prepared set of detection point coordinates. Select a dedicated miniaturized phased array probe with a sound velocity of 2700m / s. The probe has 32 crystals and a frequency range of 10-15MHz. The higher the frequency, the shorter the ultrasonic wavelength, and the stronger the ability to distinguish small defects (such as microcracks), which is conducive to the detection and quantification of finer cracks. S4.2. Apply polydimethylsiloxane organosilicon high-temperature coupling agent evenly to each preparation point, fix the probe to the pipe surface with a magnetic clamp, adjust the probe angle to keep the sound beam center line perpendicular to the pipe axis, and ensure that the coupling layer thickness is stable within 0.1mm. S4.3 Set the scanning parameters of the phased array detector, using a combination of linear scanning and sector scanning, with focusing depths set to 3mm and 6mm respectively, scanning angle range of 0-60 degrees, and pulse repetition frequency set to 2kHz. S4.4 Start the automatic scanning program to collect data. Save a complete data package for each detection point, including the original A-scan signal and S-scan image. The file name should be consistent with the coordinate set number. At the same time, record the ambient temperature and instrument setting parameters.

[0038] S5. Extract defect signal features and perform 3D digital modeling: S5.1. Import the collected raw data into professional analysis software. First, perform data preprocessing, including signal filtering and gain compensation. Use wavelet transform algorithm to enhance the signal-to-noise ratio of weak signals. Perform noise reduction and feature enhancement on the collected raw ultrasonic signals to eliminate the influence of material noise and structural noise. S5.2 Set the defect identification threshold to 20% of the full screen height, and use a combination of automatic identification and manual interpretation to identify abnormal signals. Mark the time flight and amplitude characteristic parameters for each signal. S5.3 Utilize 3D imaging algorithms to reconstruct the spatial morphology of defects, calculate the approximate orientation and planar projection size of defects based on signal characteristics, and generate a 3D model image containing color depth information with a model accuracy of 0.1mm. S5.4 Output a 3D model file for each defect and support formats including STL and PLY. At the same time, generate a table of key dimension parameters including the maximum depth and orientation angle of the defect as the basis for quantitative depth analysis.

[0039] S6. Accurate quantification of crack depth based on diffraction time-difference mode: S6.1. Based on the key dimension parameter table, select the defects that need to be re-inspected, select the defects with a depth display exceeding 0.5mm, start the diffraction time difference scanning mode, adopt the dual probe arrangement method, and set the probe center distance to 10mm; S6.2 Adjust the instrument parameters: set the pulse width to 100ns, the sampling frequency to 100MHz, and use time window control when acquiring diffraction signals, with the window width set to the μs level to ensure that the complete diffraction waveform is captured. When acquiring diffraction signals, the sampling parameters need to be set as follows: the sampling interval is 10 ns, the recording length is 1024 sampling points, and the Hanning window function is used to reduce spectral leakage. The time window width is calculated based on the acoustic path distance and is set to range from 0.5 μs before to 2 μs after the expected arrival time of the diffraction signal. S6.3 Measure the time difference of the diffraction wave packet and calculate the time difference to an accuracy of 0.1 ns using the cross-correlation method; The specific calculation process is as follows: First, acquire the original waveform signals from the reference probe and the receiving probe, and use digital filtering technology to eliminate noise interference; then, perform cross-correlation calculation on the two waveform signals to find the time offset Δt corresponding to the maximum correlation coefficient; according to the formula: d=(v×Δt) / 2 Calculate the defect depth, where v is the sound velocity of the material (2700 m / s) and Δt is the time difference of the precise measurement. Repeat the measurement three times at each measurement point, and take the arithmetic mean after eliminating gross errors as the final result. S6.4 Update the depth measurement results into the 3D model to generate a corrected model that includes depth dimensions. The output depth measurement report includes the depth value at the measurement location and the measurement uncertainty, providing accurate data for safety assessment.

[0040] S7. Conduct comprehensive data analysis and assess the security level: S7.1 The material parameters that need to be collected when establishing the defect assessment database include: the yield strength of 12Cr1MoVG at 540℃ ≥175MPa, tensile strength ≥420MPa, and fracture toughness. Creep strength: The parameters were obtained through high-temperature testing and entered into the database. S7.2 Safety assessment shall be conducted using failure assessment chart technology in accordance with BS7910:2019 standard; The calculation process includes: First, calculating the stress intensity factor based on the defect size parameters. ; Where Y is the geometry factor, σ is the working stress, and α is the defect depth; then the reference stress is calculated: ; Where P is the internal pressure (17.5 MPa), t is the wall thickness (6 mm), and D is the pipe diameter (51 mm); creep rupture parameters are calculated based on material creep data; finally, the calculated parameters are... Points are plotted on the failure assessment map, and the safety factor is determined based on the distance between the point's location and the failure boundary. S7.3. For defects that can be monitored, provide a suggestion for the next inspection cycle; for defects that need to be ground and repaired, provide the maximum allowable grinding depth; for defects that must be replaced, indicate the urgency and temporary handling measures. S7.4 Generate a complete assessment report containing recommendations and justifications for handling each defect at its safety level. The report format should conform to industry standards and provide comparative analysis of assessment results under different assumptions.

[0041] S8. Integrate test results and generate digital archives based on the results: S8.1 Integrate all detection data, including preliminary suspicious area list, endoscopic image report, ultrasound detection data, 3D model and safety assessment report, and standardize the data according to a unified format to establish data correlation; S8.2 Generate a digital inspection report. The main content includes an overview of the inspection, a detailed description of the defects in the inspection method, a safety assessment, and handling recommendations. The appendix contains all the raw data and detailed analysis process. The report is in a searchable PDF format. S8.3 Update boiler life prediction model parameters: The calculation process includes: First, establish a creep damage model: ; Where A and n are material constants, Q is activation energy, R is gas constant, and T is absolute temperature (540+273) K; then the cumulative damage degree is calculated based on historical operating data; ; Where t i For runtime, t r The fracture time is given at this stress temperature; finally, the Monte Carlo method is used to simulate the remaining lifetime distribution, considering parameter dispersion, and output a lifetime prediction curve with confidence intervals. S8.4 All data is uploaded to the boiler's lifelong digital twin archive system to establish the correlation between this inspection and historical data, update the equipment status rating, and provide data support for full life cycle management.

[0042] Based on the above steps, this invention establishes a systematic inspection process from macroscopic to microscopic, enabling accurate identification and quantitative assessment of internal wall defects. The combination of endoscopy and phased array ultrasonic technology ensures both comprehensiveness of the inspection and improved quantitative accuracy of defects. At the same time, the introduction of digital modeling and safety assessment systems makes the inspection results more objective and reliable, achieving standardization of the inspection process and systematization of data management, greatly improving inspection efficiency and accuracy, providing a scientific basis for equipment life prediction and maintenance decisions, and effectively improving the safe operation level of boilers.

[0043] All matters not covered in this invention are common knowledge.

[0044] The embodiments described above are merely preferred embodiments of the present invention and are not intended to limit the scope of the present invention. Various modifications and improvements made by those skilled in the art to the technical solutions of the present invention without departing from the spirit of the present invention should fall within the protection scope defined by the claims of the present invention.

Claims

1. A method for detecting microcracks in the inner wall of a boiler superheater, characterized in that, The method includes the following steps: S1. Conduct macroscopic identification and preliminary screening of high-risk areas; S2. Endoscopic channel creation and initial examination of the inner wall based on the preliminary list of suspicious areas; S3. Perform detection surface preparation and acoustic coupling optimization; S4. Data acquisition is performed through the precise deployment of low-velocity self-coupled phased array ultrasonic probes. S5. Extract defect signal features and perform three-dimensional digital modeling; S6. Accurate quantification of crack depth based on diffraction time-difference mode; S7. Conduct comprehensive data analysis and assess the security level; S8. Integrate test results and generate digital archives based on the results.

2. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 1, characterized in that, Step S1 specifically includes: S1.1 First, develop a detailed visual inspection plan, determine the inspection path and key area division plan, and carry out the inspection in zones according to the boiler heating surface layout diagram; S1.2 When conducting macroscopic inspections, the overall color difference of the tube screen is recorded using a color comparison card, the surface condition of the fin weld area is inspected using a magnifying glass, and the surface morphology of the elbow is observed using oblique lighting. S1.3 When a suspicious area is discovered, it shall be marked immediately. The numbering rule shall be the area code plus the serial number. At the same time, use an explosion-proof digital camera to take overall and close-up photos of the parts. When recording, note the location information and the preliminary judgment conclusion. S1.4 After each inspection, organize all recorded data and generate a preliminary list of suspicious areas containing area number, location description, defect characteristics, and photo number. The list is arranged in order of area and marked with priority level.

3. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 2, characterized in that, Step S2 specifically includes: S2.

1. Based on the priority level marked in the preliminary suspicious area list, select the three areas with the highest degree of suspicion as the starting point for endoscopic examination, and measure to determine the location of the nearest handhole or manhole. S2.2 After installing the endoscope catheter sealing device, the flexible high-definition endoscope probe is introduced into the tube. The probe is controlled to move slowly, and the condition of the inner wall is observed in real time and the probe insertion depth is recorded. S2.3 After arriving at the predetermined inspection position, adjust the probe angle and focus, and conduct a full-coverage inspection of the suspicious area, paying special attention to the color change, corrosion pit shape and linear traces on the inner wall. If any abnormality is found, immediately acquire images and record videos. S2.4 During the inspection, the location of each suspicious point is recorded, including the pipe row number, pipe serial number and distance from the nearest weld. All image data are sorted out and an internal wall defect image report containing location information and defect description is generated. At the same time, a detailed list of suspicious points for specific pipe sections is output.

4. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 3, characterized in that, Step S3 specifically includes: S3.

1. Based on the specific location information provided by the detailed list of suspicious points, use a laser rangefinder to locate each point to be inspected on site, and mark the boundary line of the grinding area on the outer wall of the pipe accordingly. S3.

2. Use an angle grinder for grinding. Grind in three directions until the metal substrate is exposed. The surface roughness should be controlled to be no more than 6.3μm. S3.

3. Thoroughly clean the polished area with anhydrous ethanol and non-woven cloth to remove all oil and dust residue. After the ethanol has completely evaporated, use thin film test paper to test the surface cleanliness to ensure that the coupling standard required for acoustic wave testing is met. S3.4 Record the specific location and surface condition of each preparation point, and generate a set of coordinates of the prepared test points containing the tube row number, tube serial number and axial and circumferential coordinates, so as to provide a positioning basis for subsequent testing.

5. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 4, characterized in that, Step S4 specifically includes: S4.1 Prepare the detection equipment based on the prepared set of detection point coordinates. Select a miniaturized phased array probe as the detection equipment. S4.2 Apply high-temperature coupling agent evenly to each preparation point, fix the phased array probe to the pipe surface with a magnetic clamp, adjust the angle of the phased array probe to keep the sound beam center line perpendicular to the pipe axis, and ensure that the coupling layer thickness is stable within 0.1mm. S4.3 Set the scanning parameters of the phased array detector, and adopt a combination of linear scanning and sector scanning; S4.4 Start the automatic scanning program to collect data. Save a complete data package for each detection point, including the original A-scan signal and S-scan image. The file name should be consistent with the coordinate set number. At the same time, record the ambient temperature and instrument setting parameters.

6. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 5, characterized in that, Step S5 specifically includes: S5.1 Import the collected raw data into professional analysis software. First, perform data preprocessing, including signal filtering and gain compensation. Use wavelet transform algorithm to enhance the signal-to-noise ratio of weak signals and eliminate the influence of material noise and structural noise. S5.2 Set the defect identification threshold to 20% of the full screen height, and use a combination of automatic identification and manual interpretation to identify abnormal signals. Mark the time flight and amplitude characteristic parameters for each signal. S5.3 Utilize 3D imaging algorithms to reconstruct the spatial morphology of defects, calculate the approximate orientation and planar projection size of defects based on signal characteristics, and generate a 3D model image containing color depth information. S5.4 Output a 3D model file for each defect and support formats including STL and PLY. At the same time, generate a table of key dimension parameters including the defect length, maximum depth and orientation angle as the basis for quantitative depth analysis.

7. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 6, characterized in that, Step S6 specifically includes: S6.

1. Based on the key dimension parameter table, select the defects that need to be re-inspected, select the defects with a depth display exceeding 0.5mm, start the diffraction time difference scanning mode, adopt the dual probe arrangement method, and set the probe center distance to 8-12mm; S6.2 Adjust the instrument parameters and use time window control when acquiring diffraction signals. Set the window width to the μs level to ensure that the complete diffraction waveform is captured. S6.3 Measure the time difference of the diffraction wave packet, calculate the time difference to an accuracy of 0.1 ns using the cross-correlation method, calculate the crack depth of the defect based on the sound velocity value of the material, and repeat the measurement three times for each measurement point and take the average value. S6.4 Update the depth measurement results into the 3D model to generate a corrected model that includes depth dimensions. The output depth measurement report includes the depth value at the measurement location and the measurement uncertainty, providing accurate data for safety assessment.

8. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 7, characterized in that, Step S7 specifically includes: S7.1 Establish a defect assessment database; S7.

2. Safety assessment is carried out using failure assessment diagram technology. The equivalent size and driving force parameters of each defect are calculated. The safety factor is determined according to the standard specifications, and the safety level is divided into three levels: supervised operation, grinding and repair required, and replacement required. S7.

3. For defects that can be monitored, provide a suggestion for the next inspection cycle; for defects that need to be ground and repaired, provide the maximum allowable grinding depth; for defects that must be replaced, indicate the urgency and temporary handling measures. S7.4 Generate a complete assessment report containing recommendations and justifications for handling each defect at its safety level. The report format should conform to industry standards and provide comparative analysis of assessment results under different assumptions.

9. The method for detecting microcracks in the inner wall of a boiler superheater according to claim 8, characterized in that: In step S7.2, the safety assessment is based on the BS7910:2019 standard.

10. A method for detecting microcracks in the inner wall of a boiler superheater according to claim 8, characterized in that, Step S8 specifically includes: S8.1 Integrate all test data, standardize the data according to a unified format, and establish data correlation relationships; S8.2 Generate a digital inspection report, the main content of which includes an overview of the inspection, a detailed description of the defects in the inspection method, a safety assessment and handling recommendations, and an appendix containing all raw data and a detailed analysis process; S8.3 Compare and analyze the results of this inspection with historical inspection data, update the parameters in the boiler life prediction model, calculate the remaining life distribution curve, and propose the key areas to focus on and the recommended inspection time for the next inspection. S8.4 All data is uploaded to the boiler's lifelong digital twin archive system to establish the correlation between this inspection and historical data, update the equipment status rating, and provide data support for full life cycle management.

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