Nondestructive testing method and system for welding quality of special steel for electric power engineering construction

Abstract

The application relates to the technical field of nondestructive testing, in particular to a special steel welding quality nondestructive testing method and system for power engineering construction, which comprises the following steps: collecting an ultrasonic signal after special steel welding in real time, which is recorded as an ultrasonic signal of current detection; acquiring a historical signal set of each defect type; calculating a signal noise resistance coefficient of each defect type; determining signal non-stationarity and signal disturbance; determining a signal adjustment coefficient and a corrected wavelet threshold value, acquiring a denoised ultrasonic signal, and combining the denoised ultrasonic signal with the correlation of the ultrasonic signal in the historical signal set of the corresponding defect type to evaluate the welding defects of the special steel. The application can avoid the situation that the ultrasonic signal is excessively smoothed or the denoising degree is low by dynamically adjusting the wavelet threshold value, so that the feature information of the welding defects can be more accurately retained, the denoising effect of the ultrasonic signal is improved, and the recognition accuracy of the welding defects of the special steel is improved.

Description

TECHNICAL FIELD

[0001] The application relates to the technical field of nondestructive testing, in particular to a special steel welding quality nondestructive testing method and system for power engineering construction. BACKGROUND

[0002] Special steel is a key material in power engineering and is widely used in the construction of power generation equipment, power transmission lines and substations. The welding process of special steel is complex and easily affected by various factors, which may result in welding defects. Steel with welding defects may cause serious accidents if used.

[0003] During ultrasonic testing, the collection of ultrasonic signals is easily affected by noise interference, and the real-time collected ultrasonic signals need to be denoised. In the prior art, a fixed wavelet threshold is usually used for denoising when a wavelet denoising algorithm is used. However, it is difficult to adapt to the different performances of ultrasonic signals under noise interference under different defect types, resulting in poor denoising effect of ultrasonic signals, and the ultrasonic signals may be excessively smoothed or the denoising degree may be too low, which makes the identification of welding defects of special steel inaccurate and reduces the detection accuracy of the welding quality of special steel. SUMMARY

[0004] In order to solve the above technical problems, the special steel welding quality nondestructive testing method and system for power engineering construction are provided to solve the existing problems.

[0005] The technical problem of the application is solved by providing a special steel welding quality nondestructive testing method and system for power engineering construction, which comprises the following steps:

[0006] In a first aspect, the application provides a special steel welding quality nondestructive testing method for power engineering construction, which comprises the following steps:

[0007] Real-time collection of ultrasonic signals after welding of special steel, denoted as current detection ultrasonic signals; obtaining a plurality of ultrasonic signals of each defect type in the historical detection process to form a historical signal set;

[0008] Analyzing the correlation of different ultrasonic signals in the historical signal set and the difference change of the correlation of different ultrasonic signals before and after denoising, and calculating the signal noise coefficient of each defect type;

[0009] Dividing the current detection ultrasonic signals into a plurality of signal segments; determining the non-stationarity of the current detection signals by the difference of the change rates of different signal amplitudes in different signal segments of the current detection ultrasonic signals and the difference change of the change rates of different signal amplitudes before and after smoothing in the signal segment;

[0010] According to the discrete condition of the signal amplitude of the current detected ultrasonic signal in different signal segments, and in combination with the non-stationarity of the signal, the disturbance degree of the current detected signal is obtained;

[0011] Based on the signal anti-noise coefficient and the signal disturbance degree, the signal adjustment coefficient corresponding to the current detection under each defect type is determined; the wavelet threshold is corrected based on the signal adjustment coefficient, to obtain the corrected wavelet threshold corresponding to the current detection under each defect type, and the current detected ultrasonic signal is denoised respectively to obtain the denoised ultrasonic signal corresponding to the current detection under each defect type, and the special steel welding defects are evaluated in combination with the correlation between the ultrasonic signals in the historical signal set of the corresponding defect type.

[0012] Preferably, the signal anti-noise coefficient of each defect type is calculated, including:

[0013] The correlation degree of any two ultrasonic signals in the historical signal set of each defect type is calculated, denoted as a first correlation degree;

[0014] The two ultrasonic signals are denoised respectively, and the correlation degree of the two denoised ultrasonic signals is calculated, denoted as a second correlation degree; the difference between the first correlation degree and the second correlation degree is calculated, denoted as a first difference;

[0015] The ratio of the first correlation degree to the first difference is calculated, denoted as a relative ratio;

[0016] The signal anti-noise coefficient is the normalized result of the sum of the relative ratios of all the arbitrary two ultrasonic signals in the historical signal set.

[0017] Preferably, the signal non-stationarity of the current detection is determined, including:

[0018] All signal amplitudes in each signal segment are smoothed, and all signal amplitudes before smoothing and all signal amplitudes after smoothing in each signal segment are respectively curve-fitted, and the corresponding tangent slopes of each signal amplitude on the fitted curve are calculated, respectively denoted as a slope before smoothing and a slope after smoothing;

[0019] The average difference of the slopes before smoothing and the average difference of the slopes after smoothing between any signal amplitude and all adjacent signal amplitudes in each signal segment are respectively denoted as the relative change rate before smoothing and the relative change rate after smoothing of the any signal amplitude;

[0020] The difference between the relative change rate before smoothing and the relative change rate after smoothing is calculated, denoted as a second difference;

[0021] The result of fusing the relative change rate of the current detected ultrasonic signal before smoothing in all signal segments with the second difference is taken as the non-stationarity of the current detected signal.

[0022] Preferably, the specific process of fusing is calculating the cumulative sum of the product of the relative change rate of all signal amplitudes in each signal segment before smoothing with the second difference; and taking the sum of the cumulative sums of all signal segments as the non-stationarity of the current detected signal.

[0023] Preferably, the obtaining of the signal disturbance degree comprises:

[0024] calculating the dispersion degree of all signal amplitudes in each signal segment; and taking the mean value of the dispersion degrees of all signal segments as the signal fluctuation degree.

[0025] The product of the signal disturbance degree, the signal fluctuation degree and the non-stationarity of the signal.

[0026] Preferably, the signal adjustment coefficient is the normalized result of the ratio of the signal disturbance degree to the signal noise resistance coefficient corresponding to each defect type.

[0027] Preferably, the calculation formula of the corrected wavelet threshold corresponding to the current detection under the i-th defect type is wherein, a is a preset initial threshold value, and b is the signal adjustment coefficient corresponding to the current detection under the i-th defect type.

[0028] Preferably, the obtaining of the denoised ultrasonic signal corresponding to the current detection under each defect type comprises: performing denoising processing on the current detected ultrasonic signal by a wavelet denoising algorithm based on the corrected wavelet threshold corresponding to the current detection under each defect type, to obtain the denoised ultrasonic signal corresponding to the current detection under each defect type.

[0029] Preferably, the evaluation of the welding defects of the special steel material comprises:

[0030] calculating the mean value of the correlation degree between the denoised ultrasonic signal corresponding to the current detection under each defect type and all ultrasonic signals in the historical signal set of the i-th defect type, and taking the mean value as the similarity;

[0031] If the similarity is greater than a preset threshold value, the special steel material has the welding defect of the i-th defect type, otherwise, the special steel material does not have the welding defect of the i-th defect type.

[0032] ​​​​In a second aspect, the embodiments of the present application further provide a non-destructive testing system for welding quality of special steel used in power engineering construction, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of the non-destructive testing method for welding quality of special steel used in power engineering construction when executing the computer program.

[0033] The present application has at least the following beneficial effects:

[0034] The present application calculates the signal noise immunity coefficient of each defect type by the change of the correlation of different ultrasonic signals in the historical signal set of the same defect type before and after denoising, which has the beneficial effect of considering the difference of the correlation between the ultrasonic signals before and after denoising to reflect the interference of noise on the signal of each defect type and evaluate the noise immunity of the ultrasonic signal of each defect type. Secondly, the non-stationarity of the current detected signal is determined, which has the beneficial effect of considering the stationary condition of the ultrasonic signal in the local range to reflect the interference degree of the ultrasonic signal by noise. The interference degree of the current detected signal is obtained, which has the beneficial effect of comprehensively evaluating the interference of the ultrasonic signal by noise through the fluctuation of the ultrasonic signal. The signal adjustment coefficient corresponding to the current detection under each defect type is determined, which has the beneficial effect of comprehensively evaluating the interference of the current detected ultrasonic signal under each defect type through the interference condition of the current detected ultrasonic signal and the noise immunity condition of each defect type. The wavelet threshold after correction corresponding to the current detection under each defect type is obtained, and the current detected ultrasonic signal is denoised to obtain the denoised ultrasonic signal corresponding to the current detection under each defect type, which has the beneficial effect of improving the denoising effect of the ultrasonic signal by dynamically adjusting the wavelet threshold, and judging whether the welding of the special steel has the welding defect of the corresponding defect type through the similarity of the denoised ultrasonic signal corresponding to the current detection under each defect type and the ultrasonic signal in the historical signal set of the corresponding defect type, which can avoid the situation of excessive smoothing or low denoising degree of the ultrasonic signal, so as to more accurately retain the characteristic information of the welding defect and improve the recognition accuracy of the welding defect of the special steel. BRIEF DESCRIPTION OF DRAWINGS

[0035] The non-destructive testing method for welding quality of special steel used in power engineering construction of the present application will be further described in detail below with reference to the accompanying drawings.

[0036] Figure 1 The step flow chart of the non-destructive testing method for welding quality of special steel used in power engineering construction provided by the embodiments of the present application is shown in the figure.

[0037] Figure 2 A step flow chart of the method for acquiring signal interference degree provided by the embodiment of the present application is provided. DETAILED DESCRIPTION

[0038] In order to make the purpose, technical scheme and advantages of the present application more clear, the method and system for nondestructive testing of welding quality of special steel for power engineering construction provided by the present application are further described in detail below in combination with the drawings and examples. It should be understood that the specific examples described herein are only used to explain the present application and do not limit the present application.

[0039] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which the present application belongs.

[0040] Please refer to Figure 1 which shows a step flow chart of the method for nondestructive testing of welding quality of special steel for power engineering construction provided by an embodiment of the present application. The method comprises the following steps:

[0041] Step 1, real-time acquisition of ultrasonic signals after welding of special steel, denoted as current detection ultrasonic signals; acquisition of multiple ultrasonic signals of each defect type in the historical detection process to form a historical signal set.

[0042] In the process of power engineering construction, the welding quality of special steel directly affects the service life of the welded structure, therefore, the ultrasonic detection device is moved along the weld of the special steel and ultrasonic detection is performed to acquire the reflected ultrasonic signals in real time, denoted as current detection ultrasonic signals;

[0043] Secondly, in the welding process of special steel, the common dangerous defect types are crack defects, incomplete penetration defects and incomplete fusion defects. When ultrasonic detection is performed on each defect type, the ultrasonic signals have different characteristics, which are as follows:

[0044] The ultrasonic signals corresponding to the crack defects have single wave shape, wide amplitude and high reflection wave height, and the reflection wave has multiple peaks in a zigzag shape. When the probe is moved, the wave peaks will fluctuate, and the wave shape is unstable. The ultrasonic signals corresponding to the incomplete penetration defects have double-peak wave shape, single wave shape, high reflection wave amplitude and high reflection wave height. When the probe is translated, the wave shape is stable, and when the probe is rotated or oscillated, the wave shape disappears quickly. When the probe is detected from both sides of the weld, the wave shapes are similar, the reflection wave heights are basically the same, and the wave shape differences are small. The ultrasonic signals corresponding to the incomplete fusion defects have single wave shape, small reflection wave amplitude and obvious wave height difference on both sides of the fusion line.

[0045] By collecting multiple ultrasonic signals of each defect type in the historical detection process, a historical signal set is formed.

[0046] In the embodiment, the number of ultrasonic signals in the historical signal set is 100. As another embodiment, the implementer can set it according to the actual situation.

[0047] So far, the ultrasonic signals after the special steel welding are collected in real time, and the historical signal set of each defect type is obtained.

[0048] In step 2, the correlation of different ultrasonic signals in the historical signal set and the difference between the correlation of different ultrasonic signals before and after denoising are analyzed, and the signal noise immunity coefficient of each defect type is calculated.

[0049] Further, if the ultrasonic detection process for each defect type is not sensitive to noise interference, that is, the degree of noise interference is small, the denoising effect of the ultrasonic signal is relatively low, therefore, when the ultrasonic signal of the defect type is denoised by wavelet, the wavelet threshold can be reduced to retain more original information; on the contrary, the noise interference is more sensitive, the denoising effect of the ultrasonic signal is relatively high, the wavelet threshold needs to be increased to reduce the interference of noise as much as possible and improve the accuracy of the detection of welding defects.

[0050] Because the ultrasonic signals of different defect types behave differently when facing noise interference, the characteristics of some defects are obvious, and the existence of defects can be judged according to these characteristics even if there is a lot of noise interference around. The characteristics of some defects are not obvious enough and can only be identified in the case of little noise. Therefore, by analyzing the change characteristics of the correlation of different ultrasonic signals of the same defect type before and after denoising, the signal noise immunity coefficient is calculated to evaluate the anti-interference situation of the ultrasonic signal of each defect type, which is specifically:

[0051] The correlation degree of any two ultrasonic signals in the historical signal set of each defect type is calculated, which is denoted as the first correlation degree;

[0052] The two ultrasonic signals are denoised respectively, and the correlation degree of the two ultrasonic signals after denoising is calculated, which is denoted as the second correlation degree;

[0053] In the embodiment, the wavelet denoising algorithm is used for denoising processing, wherein the wavelet threshold in the wavelet denoising algorithm is set as wherein, is the variance of the ultrasonic signal, and N is the length of the ultrasonic signal, For the natural constant as the base of the logarithmic function, the wavelet denoising algorithm is a known technology, which will not be described here; secondly, the correlation degree is measured by calculating the Pearson correlation coefficient of the two ultrasonic signals, wherein the calculation of the Pearson correlation coefficient is a known technology, which will not be described here, as other embodiments, the implementer can use other methods of prior art, for example, cosine similarity, Spearman correlation coefficient, etc., which are not specially limited in the present embodiment.

[0054] Calculate the difference between the first correlation degree and the second correlation degree, denoted as the first difference;

[0055] In the present embodiment, the absolute value of the difference between the first correlation degree and the second correlation degree is calculated, denoted as the relative difference.

[0056] Calculate the ratio of the first correlation degree and the first difference, denoted as the relative ratio.

[0057] The normalized result of the sum value of the relative ratio of all the arbitrary two ultrasonic signals in the historical signal set of each defect type is taken as the signal noise immunity coefficient corresponding to each defect type.

[0058] In the present embodiment, sigmoid function is used for normalization processing, wherein the sigmoid function is a known technology, which will not be described here, as other embodiments, the implementer can use other methods of prior art, for example, softmax function, tanh function, etc., which are not specially limited in the present embodiment.

[0059] It should be noted that the greater the first difference, the greater the correlation change of the two ultrasonic signals before and after denoising, if the second correlation degree of the two ultrasonic signals after denoising increases, the greater the interference degree of the ultrasonic signal of this defect type by noise, and the lower the noise immunity; the greater the first correlation degree, the higher the similarity of the ultrasonic signal of the same defect type, which indicates that the signal of this defect type is less disturbed by noise, and the signal noise immunity coefficient is greater, which indicates that the noise immunity of the ultrasonic signal corresponding to this defect type is higher.

[0060] Thus, the signal noise immunity coefficient corresponding to each defect type is obtained.

[0061] Step 3, divide the currently detected ultrasonic signal into multiple signal segments; determine the non-stationarity of the currently detected signal by the difference of the change rate of different signal amplitudes of the currently detected ultrasonic signal in different signal segments, and the difference change of the change rate of different signal amplitudes before and after smoothing in the signal segment; according to the dispersion of the signal amplitude of the currently detected ultrasonic signal in different signal segments, and combining the non-stationarity of the signal, the disturbance degree of the currently detected signal is obtained.

[0062] Further, if the current detected ultrasonic signal is greatly disturbed by noise, the stationarity of the ultrasonic signal changes greatly, therefore, the signal disturbance degree is calculated according to the fluctuation of the current detected ultrasonic signal in a local range, so as to evaluate the situation of the current detected ultrasonic signal being disturbed by noise, wherein, the step flow chart of the method for obtaining the signal disturbance degree provided by the embodiment of the application is shown in Figure 2 , and specifically comprises:

[0063] The current detected ultrasonic signal is divided into multiple signal segments;

[0064] In the embodiment, the number of signal amplitudes in each signal segment is 31, and as other implementation manners, the implementer can set it according to actual conditions.

[0065] The curve fitting is performed on all signal amplitudes in each signal segment, the tangent slope corresponding to each signal amplitude on the fitting curve is calculated, and is recorded as the slope before smoothing;

[0066] In the embodiment, the least square method is used for curve fitting, and the calculation of the least square method and the tangent slope is a known technology, which will not be described here.

[0067] The average of the difference between any signal amplitude and the slope before smoothing of all adjacent signal amplitudes in each signal segment is recorded as the relative change rate before smoothing of the any signal amplitude;

[0068] In the embodiment, the average of the absolute value of the difference between the any signal amplitude and the slope before smoothing of all adjacent signal amplitudes is recorded as the relative change rate before smoothing of the any signal amplitude.

[0069] The smoothing processing is performed on all signal amplitudes in each signal segment, the curve fitting is performed on the signal amplitudes after smoothing in each signal segment, the tangent slope corresponding to each signal amplitude on the fitting curve is calculated, and is recorded as the slope after smoothing;

[0070] In the embodiment, the mean filtering algorithm with a step of 3 is used for smoothing processing, and the mean filtering algorithm is a known technology, which will not be described here, and as other implementation manners, the implementer can use other methods in the prior art, for example, the median filtering algorithm; secondly, the least square method is used for curve fitting, and the calculation of the least square method and the tangent slope is a known technology, which will not be described here.

[0071] The average of the difference between the any signal amplitude and the slope after smoothing of all adjacent signal amplitudes in each signal segment is recorded as the relative change rate after smoothing of the any signal amplitude;

[0072] In the present embodiment, the average of the absolute values of the differences between the smoothed slope of any signal amplitude and all of its adjacent signal amplitudes is denoted as the smoothed relative change rate of the any signal amplitude.

[0073] The difference between the unsmoothed relative change rate and the smoothed relative change rate is calculated and denoted as a second difference.

[0074] In the present embodiment, the absolute value of the difference between the unsmoothed relative change rate and the smoothed relative change rate is calculated and denoted as a second difference.

[0075] The accumulated sum of the products of the unsmoothed relative change rate of all signal amplitudes in each signal segment and the second difference is calculated; and the sum of the accumulated sums of the current detected ultrasonic signal in all signal segments is taken as the signal non-stationarity of the current detected signal.

[0076] In the present embodiment, the formula for calculating the signal non-stationarity of the current detected signal is:

[0077] wherein, is the signal non-stationarity of the current detected signal, is the unsmoothed relative change rate of the jth signal amplitude in the ith signal segment of the current detected ultrasonic signal, is the second difference of the jth signal amplitude in the ith signal segment of the current detected ultrasonic signal, is the number of all signal amplitudes in the ith signal segment of the current detected ultrasonic signal, is the number of all signal segments into which the current detected ultrasonic signal is divided.

[0078] The dispersion of all signal amplitudes in each signal segment is calculated; and the average of the dispersion of the current detected ultrasonic signal in all signal segments is denoted as the signal fluctuation.

[0079] In the present embodiment, the dispersion is calculated by calculating the variance of all signal amplitudes in the signal segment of the current detected ultrasonic signal. Alternatively, other methods known in the art, such as standard deviation, etc., can be used, and the present embodiment does not make special limitations thereon.

[0080] The product of the signal fluctuation and the signal non-stationarity is taken as the signal disturbance of the current detected signal.

[0081] It should be noted that the smaller the second difference is, the smaller the difference between the slopes of the ultrasonic signals before and after the smoothing is, reflecting that the change of the ultrasonic signal has strong stationarity; the smaller the relative change rate before the smoothing is, the higher the stationarity of the change of the ultrasonic signal is, and the smaller the non-stationarity of the obtained signal is; the greater the signal fluctuation degree is, the greater the degree of interference of the ultrasonic signal by the noise is, the greater the degree of disturbance of the obtained signal is, the lower the change stationarity of the ultrasonic signal is, and the greater the fluctuation is, and the greater the degree of interference of the ultrasonic signal by the noise is.

[0082] At this point, the degree of disturbance of the current detected signal is obtained;

[0083] Step 4, based on the signal anti-noise coefficient and the signal disturbance degree, determining the signal adjustment coefficient corresponding to the current detection under each defect type; based on the signal adjustment coefficient, correcting the wavelet threshold value to obtain the corrected wavelet threshold value corresponding to the current detection under each defect type, and performing denoising on the current detected ultrasonic signal respectively to obtain the denoised ultrasonic signal corresponding to the current detection under each defect type, and evaluating the welding defects of the special steel in combination with the relevant situation of the ultrasonic signal in the historical signal set corresponding to the defect type.

[0084] Further, based on the signal disturbance degree and the signal anti-noise coefficient, a signal adjustment coefficient is determined, specifically:

[0085] The normalized result of the ratio of the signal disturbance degree of the current detection to the signal anti-noise coefficient corresponding to each defect type is taken as the signal adjustment coefficient corresponding to the current detection under each defect type;

[0086] In the embodiment, the sigmoid function is used for normalization processing, wherein the sigmoid function is a known technology and will not be described here. As other implementation manners, the implementer can use other methods of the prior art, for example, the softmax function, the tanh function, etc., and the embodiment does not specially limit this.

[0087] It should be noted that the greater the degree of interference of the current detected ultrasonic signal is, the lower the anti-noise ability of the ultrasonic signal under each defect type is, and the greater the obtained signal adjustment coefficient is, so that the wavelet threshold value needs to be increased to reduce the interference of the noise as much as possible. Conversely, the smaller the degree of interference of the current detected ultrasonic signal is, and the higher the anti-noise ability of the ultrasonic signal under each defect type is, and the smaller the obtained signal adjustment coefficient is, so that the wavelet threshold value can be reduced to retain more original information and thus increase the accuracy of defect recognition.

[0088] Therefore, the wavelet threshold value in the wavelet denoising algorithm is corrected through the signal adjustment coefficient, specifically: wherein, the current detection is in the first a wavelet threshold corresponding to the current detection in the first a preset initial threshold, a signal adjustment coefficient corresponding to the current detection in the first a signal adjustment coefficient corresponding to the current detection in the first

[0089] In the embodiment, the preset initial threshold is set as wherein, is the variance of the ultrasonic signal, and N is the length of the ultrasonic signal, is a natural constant as the base of the logarithmic function.

[0090] Based on the wavelet threshold corresponding to the current detection in each defect type, the current detection ultrasonic signal is denoised by a wavelet denoising algorithm to obtain a denoised ultrasonic signal corresponding to the current detection in each defect type;

[0091] In the embodiment, the wavelet denoising algorithm adopts Daubechies Wavelet algorithm for denoising, wherein the wavelet decomposition layer is set to 5, and as other implementation manners, the implementer can set it according to the actual situation; secondly, the Daubechies Wavelet algorithm is a known technology, which will not be described here.

[0092] It should be noted that, by using the three defect type corresponding wavelet threshold to denoise the current detection ultrasonic signal, three denoised ultrasonic signals can be obtained, and by comparing each denoised ultrasonic signal with the ultrasonic signals in the historical signal set of the corresponding defect type, it can be determined whether the special steel material has the welding defect of the defect type.

[0093] The average of the correlation degree between the denoised ultrasonic signal corresponding to the current detection in each defect type and all ultrasonic signals in the historical signal set of the corresponding defect type is calculated, and is recorded as similarity;

[0094] In the embodiment, the correlation degree is measured by calculating the inverse of the DTW distance between the denoised ultrasonic signal corresponding to the current detection in each defect type and all ultrasonic signals in the historical signal set of the corresponding defect type, wherein the calculation of the DTW distance is a known technology, which will not be described here, and as other implementation manners, the implementer can use other methods of prior art, for example, cosine similarity, etc., and the embodiment does not specially limit this.

[0095] If the similarity is greater than a preset threshold, the special steel material has the welding defect of the corresponding defect type, otherwise, the special steel material does not have the welding defect of the corresponding defect type;

[0096] In the embodiment, the preset threshold value is 0.8. As another implementation, the implementer can set the threshold value according to actual conditions.

[0097] It should be noted that the greater the similarity, the higher the similarity between the de-noised ultrasonic signal and the ultrasonic signal of the defect type, and the greater the possibility of the corresponding defect type of the welding quality of the special steel material.

[0098] Based on the same inventive concept as the above method, the embodiments of the present application also provide a non-destructive testing system for welding quality of special steel material for power engineering construction, which comprises a memory, a processor, and a computer program stored in the memory and running on the processor, and the processor implements the steps of any one of the above non-destructive testing methods for welding quality of special steel material for power engineering construction when executing the computer program.

[0099] It should be understood that, although Figure 1 The steps in the flowchart of the method are displayed in sequence according to the arrows, but these steps are not necessarily executed in the order indicated by the arrows. Unless otherwise specified herein, the execution of these steps is not strictly limited in sequence, and these steps can be executed in other orders. Moreover, Figure 1 At least part of the steps in the method can include multiple sub-steps or multiple stages, which are not necessarily executed at the same time, but can be executed at different times, and the execution order of these sub-steps or stages is not necessarily sequential, but can be executed in rotation or alternation with other steps or sub-steps or stages of other steps.

[0100] The technical features of the above embodiments can be combined in any way. To make the description concise, all possible combinations of the technical features in the above embodiments are not described, but as long as the combination of the technical features does not exist contradictory, it should be considered as the scope of the present application.

[0101] The above embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation of the present application. It should be noted that for ordinary skilled in the art, without departing from the concept of the present application, several modifications and improvements can be made, therefore, any simple modification, equivalent change and modification of the above embodiments according to the technical essence of the present application, which does not deviate from the technical solution of the present application, all belong to the protection scope of the technical solution of the present application.

Claims

1. A non-destructive testing method for welding quality of special steel used in power engineering construction, characterized in that, The method includes the following steps: The ultrasonic signals of special steel after welding are acquired in real time and recorded as the ultrasonic signals of the current detection; multiple ultrasonic signals of each defect type are acquired in the historical detection process and formed into a historical signal set. The correlation of different ultrasonic signals within the historical signal set is analyzed, as well as the differences in the correlation between different ultrasonic signals before and after denoising, and the signal noise immunity coefficient for each defect type is calculated. The currently detected ultrasonic signal is divided into multiple signal segments; the non-stationarity of the currently detected signal is determined by the difference in the rate of change of different signal amplitudes in different signal segments, and the difference in the rate of change of different signal amplitudes before and after smoothing. Based on the dispersion of the amplitude of the currently detected ultrasonic signal in different signal segments, and combined with the signal non-stationarity, the disturbance degree of the currently detected signal is obtained; Based on the signal noise immunity coefficient and signal perturbation degree, the signal adjustment coefficient corresponding to each defect type of the current detection is determined; the wavelet threshold is corrected based on the signal adjustment coefficient to obtain the corrected wavelet threshold corresponding to each defect type of the current detection; the ultrasonic signal of the current detection is denoised to obtain the denoised ultrasonic signal corresponding to each defect type of the current detection; and the welding defects of special steel are evaluated by combining the correlation between the ultrasonic signal and the historical signal set of the corresponding defect type. The calculation of the signal noise immunity coefficient for each defect type includes: Calculate the correlation between any two ultrasonic signals within the historical signal set for each defect type, and denote it as the first correlation degree; Denoising is performed on any two ultrasonic signals respectively, and the correlation between the two denoised ultrasonic signals is calculated and denoted as the second correlation; the difference between the first correlation and the second correlation is calculated and denoted as the first difference. Calculate the ratio of the first correlation to the first difference, and denot it as the relative ratio; The signal noise immunity coefficient is the normalized result of the sum of the relative ratios of all two ultrasonic signals in the historical signal set. The signal adjustment coefficient is the normalized result of the ratio of the signal disturbance degree to the signal noise immunity coefficient corresponding to each defect type.

2. The non-destructive testing method for welding quality of special steel used in power engineering construction as described in claim 1, characterized in that, Determining the non-stationarity of the currently detected signal includes: Smoothing is performed on all signal amplitudes within each signal segment. Curve fitting is performed on all signal amplitudes before and after smoothing within each signal segment. The slope of the tangent at each signal amplitude on the fitted curve is calculated and recorded as the slope before smoothing and the slope after smoothing, respectively. The mean difference between the slopes of any signal amplitude and all its adjacent signal amplitudes before smoothing and the mean difference between the slopes after smoothing are respectively denoted as the relative rate of change of the signal amplitude before smoothing and the relative rate of change after smoothing. The difference between the relative rate of change before smoothing and the relative rate of change after smoothing is calculated and denoted as the second difference. The non-stationarity of the currently detected ultrasonic signal is obtained by fusing the relative rates of change of the current ultrasonic signal before smoothing across all signal segments with the second difference.

3. The non-destructive testing method for welding quality of special steel used in power engineering construction as described in claim 2, characterized in that, The specific process of the fusion is as follows: calculate the sum of the products of the relative change rate of all signal amplitudes before smoothing and the second difference in each signal segment; and take the sum of the sums of the current detected ultrasonic signal in all signal segments as the non-stationarity of the current detected signal.

4. The non-destructive testing method for welding quality of special steel used in power engineering construction as described in claim 1, characterized in that, The determination of the current detected signal disturbance level includes: Calculate the dispersion of all signal amplitudes within each signal segment; the mean of the dispersion of the currently detected ultrasonic signal across all signal segments is denoted as the signal fluctuation. The signal perturbation degree is the product of the signal volatility and the signal non-stationarity.

5. The non-destructive testing method for welding quality of special steel used in power engineering construction as described in claim 1, characterized in that, Current detection at the Corrected wavelet thresholds for each defect type The calculation formula is as follows: ,in, To preset the initial threshold, For the current detection in the first The signal adjustment coefficient corresponding to each defect type.

6. The non-destructive testing method for welding quality of special steel used in power engineering construction as described in claim 1, characterized in that, The step of obtaining the denoised ultrasonic signal corresponding to each defect type of the current detection includes: performing denoising processing on the ultrasonic signal of the current detection using a wavelet denoising algorithm based on the corrected wavelet threshold corresponding to each defect type of the current detection, to obtain the denoised ultrasonic signal corresponding to each defect type of the current detection.

7. The non-destructive testing method for welding quality of special steel used in power engineering construction as described in claim 1, characterized in that, The assessment of welding defects in special steel includes: The mean value of the correlation between the denoised ultrasonic signal corresponding to each defect type and all ultrasonic signals in the historical signal set of the corresponding defect type is calculated and denoted as the similarity. If the similarity is greater than a preset threshold, the special steel has a welding defect of the corresponding defect type; otherwise, the special steel does not have a welding defect of the corresponding defect type.

8. A non-destructive testing system for welding quality of special steel used in power engineering construction, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the non-destructive testing method for welding quality of special steel used in power engineering construction as described in any one of claims 1-7.

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