CN111795931A - Reconstruction extraction method for laser ultrasonic defect detection diffraction echo signal - Google Patents
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Abstract
The invention discloses a reconstruction extraction method aiming at a laser ultrasonic defect detection diffraction echo signal, which comprises the steps of firstly inputting a laser ultrasonic defect detection diffraction echo signal to be processed, and sequentially carrying out subtraction and white noise addition processing to generate a noise addition signal; decomposing the noise-added signal by adopting an EMD method to generate a plurality of IMF components, sequencing the IMF components according to the frequency, calculating the discrete degree of each IMF component, and selecting the corresponding IMF component according to the discrete degree distribution and the IMF component relation to reconstruct to obtain a reconstructed signal; and denoising the reconstructed signal by using an exponential weighted average mode, extracting extreme points of the denoised reconstructed signal, selecting a certain number of maximum extreme points with a certain length and outputting the maximum extreme points. The useful signals are not lost; improving the decomposition effect of EMD; avoiding the useful signal being submerged in the incident signal; the method effectively retains useful signals containing defect information, abandons high-frequency noise signals from the environment, and achieves the purposes of accurately outputting the useful signals and visually judging defects.
Description
Technical Field
The invention belongs to the technical field of laser ultrasonic detection, and particularly relates to a reconstruction extraction method for a laser ultrasonic defect detection diffraction echo signal.
Background
With the rapid development of the manufacturing industry, the quality requirements of people for products are higher and higher, particularly in strategic fields of aviation, nuclear energy, petrochemical industry and the like, the service environment of the products is relatively worse, and the reliability requirements are higher.
The principle of the laser ultrasonic detection technology is that ultrasonic waves are excited by the surface of a laser radiation material, if the material has defects, when the ultrasonic waves propagate to the defects, the ultrasonic waves can act on the defects and diffract, so that defect diffraction echoes are generated, when the defect diffraction echoes propagate to the positions for receiving laser radiation, the defect diffraction echoes can be received by receiving laser, and finally, the reception of laser ultrasonic defect detection diffraction echo signals is completed. The laser ultrasonic defect detection diffraction echo signal contains a useful signal capable of reflecting defect information, but the useful signal is often covered by other noises, so that reconstruction and extraction of the laser ultrasonic defect detection diffraction echo signal are performed to obtain the useful signal containing the defect information, and the premise of accurately judging the defect is achieved.
The current method for processing ultrasonic signals mainly includes wavelet transformation, wavelet packet transformation, and Empirical Mode Decomposition (EMD). The wavelet transform overcomes the disadvantage that the traditional Fourier transform is not ideal for processing ultrasonic signals, namely non-stationary signals, and can well represent ultrasonic signals, namely abrupt signals, from a time domain and a frequency domain, but the wavelet transform needs to select proper wavelet functions, decomposition layer numbers, threshold functions and threshold rules, so that the stability of processing results is difficult to ensure. Compared with wavelet transformation, the wavelet packet transformation not only processes the low-frequency part of the ultrasonic signals, but also correspondingly processes the high-frequency part, so that the useful signals in the ultrasonic signals can be well reserved by the wavelet packet transformation, but most of the noises are high-frequency noises for the ultrasonic signals, and therefore the high-frequency signals do not need to be analyzed and processed independently. The EMD method can decompose a non-stationary signal into a series of Intrinsic Mode Functions (IMFs) according to the characteristics of the non-stationary signal, but in the decomposition process, there are problems of mode aliasing and end-point effects, and when the signal is reconstructed, a useful signal is easily lost. In addition to the above method, wavelet transform or wavelet packet transform may be combined with the EMD method to process the ultrasonic signal, but this is only for the analysis processing of the conventional ultrasonic signal, and for the laser ultrasonic defect detection diffraction echo signal, its noise includes the low-frequency incident signal of the laser ultrasonic itself in addition to the high-frequency noise from the environment, which would also drown out the required useful signal, so when performing the signal processing, it is necessary to consider both the high-frequency noise from the environment and the low-frequency incident signal itself.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a reconstruction extraction method for detecting diffraction echo signals aiming at laser ultrasonic defects aiming at the defects of the laser ultrasonic detection technology, so that the influence of high-amplitude incident signals on EMD decomposition is effectively avoided, and useful signals are ensured not to be lost; the end effect existing when EMD decomposes the noise-added signal is effectively overcome, and the decomposition effect of EMD is improved; the incident signals with low amplitude and low frequency are removed to the maximum extent, and the useful signals are prevented from being submerged in the incident signals; the method effectively retains useful signals containing defect information, abandons high-frequency noise signals from the environment, and achieves the purposes of accurately outputting the useful signals and visually judging defects.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a reconstruction extraction method for a laser ultrasonic defect detection diffraction echo signal is characterized by comprising the following steps:
s1: inputting a laser ultrasonic defect detection diffraction echo signal to be processed, and sequentially carrying out subtraction and white noise addition processing to generate a noise addition signal;
s2: decomposing the noise-added signal of the step S1 by adopting an EMD method to generate a plurality of IMF components, sequencing the IMF components according to frequency, calculating the discrete degree of each IMF component, and selecting the corresponding IMF component according to the discrete degree distribution and the IMF component relation to reconstruct to obtain a reconstructed signal;
s3: and (3) denoising the reconstructed signal of the S2 by using an exponential weighted average mode, extracting extreme points of the denoised reconstructed signal, selecting and outputting maximum extreme points with a certain number and a certain length.
In the above technical solution, the process of generating the noise signal in step S1 specifically includes the following two steps:
s11: carrying out difference processing on a laser ultrasonic defect detection diffraction echo signal to be processed and a laser ultrasonic reference signal to obtain a difference signal of the two signals;
s12: white noise addition treatment: white noise with equal length and consistent fluctuation range is added at two ends of the difference signal to generate a noise-added signal.
In the above technical solution, the laser ultrasonic reference signal in step S11 is a signal obtained by zeroing out other signals on the basis of only retaining the high-amplitude incident signal of the laser ultrasonic receiving signal without the defect signal.
In the above technical solution, in step S12, the length of the white noise is 100.
In the above technical solution, in step S12, the fluctuation range of the white noise should be consistent with the fluctuation range of the signal with the length of 100 at both ends of the difference signal.
In the above technical solution, step S2 includes the following two steps:
s21: and decomposing the noise-added signal by an EMD method to obtain a series of IMF components with frequencies from high to low.
S22: the degree of dispersion of each IMF component is calculated according to the following formula:
wherein n is the total number of IMF points, xiFor the i-th point of the IMF,
the IMF mean value.
In the above technical solution, in step S22, S, which is the minimum discrete degree, is selectedDispersingThe minimum IMF component and the IMF components on the left and right sides thereof to obtain a reconstructed signal.
In the above technical solution, step S3 includes the following two steps:
step 31: carrying out noise reduction processing on the reconstructed signal by using an exponential weighted average mode;
step 32: extracting the extreme points of the reconstructed signal after noise reduction, and selecting a certain number of maximum extreme points with certain intervals to output, wherein the certain number refers to the number of the defect waveforms to be extracted and is consistent with the number of the selected maximum extreme points, and the certain intervals refer to the intervals of the selected maximum extreme points, and the intervals of the selected maximum extreme points are not less than the minimum intervals of the known defect waveforms.
In the above technical solution, all maximum extreme points of the noise reduction signal need to be extracted in step S32; determining the number of candidate maximum extremum points of the length interval on the points of the interval minimum length according to the number of missing waveforms to be extracted and the points of the interval minimum length between the waveforms; and reserving the candidate maximum extreme point and the point of the half of the minimum length of the interval on the left and right sides of the candidate maximum extreme point, and performing zero setting on other signals.
The invention reconstructs and extracts the laser ultrasonic defect detection diffraction echo signal to obtain a useful signal containing defect information, realizes accurate judgment of the defect, removes the low-frequency incident signal of the laser ultrasonic by preprocessing and reconstructing the signal, removes the high-frequency noise of the environment by using the exponential weighted average processing, and finally realizes the output of the useful signal by extracting and selecting a proper extreme point.
The invention has the beneficial effects that:
(1) by performing difference processing on the laser ultrasonic defect detection diffraction echo signal to be processed and the laser ultrasonic reference signal, the amplitude of the signal to be processed can be greatly reduced, the influence of a high-amplitude incident signal on EMD decomposition is effectively avoided, the problem of modal aliasing of an IMF component generated by EMD decomposition is solved to a certain extent, and a useful signal is ensured not to be lost;
(2) the method of adding white noise with equal length and consistent fluctuation range at two ends of the difference signal is adopted to generate the noise adding signal, so that the end effect existing when the EMD decomposes the noise adding signal is effectively overcome, and the decomposition effect of the EMD is improved;
(3) by calculating the discrete degree of the IMF components and selecting proper IMF components to reconstruct the signal, the incident signal with low amplitude and low frequency can be removed to the greatest extent, and the useful signal is prevented from being submerged in the incident signal;
(4) the reconstructed signal is processed in an exponential weighted average mode, and a proper extreme point is selected for output, so that a useful signal containing defect information can be effectively reserved, a high-frequency noise signal from the environment is abandoned, and the purposes of accurately outputting the useful signal and visually judging defects are achieved.
Drawings
Fig. 1 is a flowchart of a reconstruction and extraction method for a laser ultrasonic defect detection diffraction echo signal according to the present invention.
Fig. 2 is a graph of the difference between the signal to be processed and the reference signal.
Fig. 3 is a graph of a noise-added signal obtained by adding white noise to a difference signal.
Fig. 4 shows a graph of the total IMF component obtained by the noisy signal decomposition.
FIG. 5 is a graph of the degree of dispersion of each IMF component.
Fig. 6 reconstructs and extracts a useful signal map.
Detailed Description
The present invention is described in further detail below with reference to the attached drawings.
The embodiment provides a reconstruction and extraction method for a laser ultrasonic defect detection diffraction echo signal, the flow of the method is shown in fig. 1, and the method comprises the following specific steps:
step 1:
step 11: carrying out difference processing on the laser ultrasonic defect detection diffraction echo signal to be processed to obtain a difference signal;
(1) respectively obtaining a laser ultrasonic defect detection diffraction echo signal to be processed and a laser ultrasonic receiving signal without a defect signal, wherein the signal to be processed comprises three useful signals of SL wave, SS wave and SCS wave, and the lengths of the two useful signals are 2500 points;
(2) processing the laser ultrasonic signals without the defect signals, only keeping high-amplitude incident signals of the laser ultrasonic signals, and enabling other signals to return to zero to obtain laser ultrasonic reference signals;
(3) as shown in fig. 2, the laser ultrasonic defect detection diffraction echo signal to be processed is differentiated from the laser ultrasonic reference signal to obtain a difference signal;
step 11: performing white noise adding processing on the difference signal to obtain a noise adding signal;
(1) the fluctuation ranges of signals with the length of 100 at both ends of the difference signal are calculated to be-0.02 respectively<pLeft side of<0.02、-0.01<pRight side<0.01；
(2) As shown in fig. 3, a signal having a length of 100 and a fluctuation range in accordance with the above is added to both ends of the difference signal, and a noise signal having a length of 2700 is generated;
step 2:
step 21: performing EMD on the noise-added signal to obtain a series of IMF components with frequencies from high to low, wherein all the IMF components obtained by decomposition are shown in FIG. 4;
step 22: selecting a proper IMF component to carry out signal reconstruction to obtain a reconstructed signal;
(1) the degree of dispersion of each IMF component is calculated according to the following formula:
wherein n is the total number of IMF points, xiFor the i-th point of the IMF,
the calculation result is shown in fig. 5 as the IMF mean value;
(2) as can be seen from FIG. 5, SDispersingThe IMF component with the minimum, i.e., the minimum degree of dispersion is IMF2, so the IMF1, IMF2 and IMF3 are selected for signal reconstruction, and the reconstructed signal is shown in fig. 6;
and step 3:
step 31: performing noise reduction processing with exponential average weighting on the reconstructed signal to obtain a noise reduction signal as shown in fig. 6;
step 32: extracting an extreme point of the noise reduction signal, and selecting a proper extreme point for outputting;
(1) extracting all maximum value points of the noise reduction signal;
(2) the number of the defective waveforms to be extracted is 3, and the minimum interval length between the known waveforms is 150 points, so 3 maximum extreme points with the length interval more than 150 points are selected;
(3) since the useful signal to be extracted contains more energy and has high amplitude characteristic compared with other noise signals, the maximum value is selected from the extreme points, the maximum 3 maximum value points and 75 points on the left side and the right side of the maximum 3 maximum value points are finally selected, the other signals are subjected to zero-returning processing, the obtained signals are shown in fig. 6, the positions of the useful signal in the signal to be processed and the final extracted signal are consistent from fig. 2 and 6, and the effectiveness of the method is proved.
Claims (9)
1. A reconstruction extraction method for a laser ultrasonic defect detection diffraction echo signal is characterized by comprising the following steps:
s1: inputting a laser ultrasonic defect detection diffraction echo signal to be processed, and sequentially carrying out subtraction and white noise addition processing to generate a noise addition signal;
s2: decomposing the noise-added signal of the step S1 by adopting an EMD method to generate a plurality of IMF components, sequencing the IMF components according to frequency, calculating the discrete degree of each IMF component, and selecting the corresponding IMF component according to the discrete degree distribution and the IMF component relation to reconstruct to obtain a reconstructed signal;
s3: and (3) denoising the reconstructed signal of the S2 by using an exponential weighted average mode, extracting extreme points of the denoised reconstructed signal, selecting and outputting maximum extreme points with a certain number and a certain length.
2. The method for reconstructing and extracting a diffraction echo signal for laser ultrasonic defect detection according to claim 1, wherein the step S1 of generating the noise signal specifically includes the following two steps:
s11: carrying out difference processing on a laser ultrasonic defect detection diffraction echo signal to be processed and a laser ultrasonic reference signal to obtain a difference signal of the two signals;
s12: white noise addition treatment: white noise with equal length and consistent fluctuation range is added at two ends of the difference signal to generate a noise-added signal.
3. The method for reconstructing and extracting diffraction echo signals aiming at laser ultrasonic defect detection according to claim 2, wherein the laser ultrasonic reference signal in step S11 is a signal obtained by returning to zero other signals of the laser ultrasonic received signal without defect signals on the basis of only retaining the incident signal with high amplitude.
4. The method for reconstructing and extracting a diffraction echo signal aiming at laser ultrasonic defect detection as claimed in claim 2, wherein in the step S12, the length of white noise is 100.
5. The method for reconstructing and extracting a diffraction echo signal aiming at laser ultrasonic defect detection as claimed in claim 2, wherein in step S12, the fluctuation range of white noise is consistent with the fluctuation range of the signal with the length of 100 at both ends of the difference signal.
6. The method for reconstructing and extracting diffraction echo signals for laser ultrasonic defect detection according to claim 1, wherein the step S2 includes the following two steps:
s21: and decomposing the noise-added signal by an EMD method to obtain a series of IMF components with frequencies from high to low.
S22: the degree of dispersion of each IMF component is calculated according to the following formula:
wherein n is the total number of IMF points, xiFor the i-th point of the IMF,
the IMF mean value.
7. The method for reconstructing and extracting diffraction echo signal aiming at laser ultrasonic defect detection as claimed in claim 6, wherein in step S22, S is selected as the minimum discrete degreeDispersingThe minimum IMF component and the IMF components on the left and right sides thereof to obtain a reconstructed signal.
8. The method for reconstructing and extracting diffraction echo signals for laser ultrasonic defect detection according to claim 1, wherein the step S3 includes the following two steps:
step 31: carrying out noise reduction processing on the reconstructed signal by using an exponential weighted average mode;
step 32: extracting the extreme points of the reconstructed signal after noise reduction, and selecting a certain number of maximum extreme points with certain intervals to output, wherein the certain number refers to the number of the defect waveforms to be extracted and is consistent with the number of the selected maximum extreme points, and the certain intervals refer to the intervals of the selected maximum extreme points, and the intervals of the selected maximum extreme points are not less than the minimum intervals of the known defect waveforms.
9. The method for reconstructing and extracting a diffraction echo signal aiming at laser ultrasonic defect detection according to claim 8, wherein all maximum extreme points of the noise reduction signal need to be extracted in step S32; determining the number of candidate maximum extremum points of the length interval on the points of the interval minimum length according to the number of missing waveforms to be extracted and the points of the interval minimum length between the waveforms; and reserving the candidate maximum extreme point and the point of the half of the minimum length of the interval on the left side and the right side of the candidate maximum extreme point, and performing zero setting processing on other signals to obtain useful signals.
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| CN112415011A (en) * | 2020-10-23 | 2021-02-26 | 武汉理工大学 | Robot laser ultrasonic detection device and method for defects of electric arc additive part |
| CN112858182A (en) * | 2021-01-22 | 2021-05-28 | 西安增材制造国家研究院有限公司 | Laser ultrasonic signal defect echo signal extraction method based on waveform separation |
| CN112858183A (en) * | 2021-01-22 | 2021-05-28 | 西安增材制造国家研究院有限公司 | Additive manufacturing laser ultrasonic signal defect imaging method based on waveform separation |
| CN114088817A (en) * | 2021-10-28 | 2022-02-25 | 扬州大学 | Deep learning flat ceramic membrane ultrasonic defect detection method based on deep features |
| CN114740093A (en) * | 2022-04-07 | 2022-07-12 | 中南大学 | Ultrasonic guided wave defect quantitative detection method and application thereof |
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| CN112415011A (en) * | 2020-10-23 | 2021-02-26 | 武汉理工大学 | Robot laser ultrasonic detection device and method for defects of electric arc additive part |
| CN112858182A (en) * | 2021-01-22 | 2021-05-28 | 西安增材制造国家研究院有限公司 | Laser ultrasonic signal defect echo signal extraction method based on waveform separation |
| CN112858183A (en) * | 2021-01-22 | 2021-05-28 | 西安增材制造国家研究院有限公司 | Additive manufacturing laser ultrasonic signal defect imaging method based on waveform separation |
| CN112858182B (en) * | 2021-01-22 | 2022-12-16 | 西安增材制造国家研究院有限公司 | Laser ultrasonic signal defect echo signal extraction method based on waveform separation |
| CN114088817A (en) * | 2021-10-28 | 2022-02-25 | 扬州大学 | Deep learning flat ceramic membrane ultrasonic defect detection method based on deep features |
| CN114088817B (en) * | 2021-10-28 | 2023-10-24 | 扬州大学 | Deep learning flat ceramic membrane ultrasonic defect detection method based on deep features |
| CN114740093A (en) * | 2022-04-07 | 2022-07-12 | 中南大学 | Ultrasonic guided wave defect quantitative detection method and application thereof |
| CN114740093B (en) * | 2022-04-07 | 2024-05-07 | 中南大学 | Ultrasonic guided wave defect quantitative detection method and application thereof |
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