CN110568073B - Method for picking up impact signal in noise environment - Google Patents
Method for picking up impact signal in noise environment Download PDFInfo
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- CN110568073B CN110568073B CN201910871148.5A CN201910871148A CN110568073B CN 110568073 B CN110568073 B CN 110568073B CN 201910871148 A CN201910871148 A CN 201910871148A CN 110568073 B CN110568073 B CN 110568073B
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Abstract
The invention discloses a method for picking up an impact signal in a noise environment, which comprises the following steps: step S1: collecting an audio signal of the tunnel lining in a knocking mode, and recording the audio signal as a sampling signal; step S2: extracting and separating a knocking signal and a noise signal from the sampling signal by a short-time Fourier transform analysis method according to the sampling signal obtained in the step S1, thereby extracting an excitation signal, wherein the signal before the position of the knocking signal is the noise signal; step S3: carrying out hamming window filtering and FFT spectrum analysis on the excitation signal and the noise signal extracted in the step S2, and subtracting the spectrum of the noise signal from the spectrum of the effective signal interval to obtain a real excitation signal spectrum; meanwhile, analyzing the excellent period and the gravity center period of the frequency spectrum of the real excitation signal, and analyzing the knocking detection defects. The method can effectively filter noise, can realize knocking detection under environmental noise, and greatly improves the accuracy and the applicability of the knocking detection.
Description
Technical Field
The invention relates to the technical field of nondestructive testing methods, in particular to a method for picking up impact signals in a noise environment.
Background
In the field of engineering detection, the sounding method is a simple and effective method. The surface of the test structure is knocked by using a vibration hammer to generate a vibration signal, and the existence of the void defect of the structure is analyzed and distinguished through human hearing discrimination or after the signal is picked up by using a sound pickup device. Particularly in the field of railway tunnel lining detection, the application is wider, and in the specification TB 104172018 of 'railway tunnel engineering construction quality acceptance standard' issued by the State railway administration, the detection technology of knocking (sounding method) is adopted for detecting and accepting the railway large lining quality.
In practical engineering application, a method that workers knock human ears to listen to sound is often adopted for distinguishing, the human ears listen to sound which is greatly influenced by main observation, the accuracy is related to personal experience, signals knocked on site cannot be stored and traced, and the operability is high.
Therefore, on the basis, a method for receiving and analyzing the knocking signal by adopting a professional sound pickup device is developed, so that the knocking signal is objectively interpreted, the detection precision can be effectively improved, but when the sound pickup device is used for receiving the signal, a lot of noise signals can be inevitably received, and particularly in the conditions of noise, such as noise in a tunnel, a ventilation system, human voice of a vehicle and the like, in a construction site, the noise influence is larger, so that an effective signal is difficult to extract from the received signal.
Disclosure of Invention
The invention aims to solve the technical problem that in actual engineering, noise signals are quite complex, and various types of noises such as ventilator noise, vehicle noise, construction noise, human voice noise and the like are contained in a tunnel under construction. Therefore, if the audio signal picked up on site does not undergo denoising processing, an effective signal generated by actual knocking cannot be found, and the existing method for picking up the impact signal in a noisy environment is low in efficiency and cannot effectively extract and filter the noise signal.
The invention provides a method for picking up impact signals in a noise environment, which solves the problems, researches and designs a signal denoising mode, and can effectively filter noise signals in different environmental noises.
The invention is realized by the following technical scheme:
a method of picking up a hit signal in a noisy environment, the method comprising the steps of:
step S1: collecting an audio signal of the tunnel lining in a knocking mode, and recording the audio signal as a sampling signal;
step S2: extracting and separating a knocking signal and a noise signal from the sampling signal by a short-time Fourier transform analysis method according to the sampling signal obtained in the step S1, thereby extracting an excitation signal, wherein the signal before the position of the knocking signal is the noise signal;
step S3: carrying out hamming window filtering and FFT spectrum analysis on the excitation signal and the noise signal extracted in the step S2, and subtracting the spectrum of the noise signal from the spectrum of the effective signal interval to obtain a real excitation signal spectrum; meanwhile, analyzing the excellent period and the gravity center period of the frequency spectrum of the real excitation signal, and analyzing the knocking detection defects.
The working principle is as follows: for the acoustic signals picked up by the tunnel lining knocking, the noise frequency and the frequency of the knocking signals have larger difference, aiming at the noise condition of the actual construction site, the invention provides a method for picking up the knocking signals in the noise environment, which is a knocking signal denoising method based on a short-time Fourier transform (STFT) method, the acoustic signals are divided into a plurality of short sections through short-time Fourier transform analysis (the FFT is used for transforming the waveform (instant domain signals) to the frequency domain), the acoustic signals of each section are subjected to Hamming window filtering and FFT spectrum transformation, the spectrum values of the acoustic signals are extracted, then the spectrum values of each section are compared, and the spectrum mutation position is the starting position of the knocking signals, so that the excitation signals are extracted; carrying out spectrum analysis on the extracted effective excitation signal, and simultaneously identifying a signal before the starting point of the effective signal as a noise signal to carry out spectrum analysis; after the spectrum analysis, the noise signal spectrum is subtracted from the effective signal interval spectrum, and the obtained spectrum value is the real excitation signal spectrum. And knock detection defect analysis is carried out by analyzing the excellent period and the gravity center period of the frequency spectrum of the real excitation signal. The method can rapidly and accurately de-noise the audio signal, thereby extracting an effective excitation signal; the method can realize knocking detection under the environment noise, and greatly improves the accuracy and the applicability of the knocking detection.
Further, in the step S2, a tap signal and a noise signal are extracted and separated from the sampling signal by a short-time fourier transform analysis method, wherein the extracting the tap signal specifically includes the following steps:
s21: dividing the sampling signal into a plurality of short intervals by a short-time Fourier transform (STFT) analysis method, carrying out Hamming window filtering and Fast Fourier Transform (FFT) spectrum transformation on the signal of each interval, and extracting the spectrum value of the signal;
s22: comparing and analyzing the frequency spectrum values of all intervals, and taking the frequency spectrum mutation position as the starting position of the knocking signal so as to extract the excitation signal; specifically, the method comprises the following steps:
s221: calculating the gravity center period, the excellent period and the maximum amplitude of each short time domain FFT spectrum extracted in the step S21;
s222: calculating the average value and the standard deviation of the maximum amplitude of each section, and summing the average value and the standard deviation;
s223: in the part of each section, the maximum amplitude exceeds the sum of the maximum amplitudes, the gravity center period changes most and is used as a knocking signal;
s224: and extracting a group of knocking signals by finding out the starting point of the knocking signals in the test waveform, wherein the group of knocking signals are 1024 sampling points.
Further, the short-time fourier transform analysis method comprises the following steps:
1) let the total number of sampling signals be NT；
2) Calculating a Gibor weight function omega (j) of the sampling data and storing the Gibor weight function omega (j) into an array, wherein j is 0-255, and the formula omega (j) is as follows:
wherein m is 127, a is 1024;
3) in the test data interval, from 0 to NT256, taking a set of data x every 64 data intervalsi(j) The number of the groups is 256, and j is 0-255, so as to perform short time domain FFT spectrum analysis;
4) x is to bei(j) Performing a Gibor transformation:
yi(j)＝xi(j)ω(j)
5) will yiPerforming FFT spectrum conversion and calculating the center of gravity frequency fci，fciPeriod of center of gravity TciThe reciprocal of (a);
6) calculating the maximum amplitude A of FFT frequency spectrum in each sectioniAnd an excellent frequency fdi。
Further, the step flow from step S222 to step S224 specifically includes:
1) for maximum amplitude A of each intervaliCalculating the mean value thereof
And standard deviation σAAnd calculating a cutoff threshold
2) For the center of gravity frequency f of the front 128 groups of each blockciAnd an excellent frequency fdiObtaining the mean value of the center of gravity frequency
And excellent frequency mean
3) When A isi≥CAWhen the vibration excitation signal is considered to be contained in the interval, f of the relevant group is consideredciAnd fdiTo find out it
And
deviation ratio delta ofi：
4)δiIn the largest group, the excitation signal is considered to be contained, and the maximum value δ in the group is foundiCorresponding data sampling point numbers;
5) and extracting 1024 sampling points as excitation signals.
Further, in step S2, a tap signal and a noise signal are extracted and separated from the sampling signal by a short-time fourier transform analysis method, where the signal before the position of the tap signal is the noise signal, five groups of noise signals are extracted, and each group of noise signals is 1024 sampling points.
Further, in step S3, the hamming window filtering and FFT spectrum analysis are performed on the extracted excitation signal and the extracted noise signal to obtain a spectrum amplitude and a noise spectrum amplitude of the excitation signal, and the spectrum characteristic of the excitation signal is obtained by subtracting the noise spectrum amplitude from the spectrum amplitude of the excitation signal.
Compared with the prior art, the invention has the following advantages and beneficial effects:
1. the invention is a method capable of beating signals in a noise environment, which can effectively filter noise;
2. the invention discloses a method for picking up a beating signal in a noise environment, which is a signal denoising method aiming at an audio signal picked up by a beating method in nondestructive testing, can realize beating detection under environmental noise, and greatly improves the precision and the applicability of the beating detection.
Drawings
The accompanying drawings, which are included to provide a further understanding of the embodiments of the invention and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the invention and together with the description serve to explain the principles of the invention. In the drawings:
FIG. 1 is a flow chart of a method of picking up a hit signal in a noisy environment according to the present invention.
FIG. 2 is a graph of measured data in the detection of a tunnel lining of Zhengwan railway according to the embodiment of the present invention.
Fig. 3 is a statistical chart of the barycentric period of the original signal obtained by directly performing spectrum analysis on the original data and calculating the barycentric period in the lining detection of a certain tunnel of zhengwan railway in the embodiment of the present invention.
Fig. 4 is a diagram illustrating de-noising of an actual measurement signal according to an embodiment of the method of the present invention.
FIG. 5 is a statistical graph of the gravity center period of the denoised signal according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail below with reference to examples and accompanying drawings, and the exemplary embodiments and descriptions thereof are only used for explaining the present invention and are not meant to limit the present invention.
Examples
As shown in fig. 1 to 5, the present invention is a method of picking up an impact signal in a noisy environment, the method comprising the steps of:
step S1: collecting an audio signal of the tunnel lining in a knocking mode, and recording the audio signal as a sampling signal;
step S2: extracting and separating a knocking signal and a noise signal from the sampling signal by a short-time Fourier transform analysis method according to the sampling signal obtained in the step S1, thereby extracting an excitation signal, wherein the signal before the position of the knocking signal is the noise signal;
specifically, the extracting the tapping signal specifically includes the following steps:
s21: dividing the sampling signal into a plurality of short intervals by a short-time Fourier transform (STFT) analysis method, carrying out Hamming window filtering and Fast Fourier Transform (FFT) spectrum transformation on the signal of each interval, and extracting the spectrum value of the signal;
wherein, the short-time Fourier change analysis method comprises the following steps:
1) let the total number of sampling signals be NT；
2) Calculating a Gibor weight function omega (j) of the sampling data and storing the Gibor weight function omega (j) into an array, wherein j is 0-255, and the formula omega (j) is as follows:
wherein m is 127, a is 1024;
3) in the test data interval, from 0 to NT256, taking a set of data x every 64 data intervalsi(j) The number of the groups is 256, and j is 0-255, so as to perform short time domain FFT spectrum analysis;
4) x is to bei(j) Performing a Gibor transformation:
yi(j)＝xi(j)ω(j)
5) will yiPerforming FFT spectrum conversion and calculating the center of gravity frequency fci，fciPeriod of center of gravity TciThe reciprocal of (a);
6) calculating the maximum amplitude A of FFT frequency spectrum in each sectioniAnd an excellent frequency fdi。
S22: comparing and analyzing the frequency spectrum values of all intervals, and taking the frequency spectrum mutation position as the starting position of the knocking signal so as to extract the excitation signal; specifically, the method comprises the following steps:
1) for maximum amplitude A of each intervaliCalculating the mean value thereof
And standard deviation σAAnd calculating a cutoff threshold
2) For the center of gravity frequency f of the front 128 groups of each blockciAnd an excellent frequency fdiObtaining the mean value of the center of gravity frequency
And excellent frequency mean
3) When A isi≥CAWhen the vibration excitation signal is considered to be contained in the interval, f of the relevant group is consideredciAnd fdiTo find out it
And
deviation ratio delta ofi：
4)δiIn the largest group, the excitation signal is considered to be contained, and the maximum value δ in the group is foundiCorresponding data sampling point numbers;
5) 1024 sampling points are extracted as excitation signals, and a group of knocking signals are 1024 sampling points.
Specifically, extracting the noise signal specifically includes:
signals before the position of the knocking signal (effective signal) are noise signals, five groups of noise signals are extracted, and each group of noise signals are 1024 sampling points.
Step S3: carrying out hamming window filtering and FFT spectrum analysis on the excitation signal and the noise signal extracted in the step S2, and subtracting the spectrum amplitude of the noise signal from the spectrum amplitude of the effective signal interval to obtain a real excitation signal spectrum; meanwhile, analyzing the excellent period and the gravity center period of the frequency spectrum of the real excitation signal, and analyzing the knocking detection defects.
Specifically, the hamming window filtering and FFT spectrum analysis are performed on both the excitation signal and the noise signal extracted in step S2, and the method includes:
(1) carrying out hamming window filtering on the extracted excitation signal, and carrying out FFT (fast Fourier transform) spectrum analysis;
(2) respectively carrying out hamming window filtering and FFT spectrum analysis on the extracted five groups of noise signals;
(3) and averaging the FFT spectrum amplitude values of the noise to obtain the spectrum characteristic of the noise.
The working principle is as follows: for the acoustic signals picked up by the tunnel lining knocking, the noise frequency and the frequency of the knocking signals have larger difference, aiming at the noise condition of the actual construction site, the invention provides a method for picking up the knocking signals in the noise environment, which is a knocking signal denoising method based on a short-time Fourier transform (STFT) method, the acoustic signals are divided into a plurality of short sections through short-time Fourier transform analysis (the FFT is used for transforming the waveform (instant domain signals) to the frequency domain), the acoustic signals of each section are subjected to Hamming window filtering and FFT spectrum transformation, the spectrum values of the acoustic signals are extracted, then the spectrum values of each section are compared, and the spectrum mutation position is the starting position of the knocking signals, so that the excitation signals are extracted; carrying out spectrum analysis on the extracted effective excitation signal, and simultaneously identifying a signal before the starting point of the effective signal as a noise signal to carry out spectrum analysis; after the spectrum analysis, the noise signal spectrum is subtracted from the effective signal interval spectrum, and the obtained spectrum value is the real excitation signal spectrum. And knock detection defect analysis is carried out by analyzing the excellent period and the gravity center period of the frequency spectrum of the real excitation signal. The method can rapidly and accurately de-noise the audio signal, thereby extracting an effective excitation signal; the method can realize knocking detection under the environment noise, and greatly improves the accuracy and the applicability of the knocking detection.
A knocking audio frequency detection method is applied to the detection of a certain tunnel lining of Zhengwan railway, and related data are extracted. The tunnel is a construction tunnel under construction, measured data (namely original data) is shown in fig. 2, and knocking signals cannot be directly picked up from waveforms due to noise interference.
The original data are directly subjected to frequency spectrum analysis and the gravity center period of the original data is calculated, a statistical graph of the gravity center period of the knocking signal at the dense and empty position is shown in fig. 3, the gravity center period of the signal at the dense position and the gravity center period of the signal at the defect position have no obvious difference, and therefore, whether the knocking position is dense or not cannot be distinguished by directly analyzing the noise-containing signal.
Therefore, the method for picking up the beating signal in the noise environment is used for denoising and analyzing the collected data, the denoising process is shown in fig. 4, and in fig. 4, it can be clearly seen that the method can effectively extract the frequency spectrum value of the beating signal from the noisy signal. After the on-site actual measurement signals are subjected to denoising processing, the gravity center period statistics is as shown in FIG. 5, and the difference between the dense and defect position gravity center periods is obvious.
Therefore, the knocking signal denoising method in the method has excellent denoising effect and wide applicability.
The above-mentioned embodiments are intended to illustrate the objects, technical solutions and advantages of the present invention in further detail, and it should be understood that the above-mentioned embodiments are merely exemplary embodiments of the present invention, and are not intended to limit the scope of the present invention, and any modifications, equivalent substitutions, improvements and the like made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (4)
1. A method of picking up a hit signal in a noisy environment, the method comprising the steps of:
step S1: collecting an audio signal of the tunnel lining in a knocking mode, and recording the audio signal as a sampling signal;
step S2: extracting and separating a knocking signal and a noise signal from the sampling signal by a short-time Fourier transform analysis method according to the sampling signal obtained in the step S1, thereby extracting an excitation signal, wherein the signal before the position of the knocking signal is the noise signal;
step S3: carrying out hamming window filtering and FFT spectrum analysis on the excitation signal and the noise signal extracted in the step S2, and subtracting the spectrum of the noise signal from the spectrum of the effective signal interval to obtain a real excitation signal spectrum; meanwhile, analyzing the excellent period and the gravity center period of the frequency spectrum of the real excitation signal, and analyzing the knocking detection defects;
in step S2, a tap signal and a noise signal are extracted and separated from the sampling signal by a short-time fourier transform analysis method, wherein extracting the tap signal specifically includes the following steps:
s21: dividing the sampling signal into a plurality of short intervals by a short-time Fourier transform (STFT) analysis method, carrying out Hamming window filtering and Fast Fourier Transform (FFT) spectrum transformation on the signal of each interval, and extracting the spectrum value of the signal;
s22: comparing and analyzing the frequency spectrum values of all intervals, and taking the frequency spectrum mutation position as the starting position of the knocking signal so as to extract the excitation signal; specifically, the method comprises the following steps:
s221: calculating the gravity center period, the excellent period and the maximum amplitude of each short time domain FFT spectrum extracted in the step S21;
s222: calculating the average value and the standard deviation of the maximum amplitude of each section, and summing the average value and the standard deviation;
s223: in the part of each section, the maximum amplitude exceeds the sum of the maximum amplitudes, the gravity center period changes most and is used as a knocking signal;
s224: extracting a group of knocking signals by finding out the starting point of the knocking signal in the test waveform, wherein the group of knocking signals are 1024 sampling points;
the step flow from step S222 to step S224 specifically includes:
1) for maximum amplitude A of each intervaliCalculating the mean value thereof
And standard deviation σAAnd calculating a cutoff threshold
2) For the center of gravity frequency f of the front 128 groups of each blockciAnd an excellent frequency fdiObtaining the mean value of the center of gravity frequency
And excellent frequency mean
3) When A isi≥CAWhen the vibration excitation signal is considered to be contained in the interval, f of the relevant group is consideredciAnd fdiTo find out it
And
deviation ratio delta ofi：
4)δiIn the largest group, the excitation signal is considered to be contained, and the maximum value δ in the group is foundiCorresponding data sampling point numbers;
5) and extracting 1024 sampling points as excitation signals.
2. A method of picking up a hit signal in a noisy environment according to claim 1, characterized in that the short time fourier transform analysis method comprises the steps of:
1) let the total number of sampling signals be NT；
2) Calculating a Gabor weight function omega (j) of sampling data and storing the Gabor weight function omega (j) into an array, wherein j is 0-255, and the formula omega (j) is as follows:
wherein m is 127, a is 1024;
3) in the test data interval, from 0 to NT256, taking a set of data x every 64 data intervalsi(j) The number of the groups is 256, and j is 0-255, so as to perform short time domain FFT spectrum analysis;
4) x is to bei(j) Performing a Gibor transformation:
yi(j)＝xi(j)ω(j)
5) will yiPerforming FFT spectrum conversion and calculating the center of gravity frequency fci，fciPeriod of center of gravity TciThe reciprocal of (a);
6) calculating the maximum amplitude A of FFT frequency spectrum in each sectioniAnd an excellent frequency fdi。
3. The method of claim 1, wherein the tapping signal and the noise signal are extracted and separated from the sampled signal by short-time fourier transform analysis in step S2, wherein the signal before the location of the tapping signal is the noise signal, and five sets of noise signals are extracted, each set of noise signals being 1024 samples.
4. The method as claimed in claim 1, wherein the step S3 is performed by applying hamming window filtering and FFT spectral analysis to the extracted excitation signal and noise signal to obtain the spectral amplitude and noise spectral amplitude of the excitation signal, and subtracting the noise spectral amplitude from the spectral amplitude of the excitation signal to obtain the spectral characteristic of the excitation signal.
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| CN111487315A (en) * | 2020-04-17 | 2020-08-04 | 四川陆通检测科技有限公司 | Audio frequency nondestructive testing method for tunnel lining thickness and void |
| CN112255308A (en) * | 2020-09-09 | 2021-01-22 | 中国大唐集团科学技术研究院有限公司火力发电技术研究院 | Bolt knocking detection method based on K-means clustering algorithm |
| CN115201335B (en) * | 2022-07-25 | 2024-09-06 | 四川升拓检测技术股份有限公司 | Vibration excitation device with self-power function for tunnel lining detection |
| CN116203131B (en) * | 2023-04-28 | 2023-09-15 | 中国铁建高新装备股份有限公司 | Method and device for detecting tunnel void, electronic equipment and storage medium |
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