CN113484417A - Pipeline corrosion detection method based on wavelet transformation - Google Patents
Pipeline corrosion detection method based on wavelet transformation Download PDFInfo
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Abstract
The invention provides a pipeline corrosion detection method based on wavelet transformation, which comprises the following steps: s1, knocking the pipeline by using a force hammer, and collecting signals of an acceleration sensor; s2, selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain time-frequency domain expression of x (t) wavelet transformation; s3, filtering the time-frequency domain expression to obtain WTfilter(t, f); s4, calculating WT in time rangefilter(t, f) double integration with respect to frequency and time; s5, setting a judgment index y of the corrosion degree of the pipeline1And y2And judging the mass of the pipeline according to the integral value. After threshold value screening is creatively extracted, the integral of the characteristic range in the time-frequency plane is used as the pipeline quality distinguishing characteristic, and a new thought is provided for a characteristic extraction algorithm based on a time-frequency domain; the testing method is simple and convenient, avoids the limitation of manually comparing and searching damage characteristics, and realizes the automatic judgment of the corrosion quality of the pipeline。
Description
Technical Field
The invention relates to the technical field of nondestructive testing, in particular to a pipeline corrosion detection method based on wavelet transformation.
Background
Pipeline transportation is an important mode of modern fluid transportation, and pipelines are used as transportation tools to transport gas or liquid such as oil, natural gas, water, slurry, gas and chemical gas for a long distance. The pipeline transportation has the advantages of large transportation amount, low cost, safety, reliability, small occupied area, capability of continuous transportation without weather influence and the like, and is widely applied to the fields of oil and gas industry and the like. However, because the pipeline transportation work environment is harsh, the metal pipeline is easily corroded and damaged by dark, humid and cold air outside and liquid or gas conveyed inside, and the pipeline leakage accident may be caused, so that serious consequences such as environmental pollution, personnel and property loss are caused. Therefore, in order to ensure the safe transportation of the pipeline and prevent serious accidents, it is necessary to perform quality inspection on the metal pipeline and evaluate the corrosion degree of the metal pipeline.
With the development of sound vibration detection technology, the force hammer equipment is widely applied to vibration analysis and quality detection of engineering structures. The sound vibration knocking detection generates a vibration signal by applying force hammer knocking to a test piece, and judges the physical state of an object to be detected by acquiring the vibration signal and analyzing information such as frequency, energy, amplitude and the like. Compared with other acoustic detection technologies such as ultrasonic detection, acoustic emission detection and the like, the acoustic vibration and knocking detection has the advantages of easiness in operation and capability of acquiring vibration information at any time, so that the acoustic vibration and knocking detection is widely researched and applied to the field of detection of internal defects of some products. To the pipeline corrosion detection problem that this patent relates to, the sound shakes to strike that detection operation is simple and convenient, adaptable complicated external field environment, can realize the short-term test of pipeline quality.
The sound vibration signal obtained by the force hammer knocking contains the physical state information of the test piece, but the effective pipeline quality characteristic index is difficult to extract from the time domain analysis. In general, the original time domain signal can be converted to the frequency domain or the time-frequency domain to highlight the impairment feature. Wavelet Transform (WT) is a time-frequency localization analysis method with a fixed window size but a variable shape, and with variable time and frequency resolutions, and has a higher frequency resolution and a lower time resolution in the low frequency part and a higher time resolution and a lower frequency resolution in the high frequency part, and has better adaptivity to signals. The wavelet transformation can convert the original time domain signal into a time-frequency domain, and is beneficial to searching for damage characteristics. However, the way of extracting the damage features is different for different application scenarios and objects. Therefore, with the aid of the force hammer detection means and the wavelet transformation method, a detection method and a characteristic index extraction means specially designed for metal pipeline corrosion degree evaluation are required to be designed.
Disclosure of Invention
The invention aims to provide a detection method capable of realizing rapid evaluation of the corrosion degree of a metal pipeline in a complex outfield working environment, which can accurately acquire the quality characteristics of the pipeline to be detected, greatly reduce the complexity of a test flow and conveniently and rapidly realize quality detection of a test piece.
The invention provides a pipeline corrosion detection method based on wavelet transformation, which comprises the following steps:
the method comprises the following steps: adsorbing an acceleration sensor with a magnetic seat on the surface of a pipeline to be tested, knocking the pipeline by using a force hammer, and reading an acceleration sensor signal x (t) by using a data acquisition board card;
step two: selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation;
step three: setting a threshold value, and performing screening filtering on the wavelet transform WT (t, f) to obtain WTfilter(t, f), the specific operation is as follows:
wherein, the lambda is a screening and filtering threshold value;
step four: selecting a time range (t)1,t2) Calculate WT in the time rangefilter(t, f) double integral Y with respect to frequency and time. The specific calculation method is as follows:
wherein f issIs the sampling frequency, t, of the data acquisition board card1And t2Two endpoints of the selected time range;
step five: setting a pipeline corrosion degree judgment index y1And y2And y is1<y2When Y > Y2Judging the quality of the pipeline to be good; when y is1≤Y≤y2Judging the quality of the pipeline to be 'medium'; when Y is less than Y1And judging that the quality of the pipeline is poor.
Preferably, in the second step, a cmor wavelet is selected as a mother wavelet, and continuous wavelet transformation and mapping are performed on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation; the method comprises the following specific steps:
selecting cmor wavelet as mother wavelet, and making continuous wavelet transformation on input signal x (t) to obtain Wf(a, b), the continuous wavelet transform formula is specifically as follows:
wherein a and b are respectively a scale factor and a displacement factor, a and b belong to R, and a is more than 0; phi (t) represents mother wavelet, which is subjected to scale expansion and time shift to obtain wavelet basis phia,b(t), as specified by
t is time;
known wavelet basis psia,b(t) has a center frequency of faAccording to the scale factor a and the frequency faWill be Wf(a, b) mapping to time-frequencyDomain, obtaining the time-frequency domain expression WT (t, f) of x (t) wavelet transform;
preferably, the first step further comprises: the distance between the knocking position and the acceleration sensor position is 10-40 cm.
Preferably, in the third step, λ is 0.001.
Preferably, in the fourth step, t1＝0.5s，t2＝0.8s，fs＝100kHz。
Preferably, in said step five, y1＝2.7×106And y2＝3.5×106。
Compared with the prior art, the invention has the following beneficial effects:
1. the invention takes the force hammer test signal as the analysis basis, the force hammer knocking test is simple and convenient, and the pipeline test is favorably carried out in the complex outfield environment.
2. The method is based on a mature wavelet transform algorithm, has high operation rate, is simple and efficient, and can realize real-time and rapid detection of the pipeline.
3. The method avoids the limitation of manually searching for the damage characteristics by contrast, gets rid of the dependence on engineering experience, and can realize the automatic judgment of the pipeline corrosion quality.
4. The invention creatively extracts the integral of the characteristic range in the time-frequency plane after the threshold value is screened as the pipeline quality distinguishing characteristic, and provides a new idea for a characteristic extraction algorithm based on the time-frequency domain.
Drawings
FIG. 1 is a flow chart of the method of the present invention;
FIG. 2(a) is an acceleration signal of a pipeline with better quality acquired in the embodiment;
FIG. 2(b) is the acceleration signal of the medium-quality pipeline collected in the embodiment;
FIG. 2(c) is an acceleration signal of a poor-quality pipeline collected in the embodiment;
FIG. 3(a) is the result of threshold value screening of wavelet transform of acceleration signal of pipeline with better quality in the embodiment;
FIG. 3(b) is the result of threshold value screening of the wavelet transform of the acceleration signal of the medium-quality pipeline in the embodiment;
FIG. 3(c) is the result of threshold value screening of the wavelet transform of the acceleration signal of the pipeline with poor quality in the embodiment;
fig. 4 shows the final extracted index and the classification effect.
Detailed Description
In order to better understand the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings and examples. In the drawings, like reference numbers can indicate functionally identical or similar elements. While the various aspects of the embodiments are presented in drawings, the drawings are not necessarily drawn to scale unless specifically indicated.
The whole testing system comprises a force hammer device, an acceleration sensor with a magnetic seat, a data acquisition board card, an industrial personal computer and a metal pipeline to be tested. The acceleration sensor is adsorbed on the surface of the metal pipeline through the magnetic seat, and when a force hammer is used for knocking the pipeline, a sound vibration signal of the metal pipeline is acquired through the data acquisition board card on the industrial personal computer.
As shown in fig. 1, a pipeline corrosion detection method based on wavelet transform includes the following steps:
the method comprises the following steps: adsorbing an acceleration sensor with a magnetic seat on the surface of a pipeline to be tested, knocking the pipeline by using a force hammer, wherein the distance between the knocking position and the acceleration sensor is about 10-40 cm, and reading an acceleration sensor signal x (t) by using a data acquisition board card.
To prove the effectiveness of the present invention, three tubes are used in this embodiment, the three tubes are respectively good, medium and poor quality tubes, the acceleration signals of the three tubes are respectively collected, and the acceleration signals are correspondingly recorded as x1(t)、x2(t) and x3(t) as shown in FIGS. 2(a) - (c).
Step two: selecting cmor wavelet as mother wavelet, and making continuous wavelet transformation on input signal x (t) to obtain Wf(a, b), the continuous wavelet transform formula is specifically as follows:
wherein a and b are respectively a scale factor and a displacement factor, a and b belong to R, and a is more than 0; phi (t) represents mother wavelet, which is subjected to scale expansion and time shift to obtain wavelet basis phia,b(t), as specified by
t is time.
Known wavelet basis psia,b(t) has a center frequency of faAccording to the scale factor a and the frequency faWill be Wf(a, b) mapping to the time-frequency domain to obtain the time-frequency domain expression WT (t, f) of the x (t) wavelet transform. Wavelet transformation of the signal x (t) into the time-frequency domain to express WT (t, f) is prior art and the implementation steps are not expanded here.
In this embodiment, the sampling frequency fs is 100000; setting minimum fmin as 200 as wavelet base center frequency; the maximum wavelet base center frequency is 50000 as fmax; the frequency step of the wavelet base is dF-200, and the central frequency of the mother wavelet is Fc; t is 1/fs; the wavelet base frequency requirement is converted to a corresponding a, a is Fc./((fmin: dF: fmax) × T), where "/" denotes dot division, fmin: dF: fmax denotes the increase of frequency step dF each time starting from fmin until fmax, Fc./((fmin: dF: fmax) × T) denotes the cycle starting from Fc divided by fmin, each cycle increasing the divisor by one step until the divisor exceeds fmax. b is set to x (t) at each instant, i.e.: b ═ T (1: length (x)); length (x) represents the length of the acceleration signal, and b is a vector, which is exactly every point of the time axis, such as: t (1:10), then 1:10 is the vector [ 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 ] which multiplied by the sampling interval becomes the time instant.
Acceleration signal x for three different quality pipelines1(t)、x2(t) and x3(t) performing the above operations to obtain WT respectively1(t,f)、WT2(t, f) and WT3(t,f)。
Step three: setting a threshold value, and performing wavelet transform WT (T, f) performs screening filtering to obtain WTfilter(t, f), the specific operation is as follows:
in general, λ is about 0.1% of the maximum absolute value of WT (t, f), e.g. selected from 0.09% -0.11% of the maximum absolute value of WT (t, f), and in this embodiment, λ is set to 0.001, and wavelet transform results WT for three different quality channel signals are obtained1(t,f)、WT2(t, f) and WT3(t, f) performing the above operations to obtain WT respectively1filter(t,f)、 WT2filter(t, f) and WT3filter(t, f), as shown in FIGS. 3(a) - (c), the white region value is 1, and the black region value is 0.
Step four: selecting a time range (t)1,t2) Calculate WT in the time rangefilter(t, f) double integral Y with respect to frequency and time. The specific calculation method is as follows:
wherein f issThe sampling frequency of the data acquisition board card. In this example, t1＝0.5s，t2＝0.8s，fs＝100kHz。
For three pipelines with different qualities, 50 groups of signals are respectively collected for each pipeline in order to verify the robustness of the method. The integration result Y obtained after all the signals are processed through the above steps is shown in fig. 4, wherein a circle represents the pipeline data with better quality, a square represents the pipeline data with medium quality, and a triangle represents the pipeline data with poor quality.
Step five: setting a pipeline corrosion degree judgment index y1And y2And y is1<y2When Y > Y2Judging the quality of the pipeline to be good; when y is1≤Y≤y2Judging the quality of the pipeline to be 'medium'; when Y is less than Y1In time, judge the pipelineThe quality was "poor". In this embodiment, a pipeline corrosion degree judgment index y is set1＝2.7×106And y2＝3.5×106As shown in fig. 4, the method can perfectly distinguish three types of pipelines with different qualities.
The invention takes the force hammer signal as the analysis basis, has simple operation and can adapt to the complicated and severe external field conditions. By means of a relatively mature wavelet transform algorithm, the operation rate is high, and real-time rapid detection can be achieved. Meanwhile, the integral of the characteristic range in the time-frequency plane after threshold screening is creatively extracted to serve as the pipeline quality distinguishing characteristic, so that the limitation of manually searching for the damage characteristic in a contrast manner is avoided, and the automatic distinguishing of the pipeline corrosion quality can be realized.
Finally, it should be noted that: the above-mentioned embodiments are only used for illustrating the technical solution of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (6)
1. A pipeline corrosion detection method based on wavelet transform is characterized in that: which comprises the following steps:
the method comprises the following steps: adsorbing an acceleration sensor with a magnetic seat on the surface of a pipeline to be tested, knocking the pipeline by using a force hammer, and reading an acceleration sensor signal x (t) by using a data acquisition board card;
step two: selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation;
step three: setting a threshold value, and performing screening filtering on the wavelet transform WT (t, f) to obtain WTfilter(t, f), the specific operation is as follows:
wherein, the lambda is a screening and filtering threshold value;
step four: selecting a time range (t)1,t2) Calculate WT in the time rangefilter(t, f) double integral Y with respect to frequency and time; the specific calculation method is as follows:
wherein f issIs the sampling frequency, t, of the data acquisition board card1And t2Two endpoints of the selected time range;
step five: setting a pipeline corrosion degree judgment index y1And y2And y is1<y2When Y > Y2Judging that the quality of the pipeline is good; when y is1≤Y≤y2Judging the quality of the pipeline to be medium; when Y is less than Y1And judging that the quality of the pipeline is poor.
2. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: selecting a cmor wavelet as a mother wavelet, and performing continuous wavelet transformation and mapping on an input signal x (t) to obtain a time-frequency domain expression WT (t, f) of the x (t) wavelet transformation; the method comprises the following specific steps:
selecting cmor wavelet as mother wavelet, and making continuous wavelet transformation on input signal x (t) to obtain Wf(a, b), the continuous wavelet transform formula is specifically as follows:
wherein a and b are respectively a scale factor and a displacement factor, a and b belong to R, and a is more than 0; phi (t) represents mother wavelet, which is subjected to scale expansion and time shift to obtain wavelet basis phia,b(t), as specified by
t is time;
known wavelet basis psia,b(t) has a center frequency of faAccording to the scale factor a and the frequency faWill be Wf(a, b) mapping to the time-frequency domain to obtain the time-frequency domain expression WT (t, f) of the x (t) wavelet transform.
3. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: the first step further comprises: the distance between the knocking position and the acceleration sensor position is 10-40 cm.
4. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: in the third step, the value of lambda is 0.001.
5. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: in the fourth step, t1＝0.5s，t2＝0.8s，fs＝100kHz。
6. The pipeline corrosion detection method based on wavelet transform as recited in claim 1, wherein: in the fifth step, y1＝2.7×106And y2＝3.5×106。
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