CN115097012A - Steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition - Google Patents

Steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition Download PDF

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CN115097012A
CN115097012A CN202210567458.XA CN202210567458A CN115097012A CN 115097012 A CN115097012 A CN 115097012A CN 202210567458 A CN202210567458 A CN 202210567458A CN 115097012 A CN115097012 A CN 115097012A
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acoustic emission
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付勇高
朱军
张国春
王磊
蔡嘉
唐袁袁
曹文斌
虞赟烽
王睿智
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Wuxi Municipal Facilities Maintenance Management Co ltd
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Abstract

The invention relates to the technical field of steel pipe defect detection and positioning, in particular to a steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition, which comprises the following steps: s1, system deployment, S2, signal analysis, S3, signal algorithm decomposition, S4, signal classification, S5 and optimal position defect determination. The steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition can realize simultaneous detection of multiple defects in a steel pipe, and provides an improved acoustic emission signal detection algorithm and an acoustic emission source arrival time difference positioning algorithm.

Description

Steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition
Technical Field
The invention relates to the technical field of steel pipe defect detection and positioning, in particular to a steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition.
Background
The current common steel medium defect detection and positioning methods include an ultrasonic flaw detection and positioning method, an X-ray detection and positioning algorithm, a magnetic leakage defect detection and positioning algorithm and the like, the ultrasonic flaw detection has the advantages of higher flaw detection sensitivity, more X-ray industrial flaw detection is applied in large-scale factories and flow lines, the automation degree is higher, the method is convenient to match with other automatic operations, and the industrialization efficiency is increased, the defect magnetic leakage detection principle is that when the steel material is electrified, the magnetic field curve amplitude of a defective part is reduced or lowered, according to the principle, when a magnetic field signal is converted into an electric signal, the strength of the electric signal at the defect part is also reduced, so that the defect of the steel pipe can be judged, the magnetic leakage detection is not suitable for the flaw detection of an object with a covering layer and a coating layer, and when a defect exists on a steel workpiece, an interface between different media can be formed between the defect and the steel material, the acoustic impedances between the interfaces are different, and the reflected waves are different from the original waves in amplitude and frequency due to defects after the transmitted ultrasonic waves meet the interfaces, so that the purpose of simultaneously detecting multiple defect sources with low equipment requirements can be realized by utilizing the acoustic transmitted wave signals.
The conventional steel medium defect detection and positioning method comprises an ultrasonic flaw detection and positioning method, an X-ray detection and positioning algorithm, a magnetic leakage defect detection and positioning algorithm and the like, wherein the ultrasonic flaw detection has high flaw detection sensitivity, but the ultrasonic flaw detection requires smooth surface of a detected workpiece and serious attenuation of ultrasonic energy, so that the ultrasonic flaw detection has high requirement on a signal receiving device, and the X-ray initial image effect is important, so that the ultrasonic flaw detection has high requirement on the environment for image acquisition and high application cost, and is not suitable for steel pipe defect detection in an underground pipe gallery scene.
Disclosure of Invention
Technical problem to be solved
Aiming at the defects of the prior art, the invention provides a steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition, which has the advantages of improving the defect detection precision and the positioning precision, being simple in generation mode, low in detection cost, not being influenced in operation in various environments and the like, and solves the problems that the environmental influence factors are large and the precision cannot be guaranteed in the use process of other existing positioning methods.
(II) technical scheme
In order to achieve the purposes of improving the defect detection precision and the positioning precision, having simple generation mode and low detection cost, not influencing the operation in various environments and the like, the invention provides the following technical scheme: the method for positioning the defects of the steel pipe on the basis of the modal decomposition of the acoustic emission signals comprises the following steps:
s1, system deployment
The system composed of an acoustic emission defect detection device, an induction device for receiving acoustic emission signals and a central data processing server is deployed in an environment (such as an underground pipe gallery and the like) in which the defect of a steel pipe is difficult to detect manually, wherein an acoustic emission defect detection device is used for detecting the defect of the steel pipe, so that the acoustic emission signals generated at the defect of the steel pipe are transmitted along the wall of the steel pipe, an acoustic emission signal receiving device is used for collecting the acoustic emission signals, the data processing center and the defect positioning system comprise a steel pipe body model and a relative attachment position of an inductor, an acoustic emission signal processing classification system and a defect positioning analysis algorithm, the acoustic emission signals are received through an external inductor module, and receiving inductors at different positions can receive the acoustic emission signals simultaneously transmitted by the same acoustic emission signal source.
S2, analyzing the signal
The system model analyzes acoustic emission signals generated by the defects arriving at the same time in consideration of the condition that the defect detection device detects a plurality of defects at the same time or the difference time is not large, and optimizes a positioning algorithm by using an acoustic emission source with the time difference of arrival to distinguish and accurately position to a multi-defect coordinate, wherein the acoustic emission signal processing module is used for sensing, decomposing and classifying all signals acquired by each sensor at each moment to obtain the optimal positioning precision.
S3, signal algorithm decomposition
The received acoustic emission signals are decomposed by an improved acoustic emission signal detection algorithm, and an original acoustic emission signal can be converted into a time-frequency spectrum signal (Hilbert-Huang spectrum) by a modified adaptive noise-based modal decomposition algorithm (I-CEEMDAN), so that the time-frequency characteristics of each acoustic emission signal can be clearly shown.
S4, signal classification
After the time-frequency spectrum signals are obtained, the data volume can be effectively compressed and the input characteristics of the signals can be extracted through a singular value decomposition algorithm, so that the signals of each frequency can be classified, namely, the signals of the same acoustic emission signal source collected by different sensors at the same moment can be classified, the time when the signals reach each sensor can be obtained, the processes are circularly processed, the data of the signals emitted by all defects detected in the pipeline by the detection equipment can be obtained, and the data are stored in real time.
S5, determining the optimal position defect
Aiming at the problem that the positioning accuracy of acoustic emission events under a small sample is low, a VFOM (visual field optimization method) positioning algorithm based on multi-scale grid search is provided to determine the optimal position defect.
Preferably, in step S3, after the acoustic emission sensors attached to both sides of the steel pipe acquire acoustic emission signals, these signals need to be processed first, and the acoustic emission signals are decomposed by using a modal decomposition model based on the improved adaptive noise to obtain modal components, and the acoustic emission signals are assumed to be x (n):
x (i) (n)＝x(n)+β 0 E 1 (ω (i) (n)),(i＝1,2,...,I)
wherein x is (i) (n) represents the signal x (n) at time i and white Gaussian noise ω (i) The sum of the weights of (n),
Figure BDA0003658083200000031
a complex number (k 1, 2.) representing the k-th empirical mode component obtained by empirical mode decomposition, and a first residual wave component r of the signal x (n) is calculated 1 、
Figure BDA0003658083200000041
Wherein
Figure BDA0003658083200000042
Representing an averaging operation by determining a first residual component r 1 Combining with the original signal x (n), the first IMF component can be calculated 1 ：
IMF 1 ＝x(n)-r 1 (n)。
Preferably, the first IMF is calculated 1 Thereafter, the calculation of the second IMF component is started 2 ：
Figure BDA0003658083200000043
Wherein r is 2 Is the second residual wave component, and by analogy, the kth residual wave component r can be obtained k (K3.., K), the K-th modal component IMF can be obtained k ：
Figure BDA0003658083200000044
IMF k ＝r k-1 -r k
Finally, the original signal can be seen as the sum of the plurality of modal and residual components, i.e.:
Figure BDA0003658083200000045
preferably, after obtaining the IMF components, the time-frequency spectrum signal of each modal component is obtained by using a hilbert transform, and for the signal x (t), the hilbert transform is:
Figure BDA0003658083200000046
where P is the Cauchy principal value, the instantaneous amplitude of the signal is α (t), the instantaneous phase is θ (t), and the instantaneous frequency is ω (t):
Figure BDA0003658083200000047
Figure BDA0003658083200000051
Figure BDA0003658083200000052
preferably, in step S3, the time-frequency spectrum may be represented as H (ω, t), generally, the time-frequency spectrum of a signal may be regarded as a matrix, and according to the singular value decomposition principle, all key features of the matrix may be sequentially obtained in the form of a series of singular values, and according to the similarities and differences of the key features, the acoustic emission signals from different defects obtained by each sensor may be classified, so that the time of arrival of each acoustic emission signal at each sensor may be obtained, and the signals from the same acoustic emission source at different sensors may be classified according to the frequency amplitude, thereby implementing the simultaneous multi-defect detection.
Preferably, in step S5, the classification data obtained in the above steps are integrated to obtain the time when the acoustic emission signal reaches different sensors, and whether the acoustic emission signal is the same acoustic emission source signal can be distinguished according to the parameters such as frequency and amplitude of the signal, and then, according to the actual size and structure of the steel pipe, the planned position coordinates and acoustic emission wave speed of the sensors are determined, and under the condition of considering the system clock error, the acoustic emission signal positioning model can be expressed as:
Figure BDA0003658083200000053
wherein (x) j ,y j ,z j ) Indicates the physical location of the jth sensor, (x) 0 ,y 0 ,z 0 ) Indicating the coordinates of the defect position, is to be calculated, t 0 Representing the starting time, t, of the acoustic emission signal from the defect j Indicates the time, T, at which the signal reaches the physical location of the jth sensor j Represents the system error time of the jth sensor, v ae The method comprises the following steps of representing the propagation speed of acoustic emission signals in a steel pipe, enabling the acoustic emission signals emitted based on different defects to reach different sensors in different time, classifying the same acoustic emission signals, and performing a virtual field optimization algorithm based on multi-scale search:
Figure BDA0003658083200000054
the above formula represents the simultaneous equations that the same acoustic emission source signal respectively reaches the ith and jth inductors at different times, and after the local coordinate systems of the pipeline and the inductors are determined, the above formula can be rewritten as follows according to the operation rule of the time difference hyperbolic equation:
Figure BDA0003658083200000061
wherein:
Figure BDA0003658083200000062
Figure BDA0003658083200000063
Figure BDA0003658083200000064
preferably, c is the coordinate of the local coordinate system of the sensor i and the sensor j is known ij Can be calculated. Establishing an attenuation function f for the acoustic emission signals emitted by the defect coordinates (X, Y, Z) as the acoustic emission signals decrease in energy with increasing distance ij (X,Y,Z)：
Figure BDA0003658083200000065
Wherein d is ij Representing the distance from the acoustic emission signal source to the coordinate curved surface of the sensor:
Figure BDA0003658083200000066
then the local coordinate system f ij (X, Y, Z) to a global coordinate system f ij (x, y, z), we can obtain:
Figure BDA0003658083200000067
preferably, said R is ij Is composed of (x) i ,y i ,z i ) And (x) j ,y j ,z j ) Constant matrix of representation:
Figure BDA0003658083200000071
wherein:
Figure BDA0003658083200000072
Figure BDA0003658083200000073
Figure BDA0003658083200000074
Figure BDA0003658083200000075
Figure BDA0003658083200000076
where (x, y, z) represents global coordinate system coordinates, the total proximity length may be expressed as:
Figure BDA0003658083200000077
n represents the total number of sensors receiving the acoustic emission signals, and the coordinate where the TCF maximum value is located in the model space is generally regarded as a defect positioning result.
(III) advantageous effects
Compared with the prior art, the invention provides a method for positioning multiple defects of a steel pipe based on acoustic emission signal modal decomposition, which has the following beneficial effects:
1. the steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition can realize simultaneous detection of multiple defects in a steel pipe, and provides an improved acoustic emission signal detection algorithm and an acoustic emission source arrival time difference positioning algorithm.
2. According to the method for positioning the defects at multiple positions of the steel pipe based on acoustic emission signal modal decomposition, the traditional acoustic emission signal is a vibration wave signal (hereinafter collectively referred to as a p-wave signal) generated at the defect position when the pressure is applied to the pipe wall of the steel pipe from the outside and the pipe wall is subjected to structural stress, and the defect position is unknown, so that the initial time position of the acoustic emission wave is generated, and the system has the problems of reaction time of an inductor, uncertain system delay time, clock errors among system synchronous inductors and the like, therefore, the error factors can be eliminated by adopting the positioning algorithm provided by the model, and the optimal value is taken as a defect coordinate, so that the positioning accuracy is improved. The method is also suitable for positioning a plurality of defect positions which are detected simultaneously or in extremely small time difference.
Drawings
FIG. 1 is a general equipment map of acoustic emission signal detection and localization;
FIG. 2 is a flow chart of defect detection of acoustic emission signals.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without making any creative effort based on the embodiments in the present invention, belong to the protection scope of the present invention.
Referring to fig. 1-2, the present invention provides a technical solution: the method for positioning the defects of the steel pipe on the basis of the modal decomposition of the acoustic emission signals comprises the following steps:
s1, system deployment
The system composed of an acoustic emission defect detection device, an induction device for receiving acoustic emission signals and a central data processing server is deployed in an environment (such as an underground pipe gallery and the like) in which the defect of a steel pipe is difficult to detect manually, wherein an acoustic emission defect detection device is used for detecting the defect of the steel pipe, so that the acoustic emission signals generated at the defect of the steel pipe are transmitted along the wall of the steel pipe, an acoustic emission signal receiving device is used for collecting the acoustic emission signals, the data processing center and the defect positioning system comprise a steel pipe body model and a relative attachment position of an inductor, an acoustic emission signal processing classification system and a defect positioning analysis algorithm, the acoustic emission signals are received through an external inductor module, and receiving inductors at different positions can receive the acoustic emission signals simultaneously transmitted by the same acoustic emission signal source.
S2, analyzing the signal
The system model analyzes acoustic emission signals generated by the defects arriving at the same time in consideration of the condition that the defect detection device detects a plurality of defects at the same time or the difference time is not large, and optimizes a positioning algorithm by using an acoustic emission source with the time difference of arrival to distinguish and accurately position to a multi-defect coordinate, wherein the acoustic emission signal processing module is used for sensing, decomposing and classifying all signals acquired by each sensor at each moment to obtain the optimal positioning precision.
S3, signal algorithm decomposition
The method comprises the following steps of decomposing received acoustic emission signals by using an improved acoustic emission signal detection algorithm, converting original acoustic emission signals into time-frequency spectrum signals (Hilbert-Huang spectrum) by using an improved adaptive noise-based modal decomposition algorithm (I-CEEMDAN), clearly expressing the time-frequency characteristics of each acoustic emission signal, processing the signals after acoustic emission signals are acquired by acoustic emission sensors attached to two sides of a steel pipe, decomposing the acoustic emission signals by using an improved adaptive noise-based modal decomposition model to obtain modal components, and assuming that the acoustic emission signals are x (n):
x (i) (n)＝x(n)+β 0 E 1 (ω (i) (n)),(i＝1,2,...,I)
wherein x (i) (n) represents the signal x (n) at time i and white Gaussian noise ω (i) The sum of the weights of (n),
Figure BDA0003658083200000091
a complex number (k 1, 2.) representing the k-th empirical mode component obtained by empirical mode decomposition, and then a first residual wave component r of the signal x (n) is calculated 1 、
Figure BDA0003658083200000092
Wherein
Figure BDA0003658083200000093
Representing an averaging operation by determining a first residual component r 1 Then, combining with the original signal x (n), the first IMF can be calculated 1 ：
IMF 1 ＝x(n)-r 1 (n) performing the calculation of the IMF of the first empirical mode component 1 Thereafter, the calculation of the second IMF component is started 2 ：
Figure BDA0003658083200000101
Wherein r is 2 Is the second residual wave component, and so on, the k-th residual wave component r can be obtained k (K3.., K), the K-th modal component IMF can be obtained k ：
Figure BDA0003658083200000102
IMF k ＝r k-1 -r k
Finally, the original signal can be seen as the sum of the modal and residual components, i.e.:
Figure BDA0003658083200000103
after obtaining the IMF components, the time-frequency spectrum signal of each modal component is obtained using a hilbert transform, which for signal x (t) is hilbert transformed to:
Figure BDA0003658083200000104
where P is the Cauchy principal value, the instantaneous amplitude of the signal is α (t), the instantaneous phase is θ (t), and the instantaneous frequency is ω (t):
Figure BDA0003658083200000105
Figure BDA0003658083200000106
Figure BDA0003658083200000107
the time-frequency spectrum can be expressed as H (ω, t), generally speaking, the time-frequency spectrum of a signal can be regarded as a matrix, and according to the singular value decomposition principle, all key features of the matrix can be sequentially obtained in the form of a series of singular values, and according to the similarities and differences of the key features, acoustic emission signals from different defects obtained by each inductor can be classified, so that the time of each acoustic emission signal reaching each inductor can be obtained, and signals from the same acoustic emission source reaching different inductors can be classified according to the frequency amplitude, thereby realizing the simultaneous detection of multiple defects.
S4, signal classification
After the time-frequency spectrum signals are obtained, the data volume can be effectively compressed and the input characteristics of the signals can be extracted through a singular value decomposition algorithm, so that the signals of each frequency can be classified, namely, the signals of the same acoustic emission signal source collected by different sensors at the same moment can be classified, the time of the signals reaching each sensor can be obtained, the processes are circularly processed, the data of the signals emitted by all defects detected in the pipeline by the detection equipment can be obtained, and the data are stored in real time.
S5, determining the optimal position defect
Aiming at the problem that the positioning accuracy of an acoustic emission event is low under a small sample, a VFOM (virtual field optimization method) positioning algorithm based on multi-scale grid search is provided to determine the optimal position defect, the classification data obtained in the steps are integrated, the time of the acoustic emission signal reaching different sensors can be obtained, whether the acoustic emission signal is the same acoustic emission source signal or not can be distinguished through parameters such as the frequency, the amplitude and the like of the signal, then, according to the actual size and structure condition of the steel pipe, the planning position coordinate and the acoustic emission wave speed of the determined sensors are determined, and under the condition of considering the system clock error, an acoustic emission signal positioning model can be expressed as follows:
Figure BDA0003658083200000111
wherein (x) j ,y j ,z j ) Denotes the physical position of the jth sensor, (x) 0 ,y 0 ,z 0 ) Indicating the coordinates of the defect position, is to be calculated, t 0 Representing the starting time, t, of the acoustic emission signal from the defect j Indicates the time, T, at which the signal reaches the physical location of the jth sensor j Represents the system error time, v, of the jth sensor ae The method comprises the following steps of representing the propagation speed of acoustic emission signals in a steel pipe, enabling the acoustic emission signals emitted based on different defects to reach different sensors in different time, classifying the same acoustic emission signals, and performing a virtual field optimization algorithm based on multi-scale search:
Figure BDA0003658083200000112
the above formula represents the simultaneous equations that the same acoustic emission source signal respectively reaches the ith and jth inductors at different times, and after the local coordinate systems of the pipeline and the inductors are determined, the above formula can be rewritten as follows according to the operation rule of the time difference hyperbolic equation:
Figure BDA0003658083200000121
wherein:
Figure BDA0003658083200000122
Figure BDA0003658083200000123
Figure BDA0003658083200000124
since the local coordinate system coordinates of sensor i and sensor j are known, c ij Can be calculated. Establishing an attenuation function f for the acoustic emission signals emitted by the defect coordinates (X, Y, Z) as the acoustic emission signals decrease in energy with increasing distance ij (X,Y,Z)：
Figure BDA0003658083200000125
Wherein d is ij Representing the distance from the acoustic emission signal source to the coordinate curved surface of the sensor:
Figure BDA0003658083200000126
then the local coordinate system f ij (X, Y, Z) to a global coordinate system f ij (x, y, z), we can obtain:
Figure BDA0003658083200000127
R ij is composed of (x) i ,y i ,z i ) And (x) j ,y j ,z j ) Constant matrix of representation:
Figure BDA0003658083200000131
wherein:
Figure BDA0003658083200000132
Figure BDA0003658083200000133
Figure BDA0003658083200000134
Figure BDA0003658083200000135
Figure BDA0003658083200000136
where (x, y, z) represents global coordinate system coordinates, the total proximity length may be expressed as:
Figure BDA0003658083200000137
n represents the total number of the sensors receiving the acoustic emission signals, and the coordinate of the TCF maximum value in the model space is generally regarded as a defect positioning result.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.

Claims (8)

1. The method for positioning the defects of the steel pipe based on acoustic emission signal modal decomposition is characterized by comprising the following steps of:
s1, system deployment
The system composed of an acoustic emission defect detection device, an induction device for receiving acoustic emission signals and a central data processing server is deployed in an environment (such as an underground pipe gallery and the like) in which the defect of a steel pipe is difficult to detect manually, wherein an acoustic emission defect detection device is used for detecting the defect of the steel pipe, so that the acoustic emission signals generated at the defect of the steel pipe are transmitted along the wall of the steel pipe, an acoustic emission signal receiving device is used for collecting the acoustic emission signals, the data processing center and the defect positioning system comprise a steel pipe body model and a relative attachment position of an inductor, an acoustic emission signal processing classification system and a defect positioning analysis algorithm, the acoustic emission signals are received through an external inductor module, and receiving inductors at different positions can receive the acoustic emission signals simultaneously transmitted by the same acoustic emission signal source.
S2, analyzing the signal
The system model analyzes acoustic emission signals generated by the defects arriving at the same time in consideration of the condition that the defect detection device detects a plurality of defects at the same time or the difference time is not large, and optimizes a positioning algorithm by using an acoustic emission source with the time difference of arrival to distinguish and accurately position to a multi-defect coordinate, wherein the acoustic emission signal processing module is used for sensing, decomposing and classifying all signals acquired by each sensor at each moment to obtain the optimal positioning precision.
S3, signal algorithm decomposition
The received acoustic emission signals are decomposed by an improved acoustic emission signal detection algorithm, and an original acoustic emission signal can be converted into a time-frequency spectrum signal (Hilbert-Huang spectrum) by a modified adaptive noise-based modal decomposition algorithm (I-CEEMDAN), so that the time-frequency characteristics of each acoustic emission signal can be clearly shown.
S4, signal classification
After the time-frequency spectrum signals are obtained, the data volume can be effectively compressed and the input characteristics of the signals can be extracted through a singular value decomposition algorithm, so that the signals of each frequency can be classified, namely, the signals of the same acoustic emission signal source collected by different sensors at the same moment can be classified, the time of the signals reaching each sensor can be obtained, the processes are circularly processed, the data of the signals emitted by all defects detected in the pipeline by the detection equipment can be obtained, and the data are stored in real time.
S5, determining the optimal position defect
Aiming at the problem that the positioning accuracy of acoustic emission events under a small sample is low, a VFOM (visual field optimization method) positioning algorithm based on multi-scale grid search is provided to determine the optimal position defect.
2. The method for locating the defects on the steel pipe based on the modal decomposition of the acoustic emission signal according to claim 1, wherein in the step S3, after the acoustic emission sensors attached to the two sides of the steel pipe acquire the acoustic emission signals, the acoustic emission sensors first need to process the acoustic emission signals, and the acoustic emission signals are decomposed by using a modal decomposition model based on the improved adaptive noise to obtain a modal component, wherein the acoustic emission signals are assumed to be x (n):
x (i) (n)＝x(n)+β 0 E 1 (ω (i) (n)),(i＝1,2,...,I)
wherein x (i) (n) represents the signal x (n) at time i and white Gaussian noise ω (i) The sum of the weights of (n),
Figure FDA0003658083190000021
a complex number (k 1, 2.) representing the k-th empirical mode component obtained by empirical mode decomposition, and then calculating a first residual wave component of the signal x (n)
Figure FDA0003658083190000022
Wherein
Figure FDA0003658083190000023
Representing an averaging operation by determining a first residual component r 1 Then, combining with the original signal x (n), the first IMF can be calculated 1 ：
IMF 1 ＝x(n)-r 1 (n)。
3. Acoustic emission-based message in accordance with claim 2The method for positioning the defects of the steel pipe by number modal decomposition is characterized in that the calculation is completed to obtain a first empirical mode component IMF 1 Thereafter, the calculation of the second IMF component is started 2 ：
Figure FDA0003658083190000031
Wherein r is 2 Is the second residual wave component, and so on, the k-th residual wave component r can be obtained k (K3.., K), the K-th modal component IMF can be obtained k ：
Figure FDA0003658083190000032
IMF k ＝r k-1 -r k
Finally, the original signal can be seen as the sum of the modal and residual components, i.e.:
Figure FDA0003658083190000033
4. the method for locating defects in a steel pipe based on modal decomposition of acoustic emission signals as claimed in claim 3, wherein after obtaining IMF components, time-frequency spectrum signals of each modal component are obtained by Hilbert transform, and for signals x (t), the Hilbert transform is:
Figure FDA0003658083190000034
where P is the Cauchy principal value, the instantaneous amplitude of the signal is α (t), the instantaneous phase is θ (t), and the instantaneous frequency is ω (t):
Figure FDA0003658083190000035
Figure FDA0003658083190000036
Figure FDA0003658083190000037
5. the method for locating multiple defects in a steel pipe based on modal decomposition of acoustic emission signals of claim 1, wherein in step S3, the time-frequency spectrum can be represented as H (ω, t), generally speaking, the time-frequency spectrum of a signal can be regarded as a matrix, and according to the singular value decomposition principle, all the key features of the matrix can be sequentially obtained in the form of a series of singular values, and according to the similarities and differences of the key features, the acoustic emission signals from different defects obtained by each sensor can be classified, so that the time of arrival of each acoustic emission signal at each sensor can be obtained, and according to the frequency amplitude, the signals from the same acoustic emission source at different sensors can be classified, thereby realizing the simultaneous detection of multiple defects.
6. The steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition according to claim 1, wherein in step S5, the classification data obtained in the above steps are integrated to obtain the time when the acoustic emission signal reaches different sensors, and through the parameters such as frequency and amplitude of the signal, whether the acoustic emission signal is the same acoustic emission source signal can be distinguished, and then according to the actual size structure condition of the steel pipe, according to the determined sensor planning position coordinates and acoustic emission wave speed, under the condition of considering the system clock error, the acoustic emission signal positioning model can be expressed as:
Figure FDA0003658083190000041
wherein (x) j ,y j ,z j ) Indicates the physical location of the jth sensor, (x) 0 ,y 0 ,z 0 ) Indicating the coordinates of the defect position, is to be calculated, t 0 Representing the starting time, t, of the acoustic emission signal from the defect j Indicates the time, T, at which the signal reaches the physical location of the jth sensor j Represents the system error time of the jth sensor, v ae The method comprises the following steps of representing the propagation speed of acoustic emission signals in a steel pipe, enabling the acoustic emission signals emitted based on different defects to reach different sensors in different time, classifying the same acoustic emission signals, and performing a virtual field optimization algorithm based on multi-scale search:
Figure FDA0003658083190000042
the above formula represents the simultaneous equations that the same acoustic emission source signal respectively reaches the ith and jth inductors at different times, and after the local coordinate systems of the pipeline and the inductors are determined, the above formula can be rewritten as follows according to the operation rule of the time difference hyperbolic equation:
Figure FDA0003658083190000051
wherein:
Figure FDA0003658083190000052
Figure FDA0003658083190000053
Figure FDA0003658083190000054
7. the method of claim 6 based onThe method for positioning the defects of the steel pipe by modal decomposition of the acoustic emission signals is characterized in that c is obtained by knowing the coordinates of the local coordinate systems of the sensor i and the sensor j ij Can be calculated. Establishing an attenuation function f for the acoustic emission signals emitted by the defect coordinates (X, Y, Z) as the acoustic emission signals decrease in energy with increasing distance ij (X,Y,Z)：
Figure FDA0003658083190000055
Wherein d is ij Representing the distance from the acoustic emission signal source to the curved surface of the sensor coordinate:
Figure FDA0003658083190000056
then the local coordinate system f ij (X, Y, Z) to a global coordinate system f ij (x, y, z), we can obtain:
Figure FDA0003658083190000057
8. the method for locating the defects of the steel pipe based on the modal decomposition of the acoustic emission signal according to claim 7, wherein R is ij Is composed of (x) i ,y i ,z i ) And (x) j ,y j ,z j ) Constant matrix of representation:
Figure FDA0003658083190000061
wherein:
Figure FDA0003658083190000062
Figure FDA0003658083190000063
Figure FDA0003658083190000064
Figure FDA0003658083190000065
Figure FDA0003658083190000066
where (x, y, z) represents global coordinate system coordinates, the total proximity length may be expressed as:
Figure FDA0003658083190000067
n represents the total number of sensors receiving the acoustic emission signals, and the coordinate where the TCF maximum value is located in the model space is generally regarded as a defect positioning result.
CN202210567458.XA 2022-05-23 2022-05-23 Steel pipe multi-defect positioning method based on acoustic emission signal modal decomposition Pending CN115097012A (en)

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CN117207070B (en) * 2023-09-01 2024-04-23 四川普什宁江机床有限公司 Automatic grinding wheel dressing method based on numerical control machine tool

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