CN117053755A - Pipeline bending strain detection method, system, equipment and medium based on inertia - Google Patents
Pipeline bending strain detection method, system, equipment and medium based on inertia Download PDFInfo
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Abstract
The invention discloses a method, a system, equipment and a medium for detecting bending strain of a pipeline based on inertia, wherein the method comprises the steps of acquiring attitude information of the pipeline with preset length, acquired by an IMU; performing wavelet transformation on the attitude information to obtain first target attitude information; the sliding window is used for carrying out sliding filtration on the first target attitude information according to a preset sequence and a preset window width, and a data window is determined once in each sliding of the sliding window on the first target attitude information; determining fitting coefficients through minimum mean square error according to each data window; taking the numerical value at the central point of each data window as a filtering value, determining a filtering sequence according to each filtering value and the fitting coefficient, and taking the filtering sequence as second target attitude information; determining the bending strain force of the pipeline according to the second target attitude information; and detecting the bending strain of the pipeline according to the bending strain force of the pipeline, wherein the attitude information calculated by the IMU is affected by noise.
Description
Technical Field
The invention relates to the technical field of oil and gas pipeline detection, in particular to a pipeline bending strain detection method, system, equipment and medium based on inertia.
Background
Due to the high pressure and long distance characteristics, the pipeline may be subjected to displacement and deformation by external forces due to geological disasters (such as earthquakes, landslides, permanent frozen soil thawing or swelling) and other third party damage. These deformations and displacements subject the pipe to normal internal pressure loads and to bending strain forces. The presence of bending strain forces severely affects the structural integrity and operational safety of the pipe, and especially severe defects with bending strain forces are more prone to failure. Therefore, inspection of long-distance pipelines by bending strain is an important concern for pipeline operators in recent years, which has important significance in preventing accidents and ensuring pipeline safety.
The most important method for detecting pipeline defects is pipeline online detection. Compared with the detection of an external pipeline, the method has the advantages of low cost, high efficiency, identification and the like. The IIT robot uses a conveying medium as a driving force to perform nondestructive detection on deformation, corrosion, cracks and the like of the pipeline. Provides scientific basis for pipeline operation, maintenance and safety evaluation. IIT robots use IMUs (inertial sensors Inertial Measurement Unit) based on strapdown inertial navigation techniques to map the centerline coordinates and calculate the bending strain force. However, the cups or support wheels of ILI robots encounter the pipe walls, girth or spiral welds, and other features, resulting in slight oscillations and vibrations during the inspection process, and the calculated pose information of the IMU is affected by these noises.
Disclosure of Invention
In order to solve the problem that the attitude information calculated by the IMU is affected by noise, the invention provides an inertial-based pipeline bending strain detection method, an inertial-based pipeline bending strain detection system, inertial-based pipeline bending strain detection equipment and an inertial-based pipeline bending strain detection medium.
In order to solve the technical problem, the present invention provides a method for detecting bending strain of a pipeline based on inertia, comprising the following steps:
acquiring attitude information of a pipeline with preset length acquired by an IMU;
performing wavelet transformation on the attitude information to remove the influence of high-frequency noise from system noise and vibration in the IMU and obtain first target attitude information;
the sliding window is used for carrying out sliding filtration on the first target attitude information according to a preset sequence and a preset window width, and a data window is determined once in each sliding of the sliding window on the first target attitude information;
determining fitting coefficients through minimum mean square error according to each data window;
taking the numerical value at the central point of each data window as a filtering value, determining a filtering sequence according to each filtering value and fitting coefficients, and taking the filtering sequence as second target attitude information, wherein the second target attitude information is the influence of other noise except high-frequency noise from system noise and vibration in the IMU;
determining the bending strain force of the pipeline according to the second target attitude information;
and detecting the bending strain of the pipeline according to the bending strain force of the pipeline.
In a second aspect, the present invention provides an inertia-based pipe bending strain detection system comprising:
the attitude information acquisition module is used for acquiring the attitude information of the pipeline with the preset length acquired by the IMU;
the first target attitude information determining module is used for carrying out wavelet transformation on the attitude information, removing the influence of high-frequency noise from system noise and vibration in the IMU and obtaining first target attitude information;
the data window determining module is used for carrying out sliding filtration on the first target gesture information through the sliding window according to a preset sequence and a preset window width, and determining one data window every time the sliding window slides on the first target gesture information;
the fitting coefficient determining module is used for determining fitting coefficients through minimum mean square error according to each data window;
the fitting curve determining module is used for determining a fitting curve according to each data window and the fitting coefficient;
the second target attitude information determining module is used for determining a filtering sequence according to each filtering value and the fitting coefficient by taking the value at the central point of each data window as a filtering value, and taking the filtering sequence as second target attitude information which is the influence of other noise except high-frequency noise from system noise and vibration in the IMU;
the pipeline bending strain force determining module is used for determining pipeline bending strain force according to the second target attitude information;
and the detection module is used for detecting the pipeline bending strain according to the pipeline bending strain force.
In a third aspect, the present invention also provides a computing device comprising a memory, a processor and a program stored on the memory and running on the processor, the processor implementing the steps of the inertia-based pipeline bending strain detection method as described above when the program is executed by the processor.
In a fourth aspect, the present invention also provides a computer readable storage medium having instructions stored therein which, when run on a terminal device, cause the terminal device to perform the steps of an inertia based pipe bending strain detection method.
The inertia-based pipeline bending strain detection system provided by the invention has the beneficial effects that: the method comprises the steps of performing wavelet transformation on gesture information, removing the influence of high-frequency noise from system noise and vibration in an IMU to obtain first target gesture information, sliding on the first target gesture information through a sliding window to obtain a data window, constructing a filtering sequence by taking a corresponding numerical value at the center point of the data window as a filtering value, combining fitting coefficients, taking the filtering sequence as second target gesture information, removing the influence of other noise except the high-frequency noise from the system noise and vibration in the IMU, determining the bending strain force of a pipeline through the second target gesture information, detecting the bending strain of the pipeline according to the bending strain force of the pipeline, removing the noise twice, improving the accuracy of gesture information calculated by the IMU, and solving the problem that the gesture information calculated by the IMU is affected by noise.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the present invention is further described below with reference to the drawings and the embodiments.
FIG. 1 is a flow chart of a method for inertia-based pipeline bending strain detection according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of an S-G filter;
FIG. 3 is a diagram of the original pose information before noise processing acquired by the IIT robot;
FIG. 4 is a graph of calculated bending strain force prior to noise processing;
FIG. 5 is a diagram of the noise-removed target pose information obtained by the IIT robot;
FIG. 6 is a graph of calculated bending strain force after noise processing;
FIG. 7 is a graph comparing target pose information under different filtering methods;
FIG. 8 is a graph comparing bending strain forces for different filtering methods;
FIG. 9 is a graph comparing bending strain forces for different filtering methods with respect to straight dip and different dip prior to denoising;
FIG. 10 is a graph of different displacement of bending strain under the method of the present embodiment;
FIG. 11 is a graph comparing absolute deviations of bending strain forces for different filtering methods;
FIG. 12 is a schematic diagram of an inertial-based pipe bending strain detection system according to an embodiment of the present invention.
Detailed Description
The following examples are further illustrative and supplementary of the present invention and are not intended to limit the invention in any way.
The following describes a method, a system, a device and a medium for detecting bending strain of a pipeline based on inertia according to the embodiment of the invention with reference to the accompanying drawings.
As shown in fig. 1, an embodiment of the present invention provides an inertia-based pipe bending strain detection method, which includes the steps of:
s1, acquiring attitude information of a pipeline with a preset length, which is acquired by an IMU.
The preset length can be adjusted according to actual conditions.
S2, carrying out wavelet transformation on the attitude information, and removing the influence of high-frequency noise from system noise and vibration in the IMU to obtain first target attitude information.
The first target pose information is obtained by a Symlets wavelet transform filter.
S3, sliding and filtering the first target posture information through the sliding window according to a preset sequence and a preset window width, and determining a data window once sliding the sliding window on the first target posture information.
And S4, determining a fitting coefficient through minimum mean square error according to each data window.
S5, taking the numerical value at the central point of each data window as a filtering value, determining a filtering sequence according to each filtering value and the fitting coefficient, and taking the filtering sequence as second target posture information, wherein the second target posture information is the influence of other noise except high-frequency noise from system noise and vibration in the IMU.
The second target pose information is obtained by a S-G (Savitzky-Golay) filter.
Other noise besides high frequency noise from system noise and vibrations in the IMU include girth weld noise, spiral weld noise, etc. added to the attitude information of the ILI robot during long distance and long time detection.
S6, determining the bending strain force of the pipeline according to the second target attitude information.
And S7, detecting the bending strain of the pipeline according to the bending strain force of the pipeline.
In the embodiment, the influence of system noise and vibration high-frequency noise from the IMU is removed by carrying out wavelet transformation on the gesture information to obtain first target gesture information, then the sliding window slides on the first target gesture information to obtain a data window, the corresponding numerical value at the center point of the data window is used as a filtering value, a filtering sequence is constructed by combining fitting coefficients, the filtering sequence is used as second target gesture information, the influence of other noise except the high-frequency noise from the IMU and vibration high-frequency noise is removed, finally the pipeline bending strain force is determined through the second target gesture information, the pipeline bending strain is detected according to the pipeline bending strain force, the noise is removed twice, the accuracy of the gesture information calculated by the IMU is improved, and the problem that the gesture information calculated by the IMU is influenced by the noise is solved.
Optionally, performing wavelet transformation on the gesture information to remove the influence of high-frequency noise from system noise and vibration in the IMU, to obtain first target gesture information, including: and carrying out continuous wavelet transformation or discrete wavelet transformation on the gesture information to obtain first target gesture information.
The continuous wavelet transformation and the discrete wavelet transformation are realized by using Symlets wavelet transformation filter, the wavelet transformation is a time-frequency signal analysis method, the time domain and the frequency domain have good positioning characteristics, the wavelet consists of a series of wavelet basis functions, and the local characteristics of the signal in the time domain and the frequency domain can be described. It can perform multi-scale analysis on the signal and analyze the signal in any time or space domain, with higher frequency resolution in the high frequency part of the signal and higher time resolution in the low frequency part, thus, less information loss when processing signals with less discontinuities, which can reduce phase distortion during signal decomposition and reconstruction, thereby removing the effects of high frequency noise from system noise and vibrations in the IMU.
Optionally, performing continuous wavelet transformation on the gesture information to obtain first target gesture information, where the formula is as follows:
wherein CWT x (a, b) represents first target posture information, x (t) represents posture information, ψ * (x) Represents a mother wavelet function, a represents a scale factor, b represents a shift factor, N represents the order of a wavelet transform filter, and w k Representing coefficients of a wavelet transform filter, phi (t) representing a scale function, Q (z) representing a predetermined polynomial, the result of W (z) being taken as W k Is a value of (2).
Optionally, performing discrete wavelet transform on the gesture information to obtain first target gesture information:
wherein, DWT x (j, k) represents first target posture information, j and k represent integer parameters, x (t) represents posture information, ψ * (x) Represents a mother wavelet function, a represents a scale factor, b represents a shift factor, N represents the order of a wavelet transform filter, and w k Representing coefficients of a wavelet transform filter, phi (t) representing a scale function, Q (z) representing a predetermined polynomial, the result of W (z) being taken as W k Is a value of (2).
Whether continuous wavelet transform or discrete wavelet transform is used, the mother wavelet function must satisfy the following condition:
(1) Limited support: ψ (t) =0, when t <0 or t >1.
(2) Orthogonality:where δ (k) is a dirac function.
(3) Zero mean:
(3) High-pass filtering:wherein->Is the fourier transform of ψ (t).
In addition, symlet wavelets are derived from Daubechies wavelets, which are all generated by a low pass filter W, which can be decomposed into:
where N is the order of the filter and Q (z) is a polynomial. The Daubechies wavelet is the smallest phase root of Q (z) is chosen as the coefficient of the filter, while the Symlet wavelet is the closest linear phase root of Q (z) is chosen as the coefficient of the filter, so that the Symlet wavelet has better symmetry and approximately linear phase, while the Daubechies wavelet has better frequency selectivity and faster decay rate.
Optionally, the sliding window is used for performing sliding filtration on the first target gesture information according to a preset sequence and a preset window width, and a data window is determined once in sliding on the first target gesture information, and the formula is as follows:
wherein y (n) represents an nth data window, D represents the total number of sliding times of the sliding window, n k Represents an nth order polynomial, a k Indicating the kth slide.
The preset sequence and the preset window width can be adjusted according to actual conditions, in this embodiment, the preset window width is 2m+1, m represents a preset value, a large low-order sliding window can cause signal distortion, meanwhile, the type of an absorption line is widened, required information is difficult to be reserved, a high-order small sliding window can better reserve signal information, but the filtering effect of the high-order small sliding window on noise is weak, and therefore, a proper window width needs to be selected.
Optionally, determining the fitting coefficient by minimum mean square error according to each data window includes:
wherein ε D Representing fitting coefficients, x [ n ]]Representing the actual value of the nth data window, and M represents the number of data windows.
The fitting coefficient is determined through the minimum mean square error, so that when the filtering values corresponding to all the data windows are fitted with the weighting values, the influence caused by the error is reduced, and the accuracy of the fitting curve is improved.
As shown in fig. 2, the corresponding value y (i) =a at the center point of each data window (each black point in fig. 1) i As the filter value, where y (i) represents the filter value, a i Representing the ith window data, fitting each filtering value through a fitting coefficient to obtain a filtering sequence y [ n ]](i.e., the fitted curve in FIG. 1), y [ n ]]As second target posture information.
Optionally, determining the pipe bending strain force according to the second target attitude information, with the following formula:
wherein epsilon represents the bending strain force of the pipeline and epsilon v 、ε h Representing the strain force component of the pipeline in the vertical and horizontal directions, respectively, D representing the nominal diameter of the pipeline, k v 、k h Respectively representing the vertical curvature and the horizontal curvature of the pipeline, k represents the total curvature of the pipeline, P is the pitch angle, s is the mileage, A is the heading angle, k v 、k h K are from the second target pose information.
Since the bending strain force of the pipeline is in direct proportion to the curvature change in the elastic deformation range, the curvature of the pipeline should be calculated first, and in the calculation of pipeline positioning, in addition to obtaining the pipeline coordinates under the projection of UTM (katuo projection system Universal Test Message), the second target attitude information of the IIT robot under the local coordinate system is obtained, wherein the pitch change of the IIT robot indicates the change of the inclination angle of the pipeline relative to the horizontal plane in the fixed observation pipeline, and the course angle indicates the included angle between the pipeline along the line direction and the north direction, so that the curvature of the pipeline and the second target attitude information have the following relationship:
the bending strain of a pipe has a direct relationship with the curvature of the pipe, and the bending strain of a pipe has two main components, namely: the circumferential strain amount and the longitudinal strain amount are further divided into an axial parameter and a bending parameter. If the centerline of the pipe is assumed to be the neutral axis, then the bending strain of the pipe and the centerline curvature have the following relationship:
if the pipeline bears real bending strain force, the bending influence range of the pipeline should be large, and the characteristics such as the circumferential weld and the like can cause local strain change, so the strain change caused by the circumferential weld is filtered, and when the primary bending strain force result is obtained as a judging condition of self-adaptive filtering, if the bending strain force is more than 0.125% in the 3m range, the order of the S-G filter is modified, and the self-adaptive S-G filter processes noise attitude information until the bending strain force meets the requirement.
Alternatively, in order to better explain the present embodiment, description will be made by way of a specific example:
9 pipelines with the diameter of about 90m are arranged on 5 supporting walls, strain gauges are arranged in the center of the pipelines, when the strain change is largest, the bending strain detected by the IIT robot is obviously compared, the bending strain of straight lines and different pipeline displacements is analyzed, and all pulling speeds of the test are 1m/s.
As shown in fig. 3-4, the IIT robot is affected by different vibrations during the detection process, such as girth welds, spiral welds, and the like. Gestures such as pitch can be disturbed by these noises and bending strain forces are also inaccurate, which is calculated from the gestures, three types of noises in IIT robots during inspection. The high frequency noise signal is mainly derived from the internal noise of the IIT robot and vibrations occurring during inspection. The small periodic spikes are mainly due to the small effect of IIT robots passing through the pipe spiral weld. There are some large periodic spikes, mainly due to the tremendous impact of IIT robots passing through girth welds. Therefore, the attitude information should be noise-reduced using an effective method.
By using the method of the embodiment to process the original posture information, as shown in fig. 5-6, it can be seen from fig. 5 that the fixed-frequency noise caused by vibration and impact of the spiral weld during the detection process of the IIT robot is eliminated, and the posture change caused by impact when the IIT robot passes through the girth weld is reduced. The calculated bending strain forces before and after filtration are shown in fig. 6, the bending strain forces eliminate noise caused by vibration and pipeline weld impact, and are more accurate for the bending strain forces of the linear pipeline.
In order to verify the proposed method, a comparison is made of the different filtering methods. Fig. 7 shows a comparison of target attitude information obtained by denoising original attitude information by using the wavelet transform filter alone and the method of the present embodiment. Comparison of bending strain forces as shown in fig. 8, it can be seen that the method of the present embodiment is better in denoising effect of the original attitude information, and the bending strain forces are more accurate.
Further verifies the accuracy of the bending strain force change measuring method caused by the bending deformation of the provided pipeline. The strain gauge is used for comparing the detection results of the IIT robot installed in the center of the pull-through pipeline. The pipeline is pulled through to displace different loads by 5 cm, 10 cm, 15 cm and 26.5 cm, and bending deformation of different degrees is simulated. These different bending deformations result in different bending strain forces in the pipe.
Fig. 9 shows a comparison of bending strain forces for straight sinking and different sinking before denoising. It is difficult to obtain a true change in bending strain due to noise. The different displacements of the bending strain are shown in fig. 10. It can be clearly seen that when the pipe has different bending deformations, the bending strain force will change accordingly.
To verify the effectiveness of the method of this embodiment, the pull-through test data was calculated and compared using a cubic spline interpolation filter algorithm (CSP) and Symlets Wavelet (SW) filter method and the method of this embodiment. A comparison of the different filtering methods with respect to bending strain forces is shown in table 1. According to table 1, the strain gauge variation was 0.011% when the pipe was submerged 5 cm. The strain change in the bending strain force was calculated to be 0.0007% and the absolute deviation was 0.0040% before filtering the data. The strain change calculation for the bending strain was 0.015%,0.014% and 0.014% respectively using the three filtering methods, respectively. The absolute deviation was 0.004%, 0.003% and 0.003%, respectively.
For a 10 cm sinking pipe, the strain gauge variation was 0.021%. The strain change of the PBS was calculated to be 0.0362% and the absolute deviation was 0.0152% prior to data filtration. The strain change of the PBS was calculated to be 0.0278%,0.0263% and 0.025%, respectively, using the three filtering methods, respectively. The absolute deviation was 0.0068%, 0.0053% and 0.004%, respectively.
For a 15 cm sinking pipe, the strain gage variation was 0.032%. The strain change of the PBS was calculated to be 0.033% and the absolute deviation was 0.001% prior to data filtration. The strain change of the PBS was calculated to be 0.029%,0.0342% and 0.033% using the three filtering methods, respectively. The absolute deviation was 0.003%, 0.0022% and 0.001%, respectively.
For a 26.5 cm sinking pipe, the strain gage variation was 0.055%. The strain change of PBS was calculated as 0.0778% and the absolute deviation was 0.0228% prior to data filtration. The strain change of the PBS was calculated to be 0.0712%,0.0701% and 0.068% using the three filtering methods, respectively. The absolute deviations were 0.0162%, 0.0151% and 0.013%, respectively.
Table 1 comparison of flexural strain force with different filtration methods
Wherein Pipeline Sank represents Pipeline subsidence, strain changes for gauge measurement represents change of Strain gauge, strain changes represent Strain change of bending Strain, absolute Deviation represents absolute deviation, CSP represents cubic spline interpolation filtering algorithm, SW represents Symlets Wavelet (SW) filtering method, and W-SG is the method of this embodiment.
Comparison of absolute deviations of bending strain forces under different filtering methods as shown in fig. 11, the average absolute deviation before data filtering was 0.0108%. When three filtering methods were used, the mean absolute deviations of the bending strain forces were 0.0075%, 0.0064% and 0.0053%, respectively, and the mean relative deviations were 26.9%, 21.7% and 18.2%, respectively. Clearly, the method of this embodiment has better performance and is more accurate for bending strain forces. From the pull-through test and results, the method provided by the invention can better realize the calculation performance of the bending strain force.
As shown in fig. 12, an embodiment of the present invention further provides an inertia-based pipe bending strain detection system, comprising:
the gesture information acquisition module 101 is used for acquiring gesture information of a pipeline with a preset length acquired by the IMU;
the first target gesture information determining module 102 is configured to perform wavelet transformation on gesture information, remove an influence of high-frequency noise from system noise and vibration in the IMU, and obtain first target gesture information;
the data window determining module 103 is configured to perform sliding filtering on the first target gesture information through a sliding window according to a preset sequence and a preset window width, where the sliding window determines a data window once sliding on the first target gesture information;
a fitting coefficient determining module 104, configured to determine a fitting coefficient according to each data window through a minimum mean square error;
a second target pose information determining module 105, configured to determine a filtering sequence according to each filtering value and a fitting coefficient by using a value at a center point of each data window as a filtering value, and use the filtering sequence as second target pose information, where the second target pose information is an effect of removing other noise except for high-frequency noise from system noise and vibration in the IMU;
a pipe bending strain force determination module 106 for determining a pipe bending strain force from the second target pose information;
and the detection module 107 is used for detecting the pipe bending strain according to the pipe bending strain force.
Optionally, the first target pose information determining module 102 is specifically configured to:
and carrying out continuous wavelet transformation or discrete wavelet transformation on the gesture information to obtain first target gesture information.
Optionally, the first target pose information determining module 102 is specifically configured to:
carrying out continuous wavelet transformation on the attitude information to obtain first target attitude information, wherein the formula is as follows:
wherein CWT x (a, b) represents first target posture information, x (t) represents posture information, ψ * (x) Represents a mother wavelet function, a represents a scale factor, b represents a shift factor, N represents the order of a wavelet transform filter, and w k Representing coefficients of a wavelet transform filter, phi (t) representing a scale function, Q (z) representing a predetermined polynomial, the result of W (z) being taken as W k Is a value of (2).
Optionally, the first target pose information determining module 102 is specifically configured to:
performing discrete wavelet transformation on the attitude information to obtain first target attitude information:
wherein, DWT x (j, k) represents first target posture information, j and k represent integer parameters, x (t) represents posture information, ψ * (x) Represents a mother wavelet function, a represents a scale factor, b represents a shift factor, N represents the order of a wavelet transform filter, and w k Representing coefficients of a wavelet transform filter, phi (t) representing a scale function, Q (z) representing a predetermined polynomial, the result of W (z) being taken as W k Is a value of (2).
Optionally, the data window determining module 103 is specifically configured to:
the sliding window is used for carrying out sliding filtration on the first target attitude information according to a preset sequence and a preset window width, and a data window is determined once in each sliding of the sliding window on the first target attitude information, wherein the formula is as follows:
wherein y (n) represents an nth data window, D represents the total number of sliding times of the sliding window, n k Represents an nth order polynomial, a k Indicating the kth slide.
Optionally, the fitting coefficient determining module 104 is specifically configured to:
determining fitting coefficients by minimum mean square error according to each data window, comprising:
wherein ε D Representing fitting coefficients, x [ n ]]Representing the actual value of the nth data window, and M represents the number of data windows.
Optionally, the pipe bending strain force determination module 106 is specifically configured to:
and determining the bending strain force of the pipeline according to the second target attitude information, wherein the formula is as follows:
wherein epsilon represents the bending strain force of the pipeline and epsilon v 、ε h Representing the strain force component of the pipeline in the vertical and horizontal directions, respectively, D representing the nominal diameter of the pipeline, k v 、k h Respectively represent pipelinesAnd (d) represents the total curvature of the pipe, P is pitch angle, s is mileage, A is heading angle, k v 、k h K are from the second target pose information.
The computing device of the embodiment of the invention comprises a memory, a processor and a program stored on the memory and running on the processor, wherein the processor realizes part or all of the steps of the pipeline bending strain detection method based on inertia when executing the program.
The computing device may be a computer, and correspondingly, the program is computer software, and the parameters and steps in the above embodiments of the inertia-based pipeline bending strain detection method of the present invention are referred to herein and are not described in detail.
Those skilled in the art will appreciate that the present invention may be implemented as a system, method, or computer program product. Accordingly, the present disclosure may be embodied in the following forms, namely: either entirely hardware, entirely software (including firmware, resident software, micro-code, etc.), or entirely software, or a combination of hardware and software, referred to herein generally as a "circuit," module "or" system. Furthermore, in some embodiments, the invention may also be embodied in the form of a computer program product in one or more computer-readable media, which contain computer-readable program code. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing.
In the description of the present specification, a description referring to terms "one embodiment," "some embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms are not necessarily directed to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, the different embodiments or examples described in this specification and the features of the different embodiments or examples may be combined and combined by those skilled in the art without contradiction.
While embodiments of the present invention have been shown and described above, it will be understood that the above embodiments are illustrative and not to be construed as limiting the invention, and that variations, modifications, alternatives and variations may be made to the above embodiments by one of ordinary skill in the art within the scope of the invention.
Claims (10)
1. The method for detecting the bending strain of the pipeline based on inertia is characterized by comprising the following steps of:
acquiring attitude information of a pipeline with preset length acquired by an IMU;
performing wavelet transformation on the attitude information to remove the influence of high-frequency noise from system noise and vibration in the IMU and obtain first target attitude information;
performing sliding filtration on the first target attitude information through sliding windows according to a preset sequence and a preset window width, wherein one data window is determined once the sliding window slides on the first target attitude information;
determining fitting coefficients through minimum mean square error according to each data window;
taking the numerical value at the central point of each data window as a filtering value, determining a filtering sequence according to each filtering value and fitting coefficients, and taking the filtering sequence as second target attitude information, wherein the second target attitude information is the influence of other noise except high-frequency noise from system noise and vibration in the IMU;
determining the bending strain of the pipeline according to the second target attitude information;
and detecting the bending strain of the pipeline according to the bending strain force of the pipeline.
2. The method of claim 1, wherein wavelet transforming the pose information to remove effects of high frequency noise from system noise and vibration in the IMU to obtain first target pose information, comprising:
and carrying out continuous wavelet transformation or discrete wavelet transformation on the attitude information to obtain first target attitude information.
3. The method of claim 2, wherein the pose information is subjected to continuous wavelet transform to obtain first target pose information according to the following formula:
wherein CWT x (a, b) represents first target posture information, x (t) represents posture information, ψ * (x) Represents a mother wavelet function, a represents a scale factor, b represents a shift factor, N represents the order of a wavelet transform filter, and w k Representing coefficients of a wavelet transform filter, phi (t) representing a scale function, Q (z) representing a predetermined polynomial, the result of W (z) being taken as W k Is a value of (2).
4. The method of claim 2, wherein the pose information is subjected to a discrete wavelet transform to obtain first target pose information:
wherein, DWT x (j, k) represents first target posture information, j and k represent integer parameters, x (t) represents posture information, ψ * (x) Represents a mother wavelet function, a represents a scale factor, b represents a shift factor, N represents the order of a wavelet transform filter, and w k Representing coefficients of a wavelet transform filter, phi (t) representing a scale function, Q (z) representing a predetermined polynomial, the result of W (z) being taken as W k Is a value of (2).
5. The method according to claim 1, wherein the sliding filtering is performed on the first target gesture information through a sliding window according to a preset sequence and a preset window width, and a data window is determined for each sliding of the sliding window on the first target gesture information, where a formula is as follows:
wherein y (n) represents an nth data window, D represents the total number of sliding times of the sliding window, n k Represents an nth order polynomial, a k Indicating the kth slide.
6. The method of claim 5, wherein determining fitting coefficients by minimum mean square error based on each data window comprises:
wherein ε D Representation ofFitting coefficient, x [ n ]]Representing the actual value of the nth data window, and M represents the number of data windows.
7. The method of any one of claims 1-5, wherein determining the pipe bending strain force from the second target pose information is formulated as follows:
wherein epsilon represents the bending strain force of the pipeline and epsilon v 、ε h Representing the strain force component of the pipeline in the vertical and horizontal directions, respectively, D representing the nominal diameter of the pipeline, k v 、k h Respectively representing the vertical curvature and the horizontal curvature of the pipeline, k represents the total curvature of the pipeline, P is the pitch angle, s is the mileage, A is the heading angle, k v 、k h K are from the second target pose information.
8. An inertia-based pipe bending strain detection system, comprising:
the attitude information acquisition module is used for acquiring the attitude information of the pipeline with the preset length acquired by the IMU;
the first target attitude information determining module is used for carrying out wavelet transformation on the attitude information, removing the influence of high-frequency noise from system noise and vibration in the IMU and obtaining first target attitude information;
the data window determining module is used for carrying out sliding filtration on the first target gesture information through a sliding window according to a preset sequence and a preset window width, and determining one data window every time the sliding window slides on the first target gesture information;
the fitting coefficient determining module is used for determining fitting coefficients through minimum mean square error according to each data window;
the second target attitude information determining module is used for determining a filtering sequence according to each filtering value and a fitting coefficient by taking a numerical value at the central point of each data window as a filtering value, and taking the filtering sequence as second target attitude information, wherein the second target attitude information is the influence of other noise except high-frequency noise from system noise and vibration in the IMU;
the pipeline bending strain force determining module is used for determining the pipeline bending strain force according to the second target attitude information;
and the detection module is used for detecting the bending strain of the pipeline according to the bending strain force of the pipeline.
9. A computing device comprising a memory, a processor, and a program stored on the memory and running on the processor, wherein the processor, when executing the program, implements the steps of the inertia-based pipe bending strain detection method of any of claims 1-7.
10. A computer readable storage medium having instructions stored therein, which when run on a terminal device, cause the terminal device to perform the steps of the inertia-based pipe bending strain detection method according to any of claims 1-7.
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