A horizontal bidirectional displacement reconstruction method for lattice tower structure based on multi-source heterogeneous data fusion

Abstract

The present invention belongs to the field of lattice tower structure monitoring technology and signal processing, and discloses a horizontal bidirectional displacement reconstruction method of a lattice tower structure based on multi-source heterogeneous data fusion, which simplifies the lattice tower into a thin-walled three-dimensional variable-section cantilever beam, uses a two-dimensional strain-displacement mapping method to calculate the horizontal bidirectional displacement at a low sampling rate, and finally uses a multi-rate Kalman filter algorithm to fuse it with the horizontal bidirectional acceleration to obtain the horizontal bidirectional displacement at a high sampling rate. The data fusion method of the present invention requires a small number of sensors, has a simple calculation process, and has accurate calculation results, and has strong operability and practicality.

Description

Technical Field

[0001] The invention belongs to the field of lattice tower structure health monitoring technology and signal analysis, and in particular relates to a multi-source heterogeneous data fusion method for horizontal bidirectional displacement reconstruction of a lattice tower structure. Background Art

[0002] The purpose of structural health monitoring is to evaluate the health of the structure and provide a reference for whether the structure needs maintenance. It plays a vital role in the safe operation of large-scale infrastructure during its service. In recent years, lattice towers have played an important role in signal base station construction, transmission line engineering and meteorological monitoring. It is extremely necessary to carry out relevant research on structural health monitoring of lattice towers. Dynamic displacement is one of the key parameters for evaluating the safety performance of structures. It has been widely recognized because it provides direct information related to structural deformation. However, it is difficult to directly measure the dynamic displacement of lattice towers due to their complex structural characteristics and cost reasons. It is worthwhile to use existing common data such as acceleration and strain to indirectly reconstruct dynamic displacement.

[0003] In the past few decades, scholars have done a lot of work on the research of dynamic displacement reconstruction. For example, the method of estimating the initial velocity is used to correct the double integration error of acceleration, and the static response of the structure is calculated in segments, but there is no unified guiding principle for how to segment. For details, see PARK KT, KIM SH, PARK HS, et al. The determination of bridge displacement using measured acceleration [J]. Engineering Structures, 2005, 27 (3): 371-378. In addition, strain data is also used to reconstruct dynamic displacement. The vibration displacement of the bridge is estimated using the theoretical displacement mode of the simply supported beam and the strain measured by the fiber Bragg grating sensor, or the strain and displacement-strain relationship collected by the distributed fiber Bragg grating sensor are used to estimate the shape of the rotating structure. For details, see SHIN S, LEE SU, KIM Y, et al. Estimation of bridge displacement responses using FBG sensors and theoretical mode shapes [J]. Structural Engineering and Mechanics, 2012, 42 (2): 229-245. and KIM HI, KANG LH, HAN J H. Shape estimation with distributed fiber Bragg grating sensors for rotating structures [J]. Smart Materials and Structures, 2011, 20 (3): 035011. By using the Kalman filter algorithm to fuse the velocity of the laser Doppler vibrometer and the displacement of the lidar to estimate the dynamic displacement in real time, the shortcomings of the lidar with poor accuracy and low sampling rate are improved, and the dynamic displacement with high sampling rate and high accuracy is obtained. For details, see KIM K, SOHN H. Dynamic displacement estimation by fusing LDV and LiDAR measurements via smoothing based Kalman filtering [J]. Mechanical Systems and Signal Processing, 2017, 82: 339-355. However, the existing data fusion methods are only applicable to the unidirectional displacement reconstruction of uniform cross-section beam structures, and are not applicable to variable cross-section structures such as lattice towers. In addition, most of the dynamic displacements generated by actual structures are synthesized from horizontal bidirectional displacements.There is no corresponding data fusion method for horizontal bidirectional displacement reconstruction of complex structures such as lattice tower structures.

[0004] In view of the deficiency that the existing data fusion methods are only applicable to the reconstruction of unidirectional displacement of beams with uniform cross-section, the present invention proposes a data fusion method suitable for the reconstruction of horizontal bidirectional displacement of lattice tower structures. The core of the data fusion method is to evenly arrange strain sensors within the height range of the tower body and to arrange acceleration sensors at the displacement measurement points. Then, the strain is decomposed along the in-plane and out-of-plane vibration directions, and the strain-derived displacement is calculated using the strain-displacement mapping method. The mature multi-rate Kalman filter algorithm is used to process the acceleration and strain-derived displacement to calculate the horizontal bidirectional dynamic displacement with higher sampling rate and accuracy, thereby providing a new method for indirectly measuring the horizontal bidirectional dynamic displacement of lattice tower structures. Summary of the invention

[0005] The present invention provides a new data fusion method for reconstructing the horizontal bidirectional displacement of a lattice tower structure, and provides a new idea for the indirect measurement of the horizontal bidirectional dynamic displacement of a lattice tower structure.

[0006] The technical solution of the present invention is as follows: the lattice tower is simplified into a thin-walled three-dimensional variable-section cantilever beam, a two-dimensional strain-displacement mapping method is used to calculate the horizontal bidirectional displacement with a low sampling rate, and finally a multi-rate Kalman filter algorithm is used to fuse it with the horizontal bidirectional acceleration to obtain the horizontal bidirectional displacement with a high sampling rate; the steps are as follows:

[0007] (1) 2M strain sensors are evenly arranged at the height of the two main members on the left and right of the lattice tower, and the number of strain sensors is at least 8; a horizontal bidirectional acceleration sensor is arranged at the displacement measurement point;

[0008] (2) The data collected by the strain sensor {ε left} M×1 and {ε right} M×1 According to the two main vibration directions, it can be decomposed into {ε y} M×1 and {ε z} M×1 , y and z are the directions of in-plane and out-of-plane vibration of the lattice tower, respectively;

[0009]

[0010]

[0011] (3) The decomposed strain data are processed using the random subspace analysis (SSI) method, and a stability diagram is drawn based on the processing results. The order n of the vibration mode involved is determined based on the obtained stability diagram, where n is a natural number and does not exceed M. The first n-order strain mode matrices in two directions are extracted. and

[0012] (4) Calculate the functional relationship y(x) and z(x) between the horizontal distance y and z of any point of the main material to the two neutral layers and the height x of the point from the ground based on the lattice tower;

[0013] (5) The first n-order strain vibration modes in both directions are respectively fitted with the height x of the strain sensor arrangement point from the ground to obtain the strain vibration mode function and Then put and The whole is regarded as a function, and it is expanded according to Taylor's formula. The expanded result is double-integrated and substituted into the boundary conditions of the bottom fixed connection of the lattice tower structure to obtain the displacement vibration function and

[0014]

[0015]

[0016]

[0017]

[0018] (6) Strain mode matrix of lattice tower and And the strain data after orthogonal decomposition {ε y} M×1 and {ε z} M×1 When the modal coordinates {q y} n×1 and {q z} n×1 ;

[0019]

[0020]

[0021] (7) Substitute the height coordinate x of the displacement point to be measured on the lattice tower into the two displacement vibration mode functions respectively. and The corresponding displacement vibration function value is obtained, and the obtained displacement vibration function value and the modal coordinate are multiplied to obtain the low sampling rate dynamic displacement u of the point y and u z ;

[0022] (8) The y-axis acceleration a of the displacement point to be measured collected by the acceleration sensor y and the calculated y displacement u y As a set of state variables, the z-axis acceleration a z and the calculated z-direction displacement u z As another set of state variables, they are input into the multi-rate Kalman filter algorithm to reconstruct the final high sampling rate horizontal bidirectional dynamic displacement. For details of the Kalman filter algorithm, see KIM J, KIMK, SOHN H. Autonomous dynamic displacement estimation from data fusion of acceleration and intermittent displacement measurements [J]. Mechanical Systems and Signal Processing, 2014, 42 (1-2): 194-205.

[0023] Beneficial effects of the present invention:

[0024] (1) The proposed two-dimensional strain-displacement mapping method can directly calculate the horizontal bidirectional dynamic displacement of the corresponding sampling rate from the strain, solving the problem of difficulty in directly calculating the displacement from the strain;

[0025] (2) The proposed data fusion method uses acceleration and strain-derived displacement as input values. It only needs to install strain and acceleration sensors on the lattice tower to achieve high sampling rate horizontal bidirectional displacement reconstruction.

[0026] (3) This method can comprehensively utilize the information of multiple sensors to realize the fusion of multi-source heterogeneous data, and can measure the horizontal bidirectional dynamic displacement of the lattice tower in real time. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A flow chart for the implementation of the present invention;

[0028] Figure 2 Figure 2 is the sensor layout diagram of the lattice tower; (a) is the front view of the lattice tower, where the circle represents the strain sensor, the square is the horizontal bidirectional acceleration sensor, and the X-axis also represents the imaginary neutral layer; (b) is the side view of the lattice tower, where the X-axis also represents the imaginary neutral layer. DETAILED DESCRIPTION

[0029] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] See also Figure 1 to Figure 2 , an embodiment of the present invention proposes a data fusion method for horizontal bidirectional displacement reconstruction of a lattice tower structure.

[0031] Implementation case data source: see ZHANG Q, FU X, REN L, et al. Modal parameters of a transmission tower considering the coupling effects between the tower and lines [J]. Engineering Structures, 2020, 220: 110947.

[0032] In the embodiment of the present invention, the establishment of the numerical model of the lattice tower and the transient analysis can be done by self-compiled programs or related commercial software. This embodiment takes the widely used finite element analysis software ANSYS as an example to realize the application of the data fusion method in the lattice tower structure. Figure 1 The process shown and the technical solution of the present invention are specifically described as follows:

[0033] (1) Example The lattice tower is a self-supporting iron tower with a total height of 34 m, made of Q235 equilateral angle steel. For details of the iron tower structure, see "ZHANG Q, FU X, REN L, et al. Modal parameters of a transmission tower considering the coupling effects between the tower and lines [J]. Engineering Structures, 2020, 220: 110947." in "Fig. 6". The finite element model of the iron tower was established using ANSYS software, and the BEAM188 unit was selected to simulate the lattice tower rods. The rigid joints were used to simplify the connection between the components, and the ideal elastic-plastic model was used for the steel constitutive model.

[0034] Since the two-dimensional strain-displacement mapping method in the data fusion method needs to consider the first three vibration modes, in this embodiment, a total of 8 strain measurement points are arranged on the left and right main materials, and a bidirectional acceleration measurement point is arranged at the junction of the tower head and the tower body, which is also the displacement measurement point. The numerical model of the lattice tower is established according to the design drawings.

[0035] (2) The y-direction load and z-direction load applied in this embodiment are detailed in "ZHANG Q, FU X, REN L, et al. Modal parameters of a transmission tower considering the coupling effects between the tower and lines [J]. Engineering Structures, 2020, 220: 110947." in "Fig. 6". The solution type of ANSYS software analysis is "antype, trans". After the load solution is completed, the strain response and acceleration response of the strain measuring point can be extracted. The strain sampling rate is set to 10 Hz, and the acceleration sampling rate is set to 100 Hz. The data collected by the strain sensor is decomposed according to the main directions of the two main vibrations, and then the SSI method is used to process the strain response after orthogonal decomposition. Assuming that the order is set to 100, the identified strain vibration mode and the corresponding height coordinate are extracted.

[0036] (3) According to the size of the lattice tower, calculate the functional relationship between the horizontal distance y, z from any point of the main material to the two neutral layers and the height x of the point from the ground. In this example, it is a linear functional relationship.

[0037] (4) The strain vibration modes in the two directions are respectively fitted with the height coordinates of the lattice tower by polynomials to obtain the strain vibration mode function, and and The whole is regarded as a function, and it is expanded according to Taylor's formula. The double indefinite integral is performed on the expanded result and the boundary conditions are substituted to obtain the displacement vibration function and

[0038] (5) The mode coordinates are solved from the strain response after orthogonal decomposition and the strain modes in two directions respectively by the least squares method.

[0039] (6) Substitute the height coordinates of the displacement points to be measured into the displacement mode function to obtain the function value, and multiply the displacement mode function value and the mode coordinate to obtain the horizontal bidirectional low sampling rate dynamic displacement.

[0040] (7) The existing multi-rate Kalman filter algorithm is used to process the low sampling rate dynamic displacement and acceleration in two directions respectively, so as to obtain the high sampling rate horizontal bidirectional dynamic displacement.

[0041] When using the present invention, it is necessary to pay attention to the following: first, the number of strain measuring points on each main material of the lattice tower is at least 4; second, the same number of strain measuring points need to be arranged at the same position on two adjacent main materials; third, the transient analysis technology is a mature and well-known technical means in the field, and the establishment of the numerical model of the lattice tower and the transient analysis can both use self-compiled programs or related commercial software.

[0042] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for horizontal bidirectional displacement reconstruction of a lattice tower structure based on multi-source heterogeneous data fusion, characterized in that: The lattice tower is simplified into a thin-walled three-dimensional variable-section cantilever beam. Assuming that the neutral layer is located between the two main materials, the low-sampling rate displacement is directly calculated from the strain using the two-dimensional strain-displacement mapping method. The low-sampling rate displacement and acceleration are used as the input values ​​of the Kalman filter algorithm to solve the high-sampling rate horizontal bidirectional dynamic displacement. The steps are as follows: (1) 2M strain sensors are evenly arranged along the height of two adjacent main materials of the lattice tower. The number of strain sensors is at least 8, and a horizontal bidirectional acceleration sensor is arranged at the displacement measurement point; (2) The strain response collected by the strain sensor is decomposed according to the in-plane and out-of-plane vibration directions, and the decomposed strain data {ε y } M×1 and {ε z } M×1 , and draw a stability diagram based on the processing results, and then determine the vibration mode order n based on the obtained stability diagram, where n is a natural number and does not exceed M, and extract the first n-order strain mode matrix and (3) Calculate the functional relationship y(x) and z(x) between the horizontal distance y and z of any point of the main material to the two neutral layers and the height x of the point from the ground according to the lattice tower structure; (4) The first n-order strain vibration modes are respectively fitted with the height x of the strain sensor arrangement point from the ground to obtain the strain vibration mode function and Then put and The whole is regarded as a function, and it is expanded according to Taylor's formula. The expanded result is double integrated and substituted into the boundary conditions of the bottom fixed connection of the lattice tower structure to obtain the displacement vibration function and (5) Strain mode matrix of lattice tower and And the strain data after orthogonal decomposition {ε y } M×1 and {ε z } M×1 When the modal coordinates {q y } n×1 and {q z } n×1 ; (6) Substitute the height coordinate x of the displacement point to be measured on the lattice tower into the displacement vibration function Φ i y(x) and The corresponding displacement vibration function value is obtained, and the obtained displacement vibration function value and the modal coordinate are multiplied to obtain the low sampling rate dynamic displacement u of the point y and u z ; (7) The y-axis acceleration a of the displacement point to be measured collected by the acceleration sensor y and the calculated y displacement u y As a set of state variables, the z-direction acceleration a z and the calculated z-direction displacement u z As another set of state variables, they are input into the multi-rate Kalman filter algorithm to reconstruct the final high sampling rate horizontal bidirectional dynamic displacement.

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