A bridge vortex vibration detection method based on IMU and GNSS fusion positioning

Abstract

A bridge vortex vibration detection method based on IMU and GNSS fusion positioning includes: first, collecting inertial measurement unit data, inputting it into a prediction module and calculating a double integral to generate a temporary displacement; then, performing bridge vortex-induced vibration (VIV) detection, and determining the occurrence of VIV by identifying harmonic motion and amplitude detection; if it is determined that there is a possibility of VIV vibration, the update module starts the GNSS module controller in the GNSS-RTK to activate the GNSS receiver and obtain GNSS-RTK data and multipath data; finally, after the update module receives the GNSS-RTK data and GNSS data quality, the GNSS and IMU data are fused through a Kalman filter; wherein the fused data is temporarily cached in the temporary displacement, realizing real-time monitoring of bridge vibration and VIV, and calibrating it when necessary to ensure the accuracy and reliability of the data. The present invention achieves a stable VIV detection rate while maintaining low power consumption, providing comprehensive protection for the safety and stability of the bridge structure.

Description

Technical Field

[0001] The present invention relates to a bridge vortex-induced vibration detection method based on IMU and GNSS fusion positioning, which is used for low-energy bridge vortex-induced vibration (VIV) detection of cross-sea bridges. The invention relates to the field of bridge vortex-induced vibration detection, and more particularly to a low-energy bridge vortex-induced vibration detection method based on the fusion of Global Navigation Satellite System (GNSS) data and Inertial Measurement Unit (IMU) data using an extended Kalman filter. Background Art

[0002] Vortex-induced vibration (VIV) monitoring plays a crucial role in bridge structural health monitoring, particularly for sea-crossing bridges. However, current bridge VIV monitoring methods face significant challenges. VIV monitoring of bridge box girders, a high-frequency event in bridge VIV monitoring, is a key indicator of structural health monitoring and can potentially have devastating consequences for bridge integrity.

[0003] Traditional bridge VIV monitoring methods mainly use displacement meters and IMUs, which determine whether VIV occurs by measuring the movement of the bridge box. However, the use of displacement meters requires careful planning and deployment during bridge construction, which incurs additional deployment costs. In addition, the IMU-based method has the problem of inertial drift resulting in inaccurate measurement results. Therefore, with the advancement of monitoring sensors and the development of GNSS-related technologies, GNSS receivers and related technologies have been widely used. For example, Real-Time Kinematic (RTK) positioning technology. RTK positioning uses the differential principle, that is, the known position of the base station is used to eliminate the error in satellite signal propagation, thereby achieving high-precision real-time positioning. With the application of auxiliary technologies such as Kalman filtering, RTK positioning has been widely used in urban planning, agriculture, surveying and mapping, bridge structure health monitoring and other fields.

[0004] However, these receivers are typically equipped with independent positioning chips, which, to a certain extent, leads to high power consumption. This high power consumption poses new challenges to the power supply system and overall energy efficiency of cross-sea bridges. Because independent positioning chips require a large amount of power, this can cause additional pressure on the power system, affecting the sustainability and operating costs of the bridge. Therefore, the challenges of power configuration have forced engineers and design teams to seek innovative solutions to ensure that the GNSS system can provide precise positioning while minimizing its dependence on power resources to meet the power challenges posed by the unique power supply environment of cross-sea bridges.

[0005] Secondly, in addition to the challenges posed by power supply configurations, cross-sea bridge construction also faces the problem of GNSS signal fluctuations caused by the sea surface and bridge tower structures. This can introduce multipath effects, which can seriously affect GNSS positioning accuracy. Signal fluctuations are inevitable in the sea environment, and the presence of bridge towers can cause signal reflections and refractions, resulting in multipath effects. This causes the receiver to receive signals from different paths, leading to positioning errors. Therefore, accurately assessing GNSS signal quality over the sea remains a significant challenge. Summary of the Invention

[0006] In order to overcome the above-mentioned shortcomings of the prior art, the present invention proposes a bridge vortex vibration detection method based on IMU and GNSS fusion positioning.

[0007] A bridge vortex vibration detection method based on IMU and GNSS fusion positioning includes the following steps:

[0008] (1) The acceleration and angular velocity data output by the IMU sensor are sent to the prediction module, and the temporary displacement is obtained through double integration.

[0009] (2) Mathematical modeling of bridge vortex-induced vibration is carried out based on the vibration mode detection method and is formulated as follows:

[0010]

[0011] F(t)=F0sin(ωt+φ) (2)

[0012] y(t)=y0sin(ωt) (3)

[0013] Where m is a parameter related to the bridge structure, m is the structural mass, c is the structural damping, ω = 2πf is the body oscillation frequency, k is the spring constant, F is the lateral fluid force, and I is the lateral displacement.

[0014] (3) Monitor the harmonic motion and acceleration amplitude under bridge vibration. First, perform a Hilbert transform on the temporary displacement obtained by the double integral and determine whether harmonic motion occurs based on a predefined threshold. If the value is greater than the threshold, return True. Otherwise, return False.

[0015] On the other hand, the vibration acceleration amplitude is obtained based on the acceleration and angular velocity data from the IMU. Based on the predefined amplitude threshold, the function determines whether the amplitude exceeds the threshold. If the amplitude exceeds the threshold, the function returns True. Otherwise, the function returns False.

[0016] (4) If the harmonic detection returns False or the amplitude detection returns False, the current values ​​of the temporary displacement and the bridge vortex-induced vibration results are directly output, and then the system process will return to step 1.

[0017] If both the harmonic detection and the amplitude detection return True, the present invention determines that a bridge vortex-induced vibration event may have occurred, and the bridge vortex-induced vibration detection module will output the bridge vortex-induced vibration detection result and start the update module.

[0018] (5) Start the GNSS module controller in GNSS-RTK to activate the GNSS receiver and obtain GNSS-RTK data and GNSS multipath data.

[0019] (6) The multipath strength of the signal can be obtained by determining its upper and lower envelope curves. These envelope curves can be obtained by identifying the maximum and minimum values ​​of the multipath combination. The formula is as follows:

[0020] H=Mean(Env max -Env min ) (4)

[0021]

[0022] Where H is the average envelope of the multipath effect for each satellite. Q is the confidence coefficient for the multipath effect of all satellites captured in the current GNSS signal. When H increases, Q approaches 0, and vice versa.

[0023] (7) The receiver then measures its own position using signals from multiple satellites, while also recording the pseudorange and carrier phase information for each satellite. Pseudorange is the distance obtained by multiplying the time it takes for a satellite signal to travel to the receiver by the speed of light, while phase is the number of wavelengths the satellite signal travels.

[0024] (8) Calculate the difference between the satellite signals received by the base station and the known base station position. This difference value includes systematic errors such as atmospheric delay and clock error.

[0025] (9) Use the LAMBDA algorithm to resolve the integer ambiguity.

[0026] (10) By combining the differential pseudorange and differential phase information with the integer ambiguity resolution, more accurate GNSS-RTK solution data can be obtained.

[0027] (11) Finally, after the update module receives the GNSS-RTK data and GNSS data quality, the GNSS and IMU data are fused through the Kalman filter. The fused data is temporarily cached in the temporary displacement for real-time monitoring of bridge vibration and VIV, and then the system process returns to step 2.

[0028] The present invention utilizes the multipath effect of the sea surface, which is prevalent in marine environments, to determine the quality of GNSS data. It then fuses the GNSS-RTK positioning data with the IMU acceleration data to achieve a stable VIV detection rate and reduce energy consumption. The present invention includes the following three parts:

[0029] Data acquisition part: responsible for data reception and preprocessing.

[0030] Adaptive fusion part: The core part of the system. It combines the IMU acceleration data from the data acquisition part with the displacement data from the GNSS receiver.

[0031] Result output part: This part accurately exports the longitudinal displacement data and VIV detection results of the bridge.

[0032] The present invention includes: first, collecting inertial measurement unit data, inputting it into the prediction module and calculating the double integral to generate a temporary displacement. Then, performing bridge VIV detection, and determining the occurrence of VIV by identifying harmonic motion and amplitude detection. If it is determined that there is a possibility of VIV vibration, the update module starts the GNSS module controller in the GNSS-RTK to activate the GNSS receiver and obtain GNSS-RTK data and multipath data. Finally, after the update module receives the GNSS-RTK data and GNSS data quality, the GNSS and IMU data are fused through the Kalman filter. The fused data is temporarily cached in the temporary displacement, realizing real-time monitoring of bridge vibration and VIV, and calibrated when necessary to ensure the accuracy and reliability of the data. The present invention achieves a stable VIV detection rate under the premise of low power consumption, providing comprehensive protection for the safety and stability of the bridge structure.

[0033] The advantages of the present invention are:

[0034] High recall and precision: Ensuring accurate VIV detection requires the ability to capture it when it occurs. Existing displacement recovery techniques cannot achieve this level of precision. Hardware coordination with the VIV detection module ensures that the fusion algorithm is activated when potential VIV is detected. Furthermore, the ratio output by the GNSS multipath-based data quality estimation module provides a confidence factor for the fusion method.

[0035] Energy efficiency: Considering the huge energy requirements of GNSS receivers, intermittent rather than continuous operation can significantly save energy. A GNSS receiver control module is integrated into the data acquisition section to control the operating state of the GNSS receiver (on, off, or sleep). BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1It is a schematic diagram of the arrangement of sensors on a sea-crossing bridge according to the present invention.

[0037] Figure 2 It is a schematic diagram of the arrangement of sensors on the beam box of a sea-crossing bridge according to the present invention.

[0038] Figure 3 It is a system flow chart of the present invention.

[0039] Figure 4 Schematic diagram of satellite multipath effect of the present invention.

[0040] Figure 5 It is a schematic diagram of the vortex vibration recognition accuracy of the present invention.

[0041] Figure 6 It is a schematic diagram of the correlation between the vortex vibration recognition rate and energy consumption of the present invention. DETAILED DESCRIPTION

[0042] The technical solution of the present invention is further described below with reference to the accompanying drawings.

[0043] This implementation case constructed a vibration test bench capable of precisely controlling displacement, driving a 200mm long 1650 threaded rod. This platform facilitates the simulation of motion on a bridge. The core board of the test bench is a Raspberry Pi 4B, which serves as the primary computing board. For inertial measurement, this implementation case selected a Beiwei IMU200, while a Sino K823 was used as both a GNSS receiver and an RTK reference station. The reference station was placed on top of our academic building.

[0044] The GNSS data used were collected from a heavily trafficked bridge over approximately one hour of observations, beginning at SOW (seconds per week) 285200, GPS week 2247. The bridge is oriented east-west, and the antenna was fixed to the northern guardrail. Therefore, observations in the Northern Hemisphere may be affected by multipath signal reflections from the water.

[0045] The residual data obtained from RTK positioning results can reflect the impact of multipath effects on GNSS positioning accuracy. This is because, under the premise of precise positioning, the value of the carrier residual indicates the degree of interference in the observation data. Figure 4 The case of G30, which first passed the lifting mask test and was included in the resolution process around SOW 286300, is described. G30 has a much larger residual spread compared to other satellites with relatively small residual separations.

[0046] A bridge vortex vibration detection method based on IMU and GNSS fusion positioning, the specific implementation method is as follows:

[0047] (1) First, the IMU data collected by the Beiwei IMU200 is input into the Raspberry Pi 4B, and the temporary displacement is obtained after calculation by the Raspberry Pi 4B.

[0048] (2) Mathematical modeling of bridge vortex-induced vibration is carried out based on the vibration mode detection method and is formulated as follows:

[0049]

[0050] F(t)=F0sin(ωt+φ) (2)

[0051] y(t)=y0sin(ωt) (3)

[0052] Where m is a parameter related to the bridge structure, m is the structural mass, c is the structural damping, ω = 2πf is the body oscillation frequency, k is the spring constant, F is the lateral fluid force, and I is the lateral displacement.

[0053] (3) Monitor the harmonic motion and acceleration amplitude under bridge vibration. First, perform a Hilbert transform on the temporary displacement obtained by the double integral and determine whether harmonic motion occurs based on a predefined threshold. If the value is greater than the threshold, return True. Otherwise, return False.

[0054] On the other hand, the vibration acceleration amplitude is obtained based on the acceleration and angular velocity data from the IMU. Based on the predefined amplitude threshold, the function determines whether the amplitude exceeds the threshold. If the amplitude exceeds the threshold, the function returns True. Otherwise, the function returns False.

[0055] (4) If the harmonic detection returns False or the amplitude detection returns False, the current values ​​of the temporary displacement and the bridge vortex-induced vibration results are directly output, and then the system process will return to step 1.

[0056] If both the harmonic detection and the amplitude detection return True, the present invention determines that a bridge vortex-induced vibration event may have occurred, and the bridge vortex-induced vibration detection module will output the bridge vortex-induced vibration detection result and start the update module.

[0057] (5) Start the GNSS module controller in GNSS-RTK to activate the GNSS receiver and obtain GNSS-RTK data and multipath data.

[0058] (6) The multipath strength of the signal can be obtained by determining its upper and lower envelope curves. These envelope curves can be obtained by identifying the maximum and minimum values ​​of the multipath combination. The formula is as follows:

[0059] H=Mean(Env max -Envmin ) (4)

[0060]

[0061] Where H is the average envelope of the multipath effect for each satellite. Q is the confidence coefficient for the multipath effect of all satellites captured in the current GNSS signal. When H increases, Q approaches 0, and vice versa.

[0062] (7) The receiver measures its own position using signals from multiple satellites, while also recording the pseudorange and carrier phase information for each satellite. Pseudorange is the distance obtained by multiplying the time it takes for a satellite signal to travel to the receiver by the speed of light, while phase is the number of wavelengths the satellite signal travels.

[0063] (8) Calculate the difference between the satellite signals received by the base station and the known base station position. This difference value includes systematic errors such as atmospheric delay and clock error.

[0064] (9) Use the LAMBDA algorithm to resolve the integer ambiguity.

[0065] (10) By combining the differential pseudorange and differential phase information with the integer ambiguity resolution, more accurate GNSS-RTK solution data can be obtained.

[0066] (11) Finally, after the update module receives the GNSS-RTK solution data and GNSS data quality, the GNSS and IMU data are fused through the Kalman filter. The fused data is temporarily cached in the temporary displacement for real-time monitoring of bridge vibration and VIV. The system process then returns to step 2.

[0067] The VIV detection rate is a direct indicator of the accuracy of our method. Figure 5 As shown, the IMU performs the worst, while GNSS-RTK performs better. The Kalman filter and adaptive methods are similar. This is because IMU results are affected by integral truncation errors and cannot consistently recover long-term displacements even with calibrated values. GNSS-RTK, at lower sampling rates, cannot fully capture the modal shape. In contrast, both the Kalman filter and the AF (Adaptive Filter) solution implemented in this invention can accurately detect VIV.

[0068] Next, in this implementation plan, we evaluate energy consumption indicators. To simplify the energy consumption evaluation criteria, we make the following assumptions:

[0069] (1) IMUPI's operating power consumption is 1 watt;

[0070] (2) GNSSPGi's operating power consumption is 3 watts;

[0071] (3) The operating power consumption of the computing board Pcore is 3 watts;

[0072] (4) The total power supply capacity of the system is 0.5 kWh;

[0073] Among them, IMU and computing board run continuously, and GNSS-RTK runs intermittently. Figure 6 As shown in FIG, the AF method represents an embodiment of the present invention, and it can be seen that the AF method of the embodiment of the present invention has advantages in terms of power consumption. Compared with the traditional Kalman filter (KF), the overall energy consumption of the system is reduced by 41.8%.

[0074] The contents described in the embodiments of this specification are merely an enumeration of the implementation forms of the inventive concept. The scope of protection of the present invention should not be regarded as limited to the specific forms described in the embodiments. The scope of protection of the present invention also extends to equivalent technical means that can be conceived by those skilled in the art based on the inventive concept.

Claims

1. A bridge vortex vibration detection method based on IMU and GNSS fusion positioning, comprising the following steps: (1) The IMU sensor outputs acceleration and angular velocity data to the prediction module, and after double integration, the temporary displacement is obtained; (2) Mathematically model the vortex-induced vibration of bridges and formulate it based on the vibration mode detection method; (3) Monitor harmonic motion and acceleration amplitude under bridge vibration; On the one hand, the temporary displacement obtained by the double integral is Hilbert transformed, and based on a predefined threshold, it is determined whether harmonic motion occurs; if it is greater than the threshold, True is returned; otherwise, False is returned; On the other hand, the amplitude of the vibration acceleration is obtained based on the acceleration and angular velocity data of the IMU, and a pre-defined amplitude threshold is used to determine whether it exceeds the amplitude threshold. If it is greater than the threshold, True is returned; otherwise, False is returned. (4) If the harmonic detection returns False or the amplitude detection returns False, the current value of the temporary displacement and the bridge vortex-induced vibration result is directly output, and then the system process will return to step (1); If both harmonic detection and amplitude detection return True, it is determined that a bridge vortex-induced vibration event has occurred. The bridge vortex-induced vibration detection module will output the bridge vortex-induced vibration detection result and start the update module; (5) Start the GNSS module controller in the GNSS-RTK to activate the GNSS receiver and obtain GNSS-RTK data and multipath data; (6) Obtain the multipath strength of the signal by determining the upper and lower envelope curves of the signal; (7) The receiver measures its own position through signals from multiple satellites, while recording the pseudorange and carrier phase information of each satellite. The pseudorange is the distance obtained by multiplying the time it takes for a satellite signal to travel to the receiver by the speed of light, while the phase is the number of wavelengths that the satellite signal travels. (8) Calculate the difference between the satellite signals received by the reference station and the known reference station position, and the difference value includes the systematic error; (9) Using the LAMBDA algorithm to resolve the integer ambiguity; (10) By combining the differential pseudorange and differential phase information with the integer ambiguity resolution, more accurate GNSS-RTK solution data can be obtained; (11) After the update module receives the GNSS-RTK data and GNSS data quality, the GNSS and IMU data are fused through the Kalman filter; The fused data is temporarily cached in the temporary displacement for real-time monitoring of bridge vibration, and then the system process returns to step (2).

2. The bridge vortex vibration detection method based on IMU and GNSS fusion positioning according to claim 1 is characterized by: The bridge vortex-induced vibration described in step (2) has a specific vibration mode and a specific threshold amplitude value; wherein, based on the vibration mode detection method, the vibration mode can be formulated as follows: F(t)=F0sin(ωt+φ) (2) y(t)=y0sin(ωt) (3) Where m is the structural mass, c is the structural damping, ω = 2πf is the body oscillation frequency, k is the spring constant, and F is the lateral fluid force.

3. The bridge vortex vibration detection method based on IMU and GNSS fusion positioning according to claim 1 is characterized by: The multipath strength of the signal described in step (6) is obtained by determining its upper and lower envelope curves; these envelope curves are obtained by identifying the maximum and minimum values ​​of the multipath combination respectively, and the formula is expressed as follows: H=Mean(Env max -Env min ) (4) Where H is the average envelope of the multipath effect of each satellite; Q is the multipath effect confidence coefficient of all satellites captured in the current GNSS signal; when H becomes larger, Q will approach 0, and vice versa.

4. The bridge vortex vibration detection method based on IMU and GNSS fusion positioning according to claim 1 is characterized by: The systematic errors described in step (8) include atmospheric delay and clock error.

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