A structural health online monitoring method applying RTLS and IMU

Abstract

The application provides a structural health online monitoring method using RTLS and IMU, which comprises the following steps: S1, establishing a monitoring point and a reference station, the monitoring point being provided with RTLS, IMU and a structural health monitoring device; S2, synchronously sending posture state data of the monitoring point and a ranging request to the reference station by the monitoring point provided with the RTLS and the IMU, and obtaining the distance between the monitoring point and the reference station based on the time of the ranging request or the posture state data transmission and the speed of light; S3, calibrating and compensating the data based on a Kalman filtering algorithm; S4, evaluating the change trend and safety of the structure to be monitored based on the data information calibrated and compensated in the step S3. The method adopted by the application is suitable for large quantities of products, can be used for maintenance in areas with high installation difficulty and harsh installation conditions, and can be used for a long time. Meanwhile, wireless data transmission is adopted, cable pipeline laying is avoided, and stability is good.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring technology, and more specifically to an online structural health monitoring method using RTLS and IMU. Background Technology

[0002] Currently, conventional methods for measuring settlement and landslides in online structural health monitoring systems include GNSS (General Number System) using remote sensing satellite positioning systems, hydrostatic levels using liquid level changes, fiber optic grating sensors using wavelength variations of optical signals, and laser sensors using reflections of coherent light sources for distance measurement. GNSS is significantly affected by zenith obstruction, making it unsuitable for indoor or tunnel operations, and each unit is expensive. The accuracy of hydrostatic levels and fiber optic gratings is greatly affected by temperature; large temperature differences introduce significant measurement errors. Furthermore, hydrostatic levels require conduits, and fiber optic gratings require fiber optic cables, making installation in complex environments inconvenient and limiting large-scale deployment, resulting in substantial construction costs. Laser ranging to monitor landslide locations suffers from measurement stability affected by weather and humidity, requires unobstructed line-of-sight measurements, and suffers from delayed response times. Summary of the Invention

[0003] This invention addresses one of the technical problems existing in the prior art by providing a method for online structural health monitoring using RTLS and IMU to solve at least one of the aforementioned technical problems.

[0004] According to a first aspect of the present invention, a method for online structural health monitoring using RTLS and IMU is provided, comprising the following steps:

[0005] S1. Establish monitoring points and reference stations. The monitoring points are fixed on the attachments at the unstable location of the structure to be monitored, and the reference stations are fixed on the attachments at a relatively fixed location. The reference stations serve as the reference objects for the monitoring points and perform batch data processing. The monitoring points are equipped with RTLS, IMU, and structural health monitoring devices.

[0006] S2. The monitoring point, configured with RTLS and IMU, synchronously sends the attitude status data and ranging request of the monitoring point to the base station. The distance between the monitoring point and the base station is obtained based on the time of the ranging request or attitude status data transmission and the speed of light.

[0007] S3. Data calibration and compensation based on Kalman filtering algorithm;

[0008] S4. Based on the data information after calibration and compensation in step S3, assess the change trend and safety of the structure to be monitored.

[0009] Based on the above technical solution of the present invention, the following improvements can also be made:

[0010] Optionally, the structural health monitoring device at the monitoring point includes a UWB receiver, a gyroscope, an accelerometer, a magnetometer, and several sensors for collecting structural health data. The UWB receiver is bidirectionally connected to the sensors, and the gyroscope, accelerometer, and magnetometer are integrated into an IMU. The IMU is bidirectionally connected to the sensors.

[0011] Optionally, step S2 includes:

[0012] S21. The monitoring point first sends a data packet requesting ranging to the base station and records the sending time T1. After the base station receives the data packet, it records the receiving time T2.

[0013] S22. After receiving the data packet, the base station waits for a period of time, which is recorded as TreplyB. At time T3, it sends an acknowledgment data packet to the monitoring point. After receiving the data packet, the monitoring point records the time value T4, where T3 = T2 + TreplyB.

[0014] S23. The monitoring point waits for a period of time, and the waiting time is denoted as TreplyA. At time T5, the attitude-related state values ​​of the monitoring point at this time are sent to the base station, and this time is denoted as T6, where T5 = T4 + TreplyA;

[0015] S24. Calculate the flight time Tprop of the electromagnetic wave in the air. ,

[0016] in:

[0017] ;

[0018] ;

[0019] ;

[0020] .

[0021] Optionally, step S3 includes converting the distance value between the obtained monitoring point and the base station into position coordinates, using the offset in the measured state value as a state variable, inputting the position coordinates and attitude information obtained from the first distance measurement as initial values ​​into the Kalman filter algorithm, and controlling the error of the entire algorithm by observing the changes in distance and attitude.

[0022] Optionally, step S3 includes:

[0023] S31. Define the state equation and observation equation of the monitoring station at time i as follows:

[0024]

[0025]

[0026] These represent the posterior state estimates at time i and time i-1, respectively. They are one of the results of filtering, i.e., the updated result, also called the optimal estimate.

[0027] It represents the prior state estimate at time i, which is the intermediate calculation result of the filter. That is, the result at time i predicted based on the optimal estimate at the previous time (time i-1), and is the result of the prediction equation.

[0028] Let represent the posterior estimated covariances at time i and time i-1 (i.e., the covariance of , representing the uncertainty of the state), which are one of the results of filtering.

[0029] The prior estimate covariance at time i is represented by the covariance of the filter, which is an intermediate calculation result of the filter.

[0030] A represents the state transition matrix, which is essentially a conjectured model of the target's state transitions. For example, in maneuvering target tracking, the state transition matrix is ​​often used to model the target's motion, which may be uniform linear motion or uniformly accelerated motion. When the state transition matrix does not conform to the target's state transition model, the filter will quickly diverge.

[0031] Q represents the process excitation noise covariance (the covariance of the system process). This parameter is used to represent the error between the state transition matrix and the actual process. Because we cannot directly observe the process signal, the value of Q is difficult to determine. is the state variable used by the Kalman filter to estimate the discrete-time process, also called the noise introduced by the prediction model itself. State transition covariance matrix.

[0032] B represents the matrix that transforms the input into states. The state parameters of the input include acceleration, angular velocity, magnetometer readings, velocity, and displacement.

[0033] S32. Calculate the Kalman gain K, and then you can obtain the estimated distance.

[0034] ;

[0035] ;

[0036] Kalman gain, also known as the filter gain matrix, is an intermediate calculation result of filtering, or Kalman coefficients.

[0037] H is the transformation matrix from state variables to measurements (observations), representing the relationship connecting the state and the observation. In Kalman filtering, it is a linear relationship. It is responsible for transforming m-dimensional measurements (angular velocity, magnetic sensor values, velocity values, and displacement values) to n-dimensional values, making them conform to the mathematical form of state variables. It is one of the prerequisites for filtering.

[0038] R measures the noise covariance. In actual filter implementation, the noise covariance R can generally be observed and is a known condition of the filter.

[0039] S33. Calculate the error covariance matrix between the estimated and true values ​​to prepare for the next recursive step. .

[0040] The comparison results between posterior state estimates serve as the basis for risk assessment to determine whether a structure is healthy or unhealthy.

[0041] This invention provides a method for online structural health monitoring using RTLS and IMU, which has the following advantages: This method supports all-weather operation. Compared with other current technologies, the product developed using this method has a significant cost advantage in terms of construction and maintenance costs, making it suitable for large-scale deployment. Simultaneously, the resulting product has low power consumption; in particular, the monitoring station can be powered by a portable battery, allowing for deployment in areas with high maintenance difficulty and demanding installation conditions, and enabling long-term operation. Furthermore, wireless data transmission eliminates the need for cable and conduit laying, resulting in better stability. Compared to mechanical pull-wire sensors, it is not affected by mechanical fatigue lifespan and is unaffected by high or low temperatures or humidity fluctuations. Attached Figure Description

[0042] Figure 1 This is a schematic diagram illustrating the data packet transmission and reception times between a monitoring point and a base station in an online structural health monitoring method using RTLS and IMU provided by the present invention.

[0043] Figure 2 This diagram illustrates the distance variation between a monitoring point and a base station in an online structural health monitoring method using RTLS and IMU provided by the present invention.

[0044] Figure 3 This is a schematic diagram illustrating the principle framework of a structural health online monitoring method using RTLS and IMU provided by the present invention. Detailed Implementation

[0045] Embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention.

[0046] In the description of this invention, it should be understood that the terms "longitudinal", "lateral", "up", "down", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0047] In the description of this invention, unless otherwise specified and limited, it should be noted that the terms "installation", "connection" and "linking" should be interpreted broadly. For example, they can refer to mechanical or electrical connections, or internal connections between two components. They can be direct connections or indirect connections through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms according to the specific circumstances.

[0048] like Figure 1 and Figure 2 As shown:

[0049] This embodiment provides a method for online structural health monitoring using RTLS and IMU, which includes the following steps: S1, establishing monitoring points and reference stations. The monitoring points are fixed on attachments at the unstable location of the structure to be monitored, and the reference station is fixed on attachments at a relatively fixed location. The reference station serves as a reference object for the monitoring points and performs batch data processing. The monitoring points are equipped with RTLS, IMU, and structural health monitoring devices.

[0050] S2. The monitoring point, configured with RTLS and IMU, synchronously sends the attitude status data and ranging request of the monitoring point to the base station. The distance between the monitoring point and the base station is obtained based on the time of the ranging request or attitude status data transmission and the speed of light.

[0051] S3. Data calibration and compensation based on Kalman filtering algorithm;

[0052] S4. Based on the data information after calibration and compensation in step S3, assess the change trend and safety of the structure to be monitored.

[0053] It is understood that in this embodiment, the method supports all-weather operation. Compared with other current technologies, the product formed by the method of this invention has a significant cost advantage in terms of construction and maintenance costs, making it suitable for mass production. Simultaneously, the resulting product has low power consumption; in particular, the monitoring station can be powered by a portable battery, allowing for deployment in areas with high maintenance difficulty and demanding installation conditions, and enabling long-term operation. Furthermore, wireless data transmission eliminates the need for cable and conduit laying, resulting in better stability. Compared to mechanical pull-cord sensors, it is unaffected by mechanical fatigue lifespan and is unaffected by high or low temperatures or humidity fluctuations.

[0054] Specifically, the embodiment utilizes a fusion method of ultra-wideband RTLS real-time positioning system and IMU inertial navigation technology. This method is used for real-time online monitoring of structural health and safety. Taking advantage of the characteristics of ultra-wideband pulse wireless signals—insensitivity to channel fading, low transmitted signal power spectral density, low interception capability, and low system complexity—it continuously uses picosecond-level ultra-narrow wireless pulses to accurately measure the spatial relative position between the base station and the monitoring station. Between the monitoring station and the base station, a data transmission channel established by the wireless pulses is established using their respective built-in MEMS high-precision accelerometers, gyroscopes, and magnetometers. The pulse signal carries the spatial attitude parameter values ​​of the monitoring station to the base station at that moment. By comparing the attitude changes of the base station and the monitoring station, the movement trajectory and attitude of the monitoring point are effectively and dynamically fed back. Using the relative distance and acceleration, angular velocity, and magnetic force changes between the monitoring point and the base station, as well as subsequent data processing and trend analysis, the safety performance assessment of structural landslide deformation can be performed.

[0055] By utilizing real-time spatial positioning and attitude data fusion recursive analysis for calibration and data modeling, usable information is extracted to a greater extent. Through Kalman filtering, Gaussian errors are progressively reduced to estimate position and state, thereby obtaining trend displacements in various directions. This yields accurate data on landslide, deformation, displacement, and amplitude changes.

[0056] As an optional embodiment, the structural health monitoring device at the monitoring point includes a UWB receiver, a gyroscope, an accelerometer, a magnetometer, and several sensors for collecting structural health data. The UWB receiver is bidirectionally connected to the sensors, and the gyroscope, accelerometer, and magnetometer are integrated into an IMU, which is bidirectionally connected to the sensors.

[0057] It is understandable that, such as Figure 3 As shown, sensor fusion means that multiple sensors are combined together to collect various types of required data, mainly for collecting environmental parameters and other data information required for structural health.

[0058] As an optional embodiment, step S2 includes:

[0059] S21. The monitoring point first sends a data packet requesting ranging to the base station and records the sending time T1. After the base station receives the data packet, it records the receiving time T2.

[0060] S22. After receiving the data packet, the base station waits for a period of time, which is recorded as TreplyB. At time T3, it sends an acknowledgment data packet to the monitoring point. After receiving the data packet, the monitoring point records the time value T4, where T3 = T2 + TreplyB.

[0061] S23. The monitoring point waits for a period of time, and the waiting time is denoted as TreplyA. At time T5, the attitude-related state values ​​of the monitoring point at this time are sent to the base station, and this time is denoted as T6, where T5 = T4 + TreplyA;

[0062] S24. Calculate the flight time Tprop of the electromagnetic wave in the air. ,

[0063] in:

[0064] ;

[0065] ;

[0066] ;

[0067] .

[0068] It is understood that the method involved in this embodiment has two roles: a monitoring point and a base station. The monitoring point is a product equipped with an RTLS and IMU fusion method installed during unstable periods; the base station is a relatively stationary product installed on top of the monitoring point, serving as a reference for the monitoring point and as a processing unit for batch data. The specific method is as follows:

[0069] The RTLS real-time positioning system is implemented using ultra-wideband, which involves calculating the transmission time of radio electromagnetic waves and converting the transmission time into distance.

[0070] Ask for ranging

[0071] The monitoring point first sends a data packet to the base station to request ranging and records the time of sending the packet T1. After receiving the data packet, the base station records the time of receiving the packet T2.

[0072] Respond for ask

[0073] The base station then waits for the TreplyB time. At time T3 (T3=T2+TreplyB), it sends an acknowledgment data packet to the monitoring point. After receiving the data packet, the monitoring point records the time value T4.

[0074] My status

[0075] The monitoring point waits for TreplyA time. At time T5 (T5=T4+TreplyA), it sends the MEMS attitude-related state values ​​of the monitoring point to the base station. A few minutes later, at time T6.

[0076] Then the flight time Tprop of the electromagnetic wave in the air can be calculated, and the distance between the two devices is the flight time multiplied by the speed of light.

[0077]

[0078] in:

[0079] ;

[0080] ;

[0081] ;

[0082] .

[0083] As an optional embodiment, step S3 includes converting the distance value between the obtained monitoring point and the base station into position coordinates, using the offset in the measured state value as a state variable, inputting the position coordinates and attitude information obtained from the first distance measurement as initial values ​​into the Kalman filter algorithm, and controlling the error of the entire algorithm by observing the changes in distance and attitude.

[0084] Step S3 includes:

[0085] S31. Define the state equation and observation equation of the monitoring station at time i as follows:

[0086]

[0087]

[0088] These represent the posterior state estimates at time i and time i-1, respectively. They are one of the results of filtering, i.e., the updated result, also called the optimal estimate.

[0089] It represents the prior state estimate at time i, which is the intermediate calculation result of the filter. That is, the result at time i predicted based on the optimal estimate at the previous time (time i-1), and is the result of the prediction equation.

[0090] Let represent the posterior estimated covariances at time i and time i-1 (i.e., the covariance of , representing the uncertainty of the state), which are one of the results of filtering.

[0091] The prior estimate covariance at time i is represented by the covariance of the filter, which is an intermediate calculation result of the filter.

[0092] A represents the state transition matrix, which is essentially a conjectured model of the target's state transitions. For example, in maneuvering target tracking, the state transition matrix is ​​often used to model the target's motion, which may be uniform linear motion or uniformly accelerated motion. When the state transition matrix does not conform to the target's state transition model, the filter will quickly diverge.

[0093] Q represents the process excitation noise covariance (the covariance of the system process). This parameter is used to represent the error between the state transition matrix and the actual process. Because we cannot directly observe the process signal, the value of Q is difficult to determine. is the state variable used by the Kalman filter to estimate the discrete-time process, also called the noise introduced by the prediction model itself. State transition covariance matrix.

[0094] B represents the matrix that converts the input into states. The state parameters of the input include acceleration, angular velocity, magnetic sensor values, velocity, and displacement.

[0095] S32. Calculate the Kalman gain K, and then you can obtain the estimated distance.

[0096] ;

[0097] ;

[0098] Kalman gain, also known as the filter gain matrix, is an intermediate calculation result of filtering, or Kalman coefficients.

[0099] H is the transformation matrix from state variables to measurements (observations), representing the relationship connecting the state and the observation. In Kalman filtering, it is a linear relationship. It is responsible for transforming m-dimensional measurements (angular velocity, magnetic sensor values, velocity values, and displacement values) to n-dimensional values, making them conform to the mathematical form of state variables. It is one of the prerequisites for filtering.

[0100] R measures the noise covariance. In actual filter implementation, the noise covariance R can generally be observed and is a known condition of the filter.

[0101] S33. Calculate the error covariance matrix between the estimated and true values ​​to prepare for the next recursive step. .

[0102] The comparison results between posterior state estimates serve as the basis for risk assessment to determine whether a structure is healthy or unhealthy.

[0103] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0104] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0105] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0106] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0107] Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the invention.

[0108] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of the claims of this invention and their equivalents, this invention also intends to include these modifications and variations.

Claims

1. A method for online structural health monitoring using RTLS and IMU, characterized in that, It includes the following steps: S1. Establish monitoring points and a reference station. The monitoring points are fixed on attachments at the unstable location of the structure to be monitored, while the reference station is fixed on attachments with a relatively fixed location. The reference station serves as a reference object for the monitoring points and performs batch data processing. The monitoring points are equipped with RTLS (Radio-Resistant Threatened Array), IMU (Integrated Measurement Unit), and structural health monitoring devices. The structural health monitoring devices at the monitoring points include a UWB receiver, a gyroscope, an accelerometer, a magnetometer, and several sensors for collecting structural health data. The UWB receiver is bidirectionally connected to the sensors. The gyroscope, accelerometer, and magnetometer are integrated into the IMU, which is bidirectionally connected to the sensors. S2. The monitoring point, configured with RTLS and IMU, synchronously sends the attitude status data and ranging request of the monitoring point to the base station. The distance between the monitoring point and the base station is obtained based on the time of the ranging request or attitude status data transmission and the speed of light. Step S2 includes: S21. The monitoring point first sends a data packet requesting ranging to the base station and records the sending time T1. After the base station receives the data packet, it records the receiving time T2. S22. After receiving the data packet, the base station waits for a period of time, which is recorded as TreplyB. At time T3, it sends an acknowledgment data packet to the monitoring point. After receiving the data packet, the monitoring point records the time value T4, where T3 = T2 + TreplyB. S23. The monitoring point waits for a period of time, and the waiting time is denoted as TreplyA. At time T5, the monitoring point sends the attitude-related state value of the monitoring point to the base station. The base station receives the state value at time T6, where T5 = T4 + TreplyA. S24. Calculate the flight time Tprop of the electromagnetic wave in the air. , in: ； ； ； ； S3. The data is calibrated and compensated based on the Kalman filter algorithm. Step S3 includes converting the distance between the obtained monitoring point and the base station into position coordinates, using the offset in the measured state value as a state variable, inputting the position coordinates and attitude information obtained from the first distance measurement as initial values ​​into the Kalman filter algorithm, and controlling the error of the entire algorithm by observing the changes in distance and attitude. S4. Based on the data information after calibration and compensation in step S3, assess the change trend and safety of the structure to be monitored.

2. The method for online structural health monitoring using RTLS and IMU according to claim 1, characterized in that, Step S3 includes: S31. Define the state equation and observation equation of the monitoring station at time i as follows: Let represent the posterior state estimates at time i and time i-1, respectively; This represents the control input vector, which is the input vector at time i-1; This represents the prior state estimate at time i, which is the result predicted at time i based on the optimal estimate from the previous time. Let represent the posterior estimated covariances at time i and time i-1, respectively; This represents the prior estimate of the covariance at time i; A represents the state transition matrix, Q represents the process excitation noise covariance, and B represents the matrix that transforms the input into the state. S32. Calculate the Kalman gain K, and then you can obtain the estimated distance. ； ； Kalman gain is also called the filter gain matrix; This represents the prior estimate of the covariance at time i; H is the transformation matrix from state variables to measurements, representing the relationship between the state and the observation; in Kalman filtering, it represents a linear relationship. R is the measurement noise covariance; S33. Calculate the error covariance matrix between the estimated and true values ​​to prepare for the next recursive step. I is the identity matrix; Will The comparison results between posterior state estimates serve as the basis for risk assessment to determine whether a structure is healthy or unhealthy.

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