Fiber bragg grating-based string net rack detection method

Abstract

The invention discloses a fiber bragg grating-based string net rack detection method, which comprises the following steps of: constructing a dynamic thermal inertia model containing a thermal delay coefficient by symmetrically arranging fiber bragg grating sensors at two sides of a key node, and recursively estimating the real temperature of a steel cable core part from the rapidly changed sensor temperature; therefore, high-precision microenvironment temperature decoupling is realized. On the basis, differential strain characteristics are extracted by using net wavelength drift sequences on two sides of the node, and the equivalent friction coefficient of the node is inverted. Through the mode, a pseudo strain error caused by a thermal hysteresis effect is eliminated, accurate judgment of a node friction behavior and a slippage state is realized by quantifying a cable force transmission difference on two sides of the node, safety misjudgment caused by internal force redistribution is effectively prevented, and the reliability of structural health monitoring is remarkably improved.

Description

Technical Field

[0001] This application relates to the field of intelligent detection, and more specifically, to a method for detecting tensioned steel structures based on fiber Bragg gratings. Background Technology

[0002] Tensioned space frame structures, as a highly efficient large-span prestressed spatial structure system, are widely used in large public buildings such as stadiums and convention centers. This structural system primarily relies on prestress provided by high-strength steel cables to maintain its morphological stiffness and load-bearing capacity. Its stress state is complex and extremely sensitive to changes in cable tension. During long-term service, the cable tension may relax or undergo abnormal abrupt changes due to the combined effects of environmental loads, material aging, and construction errors. This directly alters the internal force distribution of the structure, threatening the overall safety and durability. Therefore, to monitor the structural health in real time, developing a tensioned space frame inspection system capable of accurately sensing the cable stress state and nodal behavior is of paramount importance for ensuring the safe operation of large public buildings throughout their entire lifecycle.

[0003] Although some fiber Bragg grating-based monitoring technologies have been applied to such structures, they still face significant technical challenges in practical field applications, particularly in identifying the mechanical behavior at strut nodes. In the design theory of tensioned space frames, it is typically assumed that cables can slide freely when passing through nodes (cable clamps) at the lower end of struts, meaning the cable forces on both sides of the node are considered uniform and balanced. However, in actual service environments, nodes often experience significant frictional resistance due to component corrosion, foreign object jamming, or prolonged pressure, preventing cables from sliding freely and leading to severe cable force imbalances on both sides of the node. Existing fiber optic monitoring solutions mostly employ single-point measurement modes, which cannot effectively quantify the potential hazards caused by node jamming or high friction, making it difficult to accurately identify cable force friction losses and slippage states at the strut nodes of tensioned space frames. This lack of monitoring capability prevents the system from accurately assessing the actual health of the nodes, easily leading to misjudgments of safety risks arising from structural redistribution of internal forces.

[0004] Therefore, an optimized fiber Bragg grating-based method for detecting tensioned space frames is desired. Summary of the Invention

[0005] To address the aforementioned technical problems, this application provides a method for detecting tensioned space frames based on fiber Bragg gratings.

[0006] According to one aspect of this application, a method for detecting tensioned steel structures based on fiber Bragg gratings is provided, comprising: Obtain the raw wavelength dataset, which includes the first wavelength. The strain sensor at the left measuring point of each key node is at time [time]. The center wavelength, the first The strain sensor on the right measuring point of each key node at time [time] The center wavelength, the first The temperature sensor at the left measuring point of each key node is at time... The center wavelength and the first The temperature sensor at the right measuring point of each key node is at time... The center wavelength; Data preprocessing and outlier cleaning are performed on the original wavelength dataset to obtain a cleaned wavelength sequence. The cleaned wavelength sequence was decoupled from the local microenvironment temperature to obtain the net wavelength drift sequence. Based on the elastic modulus, cross-sectional area, and grating strain transfer coefficient of the steel cable, differential strain features on both sides of the nodes are extracted from the net wavelength drift sequence to obtain the cable force differential feature dataset. Friction coefficient inversion and drift state determination are performed on the cable force differential feature dataset to obtain the real-time friction coefficient and node health status assessment results of the nodes.

[0007] Compared with existing technologies, this application provides a fiber Bragg grating-based method for detecting tensioned cable structures. By symmetrically deploying fiber Bragg grating sensors on both sides of key nodes, a dynamic thermal inertia model incorporating thermal delay coefficients is constructed. This model recursively estimates the true temperature of the cable core from rapidly changing sensor temperatures, achieving high-precision decoupling of microenvironment temperature. Furthermore, differential strain characteristics are extracted using the net wavelength drift sequence on both sides of the node, inverting the equivalent friction coefficient of the node. This approach not only eliminates spurious strain errors caused by thermal hysteresis but also quantifies the differences in cable force transmission on both sides of the node, enabling accurate determination of node friction behavior and slippage state. This effectively prevents misjudgments of safety caused by internal force redistribution and significantly improves the reliability of structural health monitoring. Attached Figure Description

[0008] The above and other objects, features, and advantages of this application will become more apparent from the more detailed description of the embodiments of this application in conjunction with the accompanying drawings. The drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the embodiments of this application to explain this application and do not constitute a limitation thereof. In the drawings, the same reference numerals generally represent the same components or steps.

[0009] Figure 1 This is a flowchart of a tensioned space frame detection method based on fiber Bragg grating according to an embodiment of this application; Figure 2 This is a schematic diagram of the data flow of a tensioned space frame detection method based on fiber Bragg grating according to an embodiment of this application. Detailed Implementation

[0010] Hereinafter, exemplary embodiments according to this application will be described in detail with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments of this application. It should be understood that this application is not limited to the exemplary embodiments described herein.

[0011] As indicated in this application and claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" are not specifically singular and may include plural forms. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0012] While this application makes various references to certain modules of the systems according to embodiments of this application, any number of different modules can be used and run on user terminals and / or servers. The modules described are merely illustrative, and different aspects of the systems and methods may use different modules.

[0013] Flowcharts are used in this application to illustrate the operations performed by the system according to embodiments of this application. It should be understood that the preceding or following operations are not necessarily performed in exact order. Instead, various steps can be processed in reverse order or simultaneously as needed. Furthermore, other operations can be added to these processes, or one or more steps can be removed from them.

[0014] The technical solution of this application proposes a method for detecting tensioned wire mesh structures based on fiber Bragg gratings. Figure 1 This is a flowchart of a tensioned space frame detection method based on fiber Bragg grating according to an embodiment of this application. Figure 2 This is a system architecture diagram of a tensioned space frame detection method based on fiber Bragg gratings according to an embodiment of this application. Figure 1 and Figure 2 As shown, the fiber optic grating-based tensioned wire mesh detection method according to an embodiment of this application includes the following steps: S1, acquiring the original wavelength dataset, the original wavelength dataset including the first... The strain sensor at the left measuring point of each key node is at time [time]. The center wavelength, the first The strain sensor on the right measuring point of each key node at time [time] The center wavelength, the first The temperature sensor at the left measuring point of each key node is at time... The center wavelength and the first The temperature sensor at the right measuring point of each key node is at time... S2, perform data preprocessing and outlier cleaning on the original wavelength dataset to obtain the cleaned wavelength sequence; S3, perform local microenvironment temperature decoupling on the cleaned wavelength sequence to obtain the net wavelength drift sequence; S4, based on the elastic modulus, cross-sectional area, and grating strain transfer coefficient of the steel cable, extract differential strain features on both sides of the node from the net wavelength drift sequence to obtain the cable force differential feature dataset; S5, perform friction coefficient inversion and drift state determination on the cable force differential feature dataset to obtain the real-time friction coefficient of the node and the node health status assessment results.

[0015] Specifically, in S1, the original wavelength dataset is obtained, which includes the first wavelength data set. The strain sensor at the left measuring point of each key node is at time [time]. The center wavelength, the first The strain sensor on the right measuring point of each key node at time [time] The center wavelength, the first The temperature sensor at the left measuring point of each key node is at time... The center wavelength and the first The temperature sensor at the right measuring point of each key node is at time... The center wavelength. The original wavelength dataset refers to the data collected at a specific sampling time. For the first part of the structure The raw wavelength dataset consists of a set of unprocessed optical signals synchronously acquired by a fiber Bragg grating demodulator at key nodes (e.g., the position of the cable clamp at the lower end of the strut). The strain sensor at the left measuring point of each key node is at time [time]. The center wavelength, the first The strain sensor on the right measuring point of each key node at time [time] The center wavelength, the first The temperature sensor at the left measuring point of each key node is at time... The center wavelength and the first The temperature sensor at the right measuring point of each key node is at time... The center wavelength of the strain sensor refers to the peak value of the center wavelength reflected by the fiber optic grating, which is pasted or encapsulated on the surface of the cable and mainly senses the axial deformation of the cable (including mechanical strain and thermal strain). The center wavelength of the temperature sensor refers to the peak value of the center wavelength reflected by the fiber optic grating, which is located at the same measuring point but is physically isolated from the mechanical strain of the cable (usually encapsulated in a loose tube) and only senses changes in ambient temperature. The left and right sides of the node refer to the upstream and downstream positions along the cable direction with the strut node as the center. The symmetrical arrangement of sensors on both sides is to form a differential measurement structure.

[0016] In practice, the first step is the deployment of the sensor network and the initialization of the data acquisition system. At each monitored key node (numbered as follows): On the node, four fiber Bragg grating sensors need to be symmetrically arranged: a strain sensor and a temperature sensor are installed at the measuring point on the left side of the node, and a strain sensor and a temperature sensor are also installed at the measuring point on the right side of the node. These sensors are connected to data acquisition equipment such as a demodulator via a fiber optic network. During system initialization, the center wavelength values ​​of all sensors in the initial (stress-free, temperature-stable) state need to be recorded as the reference wavelength for subsequent calculations.

[0017] Secondly, there is the synchronous acquisition and recording of data. During system operation, the data acquisition equipment synchronously triggers and reads the center wavelength values ​​of all sensors at fixed sampling time intervals (e.g., once per second). For any given sampling moment... The system needs to record the first... The system collects readings from four sensors at key nodes. The specific operation is as follows: The system sends a data acquisition command to the demodulator, which simultaneously scans the reflectance spectra of all sensors and accurately calculates the readings of each sensor at the current moment. The center wavelength value. This process ensures that sensor data from the same node, different locations (left / right), and different functions (strain / temperature) are strictly aligned in time, avoiding errors introduced by asynchronous acquisition time.

[0018] Finally, there is the construction and output of the dataset. The data acquisition system will be constantly... The four acquired center wavelength values ​​are combined into a single data unit. This data unit specifically includes: the... The strain sensor at the left measuring point of each key node is at time [time]. The center wavelength, the The strain sensor on the right measuring point of each key node at time [time] The center wavelength, the The temperature sensor at the left measuring point of each key node is at time... The center wavelength, the The temperature sensor at the right measuring point of each key node is at time... The center wavelength. Thus, a raw wavelength dataset is formed, arranged in time series.

[0019] Specifically, S2 involves preprocessing and outlier removal of the original wavelength dataset to obtain a cleaned wavelength sequence. It should be understood that in actual engineering monitoring of tensioned wire mesh structures, the original wavelength data acquired by the fiber optic demodulator is often affected by various environmental interferences and the characteristics of the equipment itself. Directly using this data for subsequent high-precision calculations carries significant risks. First, on-site electromagnetic interference, minor vibrations in the fiber optic link, or instability of the light source can introduce random high-frequency noise into the original signal, which can mask subtle strain change trends. Second, due to loose connectors or momentary signal loss, non-physical pulse-like outliers (peaks) may appear in the data. If these extreme values ​​are not removed, they will be amplified in subsequent differential calculations, leading to incorrect cable force judgments. Most importantly, if the data from the four sensors (temperature and strain on the left and right sides) are not aligned on the time axis or have packet loss breaks, the thermal delay effect cannot be accurately calculated, leading to the failure of the entire monitoring system. Therefore, in the technical solution of this application, the original wavelength dataset is preprocessed and outlier cleaned to construct a standardized data sequence with high signal-to-noise ratio, no outliers, and time synchronization, laying a reliable data foundation for subsequent complex physical inversion algorithms.

[0020] In practice, the original wavelength dataset is first subjected to high-frequency noise filtering to obtain a denoised wavelength dataset. High-frequency noise typically refers to random fluctuations with frequencies much higher than the structure's natural frequency and the rate of change of ambient temperature. In this application's technical solution, high-frequency noise filtering of the original wavelength dataset eliminates useless high-frequency random fluctuations in the original signal, retaining low-frequency or trend signals that reflect real physical changes. Specifically, a digital filtering algorithm, such as a moving average filter, can be used. For each data point, a new average value is calculated using itself and several adjacent data points, replacing the original value of that point. Through this operation, rapid random fluctuations in the signal are smoothed out because high-frequency noise is random between adjacent points, and averaging them cancels each other out, while real physical changes are usually relatively slow and thus preserved. In this way, sharp high-frequency fluctuations in the original wavelength data are smoothed out to obtain a denoised wavelength dataset.

[0021] Next, the denoised wavelength dataset undergoes impulse outlier detection and removal to obtain a cleaned wavelength dataset. Impulse outliers are discrete points where wavelength values ​​drastically change within a very short timeframe, violating the principle of continuity in structural mechanics. In this process, the mean and standard deviation of the data points are first calculated over a short, continuous time interval. Then, a judgment threshold is set, typically a range several times the standard deviation above and below the mean. Each data point is then compared to the mean; if a data point deviates from the mean by more than the preset threshold, it is identified as an impulse outlier. Data points marked as outliers are directly removed from the dataset, forming the initially cleaned dataset. These removed locations will leave data gaps.

[0022] Next, the cleaned wavelength dataset undergoes data alignment and interpolation repair to obtain the cleaned wavelength sequence. Since the previous removal operation may cause gaps (i.e., breakpoints) in the data on the time axis, and there may be slight asynchrony between the sampling times of different sensor channels, the system needs to resample and repair the data. Specifically, a unified standard time axis is established, and interpolation algorithms (such as linear interpolation or spline interpolation) are used to fill the gaps left by removing outliers, and the data values ​​of all channels are calculated to this unified time point. After this processing, a wavelength sequence with strict temporal correspondence and numerical continuity for four channels (left strain, right strain, left temperature, and right temperature) is finally output, which can be directly input into the subsequent thermal inertia decoupling model.

[0023] Specifically, in step S3, the cleaned wavelength sequence is locally decoupled from the micro-environment temperature to obtain the net wavelength drift sequence. It should be understood that in actual monitoring scenarios of large outdoor structures such as tensioned cable trusses, existing fiber optic grating (FBG) temperature compensation mechanisms have a physical model flaw: they assume that the temperature sensor and the measured cable structure are in "instantaneous thermal equilibrium" at all times. However, cable structures exhibit significant thermal inertia due to their large thermal mass, resulting in relatively gradual temperature changes; in contrast, the small FBG sensor responds extremely quickly to ambient temperature (such as cloud cover or direct cold wind). When rapid thermal shock occurs, directly using rapidly changing sensor readings for compensation can lead to serious deviations in the calculation results. For example, overcompensation may occur during a sudden temperature rise, creating a false signal of structural compression, or undercompensation may occur during a sudden temperature drop, masking the true strain changes. Therefore, in the technical solution of this application, a decoupling method based on a dynamic thermal inertia model is introduced to estimate the lagging true temperature of the cable core from the rapidly changing sensor temperature, thereby achieving accurate strain monitoring.

[0024] In practice, firstly, the total wavelength drift of each cleaned wavelength in the cleaned wavelength sequence is calculated to obtain a total wavelength drift dataset. It should be understood that the physical basis of fiber optic grating sensing technology is monitoring the drift of its center wavelength relative to an initial "zero point" or "reference," rather than its absolute wavelength value. The initial center wavelength of a sensor can vary due to manufacturing processes, installation preload, and other factors, but its sensitivity to strain and temperature (i.e., the wavelength change caused by a unit strain or unit temperature change) is relatively stable. Therefore, meaningful physical information is contained in the change in wavelength relative to the initial state. Directly using absolute wavelength values ​​cannot perform effective decoupling calculations. Only by uniformly converting the readings of each sensor at each moment to the drift amount referenced to its respective initial reference can consistent and physically meaningful temperature compensation calculations be performed. Therefore, in the technical solution of this application, the total wavelength drift of each cleaned wavelength in the cleaned wavelength sequence is calculated to transform the original wavelength readings into a quantitative indicator reflecting changes in physical state, laying the foundation for subsequent decoupling. Here, the total wavelength drift refers to the difference between the measured wavelength of the sensor at the current moment and the reference wavelength. This difference has not yet been separated from temperature and strain, and includes the mixed effect of deformation caused by mechanical external force and thermal strain caused by thermal expansion and contraction. The final total wavelength drift dataset is a structured data set containing the total wavelength drift of the temperature and strain sensors on the left and right sides of all key nodes at each sampling moment.

[0025] In this process, firstly, the system retrieves the calibration data of each sensor, i.e., the reference wavelength, from the storage module. For the left and right measuring points of each key node, the reference center wavelengths recorded by the temperature sensor and strain sensor during system initialization are obtained respectively. Secondly, the system traverses the cleaned wavelength sequence and performs difference calculations for the data at each time point. For the temperature sensor, the system reads the real-time center wavelength at the current moment and subtracts the corresponding reference center wavelength to obtain the total wavelength drift of the temperature sensor. This value represents the total offset of the sensor's perception of ambient temperature changes relative to the initial state, and is the basis for subsequent calculations of the sensor's measured temperature difference. Similarly, for the strain sensor, the system reads the real-time center wavelength at the current moment and subtracts the corresponding reference center wavelength to obtain the total wavelength drift of the strain sensor. This value has not yet eliminated thermal effects and is the result of the superposition of mechanical strain and thermal strain, corresponding to the original input item that needs to be corrected in the subsequent net wavelength drift calculation. Finally, the calculated total wavelength drift of all temperature and strain sensors is indexed and encapsulated according to node number and time order to generate a total wavelength drift dataset.

[0026] Furthermore, based on the fiber grating temperature sensitivity coefficient, pure mechanical strain wavelength drift decoupling is performed on each total wavelength drift data in the total wavelength drift dataset to obtain the net wavelength drift sequence. It should be understood that existing fiber grating (FBG) temperature compensation mechanisms have an inherent physical model flaw, stemming from an overly idealized assumption that the FBG sensor used for temperature measurement and the measured cable structure are in instantaneous thermal equilibrium at all times. In actual monitoring scenarios of large outdoor structures like tensioned space frames, this assumption is easily broken. The root cause lies in the significant dynamic thermal conduction hysteresis between the two. Specifically, cable structures, especially large-diameter cables, exhibit significant thermal inertia due to their enormous thermal mass, resulting in relatively gradual temperature response changes. In stark contrast, FBG temperature sensors, due to their small size and extremely low thermal mass, respond extremely rapidly to transient changes in ambient temperature (such as sun exposure caused by passing clouds or direct cold wind). When such rapid thermal shocks occur, the temperature measured by the temperature sensor changes drastically, while the temperature of the steel cable itself changes much slower due to thermal inertia. In this case, directly using this rapidly changing sensor reading, which may deviate from the cable's true temperature, for compensation will lead to erroneous calculations. A sudden temperature rise can cause overcompensation, creating a false signal of structural compression; conversely, a sudden temperature drop can cause undercompensation, masking the true strain changes. This noise and error introduced by the thermal hysteresis effect severely affects the accuracy of strain decoupling, preventing it from accurately reflecting the true stress state of the structure.

[0027] To address the aforementioned technical deficiencies, this application proposes a temperature decoupling method based on a dynamic thermal inertia model. This method abandons the traditional approach of directly using sensor-measured temperatures for compensation and instead introduces a dynamic heat conduction model. This model treats the sensor temperature as the system input and the core temperature of the steel cable under test as an internal state that cannot be directly observed. Through a recursive algorithm, it estimates in real time the gradually changing temperature of the steel cable body, which is closer to the physical reality, from the rapidly changing sensor temperature. Finally, it uses this estimated temperature to complete accurate compensation.

[0028] In this process, firstly, the real-time temperature wavelength, reference temperature wavelength, real-time strain wavelength, and reference strain wavelength are extracted from the total wavelength drift data; then, based on the real-time temperature wavelength, reference temperature wavelength, and temperature sensor sensitivity coefficient, the sensor's measured temperature difference is determined. It should be understood that the sensor's measured temperature difference represents the instantaneous thermal excitation exerted on the monitoring system by the external environment (such as solar radiation and temperature changes). Therefore, the raw wavelength signal from the temperature sensor is converted into a temperature change in the physical world, thus providing a quantified and accurate starting data point for the dynamic model calculation. This process is expressed by the following formula: in, This represents the measured temperature difference of the temperature sensor relative to its initial state at time t; This is the real-time center wavelength acquired by the temperature sensor; Its initial reference center wavelength; This is the temperature sensitivity coefficient of the temperature sensor itself.

[0029] Furthermore, based on the sensor's measured temperature difference and thermal delay coefficient, the estimated temperature difference of the cable core at the previous moment is recursively estimated using a thermal delay model to obtain the estimated temperature difference of the cable core. To overcome the thermal conduction hysteresis problem between the sensor and the cable body, a first-order inertial element is used to simulate the thermal inertia of the cable, recursively estimating the gradually changing temperature of the cable body. Specifically, this process is accomplished through a recursive formula, meaning that the estimated temperature difference of the cable at the current moment is the result of comprehensively considering its own temperature state at the previous moment and the current external environmental thermal excitation. This process is expressed by the formula: in, The term represents the memory or inertia portion of the cable's temperature state, while The item represents the driving or updating part of the current external thermal environment on the temperature of the steel cable. This represents the estimated temperature difference of the steel cable body at time t after smoothing using a dynamic thermal inertia model. Estimate the temperature difference in the core of the steel cable obtained in the previous calculation cycle; It is the actual temperature difference measured by the sensor calculated in the previous step; This is the system's sampling time interval. A key parameter within it... It is a dimensionless thermal delay coefficient, ranging from 0 to 1. This coefficient accurately characterizes the thermal inertia of the cable: the smaller the value, the greater the thermal inertia of the cable, and the more sluggish and smooth its temperature change relative to the sensor. In this way, the slowly changing temperature of the cable itself, which is closer to the physical reality, can be effectively estimated from the rapidly changing and drastically altered sensor temperature signal.

[0030] Subsequently, based on the estimated temperature difference in the cable core and the temperature sensitivity coefficient of the strain sensor, strain decoupling based on the estimated temperature is performed on the real-time strain wavelength and the reference strain wavelength to obtain the net wavelength drift. That is, a temperature-related term that more accurately reflects the thermal expansion and contraction of the cable body is subtracted from the total wavelength drift of the strain sensor, thus achieving high-precision decoupling. This process is expressed by the formula: in, This is the final net wavelength drift that contains only the true mechanical strain information; The real-time center wavelength acquired by the strain sensor; Its initial reference center wavelength; It is the temperature sensitivity coefficient of the strain sensor; and This is the estimated temperature difference in the core of the steel cable, calculated recursively in the previous step. This step replaces the directly measured temperature value containing transient errors in the original mechanism with a more reliable temperature value optimized by a physical model, in order to obtain a corrected net wavelength drift sequence with data quality and reliability far exceeding that of traditional methods.

[0031] In summary, this mechanism enables high-precision and high-reliability monitoring of strain in tensioned space frame cables when dealing with dynamic temperature disturbances such as sudden changes in solar radiation and gusty cooling. Specifically, by introducing a recursive estimation algorithm based on a dynamic thermal inertia model, it successfully overcomes the problem of over- or under-compensation caused by thermal hysteresis between the sensor and the measured structure in traditional temperature compensation methods. Ultimately, it can effectively extract a smoother and more realistic temperature change trend in the cable core from noisy surface temperature measurements, thus significantly reducing spurious strain signals introduced by thermal hysteresis in decoupled calculations. In this way, the signal-to-noise ratio and accuracy of strain monitoring data in complex and dynamic environments are greatly improved, ensuring that the monitoring system can truly reflect changes in the mechanical state of the structure, avoiding misjudgments of structural safety status due to temperature artifacts, and providing more reliable data support for the health monitoring of large structures.

[0032] Specifically, in step S4, based on the elastic modulus, cross-sectional area, and strain transfer coefficient of the steel cable, differential strain features are extracted from both sides of the node to obtain a cable force differential feature dataset from the net wavelength drift sequence. It should be understood that although the net wavelength drift sequence eliminates temperature interference, it is essentially still a change in light wavelength and cannot be directly used to assess the structural load-bearing safety. The safety of a tensioned space frame highly depends on the balanced distribution of cable forces, and friction or jamming at the strut nodes can lead to uneven cable forces on both sides of the node. Therefore, in the technical solution of this application, by introducing the physical properties of the steel cable (elastic modulus, cross-sectional area) and the transmission characteristics of the sensor (strain transfer coefficient), the wavelength drift is accurately mapped to an absolute cable force value. By constructing differential features, the system can quantify the force deviation between the left and right sides of the node. This deviation is the direct physical basis for identifying node slippage obstruction, assessing friction loss, and determining the health status of the node. The final cable force differential feature dataset refers to a time series set containing the cable force difference (absolute difference) and cable force ratio (relative difference) on both sides of the node, which intuitively reflects the loss or obstruction of force when flowing through the node.

[0033] In practical implementation, firstly, based on the elastic modulus and cross-sectional area of ​​the steel cable and the strain transfer coefficient of the optical fiber, the cable force on both sides of the node is calculated in real time to obtain the node cable force time series. It should be understood that the raw signal directly acquired by the fiber optic grating sensor is an optical wavelength signal, while for the health monitoring of a tensioned wire mesh structure, the core focus is on the actual stress state of the steel cable. By converting the temperature-decoupled net wavelength drift sequence into a cable force time series, dimensionless or optically unitless data can be converted into mechanical indicators with clear physical meaning. This process can eliminate environmental interference and establish a quantitative correspondence between optical signals and cable forces, thus providing the most basic and crucial data support for subsequent extraction of cable force differential features, ratio features, and friction coefficient inversion. By obtaining an accurate cable force time series, the system can truly reflect the changes in the mechanical state of the structure under complex dynamic environments, thereby effectively identifying the health status and drift risk of the nodes.

[0034] Among them, the elastic modulus of the steel cable represents the proportional constant between normal stress and normal strain during the elastic deformation stage of the steel cable material, reflecting the material's inherent ability to resist elastic deformation; the cross-sectional area refers to the cross-sectional area of ​​the monitored steel cable, which is an important geometric parameter determining the cable's load-bearing capacity; while the strain transfer coefficient of the optical fiber (or grating strain transfer coefficient) is a correction parameter used to characterize the efficiency of the mechanical strain of the steel cable body being transferred to the fiber core through the encapsulation layer and adhesive layer. Due to the existence of physical barriers, the strain sensed by the grating is often less than the actual strain of the structure. In addition, the net wavelength drift sequence refers to the sequence of wavelength changes caused by pure mechanical strain after removing high-frequency noise and pulse anomalies from the original wavelength and processing it through a dynamic thermal inertia model to completely eliminate the influence of environmental temperature fluctuations and thermal hysteresis effects.

[0035] In this process, the preprocessed and decoupled net wavelength drift sequence is first extracted. Each value in this sequence represents the wavelength displacement caused by structural stress at a specific moment. Then, the system introduces a pre-calibrated fiber grating strain sensitivity coefficient to initially convert the wavelength drift into apparent strain at the sensor location. To obtain the true strain of the cable itself, the strain transfer coefficient of the optical fiber is used to correct the apparent strain, eliminating measurement losses caused by the coupling medium. After obtaining the true strain, based on Hooke's law, the true strain is multiplied by the elastic modulus of the cable to obtain the stress. Finally, combined with the cross-sectional area of ​​the cable, the final conversion from stress to cable force is completed. The entire process is continuous in the time dimension, thus generating a complete nodal cable force time series. This process is expressed by the following formula: in, To calculate the real-time cable force value, The elastic modulus of the steel cable. Let be the cross-sectional area of ​​the steel cable. This represents the net wavelength shift at that moment. This represents the strain sensitivity coefficient of the strain sensor. denoted as the strain transfer coefficient of the optical fiber.

[0036] Furthermore, cable force difference and ratio features are extracted from the nodal cable force time series to obtain a cable force difference feature dataset. It should be understood that while the cable force value on one side can reflect the load-bearing state of the cable, it cannot directly reflect the mechanical equilibrium relationship and working characteristics of the nodal location. By extracting the difference and ratio features of the cable forces on both sides of the nodal, common-mode interference caused by global structural force fluctuations can be eliminated, thereby accurately capturing local force imbalance signals caused by nodal slippage, changes in friction properties, or structural drift. This step provides high signal-to-noise ratio input features for subsequent equivalent friction coefficient inversion, and is the physical basis for determining the health status of the nodal and warning of slippage risks.

[0037] Among them, the cable force difference refers to the algebraic difference between the cable force values ​​on both sides of the node at the same moment, reflecting the unbalanced tension borne by the node; the cable force ratio refers to the ratio of the cable force values ​​on both sides of the node, which is usually used to characterize the asymmetry of tension transmission; and the cable force difference feature dataset refers to a multi-dimensional data matrix composed of the above difference values ​​and ratio values ​​changing over time, used to describe the evolution of the mechanical behavior of the node.

[0038] In this process, firstly, the system obtains the real-time calculated values ​​of the cable forces corresponding to both sides of the node at the same moment. To ensure the effectiveness of feature extraction, the system needs to synchronize and align the cable force sequences on the left and right sides of the node in time. Secondly, the unbalanced force features are extracted by calculating the algebraic difference between the cable force values ​​on the left and right sides of the node. This process is expressed by the formula: in, For a moment The cable tension difference value, This represents the real-time cable force value measured at the point to the left of the node. This represents the real-time cable force value measured at the point to the right of the node. Next, the correlation between the forces on both sides of the node is quantified by calculating the ratio of the cable forces on both sides. This process is expressed by the formula: in, Indicates time The system calculates the cable force ratio characteristic value. During the calculation, if the forces on both sides of the node are perfectly symmetrical and there is no frictional loss, the ratio value theoretically approaches 1. Furthermore, the system formats and encapsulates the calculated cable force difference sequence and cable force ratio sequence into a cable force difference feature dataset. This dataset, based on the time axis, stores the mechanical imbalance characteristics corresponding to each sampling moment, providing a standardized data source for subsequent friction coefficient inversion of the nodes.

[0039] Specifically, in step S5, friction coefficient inversion and drift state determination are performed on the cable force differential feature dataset to obtain the real-time friction coefficient and node health status assessment results. It should be understood that single cable force or wavelength data cannot directly reflect the physical safety status of a tensioned space frame node, especially the relative sliding risk between the cable and the node. By converting the cable force differential features into the mechanical physical quantity of friction coefficient, the force transmission efficiency and connection tightness at the node can be quantified. Abnormal fluctuations in the friction coefficient or abrupt changes in the cable force differential are core indicators for identifying the structure's "drift state." By performing friction coefficient inversion and drift state determination on the cable force differential feature dataset, the transformation from data acquisition to physical property diagnosis can be achieved, thereby accurately assessing the structure's health level and avoiding misjudgments of structural safety due to temperature artifacts or uneven local stress.

[0040] In practice, the first step is to invert the equivalent friction coefficient of the nodal cable force time series to obtain the equivalent friction coefficient time series. It should be understood that in a tensioned space frame structure, the force balance relationship of the cables when passing through turning nodes is a crucial indicator for assessing structural safety. Simple cable force values ​​only reflect the load-bearing capacity and cannot directly reflect the physical state of the node location, such as whether minor slippage or fastening failure has occurred. By converting cable force data into equivalent friction coefficients, abstract strain signals can be transformed into tribomechanical parameters with clear physical meaning. This conversion process eliminates interference caused by global load fluctuations, highlights the nonlinear changes in local nodes, and thus provides a quantitative basis for determining whether the structure has entered a "drift state," ensuring that the monitoring system can accurately reflect the changes in the mechanical properties of the structure. The equivalent friction coefficient refers to the characteristic coefficient in the mechanical model corresponding to the frictional resistance generated at the nodal nodes of the tensioned space frame due to the cables bypassing the turning device; the equivalent friction coefficient time series refers to the set of coefficients obtained by inverting the cable force data at each moment within a continuous monitoring period, reflecting the dynamic evolution of frictional characteristics.

[0041] This process can be achieved by introducing Euler's formula for cableway friction. First, the system acquires data at the same sampling time. Real-time cable force on the left side of the node and the real-time cable tension on the right Subsequently, the system extracts the pre-stored geometric parameters of the node, namely the turning wrap angle (turning radians) of the cable as it passes through the node. Then, it uses the ratio of cable forces on both sides of the node and the turning angle to calculate the equivalent friction coefficient. This process is expressed by the formula: in, For a moment The real-time equivalent friction coefficient obtained by inversion The geometric turning angle of the cable at the node (in radians). and Representing time respectively The larger and smaller values ​​of the cable force sequence on both sides of this node. By repeating the above recursive calculation for each sampling point on the time axis, the system transforms the dispersed cable force data into a continuous time series of equivalent friction coefficients.

[0042] Furthermore, a comprehensive assessment of node health status is conducted based on the equivalent friction coefficient time series and the cable force differential feature dataset to obtain the node health status time series. It is understandable that during the long-term service of a tensioned space frame, fluctuations in a single cable force or a single friction coefficient may be affected by multiple factors. By coupling the equivalent friction coefficient time series with the cable force differential feature dataset for assessment, abnormal relative slippage between the cables and nodes can be identified from multiple dimensions. This assessment can distinguish between common-mode changes caused by overall structural stress adjustments and local anomalies caused by node failures, thereby accurately identifying the structure's drift state and ensuring that the monitoring system can still provide accurate health assessment levels even under complex dynamic environments such as sudden changes in solar radiation and gusty cooling.

[0043] The cable force differential feature dataset contains key features such as the algebraic difference and ratio of cable forces on both sides of a node; while the node health status time series is a continuous quantitative classification result of node safety. Typically, health status is defined as a logical sequence consisting of states such as "normal," "warning," and "drift," used to describe the tightness and mechanical stability of structural connections.

[0044] In this process, the equivalent friction coefficient time series and cable tension differential feature dataset are first retrieved synchronously within the same time period. Then, the comprehensive deviation index of the node state is calculated. This process quantifies the node instability by comparing the degree of variation between the real-time friction coefficient and the design reference friction coefficient, combined with the instantaneous rate of change of the cable tension differential. The specific calculation process is expressed by the formula: in, for The deviation of the node state at time t. The real-time equivalent friction coefficient, is the initial reference friction coefficient of the node. This represents the real-time cable tension differential value. As the cable force differential reference, This is the average value of the cable forces on both sides. and These are the preset weighting coefficients.

[0045] Furthermore, a set of discrimination thresholds is preset, and the calculated deviation is then... This is mapped to a specific health level. The determination logic can be expressed by the formula: in, This represents the node's health status value (e.g., 1 for healthy, 2 for warning, and 3 for drift). and These are the level one and level two alarm thresholds, respectively. By performing this type of logical operation on each sampling point on the time axis, the final node health status time series is obtained.

[0046] In summary, the fiber Bragg grating-based tensioned space frame detection method according to the embodiments of this application is explained. It constructs a dynamic thermal inertia model including a thermal delay coefficient by symmetrically deploying fiber Bragg grating sensors on both sides of key nodes. The true temperature of the cable core is recursively estimated from the rapidly changing sensor temperature, thereby achieving high-precision micro-environment temperature decoupling. Based on this, differential strain characteristics are extracted using the net wavelength drift sequence on both sides of the node, and the equivalent friction coefficient of the node is inverted. In this way, not only are spurious strain errors caused by thermal hysteresis eliminated, but also the differences in cable force transmission on both sides of the node are quantified, enabling accurate determination of node friction behavior and slippage state. This effectively prevents safety misjudgments caused by internal force redistribution and significantly improves the reliability of structural health monitoring.

[0047] The various embodiments of this disclosure have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments. The terminology used herein is chosen to best explain the principles, practical application, or improvement of the technology in the market, or to enable others skilled in the art to understand the embodiments disclosed herein.

Claims

1. A fiber grating-based cable-stayed net rack detection method, characterized in that, include: Obtain the raw wavelength dataset, which includes the first wavelength. The strain sensor at the left measuring point of each key node is at time [time]. The center wavelength, the first The strain sensor at the right measuring point of each key node is at time [time]. The center wavelength, the first The temperature sensor at the left measuring point of each key node is at time... The center wavelength and the first The temperature sensor on the right measuring point of each key node is at time... The center wavelength; Data preprocessing and outlier cleaning are performed on the original wavelength dataset to obtain a cleaned wavelength sequence. The cleaned wavelength sequence was decoupled from the local microenvironment temperature to obtain the net wavelength drift sequence. Based on the elastic modulus, cross-sectional area, and grating strain transfer coefficient of the steel cable, differential strain features on both sides of the nodes are extracted from the net wavelength drift sequence to obtain the cable force differential feature dataset. Friction coefficient inversion and drift state determination are performed on the cable force differential feature dataset to obtain the real-time friction coefficient and node health status assessment results of the nodes.

2. The method for detecting tensioned wire mesh structures based on fiber Bragg gratings according to claim 1, characterized in that, Data preprocessing and outlier cleaning are performed on the original wavelength dataset to obtain a cleaned wavelength sequence, including: High-frequency noise is filtered out from the original wavelength dataset to obtain a denoised wavelength dataset. Impulse outlier detection and removal are performed on the denoised wavelength dataset to obtain a cleaned wavelength dataset. Data alignment and interpolation repair are performed on the cleaned wavelength dataset to obtain the cleaned wavelength sequence.

3. The method for detecting tensioned space frames based on fiber Bragg gratings according to claim 1, characterized in that, The cleaned wavelength sequence is decoupled from the local microenvironment temperature to obtain the net wavelength drift sequence, including: The total wavelength shift is calculated for each cleaned wavelength in the cleaned wavelength sequence to obtain the total wavelength shift dataset. Based on the fiber grating temperature sensitivity coefficient, pure mechanical strain wavelength drift decoupling is performed on each total wavelength drift data in the total wavelength drift dataset to obtain the net wavelength drift sequence.

4. The method for detecting tensioned wire mesh structures based on fiber Bragg gratings according to claim 3, characterized in that, Based on the fiber grating temperature sensitivity coefficient, pure mechanical strain wavelength drift decoupling is performed on each total wavelength drift data in the total wavelength drift dataset to obtain the net wavelength drift sequence, including: Extract the real-time temperature wavelength, reference temperature wavelength, real-time strain wavelength, and reference strain wavelength from the total wavelength drift data; The actual temperature difference measured by the sensor is determined based on the real-time temperature wavelength, the reference temperature wavelength, and the temperature sensor sensitivity coefficient. Based on the measured temperature difference and thermal delay coefficient of the sensor, the estimated temperature difference of the steel cable core at the previous moment is recursively estimated based on the thermal delay model to obtain the estimated temperature difference of the steel cable core. Based on the estimated temperature difference in the core of the steel cable and the temperature sensitivity coefficient of the strain sensor, strain decoupling based on the estimated temperature is performed on the real-time strain wavelength and the reference strain wavelength to obtain the net wavelength drift.

5. The method for detecting tensioned wire mesh structures based on fiber Bragg gratings according to claim 4, characterized in that, Based on the sensor-measured temperature difference and thermal delay coefficient, the estimated temperature difference of the steel cable core at the previous moment is recursively estimated using a thermal delay model to obtain the estimated temperature difference of the steel cable core. This includes: recursively estimating the estimated temperature difference of the steel cable core at the previous moment using a thermal delay model with the following formula: ， in, The thermal delay coefficient, The temperature difference in the core of the steel cable was estimated at the previous moment. This represents the actual temperature difference measured by the sensor.

6. The method for detecting tensioned space frames based on fiber Bragg gratings according to claim 1, characterized in that, Based on the elastic modulus and cross-sectional area of ​​the steel cable and the strain transfer coefficient of the optical fiber, differential strain features on both sides of the node are extracted from the net wavelength drift sequence to obtain a cable force differential feature dataset, including: Based on the elastic modulus and cross-sectional area of ​​the steel cable and the strain transfer coefficient of the optical fiber, the cable force on both sides of the node is calculated in real time to obtain the time series of the node cable force. The cable force difference and ratio features are extracted from the cable force time series of nodes to obtain the cable force difference feature dataset.

7. The method for detecting tensioned space frames based on fiber Bragg gratings according to claim 6, characterized in that, Friction coefficient inversion and drift state determination are performed on the cable force differential feature dataset to obtain the real-time friction coefficient and node health status assessment results, including: The equivalent friction coefficient of the nodes is inverted from the time series of nodal cable forces to obtain the time series of equivalent friction coefficients; The node health status time series is obtained by comprehensively judging the node health status based on the time series of equivalent friction coefficient and the cable force difference feature dataset.

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