Intelligent online evaluation system for structural strength of composite material

Abstract

The invention provides a hardware and software design method of a composite material structural strength intelligent online evaluation system, and belongs to the field of structural intelligent health monitoring and detection. An embedded or externally attached FBG sensor and network layout design is performed according to a composite material structure and load characteristics, a multi-dimensional analysis algorithm is fused to realize dynamic evaluation of structural strength, a real-time safety coefficient is calculated in combination with failure criteria such as Tsai-Hill and the like, and a random subspace identification (SSI) method is adopted to identify the structural strength of the composite material. Modal parameters (frequency drift rate and vibration mode difference) are extracted through Hankel matrix construction and time domain singular value decomposition, internal damage is accurately positioned, a failure mode is discriminated, and a sensing-analysis-decision full-closed-loop intelligent evaluation system is formed. The intelligent online evaluation system for the strength of the in-service composite material structure provides an effective and reliable technology for full-life-cycle digital management, dynamic stress field reconstruction, performance degradation prediction and remote collaborative operation and maintenance of service data of the composite material structure.

Description

Technical Field

[0001] The present invention belongs to the field of structural intelligent health monitoring and detection. Based on the design of fiber Bragg grating (FBG) sensors and network layout, an inlaid or externally attached intelligent composite material structure sensing and testing system is constructed. By using discrete monitoring information and composite material structure strength theory, the intelligent online evaluation of the structural strength of in-service composite material components is realized, so as to diagnose the structural performance degradation mechanism and safety status evaluation based on the full-process strength evolution information. At the same time, combined with time-domain signal analysis and structural modal parameter estimation methods, internal damage of the structure is identified. Background Art

[0002] With the continuous development of high-risk industries such as aerospace and petrochemical industries, the requirements for the safety and reliability of structures in these industries are also constantly increasing. At the same time, composite materials are widely used in these industries due to their excellent properties such as light weight, high strength and corrosion resistance. All of the above factors have made it difficult for traditional evaluation methods to meet the needs of real-time monitoring and prevention of potential failures. Therefore, it is particularly important to develop an intelligent online evaluation system for the structural strength of composite materials.

[0003] Real-time online structural strength monitoring of aerospace structures is very necessary. It can reflect the structural health status in real time, timely detect potential hidden dangers and avoid catastrophic accidents.

[0004] Moreover, with the continuous development of industries such as aerospace, aircraft manufacturers and operators are increasingly favoring the use of composite materials. The Boeing 787 airliner is a major milestone in the aviation industry. It was the first to use composite materials on a large scale to manufacture the fuselage and wing surfaces. This choice not only reduced the weight of the aircraft, but also improved fuel efficiency and allowed for the design of larger windows and lower cabin pressure. However, the widespread use of composite materials has also brought new challenges, especially in the area of structural health monitoring. According to a review report by the US Government Accountability Office (GAO) on the Boeing 787 aircraft on October 24, 2011, there are four safety hazards with the 787: insufficient information on the active state of the aircraft's composite structure; technical issues involved in the uniqueness of the composite materials; the establishment of maintenance standards; and issues related to personnel training and the addition of maintenance centers. A notable feature of composite materials is that they are not as easy as metals to detect internal damage through conventional visual inspections. When composite materials are impacted, the surface may appear intact, but there may actually be imperceptible delaminations or cracks inside, which pose a potential risk to flight safety. If such damage is not detected and repaired in a timely manner, it may lead to more serious consequences. Although traditional non-destructive testing methods such as ultrasonic and X-ray can detect internal damage to a certain extent, their limitations lie in the inability to monitor in real time and the complexity of the operation, making it difficult to cover large areas of the structure. In contrast, fiber optic sensors have excellent characteristics such as anti-electromagnetic interference, corrosion resistance, high temperature and high pressure resistance, and can work stably under service conditions such as high humidity, strong vibration, and strong electromagnetic environments. At the same time, fiber optics have strong insulation and can effectively resist lightning strikes. An intelligent monitoring system built with FBG sensing elements in fiber optic sensors can monitor the static or dynamic response state of composite material structures in real time and continuously. The small, simple, and easily integrated and networked FBG can be embedded inside composite materials or attached to the outer surface of composite materials. Its extremely high measurement sensitivity and accuracy, strong multiplexing ability, excellent corrosion resistance and chemical stability, ability to adapt to complex environments, good insulation performance, and the ability to monitor high-frequency vibration signals make it an ideal choice for structural health monitoring of composite material structures and structures in the aviation field.

[0005] Therefore, this paper proposes an intelligent online evaluation system for the strength of composite structures based on FBG sensing monitoring information. By implanting a distributed FBG sensing network at key parts of the structure, wavelength data of monitoring points under service conditions are collected in real time, and a multi-dimensional analysis algorithm is integrated to achieve dynamic evaluation of the structure strength. This system combines failure criteria such as Tsai-Hill to calculate the real-time safety factor and quantify the degree of strength degradation of composite materials. At the same time, the Stochastic Subspace Identification (SSI) method is adopted. Through the construction of Hankel matrix and time-domain singular value decomposition to extract modal parameters (frequency drift rate, mode shape difference degree), internal damage is accurately located and the failure mode is discriminated, forming a full closed-loop intelligent evaluation system of "perception - analysis - decision". At the same time, this system can be linked to the cloud to achieve digital management of the full life cycle of service data, dynamic stress field reconstruction, performance degradation prediction, and remote collaborative operation and maintenance, so as to significantly improve the data-driven preventive maintenance ability. Summary of the Invention

[0006] The object of the present invention is to construct an embedded or externally attached intelligent composite structure sensing and testing system based on the design of FBG sensor devices and network layout, and use discrete monitoring information and composite material structure strength theory to achieve intelligent online evaluation of the structure strength of in-service composite components, so as to diagnose the mechanism of structural performance degradation and safety state evaluation based on the full-process strength evolution information. At the same time, combined with time-domain signal analysis and structural modal parameter estimation methods to identify internal damage of the structure, and solve the problems related to the high-precision online tracking of the service state information of composite structures, the real-time evaluation and diagnosis of strength and damage information, etc.

[0007] The technical solution of the present invention is as follows:

[0008] Research an intelligent online evaluation system for the strength of composite structures. The hardware part of this system is an intelligent composite structure sensing and testing system with embedded or externally attached FBG sensors, a demodulator, and a computer. The software part uses Python programming to convert the wavelength signal obtained by the FBG sensor into strain information, and then obtains the safety factor of each layer of the structure measurement point according to the composite material structure strength theory and modal analysis method; comprehensively considering the mode shape difference degree and the average frequency drift of each mode, the change of the overall stiffness of the structure can be known in real time from the average frequency drift of each mode, so as to preliminarily evaluate the overall strength state of the structure; the change of the strength of the measurement point can be known in real time from the safety factor and the comprehensive mode shape difference degree, so as to evaluate the strength of the structure, and it can also be used as the basis for structural damage identification and location.

[0009] The intelligent composite material structure sensing and testing system with embedded or externally attached FBG sensors designs the sensing grid layout according to the characteristics of the composite material structure to be monitored, and selects embedded or externally attached FBG sensors according to the service requirements of the structure. For example, for the wing structure of an aircraft, FBG sensors can be arranged in areas where structural damage may occur, such as geometric discontinuities, connection parts, and high-vibration regions. For parts where double-sided external attachment of FBG sensors is possible, externally attached FBG sensors can be selected, and then the strains of each layer in the composite material are calculated according to plate and shell theories such as Kirchhoff-Love plate theory or Reissner-Mindlin plate theory. For parts where double-sided external attachment of FBG sensors is not possible, only embedded FBG sensors can be selected to directly measure the strains of each layer in the composite material. Therefore, the number and spacing of FBG sensors need to be determined according to the specific service conditions of the composite material structure where the strength structure evaluation system is to be installed.

[0010] The software part of the present invention interprets the strain response data output by the FBG sensor monitoring system based on the strength criteria of composite materials and modal analysis. The main steps are divided into three parts: strength evaluation of composite material structures, modal analysis, and comprehensive evaluation:

[0011] 1. Strength evaluation of composite material structures:

[0012] (1) Calculate the strain ε(t) of each measurement point according to the wavelength data Δλ(t) measured by the FBG at each measurement point:

[0013]

[0014] (2) If embedded FBG sensors are selected, the strain ε x (t), ε y (t) and γ xy (t) of each layer of the composite material can be directly obtained; if externally attached FBG sensors are selected, then according to the monitored and calculated ε x (t), ε y (t) and γ xy (t) on the upper and lower surfaces of the composite material. According to the actual situation of the composite material structure to be monitored, select plate and shell theories such as Reissner-Mindlin plate theory or Kirchhoff hypothesis to obtain the ε x (t), ε y (t) and γ xy (t) of each layer of the composite laminate. Here, taking Kirchhoff hypothesis as an example, according to Kirchhoff hypothesis:

[0015]

[0016] Substituting the strain data of the upper and lower surfaces into the above equations, we can get a set of six linear equations. Solving this set of equations, we can get the neutral layer as well as Then, the ε of each laminate layer is calculated based on the laminate height z of each composite material layer according to formula (1): x (t), ε y (t) and γ xy (t).

[0017] (3) According to the stress-strain relationship of the composite material, the strain required above is converted into stress:

[0018]

[0019] in:

[0020]

[0021] Among them, c 2 represents cosθ 2 , s 2 represents sinθ 2 , θ is the angle between the fiber reinforcement direction 1 and the x-axis direction, Q 66 =G 12 . Then convert the stress to the principal axis direction:

[0022]

[0023] (4) Calculate the safety factor f of stress under the strength criterion E (t):

[0024] a) Tsai-Hill Criterion:

[0025]

[0026] b) Tsai-Wu Criteria:

[0027]

[0028] in,

[0029] c)Hashin principle:

[0030] i. Fiber tensile failure:

[0031]

[0032] ii. Fiber compression failure:

[0033]

[0034] iii. Matrix tensile failure:

[0035]

[0036] iv. Matrix compressive failure:

[0037]

[0038] 2. Modal analysis: There are m FBG sensor measurement points in total, and the sampling frequency is f s , and each FBG monitoring point outputs strain ε in the time period i (t n ), where i ∈ {1,..., m} is the measurement point number, and n ∈ {0,..., N - 1} is the acquisition sequence number.

[0039] (1) Time-domain signal preprocessing: First, the collected strain ε i (t n ) is denoised and interference-removed by using Butterworth or wavelet filtering method to obtain the filtered strain Then the filtered time-domain signal is regarded as a discrete sequence of length N

[0040] (2) Modal identification: The filtered strain data are combined into a multi-channel time-domain vector:

[0041]

[0042] Then the time-domain vector x[n] is embedded into the Hankel matrix H O :

[0043]

[0044] Then the Hankel matrix H O is subjected to singular value decomposition (SVD):

[0045] H O = UΣV T (7)

[0046] where Σ = diag(σ1, σ2,..., σ r ) is the singular diagonal matrix, and σ1 ≥ σ2 ≥... ≥ σ r ≥ 0. Then, according to the singular value energy or inflection point principle, the first n significant singular values σ1, σ2,..., σ n are selected, and the corresponding subspace is regarded as the main dynamic information of the system. At the same time, the corresponding truncated Then construct the observation subspace Then according to the subspace identification principle, Internal block or combined with least squares regression to obtain the state transfer matrix and the observation matrix Again Perform eigenvalue decomposition to obtain the system pole λ p .make p=1,...,n, under the sampling period of Δt, then:

[0047] ① Modal frequency:

[0048]

[0049] ② Damping ratio:

[0050]

[0051] Indicates the damping ratio of the p-th order mode, 0<ζ p <1 means underdamped mode.

[0052] ③ Modal vibration shape:

[0053]

[0054] where φ p yes The right eigenvector of .

[0055] (3) Frequency drift determination: The frequency of the p-th mode at the current moment is The frequency in a healthy state is Then the relative drift rate Δω p for:

[0056]

[0057] Set the threshold to δ ω (The typical range is 3% to 10%, which can be calibrated by experiment), if |Δω p |>δ ω To reduce the influence of noise, it is selected to monitor Δω within κ times (such as 3 to 5 times) of independent monitoring cycles. p Perform a moving average:

[0058]

[0059] if Exceeding the threshold δ multiple times continuously ω Then an overall stiffness attenuation alarm will be issued.

[0060] (4)Determination of mode shape difference: Denote the mode shape of the p-th mode at the current moment as the mode shape in the healthy state as First, normalize and phase-align the mode vectors, using the L2 norm for normalization:

[0061]

[0062] Then, phase-align the mode vectors by calculating the inner product:

[0063]

[0064] Then, calculate the mode difference degree, and the mode shape difference degree DI i,p for the measurement point i is:

[0065]

[0066] When DI i,p significantly increases, it indicates that the measurement point i deviates more from the baseline in the p-th mode shape, and there may be damages such as local cracks, delamination, or loose connections. To improve the accuracy of positioning, the present invention selects to perform weighted summation on the difference degrees DI i,p of the measurement point i on multiple modes {p = 1,..., n} to obtain the comprehensive difference degree CDI i :

[0067]

[0068] where w p is the weight of the p-th mode. If CDI i exceeds the comprehensive threshold δ CDI , it can be preliminarily determined that there are damage signs at the measurement point i. If adjacent measurement points (such as i - 1, i + 1) also simultaneously show CDI i±1 >δ CDI , it can be inferred that there is a concentrated damage distribution in this area. CDI i±1 ≥δ CDI

[0069] 3. Comprehensive evaluation: During the real-time monitoring process, process the data collected by the FBG sensors as described above to obtain the real-time safety factor f E , the average relative drift rate Δω p and the comprehensive difference degree CDI i . Then, perform intensity alarm according to these indicators:

[0070] (1) If the average relative drift rate |Δω p | continuously exceeds δ ω , it indicates that the overall stiffness is decreasing. If the safety factor f EIf (t) < 1, it indicates that although there are signs of damage to a certain extent in the structure, the remaining load-bearing capacity is still acceptable, and it can be determined as early damage. Then search and return the measurement point positions where the comprehensive difference CDI exceeds the threshold δ CDI Meanwhile, combine the safety factor measured according to the Hashin criterion to help judge the damage type and provide guidance for subsequent detection and maintenance.

[0071] (2) If the average relative drift rate |Δω p | continuously exceeds δ ω , it indicates that the overall stiffness is decreasing. If the safety factor f E (t) ≥ 1 at this time, it means that the structure is on the verge of failure and immediate shutdown or maintenance is required. Meanwhile, search and return the measurement point positions where the comprehensive difference CDI exceeds the threshold δ CDI Meanwhile, combine the safety factor measured according to the Hashin criterion to help judge the damage type and provide guidance for detection and maintenance.

[0072] (3) If the average relative drift rate |Δω p | is less than δ ω , and the safety factor f E (t) ≥ 1 at this time, it means that there is damage to local non-critical parts of the structure. Search and return the measurement point positions where the comprehensive difference CDI exceeds the threshold δ CDI Meanwhile, combine the safety factor measured according to the Hashin criterion to help judge the damage type and provide guidance for subsequent detection and maintenance.

[0073] (4) If the average relative drift rate |Δω p | is less than δ ω , and the safety factor f E (t) < 1 at this time, it means that the structure is currently in a healthy state and only needs to continue with routine monitoring.

[0074] The operation steps of the present invention are as follows:

[0075] Step S1: According to structural theory knowledge or finite element analysis, identify the vulnerable positions of the target component, such as joints, geometric discontinuities, and high-vibration regions. Select monitoring points based on the vulnerable positions, and plan the quantity and distribution method of embedded or externally attached FBGs. The greater the number and density of measurement points, the higher the subsequent accuracy. Then connect the FBG sensors to the demodulator and computer to form a monitoring system.

[0076] Step S2: Substitute the wavelength data Δλ(t) of each measurement point collected by the FBG sensor monitoring into the system programmed with python to obtain the real-time safety factor f E (t) calculated according to the Tsai-Hill criterion, Tsai-Wu criterion, and Hashin criterion for each measurement point.

[0077] Step S3: While step S2 is running, substitute the wavelength data Δλ of each measurement point collected by the FBG sensor monitoring i (t n ) into another system programmed with Python to obtain the average relative drift rate |Δω p | and the comprehensive difference index CDI of each measurement point.

[0078] Step S4: Analyze the current condition of the structure based on the data obtained in step S2 and step S3. When it is monitored that the average relative drift amount |Δω p | continuously exceeds the set threshold δ ω , it indicates that the overall stiffness of the structure may decrease. At this time, further evaluate the safety factor f E (t) to judge the structural state. If f E (t) < 1, it is considered that there are signs of early damage to the structure, but there is still a load-bearing margin; on the contrary, if f E (t) > 1, it indicates that the structure is approaching the risk of failure, and shutdown or maintenance measures should be taken immediately. In either case, once the structure is confirmed to be damaged, the measurement point positions where the comprehensive difference index CDI exceeds the preset threshold δ CDI should be searched, and the damage type should be judged in combination with the safety factor determined by the Hashin criterion to provide guidance for subsequent maintenance work. On the other hand, if the average relative drift amount |Δω p | is less than the threshold δ ω , but the safety factor f E (t) > 1, this indicates damage to local non-critical parts. At this time, it is also necessary to search for the measurement points where the CDI exceeds the threshold δ CDI , and analyze the damage type based on the safety factor provided by the Hashin criterion. If all indicators show that the structure is in a healthy state, that is, |Δω p | < δ ω and f E (t) < 1, then only the routine monitoring program needs to be continued.

[0079] The present invention is an intelligent evaluation system for the strength of a composite material structure based on the FBG sensor as the hardware foundation, the composite material structure strength theory and modal analysis as the software. During the monitoring process, through the FBG sensor and the demodulation device, real-time signal collection and acquisition of the structure are carried out. The wavelength variable data collected at the monitoring points are processed through a Python program to obtain the safety factor of each layer at each monitoring point of the structure, the comprehensive difference of the vibration modes, and the average relative drift rate of the structure frequency under each mode. Through these parameters, the state of the structure is judged, the strength of the structure is evaluated in real-time online, and the internal damage of the structure is identified and located.

[0080] The effects and benefits of the present invention are as follows: Through the real-time monitoring information of the multi-point optical fiber sensors, local stress concentration or damage signs can be captured in a timely manner during the service of composite material structures such as aircraft or wind turbine blades, avoiding catastrophic failure accidents. Moreover, the recorded real-time parameters and data also provide a guiding direction for subsequent maintenance work. The system can embed FBGs during the composite material manufacturing stage or install them externally later, meeting the requirements of different stages and occasions. The required construction period is short, the installation and disassembly are convenient, and the sensor layout can be quickly completed during a short shutdown or grounding period in the window period. The intelligent online evaluation system supports repeated use. Once the monitoring target ends, the externally attached sensors can be disassembled and applied to the monitoring of other composite material structures. The demodulator and computer monitoring module have strong versatility and are convenient for popularization in different engineering scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0081] FIG. Figure 1 is a flowchart of the safety factor calculation part in the software system of the present invention;

[0082] FIG. Figure 2 is a flowchart of the modal analysis calculation part in the software system of the present invention;

[0083] FIG. Figure 3 is a flowchart of the comprehensive evaluation part in the software system of the present invention;

[0084] FIG. Figure 4 is a schematic diagram of the structure in the monitoring and operating state;

[0085] FIG. Figure 5 ~FIG. Figure 8 are the safety factor f E diagrams calculated by the Tsai-Hill criterion, Tsai-Wu criterion, and Hashin criterion for each monitoring point of the monitored structure,

[0086] FIG. Figure 9 is a comparison diagram of the main modal frequency drifts

[0087] FIG. Figure 10 is the comprehensive difference index CDI diagram of the vibration modes of each monitoring point of the monitored structure DETAILED DESCRIPTION OF THE INVENTION

[0088] The following describes in detail the specific implementation manners of the present invention in combination with the technical solutions and the drawings.

[0089] The present invention provides an intelligent online evaluation system based on the combination of FBG sensors and composite material structure strength theory, which is applicable to the real-time monitoring and strength evaluation of complex composite material structures such as aerospace aircraft and wind turbine blades. The system obtains strain information at multiple points by arranging a quasi-distributed FBG sensor array, and combines the structure strength theory to realize the safety diagnosis and performance evaluation of the composite material structure. The following are the specific implementation steps of the system:

[0090] Step S1: First, according to structural theory knowledge or finite element analysis, identify the vulnerable positions of the target component, such as joints, geometric discontinuities, and high-vibration regions. Select monitoring points based on the vulnerable positions, and plan the number and distribution method of embedded or externally attached FBGs. To ensure monitoring accuracy, a sufficient number of monitoring points need to be selected. As shown in the appendix Figure 4 , 20 monitoring points are selected for the monitored structure. Select an optical fiber demodulator with high resolution and high-speed sampling, connect all FBG sensors to the demodulator in series or multi-channel mode, and connect it to a computer to form the monitoring system shown in the appendix Figure 4 .

[0091] Step S2: Then, the demodulator collects the wavelength Δλ(t) at a set frequency. After processing the collected wavelength data with a python program (the detailed programming process is as shown in the appendix Figure 1 ), the instantaneous safety factor f E (t) calculated by the Tsai-Hill criterion (4-1), Tsai-Wu criterion (4-2), and Hashin criterion (4-3) can be obtained for each layer at each measurement point, as shown in the appendix Figure 5 ~the appendix Figure 8 . It can be seen that the monitoring system is in an alarm or early warning state at measurement points 13 and 14. For example, at the 13th measurement point, the safety factors f E (t) calculated according to the Tsai-Hill criterion, Tsai-Wu criterion, and Hashin criterion are all greater than 1. For example, the safety factor f E (t) of Tsai-Hill = 1.483 has exceeded 1, indicating that the material may have been damaged at this time.

[0092] Step S3: While calculating the safety factor, after processing the wavelength data collected by the FBG sensors at a set frequency with a python program (the detailed programming process is as shown in the appendix Figure 2 ), the comprehensive vibration mode difference degree CDI of each measurement point of the structure and the average frequency relative drift amount |Δω p | of the structure in each mode can be obtained, as shown in the appendix Figure 9 and the appendix Figure 10As shown. It can be seen that although the average relative drift rate of the modal frequencies of the structure under multiple main modes has reached the warning threshold of 3%, but all are less than 5%. Therefore, the overall stiffness of the structure is still in a healthy state. However, it can also be seen from the appendix Figure 10 that the CDI of the 13th monitoring point is about 7%, exceeding the threshold of 5%, and the CDI of the 14th monitoring point is about 8%, exceeding the threshold of 5%. Therefore, it can be concluded that structural failure should have occurred near these two monitoring points.

[0093] Step S4: Substitute the instantaneous safety factors f E (t) obtained at each measurement point in the first two steps under each criterion, the comprehensive mode shape difference index CDI of each measurement point, and the average relative drift of the structure frequency under each mode |Δω p | into the python program for processing (the detailed programming process is as shown in the appendix Figure 3 ), and obtain the final determination result of the structural strength: damage occurs in local non-critical parts. Then, according to the comprehensive mode shape difference index CDI of each measurement point Figure 10 it can be seen that the CDIs of the 13th and 14th measurement points significantly exceed the threshold, thus locating the approximate location of the structural failure. At the same time, combined with the Hashin criterion Figure 7 it is known that the failure mode here is matrix tensile failure, providing reference and direction for subsequent maintenance.

[0094] The intelligent online evaluation system for the strength of composite material structures provided by the present invention realizes the comprehensive monitoring and safety evaluation of composite material components through the combination of FBG sensors and the theory of composite material structure strength. This system has broad application prospects in the fields of aerospace, wind power generation, ocean engineering, etc., and can effectively improve the safety, reliability and service life of composite material structures. Through real-time online monitoring and damage assessment, this system can provide timely alarms before the occurrence of sudden or progressive damage, and guide the structural repair and maintenance, with significant engineering benefits and commercial value.

Claims

1. An intelligent online evaluation system for the structural strength of composite materials based on FBG sensing network information, characterized by including Composite material structure strength assessment, modal analysis, and comprehensive evaluation, which are divided into three parts: Step S1: Based on structural theory knowledge or finite element analysis, identify the vulnerable locations of the target component, such as joints, geometric discontinuities, and high-vibration regions. Select monitoring points according to the vulnerable locations, and plan the quantity and distribution method of embedded or externally attached FBGs. The greater the quantity and density of the measuring points, the higher the subsequent accuracy. Then connect the FBG sensors to the demodulator and computer to form a monitoring system. Step S2: Substitute the wavelength data Δλ(t) of each measurement point collected by the FBG sensor into the system programmed with Python to obtain the real-time safety factor f calculated according to the Tsai-Hill criterion, Tsai-Wu criterion, and Hashin criterion for each measurement point E (t). Step S3: While step S2 is running, substitute the wavelength data Δλ of each measuring point collected by the FBG sensor i (t n ) into another system programmed with Python to obtain the average relative drift rate |Δω p | and the comprehensive difference index CDI of each measuring point. Step S4: Analyze the current condition of the structure based on the data obtained in Step S2 and Step S3. When it is monitored that the average relative drift amount |Δω p | continuously exceeds the set threshold δ ω , it indicates that the overall stiffness of the structure may decrease. At this time, further evaluate the safety factor f E (t) to judge the structural state. If f E (t) < 1, it is considered that there are signs of early damage to the structure, but there is still a load-bearing margin; on the contrary, if f E (t) > 1, it indicates that the structure is approaching the risk of failure, and shutdown or maintenance measures should be taken immediately. In either case, once it is confirmed that the structure is damaged, the measuring point positions where the comprehensive difference index CDI exceeds the preset threshold δ CDI should be searched, and the damage type should be judged in combination with the safety factor determined by the Hashin criterion to provide guidance for subsequent maintenance work. On the other hand, if the average relative drift amount |Δω p | is less than the threshold δ ω , but the safety factor f E (t) > 1, which indicates damage to local non-critical parts. At this time, the measuring points where the CDI exceeds the threshold δ CDI also need to be searched, and the damage type is analyzed based on the safety factor provided by the Hashin criterion. If all indicators show that the structure is in a healthy state, that is, |Δω p | < δ ω and f E (t) < 1, then only the routine monitoring program needs to be continued.

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