Bridge monitoring system and method based on fiber grating array

Abstract

The invention provides a bridge monitoring system and method based on a fiber grating array, and the system and method achieve the high-precision dynamic monitoring of small and medium span bridges through the combination of dense distribution of sensors and artificial intelligence data analysis. According to the system, strain data of multiple key positions of a bridge are converted into dynamic deflection data of high-density distribution points through the fiber grating array technology, the data are processed and analyzed in real time through an artificial intelligence algorithm, and therefore efficient monitoring and early warning of the health state of a bridge structure are achieved. The method solves the problems of insufficient monitoring point density and low dynamic characteristic monitoring precision in the existing bridge monitoring system, is suitable for bridge structure health monitoring, long-term performance evaluation and operation state analysis, and particularly has a wide application prospect in dynamic monitoring of small and medium span bridges.

Description

Technical Field

[0001] The present invention relates to the field of bridge structure health monitoring, and in particular to a bridge monitoring system and method combining fiber grating array technology and artificial intelligence algorithm. Background Art

[0002] With the increase in the number of bridges and the extension of their service life, the demand for health monitoring of bridge structures has become increasingly important. At present, traditional monitoring methods have problems such as insufficient density of monitoring points and low monitoring accuracy, which can hardly meet the complexity and real-time requirements of modern bridge structures. Fiber Bragg grating technology has been widely used in bridge monitoring due to its high sensitivity, anti-interference and long-distance transmission capabilities. However, existing technologies make it difficult to fully utilize fiber grating arrays to achieve high-density point distribution and real-time processing of complex data. Therefore, how to effectively combine fiber grating technology with artificial intelligence algorithms to improve monitoring accuracy and efficiency has become a key challenge in the field of bridge monitoring. Summary of the invention

[0003] The present invention is a bridge monitoring system and method based on fiber grating array, which realizes real-time monitoring and evaluation of the health status of bridge structure by combining fiber grating technology, data acquisition and artificial intelligence algorithm. The content and implementation scheme of the present invention are described in detail below.

[0004] Fiber Bragg Grating Sensor Module Technical implementation plan: Fiber Bragg grating sensor modules are deployed in high-density arrays at key locations on the bridge, including supports, main beams, cables, etc., to sense the strain and temperature changes of the bridge in real time.

[0005] Specific steps: Array design and layout: According to the structural characteristics of the bridge and key stress locations, the finite element analysis method is used to optimize the sensor layout. Sensors are arranged at supports, main beams, cables and other locations. Each layout consists of two fiber gratings (one for strain measurement and the other for temperature compensation).

[0006] Sensor packaging and installation: The sensor adopts a double-layer packaging design. The outer layer is made of high-temperature resistant and corrosion-resistant material (such as polyimide), and the inner layer is filled with low-modulus silicone to absorb mechanical vibration. The sensor is fixed by adhesive or welding to ensure installation stability.

[0007] Real-time data perception: Fiber Bragg grating sensors sense strain and temperature through changes in reflected wavelength, based on the Bragg wavelength formula:

[0008] Data acquisition module Technical implementation plan: The data acquisition module uses a high-precision analog-to-digital conversion (ADC) device to digitize the optical signal output by the sensor to achieve high-sensitivity and high-resolution data acquisition.

[0009] Specific steps: Signal acquisition and transmission: A narrowband tunable laser is used to periodically scan the reflection wavelength of the fiber Bragg grating to generate an optical signal; a photodetector converts the optical signal into an electrical signal, and an amplifier circuit is used to enhance the signal strength.

[0010] Analog-to-digital conversion and synchronous sampling: The analog signal is converted into a digital signal through a high-resolution ADC (16 bits or higher), with a sampling rate of no less than 100kHz to meet dynamic monitoring needs. Multi-channel synchronous sampling technology is used to achieve parallel acquisition of multi-point signals.

[0011] Data transmission and storage: Data is transmitted to the analysis module via optical fiber or wireless communication, and high-speed interfaces (such as PCIe) are used to achieve low-latency data transmission. Equipped with edge computing equipment, the collected data is preliminarily processed and stored.

[0012] Technical implementation solution: The data analysis module uses a combined model of convolutional neural network (CNN) and long short-term memory network (LSTM) to decouple and analyze the wavelength change signal and generate dynamic characteristic data of the bridge.

[0013] Specific steps: Data preprocessing: Use adaptive filtering algorithms (such as Kalman filtering) to denoise the signal and enhance data quality; normalize the wavelength change data to make it fit the model input.

[0014] Model training and deployment: Convolutional neural network (CNN) extracts spatial features of wavelength variation signals:

[0015] The long short-term memory network (LSTM) captures the time series characteristics of the signal and generates dynamic deflection data. The model is trained with large-scale experimental data to optimize hyperparameters (such as learning rate and regularization weight).

[0016] Decoupling and feature extraction: Based on the model output, decouple the temperature and strain components to generate dynamic characteristic data with high density points; use Fourier transform (FFT) to analyze the vibration mode of the bridge.

[0017] Technical implementation solution: The alarm module has real-time early warning function, which determines the operation status of the bridge through dynamic threshold algorithm and detects potential risks in time.

[0018] Specific steps: Dynamic threshold setting: Generate a dynamic threshold model based on the historical data and current status of the bridge:

[0019] Health status assessment: Compare real-time data with dynamic thresholds to determine the health status of the bridge; combine dynamic deflection data to generate a bridge health score (e.g. 0-100 points).

[0020] Abnormal detection and alarm output: Once abnormal fluctuations are detected, an audible and visual alarm is triggered, and the management personnel are notified via SMS or APP; a detailed health assessment report is generated, providing abnormal cause analysis.

[0021] Technical implementation plan: The bridge monitoring method includes steps such as point layout, data collection, signal analysis and health assessment.

[0022] Specific steps: Sensor layout: Fiber Bragg grating sensors are placed at key locations on the bridge to form a high-density monitoring network; the density of the sensors is adjusted according to the structural characteristics of the bridge and monitoring requirements.

[0023] Real-time data acquisition: Use a narrow-band laser to scan the fiber grating array to obtain wavelength change signals in real time; the data acquisition module completes the digital processing of the signal and transmits it to the analysis module.

[0024] Health status assessment: The data analysis module uses artificial intelligence algorithms to generate dynamic characteristic data of the bridge; evaluates the health status of the bridge based on the analysis results and generates an assessment report; and issues an alarm through the alarm module when potential risks are found. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] Figure 1 :Data analysis and alert generation flow chart Figure 2 : is the architecture diagram of the bridge monitoring system of the present invention; Figure 3 : is a schematic diagram of the fiber grating sensor array arrangement of the present invention; Figure 4 : It is the data collection and processing flow chart of the present invention. DETAILED DESCRIPTION

[0026] Data analysis and alert generation process Figure 1 As shown in the figure. In the data analysis stage, the system uses artificial intelligence algorithms to conduct in-depth analysis of the pre-processed data. These algorithms include convolutional neural networks (CNN) and long short-term memory networks (LSTM), which can automatically extract feature information from the data and generate dynamic characteristic data of the bridge, such as dynamic deflection and vibration mode. Then, the system will evaluate the health status of the bridge based on this data and generate a health assessment report.

[0027] During the alarm generation phase, the system monitors the health status data of the bridge in real time and compares it with the preset threshold. Once the data is found to be abnormal or exceeds the threshold, the system will immediately trigger the alarm mechanism and send an alarm to relevant personnel through sound and light alarms, SMS notifications, etc., so that timely measures can be taken for maintenance or repair.

[0028] like Figure 2 As shown, the bridge monitoring system provided by the present invention mainly includes a fiber Bragg grating sensor module, a data acquisition module, a data analysis module and an alarm module. The sensor module is arranged at the key parts of the bridge, the data acquisition module is responsible for collecting sensor data, the data analysis module processes the data and generates a health assessment report, and the alarm module issues an alarm when an abnormality is found.

[0029] like Figure 3 As shown in the figure, the fiber Bragg grating sensor array is carefully arranged in multiple key parts of the bridge, including the main beam, support, cable, etc. On the main beam, the sensor array sets a node at a certain distance along the length of the bridge. Each node contains two fiber Bragg gratings, one for strain measurement and the other for temperature compensation. On the support and cable, the sensor array is arranged according to the specific structural characteristics to ensure that the deformation and stress changes of key parts can be fully covered and captured.

[0030] Data collection and processing process Figure 4 As shown. First, the data acquisition module uses a multi-channel laser to synchronously scan the fiber Bragg grating sensor array to obtain wavelength change signals. Then, these signals are converted into digital signals through a high-precision analog-to-digital conversion device and stored in a local or remote server. In the data processing stage, the system will perform pre-processing operations such as filtering and denoising on the digital signals to improve the accuracy and reliability of the data.

[0031] The following are four embodiments designed based on the uploaded patent document content and combined with the actual application of fiber grating arrays:

[0032] Objective: To utilize the high-density point distribution characteristics of fiber Bragg grating arrays to achieve dynamic strain monitoring of the bridge main beam area and capture the global deformation data of the main beam under traffic loads.

[0033] System structure: Fiber Bragg grating sensor array module: adopts a high-density array to cover the main beam area of ​​the bridge and sense local and overall dynamic strain and temperature changes; data acquisition module: multi-channel acquisition of wavelength change signals of the fiber Bragg grating array, and generates digital signals through high-precision analog-to-digital conversion; data analysis module: combines convolutional neural network (CNN) and long short-term memory network (LSTM) to analyze dynamic strain characteristics and generate deformation maps of the main beam; alarm module: triggers abnormal deformation warning based on health assessment data.

[0034] Implementation steps: Sensor array design and layout: Fiber Bragg grating arrays are arranged in the mid-span, support points and other key stress-bearing locations of the main beam. The array design covers a node every 2 meters, and each node contains two gratings (strain + temperature). The array uses coupling and branching technology to concentrate multi-grating signals into a few optical paths, thereby improving efficiency and reducing wiring complexity.

[0035] Data acquisition and synchronous processing: Use a multi-channel laser to synchronously scan the fiber grating array to collect wavelength change signals with high density distribution points; use analog-to-digital conversion (ADC) to achieve synchronous sampling of multiple nodes to form a dynamic data matrix of the main beam.

[0036] Health assessment and early warning: The data analysis module extracts dynamic strain distribution characteristics through CNN, and combines LSTM to predict future trends and generate a health assessment report. When the strain exceeds the dynamic threshold, the alarm module issues an early warning to alert maintenance personnel.

[0037] Advantages: The density of fiber grating arrays is significantly higher than that of traditional single-point monitoring, and can capture the global and local dynamic strain characteristics of the main beam. Multi-grating parallel acquisition significantly improves data acquisition efficiency and provides guarantee for dynamic monitoring in complex environments.

[0038] Example 2: Bridge cable tension monitoring based on fiber grating array Objective: To use fiber grating arrays to monitor the dynamic tension of cables at multiple points and accurately identify local tension changes and overall cable health status.

[0039] System structure: Fiber Bragg grating sensor array module: Arrange fiber Bragg grating arrays in bridge cables to densely sense changes in cable tension; Data acquisition module: Realize real-time acquisition and transmission of array signals through multi-channel laser scanning; Data analysis module: Combine Fourier transform (FFT) and artificial intelligence models to interpret cable vibration modes and tension distribution; Alarm module: Issue alarms in time for abnormal cable tension fluctuations.

[0040] Array design and layout: High-density fiber grating arrays are arranged at key locations of the cable (middle and anchor ends), with one grating point per meter. The array uses wavelength division multiplexing (WDM) technology to achieve centralized processing of multi-point data and reduce the complexity of the optical path.

[0041] Data acquisition and spectrum analysis: The laser periodically scans the fiber grating array to collect multi-point tension signals; the data analysis module combines FFT to analyze the cable vibration mode and identify local tension changes and cable fatigue.

[0042] Dynamic assessment and anomaly detection: Generate a cable tension distribution map through an artificial intelligence model to determine whether there is abnormal stress concentration in the cable; once the tension anomaly exceeds the threshold, the alarm module triggers a real-time warning.

[0043] Advantages: Fiber Bragg grating array technology significantly improves the spatial resolution of cable tension monitoring and can accurately identify local damage risks. Compared with single grating monitoring, the array layout provides a tension distribution map of the entire cable.

[0044] Objective: To monitor the multi-point displacement of the support through a fiber grating array and evaluate the operating status of the support under temperature changes and loads.

[0045] System structure: Fiber Bragg grating sensor array module: high-density fiber Bragg grating nodes are arranged on the support to monitor the support displacement and temperature changes in real time; data acquisition module: multi-channel concurrent acquisition technology is used to obtain the support array signal; data analysis module: displacement components are extracted through deep learning algorithms, and a support health status report is generated.

[0046] Array layout and node optimization: Arrange fiber grating arrays at different positions of the movable support and the fixed support to form a multi-point displacement monitoring network; improve the sensitivity of point layout by integrating multi-layer grating technology.

[0047] Data acquisition and processing: Use synchronous acquisition equipment to realize multi-point data acquisition and convert the signal into support displacement change data; use multi-threaded data transmission technology to ensure the real-time performance of array signals.

[0048] Health assessment and alarm: The data analysis module decouples temperature and displacement data to form a support displacement monitoring report; when the support displacement exceeds the safety threshold, an audible and visual alarm is triggered and maintenance personnel are notified.

[0049] Advantages: Fiber Bragg grating array realizes multi-point displacement monitoring of the support, which can provide comprehensive support health status compared with traditional single-point monitoring; the data synchronization processing capability is significantly improved to ensure real-time and accurate monitoring.

[0050] Example 4: Bridge vibration mode monitoring based on fiber grating array Objective: To capture the overall vibration modal characteristics of a bridge using fiber Bragg grating arrays and identify abnormal vibration behavior in real time.

[0051] System structure: Fiber Bragg grating sensor array module: covers the main beam, support and cables of the bridge to realize multi-region vibration modal monitoring; data acquisition module: synchronously collects multi-point vibration signals to generate high-resolution dynamic data; data analysis module: extracts modal features and identifies abnormal behaviors through artificial intelligence algorithms.

[0052] Sensor array design: High-density fiber grating arrays are arranged in different areas of the bridge to form a dynamic monitoring network covering the entire bridge; each array node is combined with wavelength multiplexing technology to improve the efficiency of the data channel.

[0053] Vibration signal acquisition: Collect vibration signals from different areas and use analog-to-digital conversion equipment to synchronize real-time data to form a multi-dimensional dynamic data matrix.

[0054] Modal analysis and evaluation: The data analysis module generates vibration modal characteristic diagrams and compares them with normal modal data to determine whether there are abnormal behaviors; it predicts vibration trends based on historical data and gives abnormal warnings.

[0055] Advantages: Fiber Bragg grating array technology realizes full-bridge dynamic modal monitoring with large coverage and high resolution; the multi-dimensional data analysis capability is significantly improved to meet the needs of early abnormality detection in complex vibration scenarios.

[0056] These examples highlight the unique advantages of fiber grating arrays in spatial distribution and multi-point monitoring, which greatly improves the resolution, real-time and global coverage of monitoring compared to single sensors. Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A bridge monitoring system based on fiber grating array, comprising: Fiber Bragg grating sensor modules are placed at multiple key locations on the bridge to sense strain and ambient temperature in real time; A data acquisition module collects wavelength change signals of the fiber grating array and generates digital signals through a high-precision analog-to-digital conversion device; The data analysis module uses artificial intelligence algorithms to process the wavelength change data of the fiber Bragg grating, decouple the temperature and strain components, and generate dynamic deflection data with high density distribution points; The alarm module generates a health status assessment report based on the results of the data analysis module and issues an alarm when an anomaly is found.

2. The bridge monitoring system according to claim 1, characterized in that: The fiber grating sensor modules are deployed in a high-density array, covering key parts of the bridge such as supports, main beams, cables, etc.

3. The bridge monitoring system according to claims 1 to 2, characterized in that: The data analysis module is based on deep learning algorithms, including a combined model of convolutional neural networks (CNN) and long short-term memory networks (LSTM), to process the dynamic data of the bridge.

4. The bridge monitoring system according to any one of claims 1 to 3, characterized in that: The alarm module includes a real-time early warning function that can generate dynamic thresholds based on the operating status of the bridge and detect potential risks in a timely manner.

5. A bridge monitoring method based on fiber grating array, comprising the following steps: Fiber Bragg grating sensor modules are arranged at key locations of the bridge. The wavelength change signal of the fiber Bragg grating is acquired in real time using the data acquisition module. The wavelength change signal is processed using the data analysis module to generate dynamic characteristic data of the bridge. The health status of the bridge is evaluated based on the analysis results, and the abnormal detection results are output through the alarm module.

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