Wireless passive bolt pretightening force monitoring method and system

Abstract

The invention discloses a wireless passive bolt pre-tightening force monitoring method and system. The method comprises the steps that bolt deformation, temperature and vibration parameters are collected; optimizing the arrangement positions of the sensors based on physical field simulation and working condition characteristics; eliminating interference by using a dynamic decoupling model, and outputting a corrected pre-tightening force value; and monitoring and evaluating the pre-tightening force state of the bolt group in combination with multi-channel sensing, and outputting a monitoring result. According to the invention, the multi-dimensional sensing unit is combined with the dynamic decoupling algorithm, so that the measurement precision under a complex working condition is remarkably improved, and on the basis of a wireless passive communication technology, the arrangement position of each sensor is determined through physical field simulation, so that the risk of a monitoring blind area caused by unreasonable arrangement is avoided. Besides, by fusing multi-dimensional parameters such as strain, temperature and vibration and constructing a decoupling algorithm, the problem of pre-tightening force measurement deviation caused by multi-interference coupling under complex working conditions is solved, and meanwhile, real-time monitoring and abnormal early warning of the bolt group pre-tightening force are realized.

Description

Technical Field

[0001] This invention relates to the field of passive wireless communication technology, and more specifically, to a wireless passive bolt preload monitoring method and system. Background Technology

[0002] In critical fields such as industrial equipment, bridge structures, wind turbine towers, and aerospace, the preload state of bolted connections directly determines the structure's vibration resistance, sealing performance, and fatigue life. Traditional monitoring methods rely on manual re-inspection or offline sampling, resulting in low efficiency and poor real-time performance. While existing online monitoring technologies have introduced wired strain gauges or piezoelectric sensors, the complex cabling and external power requirements lead to high deployment costs and maintenance difficulties, especially in rotating components or enclosed environments where long-term stable operation is challenging. In recent years, the emergence of wireless passive technology has provided a new approach to real-time monitoring; however, existing solutions are still limited by the coupling effect of a single sensing parameter and environmental interference, failing to distinguish between actual preload changes and interference factors such as temperature drift and vibration noise, leading to a significant decrease in measurement accuracy.

[0003] Further analysis reveals that the limitations of existing technologies extend beyond the single sensing parameter: current technologies often only support independent monitoring of a single sensing node—a single bolt—lacking the ability to monitor the preload of bolt groups, making it difficult to meet the global health management needs of large structures (such as wind turbine tower flanges). Furthermore, the strong reflection and eddy current effects of wireless signals in metallic environments severely restrict communication distance and stability, further limiting the applicability of the technology. These issues collectively constitute a significant obstacle to the advancement of current bolt preload monitoring technology towards intelligence and high precision. For example, the following technical solutions...

[0004] Patent CN117073882A discloses a preload monitoring device and control method. The device includes a tag circuit board comprising a radio frequency identification (RFID) chip, an antenna, and multiple strain sensors; and a tag reader. The tag reader transmits radio frequency signals and receives signals transmitted by the RFID chip through the antenna. The strain sensors detect changes in bolt preload and convert these changes into voltage signals. The antenna receives the radio frequency signals transmitted by the tag reader and generates an induced alternating current. The RFID chip converts the induced alternating current into direct current to power itself and the strain sensors, and converts the voltage signals into digital signals, which are then transmitted to the tag reader via the antenna. This passive method enables real-time acquisition of bolt preload, allowing for wireless transmission and real-time monitoring of preload in long-distance bolt connections. However, this solution has limitations due to its reliance on a single sensing parameter and manual deployment rules.

[0005] The patent with publication number CN115931204A provides a smart bolt preload measuring device based on an oscillation circuit: it includes a preload measuring module and a wireless power supply signal processing module. The wireless power supply signal processing module wirelessly supplies power to the preload measuring module installed between the nut and the fastened part. After the preload measuring module receives power, its oscillator transmits an oscillation signal with a frequency controlled by the bolt preload via a transmitting antenna. The wireless power supply signal processing module receives the oscillation signal and processes it to obtain the magnitude of the bolt preload.

[0006] Patent CN119323228A discloses a bolt connection status monitoring device and method based on passive radio frequency identification (RFID). The device includes a monitoring unit, an RFID reader, and a host computer. The monitoring unit has an ID and is installed corresponding to the paired bolts and nuts to be monitored. The lower end of the monitoring unit's housing is open and coaxially fixed to the nut, with the bolt's shank extending into the housing's inner cavity. An inductor coil is encapsulated on the inner wall of the housing, and an RFID module is fixed to the top of the housing. The two ends of the coil are connected to the RFID module's circuit to form a closed loop. Bolt loosening information is obtained by sensing the change in electrical signal generated when the nut rotates relative to the bolt. The RFID module is used for real-time monitoring of the electrical signal, the host computer is used for issuing monitoring commands and data processing, and the RFID reader is used to establish bidirectional communication between the RFID module and the host computer.

[0007] In summary, existing bolt preload monitoring technologies suffer from the following drawbacks: First, they rely on a single sensing parameter (such as a strain gauge), failing to decouple temperature drift, vibration noise, and actual preload changes, leading to a significant decrease in measurement accuracy under complex operating conditions. Furthermore, sensor deployment depends on manual experience, making it difficult to accurately cover areas with concentrated sensing parameters, resulting in monitoring blind spots. Second, existing wireless passive solutions only support independent monitoring of a single sensing node—a single bolt—lacking the ability to monitor the preload of bolt groups, making it difficult to achieve dynamic assessment of the overall health status of large structures. Summary of the Invention

[0008] In view of the above problems, the purpose of this invention is to provide a wireless passive bolt preload monitoring method and system.

[0009] The first aspect of this invention provides a wireless passive bolt preload monitoring method, the method comprising:

[0010] S1: Collect bolt deformation, temperature, and vibration parameters;

[0011] S2: Optimize sensor placement based on physical field simulation and operating condition characteristics;

[0012] S3: Use a dynamic decoupling model to eliminate interference and output the corrected preload value;

[0013] S4: Combine multi-channel sensor monitoring to evaluate the preload status of bolt groups and output the monitoring results.

[0014] Preferably, S2 includes:

[0015] S2.1: Establish a three-dimensional finite element model of the bolted connection structure using finite element simulation tools;

[0016] S2.2: Using a three-dimensional finite element model to simulate the stress distribution, temperature field, and vibration changes of bolts under typical working conditions;

[0017] S2.3: Extract bolt temperature change information based on the temperature field, and extract resonance regions and vibration-sensitive nodes in the bolt based on vibration changes;

[0018] S2.4: Analyze the stress distribution in the circumferential, radial, and axial directions at the bolt head and screw section through simulation.

[0019] S2.5: Extract high-gradient regions of stress concentration as key monitoring sites;

[0020] S2.6: Strain sensors are deployed at key monitoring points, temperature sensors are deployed in non-stressed areas of the same material and temperature as the strain sensors to obtain temperature information, and vibration sensors are installed in resonance areas or vibration-sensitive nodes determined by structural modal analysis.

[0021] Preferably, step S3, which utilizes a dynamic decoupling model to eliminate interference, includes:

[0022] S3.1: Obtain bolt temperature-strain coupling data through experiments;

[0023] S3.2: A bolt temperature drift compensation model is established by fitting the temperature-strain relationship using the least squares method.

[0024] S3.3: Combines cubic spline interpolation algorithm to correct strain sensor output in real time;

[0025] S3.4: Determine the bolt resonance frequency band through vibration modal analysis, and embed an adaptive filtering algorithm into the multi-parameter decoupling algorithm to suppress vibration noise in unrelated frequency bands.

[0026] A second aspect of the present invention provides a wireless passive bolt preload monitoring system, the system comprising: a sensor node module and a reader system module;

[0027] The reader system module includes: a host computer, a reader, and a reader antenna;

[0028] The sensor node module includes: a tag antenna, a tag chip, a microcontroller unit, a multi-channel sensor gating module, a signal conditioning module, and a sensor module;

[0029] The host computer controls the reader and transmits radio frequency signals through the reader's antenna;

[0030] After receiving the radio frequency signal, the tag antenna transmits it to the tag chip;

[0031] The tag chip is activated after receiving the radio frequency signal, and then captures the radio frequency energy to power the microcontroller and the multi-channel sensor gating module;

[0032] The microcontroller selects the corresponding sensor in the sensor module according to a preset timing sequence through the multi-sensor gating module;

[0033] The sensor collects bolt deformation, temperature, and vibration parameters, and sends these parameters to the signal conditioning module.

[0034] After the signal conditioning module conditions the bolt deformation, temperature, and vibration parameters, it sends them to the microcontroller.

[0035] The microcontroller processes the bolt deformation, temperature, and vibration parameters conditioned by the signal conditioning module and sends them to the tag chip;

[0036] The tag chip modulates the bolt deformation, temperature, and vibration parameters processed by the microcontroller into a backscatter signal, which is then transmitted back to the reader via the backscatter link of the reader antenna.

[0037] The reader demodulates, decodes, and parses the received backscattered signal, converting the raw data into readable information before sending it to the host computer.

[0038] Preferably, the sensor module includes a strain sensor, a temperature sensor, and a vibration sensor.

[0039] Preferably, the sensor node module adopts an anti-metal layered layout design.

[0040] Preferably, the anti-metal layered layout design specifically includes:

[0041] The tag chip and tag antenna are integrated on the upper layer to form the radio frequency board layer; the microcontroller, multi-channel sensor gating module, signal conditioning module and sensor module are deployed on the lower layer to form the signal board layer.

[0042] The radio frequency (RF) board layer and the signal board layer achieve interlayer signal and power transmission through board-to-board connectors.

[0043] Preferably, the sensor node module is encapsulated in a hexagonal prism plastic shell for the radio frequency board layer and the signal board layer.

[0044] Preferably, the system further includes a sensor deployment optimization module;

[0045] Before the sensor node module starts collecting signals, the sensor layout optimization module performs physical field simulation on the bolts and then optimizes the sensor layout based on the simulation results.

[0046] Preferably, the sensor placement optimization module places strain sensors in the radial stress concentration area at the bolt head, arranges temperature compensation devices for strain in the circumferential direction at the bolt head, deploys temperature sensors in the non-stressed area of ​​the bolt made of the same material, and installs vibration sensors at the resonance sensitive nodes of the bolt structure.

[0047] Compared with existing technologies, the beneficial effects of this invention are as follows: This invention proposes a wireless passive bolt preload monitoring method. By combining multi-dimensional sensing units with a dynamic decoupling algorithm, it significantly improves measurement accuracy under complex working conditions. Based on wireless passive communication technology (RFID), it determines the placement of each sensor through physical field simulation, avoiding the risk of monitoring blind spots caused by unreasonable placement. Furthermore, by fusing multi-dimensional parameters such as strain, temperature, and vibration, and constructing a decoupling algorithm, it solves the problem of preload measurement deviation caused by multi-interference coupling under complex working conditions, while simultaneously achieving real-time monitoring and anomaly warning of bolt group preload.

[0048] By extracting the key sensing locations of bolts through finite element force field simulation, and integrating multi-dimensional parameters such as strain, temperature, and vibration, a decoupled algorithm model is constructed. Combined with a multi-sensor gating module, it realizes accurate measurement of preload under complex interference coupling and collaborative monitoring of bolt groups. At the same time, it realizes data backhaul through wireless passive communication technology, providing a high-precision, maintenance-free monitoring solution for industrial scenarios. Attached Figure Description

[0049] Figure 1 This is a flowchart of a wireless passive bolt preload monitoring method as described in Example 1.

[0050] Figure 2 The diagram below shows the structural logic of a wireless passive bolt preload monitoring system as described in Example 2.

[0051] Figure 3 This is a schematic diagram of a sensor node module.

[0052] Figure 4 This is a schematic diagram showing the installation of the strain sensor and temperature sensor on the bolt.

[0053] Figure 5 This is a schematic diagram of the vibration sensor being mounted on a bolt. Detailed Implementation

[0054] To better understand the above-mentioned objectives, features, and advantages of the present invention, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that, unless otherwise specified, the embodiments and features described in these embodiments can be combined with each other.

[0055] Many specific details are set forth in the following description in order to provide a full understanding of the invention. However, the invention may also be practiced in other ways different from those described herein, and therefore the scope of protection of the invention is not limited to the specific embodiments disclosed below.

[0056] Example 1

[0057] like Figure 1 As shown in the figure, this embodiment discloses a wireless passive bolt preload monitoring method, the method comprising:

[0058] S1: Collect bolt deformation, temperature, and vibration parameters;

[0059] S2: Optimize sensor placement based on physical field simulation and operating condition characteristics;

[0060] S3: Use a dynamic decoupling model to eliminate interference and output the corrected preload value;

[0061] S4: Combine multi-channel sensor monitoring to evaluate the preload status of bolt groups and output the monitoring results.

[0062] It should be noted that this embodiment significantly improves measurement accuracy under complex working conditions by combining multi-dimensional sensing units with a dynamic decoupling algorithm; through physical field simulation, the placement of each sensor is determined, avoiding the risk of monitoring blind spots caused by unreasonable placement. In addition, a global health assessment is achieved through a multi-channel bolt group collaborative monitoring mechanism, overcoming the limitations of traditional single-sensor node-single-bolt monitoring.

[0063] In this embodiment, S2 includes:

[0064] S2.1: Establish a three-dimensional finite element model of the bolted connection structure using a finite element simulation tool (such as ABAQUS);

[0065] S2.2: Use a three-dimensional finite element model to simulate the stress distribution, temperature field and vibration changes of bolts under typical working conditions (such as axial preload of 10-50kN, vibration load of 0-500Hz and thermal cycling of 20-80℃).

[0066] S2.3: Extract bolt temperature change information based on the temperature field, and extract resonance regions and vibration-sensitive nodes in the bolt based on vibration changes;

[0067] S2.4: Analyze the stress distribution in the circumferential, radial, and axial directions at the bolt head and screw section through simulation.

[0068] S2.5: Extract high-gradient regions of stress concentration as key monitoring sites;

[0069] S2.6: Strain sensors are deployed at key monitoring points, temperature sensors are deployed in non-stressed areas of the same material and temperature as the strain sensors to obtain temperature information, and vibration sensors are installed in resonance areas or vibration-sensitive nodes determined by structural modal analysis.

[0070] It should be noted that this embodiment combines simulation results to guide sensor deployment, significantly improving monitoring accuracy and anti-interference capabilities under complex working conditions, while avoiding blind spot risks caused by manual deployment based on experience. The specific sensor deployment is as follows: strain sensors are deployed in the radial stress concentration area at the bolt head end; temperature compensation devices for strain are arranged circumferentially; temperature sensors are deployed in non-stressed areas of the same material; and vibration sensors are installed at structural resonance-sensitive nodes.

[0071] In this embodiment, step S3, which involves eliminating interference using a dynamic decoupling model, includes:

[0072] S3.1: Obtain bolt temperature-strain coupling data through experiments;

[0073] S3.2: A bolt temperature drift compensation model is established by fitting the temperature-strain relationship using the least squares method.

[0074] S3.3: Combines cubic spline interpolation algorithm to correct strain sensor output in real time;

[0075] S3.4: Determine the bolt resonance frequency band through vibration modal analysis, and embed an adaptive filtering algorithm into the multi-parameter decoupling algorithm to suppress vibration noise in unrelated frequency bands.

[0076] It should be noted that the multi-parameter decoupling algorithm described in this embodiment is used to process and output the sensor data in real time, which significantly improves the monitoring accuracy and anti-interference capability under complex working conditions.

[0077] Example 2

[0078] like Figure 2 As shown, this embodiment discloses a wireless passive bolt preload monitoring system, which includes: a sensor node module and a reader system module;

[0079] The reader system module includes: a host computer, a reader, and a reader antenna;

[0080] The sensor node module includes: a tag antenna, a tag chip, a microcontroller unit, a multi-channel sensor gating module, a signal conditioning module, and a sensor module;

[0081] The host computer controls the reader and transmits radio frequency signals through the reader's antenna;

[0082] After receiving the radio frequency signal, the tag antenna transmits it to the tag chip;

[0083] The tag chip is activated after receiving the radio frequency signal, and then captures the radio frequency energy to power the microcontroller and the multi-channel sensor gating module;

[0084] The microcontroller selects the corresponding sensor in the sensor module according to a preset timing sequence through the multi-sensor gating module;

[0085] The sensor collects bolt deformation, temperature, and vibration parameters, and sends these parameters to the signal conditioning module.

[0086] After the signal conditioning module conditions the bolt deformation, temperature, and vibration parameters, it sends them to the microcontroller.

[0087] The microcontroller processes the bolt deformation, temperature, and vibration parameters conditioned by the signal conditioning module and sends them to the tag chip;

[0088] The tag chip modulates the bolt deformation, temperature, and vibration parameters processed by the microcontroller into a backscatter signal, which is then transmitted back to the reader via the backscatter link of the reader antenna.

[0089] The reader demodulates, decodes, and parses the received backscattered signal, converting the raw data into readable information before sending it to the host computer.

[0090] It should be noted that the wireless passive bolt preload monitoring system described in this embodiment includes a sensor node module and a reader system module. It mainly consists of eight parts: reader antenna, reader, host computer, tag antenna, tag chip, microcontroller unit, multi-channel sensor selection module, and sensor module.

[0091] The reader antenna is responsible for transmitting radio frequency signals and communicating with the sensor nodes.

[0092] Reader: Responsible for demodulating radio frequency signals, decoding data and parsing protocols. After converting the raw data into readable information, it interacts with the host computer through the interface to complete data upload and command response.

[0093] Host computer: Responsible for processing received signals, decoupling algorithms, and performing health assessments and early warnings.

[0094] Tag antenna: The tag antenna is used to receive electromagnetic waves emitted by the reader to obtain radio frequency energy and communication commands, and the node's sensor data is also transmitted through the tag antenna.

[0095] Tag chip: This converts the radio frequency energy received by the tag antenna into DC energy and has a certain capacity of data storage unit. In addition, the tag chip is responsible for establishing and maintaining communication between the device and the reader.

[0096] Microcontroller Unit: A low-power microcontroller unit. The microcontroller unit controls the on / off state of the multiplexer gating module to select the sensing channel.

[0097] Multi-channel sensor gating module: Used to control the on / off state of sensor channels.

[0098] Sensor module: Used for multi-parameter sensing of bolts.

[0099] In this embodiment, the sensor node module operates and communicates by acquiring radio frequency signals. Its multi-channel sensor gating unit, controlled by the microcontroller unit, performs multi-bolt multi-parameter sensing. The node also integrates multi-dimensional sensing units for deformation, temperature, vibration, and other parameters to collect relevant bolt parameters in real time, and utilizes a signal conditioning module to initially eliminate environmental interference. After processing by the microcontroller unit, the data is transmitted to the tag chip, which performs load modulation on the data and ultimately transmits it back to the reader module via backscattering from the tag antenna. Upon receiving the signal, the reader module demodulates the signal to restore the original data and further eliminates multi-parameter coupling interference using a multi-parameter decoupling algorithm, extracting independent preload information. Finally, the health assessment unit outputs the health status and warning signal.

[0100] It should be noted that the backscatter link feedback is a technique of backscatter communication, a passive wireless communication technology consisting of RFID (Radio Frequency Identification) tags (passive nodes) and RFID readers (active devices). The reader emits continuous radio frequency signals, and the tag receives these signals to obtain energy and drive its internal circuitry. The tag dynamically adjusts its antenna load impedance (e.g., switching between short-circuit and open-circuit states) to encode sensor data into the amplitude or phase of the reflected signal, forming an echo signal. The reader captures and analyzes the modulation characteristics in the reflected signal to achieve data reading and communication from the passive tag. This technology does not require an independent power supply for the tag, relying on radio frequency energy for power supply and information transmission. It features low power consumption and high compatibility, making it suitable for large-scale passive sensor network deployments in IoT scenarios.

[0101] In this embodiment, the sensor module includes a strain sensor, a temperature sensor, and a vibration sensor.

[0102] It should be noted that this embodiment mainly collects bolt deformation, temperature and vibration parameters, so strain sensors, temperature sensors and vibration sensors are set in this embodiment.

[0103] In this embodiment, the sensor node module adopts an anti-metal layered layout design.

[0104] In this embodiment, the anti-metal layered layout design is specifically as follows:

[0105] The tag chip and tag antenna are integrated on the upper layer to form the radio frequency board layer, in order to improve energy capture efficiency and communication stability; the microcontroller unit, multi-channel sensor gating module, signal conditioning module and sensor module are deployed on the lower layer to form the signal board layer.

[0106] The radio frequency (RF) board layer and the signal board layer achieve interlayer signal and power transmission through board-to-board connectors.

[0107] In this embodiment, the sensor node module is encapsulated by a hexagonal prism plastic shell for the radio frequency board layer and the signal board layer.

[0108] It should be noted that, since the sensor node module is used in a metallic environment, and considering the strong reflection and eddy current interference of radio frequency signals caused by the metallic environment, as well as the coupling interference between radio frequency lines and DC lines, the sensor node module described in this embodiment adopts an anti-metal layered layout design.

[0109] like Figure 3 As shown. Figure 3 This is a schematic diagram of the sensor node module in use. 1 represents the outer casing, 2 the RF board layer, 3 the signal board layer, 4 the signal line, 5 the bolt, and 6 the board-to-board connector.

[0110] In this embodiment, the tag chip and tag antenna are integrated on the upper layer, forming the radio frequency board layer 2, to improve energy capture efficiency and communication stability; the remaining sensing modules are deployed on the lower layer, forming the signal board layer 3. The two layers achieve inter-layer signal and power transmission through board-to-board connectors 6. The sensor node module as a whole is encapsulated in a hexagonal prism plastic shell for both boards.

[0111] Specifically, sensors (including temperature sensors, strain sensors, vibration sensors, etc.) Figure 3 (Not shown in the diagram) is mounted on bolt 5. The sensor is connected to the signal board layer via signal line 4. The signal board layer 3 is below the radio frequency board layer 2. Signal and power transmission between the signal board layer 3 and the radio frequency board layer 2 is achieved through board-to-board connector 6. The housing 1 encapsulates the radio frequency board layer 2, the signal board layer 3, and the signal line 4 as a whole.

[0112] In this embodiment, the system further includes a sensor deployment optimization module;

[0113] Before the sensor node module starts collecting signals, the sensor layout optimization module performs physical field simulation on the bolts and then optimizes the sensor layout based on the simulation results.

[0114] In this embodiment, as Figure 4-5 As shown, Figure 4 This is a schematic diagram of the installation of the strain sensor and temperature sensor on the bolt (the bolt is shown from above). Figure 5 This is a schematic diagram of the vibration sensor mounted on a bolt (the bolt is shown in side view). In the diagram, 5 is the bolt, 7 is the strain sensor, 8 is the vibration sensor, and 9 is the temperature sensor.

[0115] The sensor layout optimization module places strain sensor 7 in the radial stress concentration area at the bolt head end, arranges temperature compensation device for strain in the circumferential direction at the bolt head end, deploys temperature sensor 9 in the non-stressed area of ​​the bolt of the same material, and installs vibration sensor 8 at the resonance sensitive node of the bolt structure.

[0116] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0117] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0118] In addition, in the various embodiments of the present invention, each functional unit can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

Claims

1. A wireless passive bolt preload monitoring method, characterized in that, The method includes: S1: Collect bolt deformation, temperature, and vibration parameters; S2: Optimize sensor placement based on physical field simulation and operating condition characteristics; S3: Use a dynamic decoupling model to eliminate interference and output the corrected preload value; S4: Combine multi-channel sensor monitoring to evaluate the preload status of bolt groups and output the monitoring results.

2. The wireless passive bolt preload monitoring method according to claim 1, characterized in that, S2 includes: S2.1: Establish a three-dimensional finite element model of the bolted connection structure using finite element simulation tools; S2.2: Using a three-dimensional finite element model to simulate the stress distribution, temperature field, and vibration changes of bolts under typical working conditions; S2.3: Extract bolt temperature change information based on the temperature field, and extract resonance regions and vibration-sensitive nodes in the bolt based on vibration changes; S2.4: Analyze the stress distribution in the circumferential, radial, and axial directions at the bolt head and screw section through simulation. S2.5: Extract high-gradient regions of stress concentration as key monitoring sites; S2.6: Strain sensors are deployed at key monitoring points, temperature sensors are deployed in non-stressed areas of the same material and temperature as the strain sensors to obtain temperature information, and vibration sensors are installed in resonance areas or vibration-sensitive nodes determined by structural modal analysis.

3. A wireless passive bolt preload monitoring method according to claim 1 or 2, characterized in that, The elimination of interference using a dynamic decoupling model, as described in S3, includes: S3.1: Obtain bolt temperature-strain coupling data through experiments; S3.2: A bolt temperature drift compensation model is established by fitting the temperature-strain relationship using the least squares method. S3.3: Combines cubic spline interpolation algorithm to correct strain sensor output in real time; S3.4: Determine the bolt resonance frequency band through vibration modal analysis, and embed an adaptive filtering algorithm into the multi-parameter decoupling algorithm to suppress vibration noise in unrelated frequency bands.

4. A wireless passive bolt preload monitoring system, characterized in that, The system includes: a sensor node module and a reader system module; The reader system module includes: a host computer, a reader, and a reader antenna; The sensor node module includes: a tag antenna, a tag chip, a microcontroller unit, a multi-channel sensor gating module, a signal conditioning module, and a sensor module; The host computer controls the reader and transmits radio frequency signals through the reader's antenna; After receiving the radio frequency signal, the tag antenna transmits it to the tag chip; The tag chip is activated after receiving the radio frequency signal, and then captures the radio frequency energy to power the microcontroller and the multi-channel sensor gating module; The microcontroller selects the corresponding sensor in the sensor module according to a preset timing sequence through the multi-sensor gating module; The sensor collects bolt deformation, temperature, and vibration parameters, and sends these parameters to the signal conditioning module. After the signal conditioning module conditions the bolt deformation, temperature, and vibration parameters, it sends them to the microcontroller. The microcontroller processes the bolt deformation, temperature, and vibration parameters conditioned by the signal conditioning module and sends them to the tag chip; The tag chip modulates the bolt deformation, temperature, and vibration parameters processed by the microcontroller into a backscatter signal, which is then transmitted back to the reader via the backscatter link of the reader antenna. The reader demodulates, decodes, and parses the received backscattered signal, converting the raw data into readable information before sending it to the host computer. The host computer uses demodulation and algorithms to decouple and separate interference signals, and combines the preload of the bolt group to determine single-point anomalies or overall loosening trends, triggering an early warning.

5. The wireless passive bolt preload monitoring system according to claim 4, characterized in that, The sensor module includes a strain sensor, a temperature sensor, and a vibration sensor.

6. The wireless passive bolt preload monitoring system according to claim 5, characterized in that, The sensor node module adopts an anti-metal layered layout design.

7. The wireless passive bolt preload monitoring system according to claim 6, characterized in that, The anti-metal layered layout design is specifically as follows: The tag chip and tag antenna are integrated on the upper layer to form the radio frequency board layer; the microcontroller, multi-channel sensor gating module, signal conditioning module and sensor module are deployed on the lower layer to form the signal board layer. The radio frequency (RF) board layer and the signal board layer achieve interlayer signal and power transmission through board-to-board connectors.

8. The wireless passive bolt preload monitoring system according to claim 7, characterized in that, The sensor node module is encapsulated in a hexagonal prism plastic shell, which encapsulates the radio frequency board layer and the signal board layer.

9. A wireless passive bolt preload monitoring system according to claim 8, characterized in that, The system also includes a sensor deployment optimization module; Before the sensor node module starts collecting signals, the sensor layout optimization module performs physical field simulation on the bolts and then optimizes the sensor layout based on the simulation results.

10. A wireless passive bolt preload monitoring system according to claim 9, characterized in that, The sensor deployment optimization module places strain sensors in the radial stress concentration area at the bolt head, arranges temperature compensation devices for strain in the circumferential direction at the bolt head, deploys temperature sensors in the non-stressed area of ​​the bolt made of the same material, and installs vibration sensors at the resonance sensitive nodes of the bolt structure.

Images