A wireless monitoring method for pre-tightening force of a multi-bolt connection structure, a wireless sensing node and a system

Abstract

The application discloses a kind of multi-bolt pre-tightening force wireless monitoring method, wireless sensing node and system, belong to structural health monitoring technical field, its method includes: by photovoltaic and electromagnetic induction collaborative power supply, and by energy management unit management node operating mode;Strain signal is collected and processed to extract the steady-state characteristic quantity representing pre-tightening force;The steady-state characteristic quantity is encoded as duty cycle time domain signal parameter;Data frame containing node ID is transmitted asynchronously by introducing random delay.This application can realize the long-term, reliable, low-power distributed monitoring of multi-bolt connection structure pre-tightening force, with high power supply reliability, monitoring data stability, easy to large-scale deployment and other advantages.

Description

Technical Field

[0001] This invention relates to the field of structural health monitoring technology, specifically to a wireless monitoring method, wireless sensing node and system for the preload of multi-bolt connection structures. Background Technology

[0002] Bolted connections are a widely used connection method in wind power generation, petrochemicals, bridges, rail transportation, and large machinery. For multi-bolt flange connections, the bolt preload directly affects the sealing performance, load-bearing capacity, and fatigue resistance of the connection interface. During long-term service, bolted connections are subject to alternating loads, mechanical vibrations, temperature changes, corrosion, and material relaxation, which can easily lead to problems such as preload decay, bolt loosening, and even connection failure.

[0003] To monitor bolt preload, various methods have been proposed in existing technologies, such as the torque method, ultrasonic method, strain gauge method, and fiber optic grating method. Among these, the torque method indirectly estimates the preload by controlling the tightening torque, but it is easily affected by the coefficient of friction, resulting in significant dispersion in the actual preload. The ultrasonic method has the advantage of non-destructive testing, but it typically relies on manual inspection and external equipment, making it unsuitable for long-term online monitoring. While strain gauge or fiber optic grating-based monitoring methods can directly sense structural strain, traditional systems generally suffer from complex wiring, power supply difficulties, limited installation space, and high maintenance costs, making it difficult to meet the long-term, distributed, and low-maintenance monitoring needs of outdoor multi-bolted connection structures.

[0004] With the development of wireless technology, monitoring solutions based on wireless sensor nodes have emerged. However, existing wireless monitoring solutions still have many shortcomings. First, in terms of power supply, traditional wireless nodes mainly rely on battery power, which is limited by battery capacity and ambient temperature, making it difficult to meet long-term monitoring needs. Using a single energy harvesting method, such as single photovoltaic power supply, the output is unstable under low light, nighttime, or shading conditions, making it impossible to guarantee continuous operation; while single electromagnetic induction power supply usually relies on external short-range excitation, making it difficult to achieve continuous operation around the clock.

[0005] Secondly, in terms of signal processing and transmission, the strain signal corresponding to bolt preload is usually very weak and susceptible to temperature drift, power supply fluctuations, vibration noise, and electromagnetic interference, making it difficult to stably extract the preload-related signal. Furthermore, when multiple wireless sensor nodes operate simultaneously, without effective identification coding and transmission timing coordination mechanisms, problems such as data packet conflicts, channel confusion, and receiver parsing errors are highly likely to occur, severely impacting the reliability of the monitoring system.

[0006] Therefore, there is an urgent need for a technical solution that can overcome the aforementioned challenges in power supply, signal processing, and multi-node communication, and achieve long-term, reliable, and low-cost wireless monitoring of the preload of multi-bolt connection structures. Summary of the Invention

[0007] In view of this, the present invention aims to solve the problems of poor power supply reliability, susceptibility to signal interference, and easy conflict in multi-node communication in existing wireless monitoring technologies. To this end, the present invention provides a wireless monitoring method, wireless sensing node, and system for the preload of multi-bolt connection structures, so as to achieve long-term, stable, and low-power wireless monitoring of the preload of multi-bolt connection structures.

[0008] This invention provides a wireless monitoring method for the preload of a multi-bolt connection structure, applied to a wireless sensing node including a strain sensor. The wireless sensing node further includes a first energy acquisition unit, a second energy acquisition unit, an energy management unit, and an energy storage element. The method comprises the following steps: the first energy acquisition unit acquires light energy from the environment, and / or the second energy acquisition unit acquires energy non-contactly in response to an external wireless excitation signal; the energy management unit manages the operating mode of the wireless sensing node based on the energy acquisition status of the first and / or second energy acquisition units and the energy storage status of the energy storage element; the strain signal, converted by the strain sensor and corresponding to the preload, is acquired; the strain signal is processed to extract steady-state characteristic quantities characterizing the preload; the steady-state characteristic quantities are encoded into time-domain signal parameters; the time-domain signal parameters, a group ID for distinguishing different monitoring areas, and a node ID for distinguishing different nodes within the group are encapsulated into a data frame; a random delay is introduced before sending the data frame for asynchronous transmission.

[0009] In one implementation, the energy management unit manages the operating mode of the wireless sensor node based on the energy acquisition status of the first energy acquisition unit and / or the second energy acquisition unit, as well as the energy storage status of the energy storage element. This includes: switching to photovoltaic power supply mode when the first energy acquisition unit acquires sufficient energy; entering a low-power sleep mode when the first energy acquisition unit cannot acquire sufficient energy; and / or entering an electromagnetic wake-up acquisition mode when the second energy acquisition unit acquires energy. This intelligent mode switching strategy maximizes energy utilization efficiency and extends the node's lifespan in complex environments.

[0010] In one embodiment, processing the strain signal to extract steady-state characteristic quantities representing the preload includes: performing a fast Fourier transform on the sampled strain signal sequence and extracting the zero-frequency component, or performing a moving average processing on the strain signal sequence. This step can effectively filter out high-frequency vibration noise and transient interference, thereby accurately extracting core information related to static or quasi-static preload.

[0011] In one implementation, encoding the steady-state feature quantity into a time-domain signal parameter includes: linearly mapping the steady-state feature quantity into a duty cycle that serves as a time-domain signal parameter. Using duty cycle encoding not only simplifies the processing logic at the node end and reduces power consumption, but also enhances the signal's anti-interference capability during wireless transmission.

[0012] This invention also provides a wireless sensing node for monitoring the preload of multi-bolted connection structures, including a strain sensor, characterized in that it further includes: a first energy harvesting unit for harvesting light energy from the environment; the first energy harvesting unit includes a photovoltaic cell, a maximum power point tracking circuit, a boost / buck converter circuit, and a reverse current protection circuit; a second energy harvesting unit for responding to an external wireless excitation signal and acquiring energy from the external wireless excitation signal in a non-contact manner; the second energy harvesting unit includes an external transmitting coil, a node-end receiving coil, a resonant matching capacitor, a rectifier bridge, a filter capacitor, and a voltage regulator circuit; and a storage... The device includes an energy storage element; an energy management unit for managing the operating mode of the wireless sensing node based on the energy acquisition status of the first energy acquisition unit and / or the second energy acquisition unit and the energy storage status of the energy storage element; and a processing unit configured to: process the strain signal converted by the strain sensor to extract steady-state characteristic quantities characterizing the preload; encode the steady-state characteristic quantities into time-domain signal parameters; encapsulate the time-domain signal parameters, a group ID for distinguishing different monitoring areas, and a node ID for distinguishing different nodes within the group into a data frame; and introduce a random delay before sending the data frame.

[0013] In one embodiment, the first energy harvesting unit is a photovoltaic energy harvesting unit; the second energy harvesting unit is an electromagnetic induction energy harvesting unit.

[0014] In one implementation, the time-domain signal parameter is the duty cycle.

[0015] In one embodiment, the strain sensor, the processing unit, the energy management unit, the first energy acquisition unit, and the second energy acquisition unit are all integrated on a circuit board with a size no larger than 15x15mm.

[0016] The present invention also provides a wireless monitoring system for the preload of a multi-bolt connection structure, characterized in that it includes: one or more wireless sensing nodes as described above; and a receiving end, the receiving end being configured to receive data frames sent by the wireless sensing nodes, and to match the parsed data with preset bolt positions according to the group ID and node ID contained in the data frames.

[0017] In one embodiment, the receiving end is further configured to: calculate the final preload value using a pre-calibrated multi-parameter model based on the received time-domain signal parameters and the measured temperature; compare the calculated final preload value with a preset threshold, and trigger an alarm when the final preload value exceeds the threshold.

[0018] Compared with the prior art, the present invention has the following beneficial effects: 1. Improved power supply reliability and environmental adaptability: By adopting photovoltaic and electromagnetic induction power supply in combination with intelligent energy management strategies, the problem of unstable power supply or limited lifespan caused by the reliance on a single energy source in traditional wireless nodes has been solved, and the long-term power supply reliability and operational availability of nodes in complex outdoor environments such as light variation and no light has been improved.

[0019] 2. Enhanced stability and accuracy of monitoring signals: By extracting steady-state characteristic quantities at the node end and encoding them as time-domain signal parameters, the influence of factors such as mechanical vibration, electromagnetic interference, and power fluctuations on weak strain signals is effectively suppressed, reducing data transmission power consumption and improving the stability and robustness of preload monitoring. Simultaneously, the multi-parameter model at the receiving end further compensates for temperature drift, improving the accuracy of the final preload value.

[0020] 3. Reliable multi-node distributed monitoring is achieved: By encapsulating group ID and node ID in the data frame for identification and introducing random delay to achieve asynchronous transmission, data conflicts and channel confusion when multiple nodes communicate simultaneously are effectively avoided, ensuring reliable data reception and correct parsing in large-scale distributed monitoring scenarios.

[0021] 4. The system achieves high integration and ease of use: By highly integrating multiple functional modules such as sensing, processing, communication and coordinated power supply into a miniaturized wireless sensor node, the system achieves miniaturization, low power consumption and easy deployment, reducing the difficulty and cost of on-site installation and subsequent maintenance. Combined with the real-time display and alarm functions of the receiver, it provides users with a complete integrated solution from monitoring to early warning. Attached Figure Description

[0022] Figure 1 This is a block diagram of the overall system structure in an embodiment of the present invention; Figure 2 This is a schematic diagram of the main and auxiliary coils for electromagnetic induction power supply in an embodiment of the present invention; Figure 3 This is a schematic diagram of the wireless data frame structure and asynchronous transmission logic in an embodiment of the present invention; Figure 4 This is a flowchart of the receiver parsing and alarm process in an embodiment of the present invention; Explanation of reference numerals in the attached figures:

[0023] 1-PCB circuit board, 2-first energy harvesting unit, 3-second energy harvesting unit, 4-strain sensor. Detailed Implementation

[0024] The technical solution of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] This invention provides a wireless monitoring system for the preload of multi-bolt connection structures. The system adopts a distributed architecture, mainly comprising one or more wireless sensor nodes and a receiver. In practical applications, such as on flanges of wind turbines, petrochemical pipelines, or bridge connections, a wireless sensor node can be deployed near each bolt requiring monitoring. These wireless sensor nodes are responsible for real-time acquisition of physical quantities related to bolt preload, processing and encoding the acquired signals, and transmitting the data wirelessly. The receiver is responsible for receiving data from one or more wireless sensor nodes, parsing, converting, displaying, and storing the data, and triggering an alarm when an anomaly is detected. This distributed wireless monitoring architecture effectively solves the problems of complex wiring, power supply difficulties, limited installation space, and high maintenance costs associated with traditional wired monitoring systems, providing a long-term, reliable, and low-maintenance-cost online monitoring solution for multi-bolt connection structures.

[0026] like Figure 1 As shown, this invention provides a wireless sensing node for monitoring the preload of multi-bolted connection structures. This node is a highly integrated small device whose core function is to sense changes in preload, process signals, and transmit them wirelessly with extremely low power consumption. In a preferred embodiment, the wireless sensing node includes a strain sensor, a processing unit, and a collaborative power supply system. This collaborative power supply system is crucial for ensuring the long-term stable operation of the node; it includes a first energy harvesting unit, a second energy harvesting unit, an energy storage element, and an energy management unit. This integrated design enables the wireless sensing node to independently complete the entire process from energy harvesting to data transmission, simplifying field deployment.

[0027] Specifically, the cooperative power supply system provides power to wireless sensor nodes through two complementary methods. For example... Figure 1As shown, the first energy harvesting unit is preferably a photovoltaic energy harvesting unit, whose function is to harvest light energy from the environment, whether natural or artificial, and convert it into electrical energy. The first energy harvesting unit includes photovoltaic cells, a maximum power point tracking circuit, a boost / buck converter circuit, and a backflow prevention protection circuit. This provides the basic energy source for the long-term autonomous operation of the wireless sensor node under illumination conditions. Simultaneously, the wireless sensor node also integrates a second energy harvesting unit, preferably an electromagnetic induction energy harvesting unit. The second energy harvesting unit includes an external transmitting coil, a node-end receiving coil, a resonant matching capacitor, a rectifier bridge, a filter capacitor, and a voltage regulator circuit. Figure 2 As shown, when the wireless sensor node needs active recharging or to be woken up in the dark, an external transmitting coil can be brought close to the node to transfer energy to the second energy harvesting unit (i.e., the receiving coil) non-contactly via electromagnetic induction. These two power supply methods are intelligently scheduled by the energy management unit, and the harvested energy is stored in energy storage elements (such as supercapacitors or rechargeable micro-batteries). This design allows the wireless sensor node to be charged using ambient light and also enables it to quickly resume operation through external discharge at critical moments, improving the power supply reliability and operational availability of the wireless sensor node in complex outdoor environments.

[0028] The signal acquisition and processing functions of wireless sensing nodes are jointly performed by strain sensors and processing units (usually microcontrollers). For example... Figure 1 As shown, a strain sensor can be attached to a localized area of ​​the bolt shank, nut, gasket, or flange to sense minute strain caused by changes in bolt preload. This strain sensor typically forms a Wheatstone bridge, and its weak output voltage signal is proportional to the change in preload. Upon receiving this signal, the processing unit first amplifies it using an internal or external low-noise amplifier circuit. To suppress the effects of mechanical vibration, power supply ripple, electromagnetic interference, and random noise on static preload measurement, the processing unit processes the amplified strain signal to extract a steady-state characteristic quantity that stably characterizes the preload. This processing enhances the signal-to-noise ratio, ensuring the accuracy of subsequent preload calculations.

[0029] After extracting the steady-state characteristic quantity, to reduce the amount of data and power consumption during wireless transmission, the processing unit further encodes the steady-state characteristic quantity into a time-domain signal parameter. In a preferred embodiment, this time-domain signal parameter is the duty cycle. The processing unit linearly maps the value of the steady-state characteristic quantity to a pulse width modulation signal with a specific duty cycle. Compared to directly transmitting multi-bit analog-to-digital converter (ADC) sample values, transmitting a value representing the duty cycle is more efficient and less susceptible to interference. Subsequently, as... Figure 3As shown, the processing unit encapsulates the time-domain signal parameters, along with the group ID used to distinguish different monitoring areas, the node ID used to distinguish different nodes within the group, and other status information and verification fields, into a complete data frame. This design, which encodes sensor information into time-domain parameters and adds identification identifiers, not only reduces power consumption but also lays the foundation for reliable communication in multi-node systems.

[0030] To meet the requirement of installation in confined spaces, the wireless sensing node of this invention features a highly miniaturized physical structure. In one specific embodiment, such as... Figure 1 As shown, all functional modules, including the strain sensor, processing unit, energy management unit, first energy acquisition unit, and second energy acquisition unit, are highly integrated onto a single printed circuit board (PCB) 1. This PCB can be made extremely small; for example, its overall size is no greater than 15mm × 15mm. For instance, the overall size of the PCB is 14mm × 14mm. This compact structure allows the wireless sensing node to be easily installed in spaces with extremely limited availability, such as bolt heads, nut sides, or flange connection gaps, greatly enhancing its versatility in engineering applications.

[0031] Based on the aforementioned system and node structure, this invention further provides a wireless monitoring method for the preload of multi-bolt connection structures. This method details the complete workflow of the wireless sensing node, from energy management and signal acquisition to data transmission. Firstly, regarding energy management, the operating mode of the wireless sensing node is dynamically managed by an energy management unit. Specifically, when the first energy acquisition unit acquires sufficient energy, the wireless sensing node enters a photovoltaic power supply mode, performing data acquisition and transmission according to a preset cycle, while simultaneously charging the energy storage element. When photovoltaic energy is insufficient and the energy storage element's charge is below a preset threshold, the wireless sensing node enters a low-power sleep mode to maximize energy savings. When the second energy acquisition unit responds to an external wireless excitation signal and acquires energy, the wireless sensing node is awakened and enters an electromagnetic wake-up acquisition mode, performing one or more data acquisition and transmission tasks. This intelligent operating mode switching based on energy status ensures that the wireless sensing node operates with optimal energy efficiency under different environmental conditions.

[0032] In the signal acquisition and processing step, after the wireless sensing node is awakened, it first acquires the strain signal corresponding to the preload, converted by the strain sensor. To accurately extract information representing the static or quasi-static preload from the noisy raw signal, this invention employs a specific algorithm to extract steady-state feature quantities. In one embodiment, this step includes processing the sampled strain signal sequence. One specific method is to perform a Fast Fourier Transform on the sequence and extract its zero-frequency component, since the static preload is mainly reflected in the DC or extremely low-frequency part of the signal. Another optional method is to perform a moving average processing on the strain signal sequence to smooth the signal and filter out high-frequency noise and transient disturbances. For example, the sampled sequence x(n) can be processed by a feature extraction operator H{·} to obtain the steady-state feature quantity S_dc, which can be mathematically expressed as: S_dc = H{x(n)}. In this way, vibration noise and electromagnetic interference can be effectively suppressed, and a stable value strongly correlated with the preload can be obtained.

[0033] The next step is encoding and encapsulation. After extracting the steady-state characteristic quantity S_dc, the processing unit encapsulates it into a time-domain signal parameter for low-power wireless transmission. In a preferred embodiment, this encoding method linearly maps the steady-state characteristic quantity to a duty cycle value D as a time-domain signal parameter. This mapping relationship can be expressed by a simple linear formula: D = D0 + βS_dc. Here, D0 is the reference duty cycle under zero load or reference conditions, and β is a sensitivity coefficient determined based on calibration experiments. This encoding method transforms complex strain information into a time-proportion parameter that is easy to generate and resolve, exhibiting strong anti-interference capabilities. Subsequently, the processing unit encapsulates the calculated time-domain signal parameter D, along with a preset group ID and node ID, into a standard data frame, preparing for subsequent asynchronous transmission and receiver parsing.

[0034] Finally, there is the asynchronous transmission step. In multi-node applications, if all wireless sensor nodes transmit data at the same time, it will lead to severe wireless channel collisions and packet loss. To solve this problem, the method of this invention introduces a random delay before transmitting the data frame. After completing the data frame encapsulation, each wireless sensor node generates a random delay time Δt_i within a predetermined time window. The actual transmission time τ_i of the wireless sensor node is determined by the reference time t_0 and the random delay Δt_i, i.e.: τ_i = t_0 + Δt_i. By introducing this random delay, asynchronous peak-shifting transmission of multiple nodes is achieved, greatly reducing the probability of data collisions. After the data transmission is completed, the wireless sensor node immediately enters a low-power sleep mode, waiting for the next working cycle. The average power consumption P_avg of the entire wireless sensor node can be expressed by the formula P_avg = (P_a t_a+P_sThe power consumption of a wireless sensor node can be evaluated using the formula t_s) / (t_a+t_s), where Pa and ta are the power consumption and time in the working state, and Ps and ts are the power consumption and time in the sleep state. By shortening the working time ta and extending the sleep time ts, the average power consumption of the wireless sensor node can be reduced, thus extending its operating life under limited energy conditions.

[0035] Corresponding to wireless sensor nodes, the receiver plays a crucial role in data reception, analysis, and decision-making, such as... Figure 4 As shown, the receiver's workflow begins with data reception and parsing. The receiver continuously monitors the wireless channel, and upon receiving a data frame, it first verifies it, checking aspects such as the frame header, data length, and checksum. If the verification fails, the data frame is deemed invalid and discarded to prevent erroneous data from entering subsequent processing. If the verification passes, the receiver parses key information from the data frame, including the group ID, node ID, and encoded time-domain signal parameters.

[0036] After successfully parsing the data, the receiver enters the node matching and data conversion stage. The receiver internally stores a node mapping table, which precisely matches the received data with a specific, pre-defined bolt position based on the parsed group ID and node ID. This step ensures that data from different wireless sensor nodes can be correctly distinguished and managed, avoiding data confusion. Next, to obtain the final preload value, the receiver needs to convert the received time-domain signal parameters. In a preferred embodiment, this conversion process utilizes a pre-calibrated multi-parameter model. For example, this model can consider the influence of temperature on the measurement, and its mathematical expression is: F = aD + b·T + c. Here, F is the final calculated preload value, D is the received time-domain signal parameter (such as duty cycle), T is the current ambient or node-measured temperature value, and a, b, and c are coefficients obtained through experimental calibration for that specific channel. By introducing temperature as a compensation parameter, this model can improve the accuracy of preload measurement and its stability in varying temperature environments.

[0037] Finally, the receiving end performs display, storage, and alarm functions. The calculated final preload value F is displayed in real time on the receiving end's interface and stored in the database along with the timestamp, node information, etc., for subsequent historical trend analysis and status assessment. Simultaneously, the receiving end internally presets safety thresholds for each monitoring point, including an upper limit threshold and a lower limit threshold. The receiving end compares the real-time calculated preload value with these thresholds. When the preload value exceeds the preset range, the bolt's preload status is determined to be abnormal, and the system will trigger an alarm. The alarm can take the form of an audible and visual alarm, a prominent prompt on the interface, a status marker, or sending alarm information to management personnel via the network. This closed-loop monitoring and alarm mechanism enables the system not only to visualize changes in preload but also to provide timely warnings when potential risks arise, providing crucial protection for the safe operation of the equipment.

[0038] It should be understood that the specific structures and methods described in the above embodiments are merely examples, and those skilled in the art can make various modifications and variations based on the core ideas of this invention. These modifications and variations should also fall within the protection scope of this invention.

[0039] For example, in terms of energy supply methods, the first energy harvesting unit can be replaced not only with a photovoltaic energy harvesting unit, but also with a piezoelectric energy harvesting unit that utilizes environmental vibrations or a thermoelectric power generation unit that utilizes temperature differences. The second energy harvesting unit can also utilize other near-field wireless power supply technologies besides electromagnetic induction. The energy storage element can also be a thin-film battery or other micro-energy storage device. Any system that achieves a coordinated working mode of environmental energy harvesting and external on-demand power replenishment is considered an equivalent substitute for this invention.

[0040] In terms of sensor selection, in addition to conventional strain gauges, other sensing elements capable of reflecting changes in preload, such as microelectromechanical system strain sensors, piezoresistive sensors, piezoelectric sensors, or fiber Bragg grating sensors, can also be used. The sensor's installation location is not limited to the bolt shank; it can be flexibly installed on the nut, gasket, or flange connection area, depending on the actual structure and stress distribution.

[0041] Regarding the signal encoding method, the present invention preferably employs duty cycle encoding, but pulse width encoding, frequency encoding, period encoding, or directly encoding the ADC sampled values ​​into digital quantities can also be used. Any method that can convert the preload-related analog signal into characteristic information suitable for low-power wireless transmission is an equivalent alternative to the encoding method of the present invention.

[0042] In terms of wireless communication methods, besides Bluetooth Low Energy, other low-power wide area network (WAN) or local area network (LAN) technologies such as LoRa, ZigBee, Sub-GHz, Wi-Fi HaLow, or NB-IoT can be selected based on transmission distance, power consumption, and networking requirements. The communication topology can also be a star network, a mesh network, or a point-to-point connection.

[0043] In asynchronous transmission strategies, besides introducing random delays, other anti-collision mechanisms such as fixed time slot allocation, carrier sense multiple access, node polling, or master node scheduling can also be used. Any mechanism that can effectively reduce channel collisions caused by multiple wireless sensor nodes transmitting data simultaneously and improve data transmission reliability can be considered an alternative to the asynchronous transmission strategy of this invention.

[0044] In terms of receiving end functionality, besides dedicated hardware receivers, smartphones, tablets, industrial gateways, or cloud platform servers integrating corresponding wireless receiving modules and software applications can also be used. Alarm methods can also be expanded to include SMS alerts, email notifications, or push alarm events to the enterprise management system.

[0045] Finally, regarding the preload conversion model, in addition to the linear multi-parameter model, polynomial models, piecewise linear models, lookup table interpolation methods, or even machine learning regression models (such as neural networks, support vector regression, etc.) trained on large amounts of data can be used to achieve higher accuracy in preload conversion, based on the actual nonlinear relationships and the influence of multiple factors. These alternative solutions do not deviate from the core idea of ​​this invention, which aims to provide a miniaturized, self-powered, multi-node collaborative wireless monitoring system, and therefore should all be included within the scope of protection of this invention.

Claims

1. A wireless monitoring method for preload in a multi-bolt connection structure, applied to a wireless sensing node including a strain sensor, wherein the wireless sensing node further includes a first energy acquisition unit, a second energy acquisition unit, an energy management unit, and an energy storage element, characterized in that, include: The first energy harvesting unit harvests light energy from the environment, and / or the second energy harvesting unit receives energy in a non-contact manner in response to an external wireless excitation signal; The energy management unit manages the working mode of the wireless sensing node based on the energy acquisition status of the first energy acquisition unit and / or the second energy acquisition unit and the energy storage status of the energy storage element. Acquire the strain signal corresponding to the preload force, converted by the strain sensor; The strain signal is processed to extract steady-state characteristic quantities that characterize the preload; The steady-state characteristic quantities are encoded as time-domain signal parameters; The time-domain signal parameters, the group ID used to distinguish different monitoring areas, and the node ID used to distinguish different nodes within the group are encapsulated into a data frame; A random delay is introduced before sending the data frame to enable asynchronous transmission.

2. The method according to claim 1, characterized in that, The management of the operating mode of the wireless sensor node by the energy management unit based on the energy acquisition status of the first energy acquisition unit and / or the second energy acquisition unit and the energy storage status of the energy storage element includes: When the first energy harvesting unit has harvested enough energy, a photovoltaic power supply mode is adopted; When the first energy harvesting unit fails to harvest enough energy, it enters a low-power sleep mode; and / or, when the second energy harvesting unit acquires energy, it enters an electromagnetic wake-up harvesting mode.

3. The method according to claim 1, characterized in that, The process of processing the strain signal to extract steady-state characteristic quantities representing the preload includes: Perform a fast Fourier transform on the sampled strain signal sequence and extract the zero-frequency component, or perform a moving average processing on the strain signal sequence.

4. The method according to claim 1, characterized in that, Encoding the steady-state characteristic quantities into time-domain signal parameters includes: The steady-state characteristic is linearly mapped to a duty cycle value.

5. A wireless sensing node for monitoring the preload of a multi-bolted connection structure, comprising a strain sensor, characterized in that, include: The first energy harvesting unit is used to harvest light energy from the environment; The first energy harvesting unit includes photovoltaic cells, a maximum power point tracking circuit, a boost / buck converter circuit, and a backflow protection circuit; The second energy harvesting unit is used to respond to an external wireless excitation signal and harvest energy from the external wireless excitation signal in a non-contact manner; the second energy harvesting unit includes an external transmitting coil, a node receiving coil, a resonant matching capacitor, a rectifier bridge, a filter capacitor, and a voltage regulator circuit. Energy storage components; An energy management unit is used to manage the working mode of the wireless sensing node based on the energy acquisition status of the first energy acquisition unit and / or the second energy acquisition unit and the energy storage status of the energy storage element. The processing unit is used to process the strain signal converted by the strain sensor to extract steady-state characteristic quantities characterizing the preload. The steady-state characteristic quantity is encoded as a time-domain signal parameter; the time-domain signal parameter, the group ID used to distinguish different monitoring areas, and the node ID used to distinguish different nodes within the group are encapsulated into a data frame; And a random delay is introduced before sending the data frame.

6. The wireless sensing node according to claim 5, characterized in that, The first energy harvesting unit is a photovoltaic energy harvesting unit; the second energy harvesting unit is an electromagnetic induction energy harvesting unit.

7. The wireless sensing node according to claim 5, characterized in that, The time-domain signal parameter is the duty cycle.

8. The wireless sensing node according to claim 5, characterized in that, The strain sensor, the processing unit, the energy management unit, the first energy acquisition unit, and the second energy acquisition unit are all integrated on a circuit board with a size no larger than 15x15mm.

9. A wireless monitoring system for the preload of a multi-bolt connection structure, characterized in that, include: One or more wireless sensing nodes as described in claim 5; The receiving end is configured to receive data frames sent by the wireless sensing node, and match the parsed data with preset bolt positions based on the group ID and node ID contained in the data frames.

10. The system according to claim 9, characterized in that, The receiving end is further configured to: calculate the final preload value using a pre-calibrated multi-parameter model based on the received time-domain signal parameters and the measured temperature; compare the calculated final preload value with a preset threshold; and trigger an alarm when the final preload value exceeds the threshold.

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