Bolt pretightening force self-sensing real-time monitoring method based on magnetostrictive effect

Abstract

The application relates to the technical field of bolt pre-tightening force measurement, in particular to a bolt pre-tightening force self-sensing real-time monitoring method based on magnetostrictive effect, which comprises the following steps: the monitoring method is used for a data processing unit in a bolt pre-tightening force self-sensing real-time monitoring system based on magnetostrictive effect; the monitoring system further comprises a bolt, an energy collecting unit, an excitation unit and an impedance measuring unit; the monitoring method comprises the following steps: receiving impedance data sent by an impedance sensor in the impedance measuring unit; the impedance data is data obtained by the impedance sensor in the impedance measuring unit when the impedance sensor measures the impedance of a measuring coil under the action of an excitation signal; the excitation signal is output by the excitation unit to the measuring coil; inputting the impedance data into a first calculation model in the data processing unit to obtain a first bolt pre-tightening force at a first target time when the impedance data is collected.

Description

Technical Field

[0001] This application relates to the field of bolt preload measurement technology, and in particular to a real-time monitoring method for bolt preload self-sensing based on magnetostrictive effect. Background Technology

[0002] Bolts, as a critical basic component, play a core role in ensuring reliable connections in numerous major equipment and infrastructure projects. The failure of a critical bolt connection can lead to the destruction of the entire structure and even cause significant loss of life and property. Bolt connection failures are rarely due to insufficient bolt strength causing breakage; the vast majority (approximately 90%) are caused by insufficient bolt preload, uneven bolt preload, or bolt loosening.

[0003] Monitoring bolt preload is a crucial technical safeguard for preventing the aforementioned accidents. However, in scenarios such as bridge steel structure connections, high-speed rail track fasteners, and wind turbine generators, it is often necessary to regularly send workers to the site to inspect each bolt individually based on experience or tools. This is not only time-consuming and labor-intensive but also prone to omissions. Therefore, a superior real-time monitoring solution for bolt preload based on magnetostrictive effect is needed to overcome these difficulties. Summary of the Invention

[0004] This application provides a real-time monitoring scheme for bolt preload based on magnetostrictive effect, which enables the real-time monitoring process of bolt preload to be self-sufficient.

[0005] In a first aspect, this application provides a real-time monitoring method for bolt preload self-sensing based on magnetostrictive effect. The monitoring method is used in a data processing unit of a real-time monitoring system for bolt preload self-sensing based on magnetostrictive effect. The monitoring system further includes a bolt, an energy harvesting unit, an excitation unit, and an impedance measurement unit. The monitoring method includes: The impedance measurement unit receives impedance data sent by the impedance sensor. The impedance data is obtained by the impedance sensor measuring the impedance of the measuring coil in the impedance measurement unit under the action of an excitation signal. The excitation signal is output from the excitation unit to the measuring coil. When the preload of the bolt changes, the sensing part of the bolt generates a change in magnetic properties. The change in magnetic properties causes a change in the impedance of the measuring coil under the action of the excitation signal. The excitation unit and the impedance measurement unit are powered by the energy harvesting unit, which harvests energy based on the change in magnetic properties. The impedance data is input into the first calculation model in the data processing unit to obtain the first bolt preload at the first target moment when the impedance data is collected.

[0006] Furthermore, the monitoring system includes a signal acquisition unit, and the monitoring method further includes: The system receives electrical signal data sent by the signal acquisition unit, wherein the electrical signal data is data collected by the signal acquisition unit from the energy harvesting unit when the energy harvesting unit is collecting energy. The electrical signal data is input into the second calculation model in the data processing unit to obtain the second bolt preload at the second target moment when the electrical signal data is collected. The first bolt preload is calibrated using the second bolt preload to obtain the calibrated first bolt preload.

[0007] Furthermore, the step of calibrating the first bolt preload using the second bolt preload to obtain the calibrated first bolt preload includes: Obtain the historical bolt preload force at multiple historical moments prior to the second target time; Construct a preload curve based on the second bolt preload and the historical bolt preload; The predicted bolt preload at the first target moment is obtained from the preload curve. The predicted bolt preload is used to calibrate the first bolt preload to obtain the calibrated first bolt preload.

[0008] Furthermore, the step of calibrating the first bolt preload using the predicted bolt preload to obtain the calibrated first bolt preload includes: If the difference between the predicted bolt preload and the first bolt preload is greater than a preset value, the predicted bolt preload is used to calibrate the first bolt preload to obtain the calibrated first bolt preload.

[0009] Furthermore, the training process of the second computational model includes: When the excitation unit outputs the excitation signal to the measuring coil, the sample electrical signal data of the signal acquisition unit are acquired when different sample preload forces are applied to the bolt. Construct multiple first data pairs, each first data pair containing the sample preload force and the sample electrical signal data corresponding to the sample preload force; The second computational model is trained using the first data pair to obtain the trained second computational model.

[0010] Furthermore, the training process of the first computational model includes: When the excitation unit outputs the excitation signal to the measuring coil, the sample impedance data of the measuring coil is obtained when different sample preloads are applied to the bolt; Construct multiple second data pairs, each second data pair containing the sample preload force and the sample impedance data corresponding to the sample preload force; The first computational model is trained using the second data pair to obtain the trained first computational model.

[0011] Secondly, this application provides a real-time monitoring system for bolt preload based on magnetostrictive effect. The monitoring system includes a bolt, an energy harvesting unit, an excitation unit, an impedance measurement unit, and a data processing unit. The bolt includes a sensing portion made of a magnetostrictive material, the sensing portion being used to generate a change in magnetic properties when the preload of the bolt changes; The energy harvesting unit is used for: Energy is harvested based on the changes in the magnetic properties. Power is supplied to the impedance measurement unit and the excitation unit; The excitation unit is used to output an excitation signal to the measuring coil included in the impedance measuring unit. Based on the change in magnetic characteristics, the measuring coil undergoes an impedance change under the action of the excitation signal. The impedance measurement unit further includes an impedance sensor, which is used to measure the impedance data of the measuring coil and send it to the data processing unit. The data processing unit is used to obtain the first bolt preload at the first target moment when the impedance data is collected, based on the received impedance data.

[0012] Furthermore, the monitoring system includes a signal acquisition unit, which is used to collect electrical signal data of the energy harvesting unit while the energy harvesting unit is collecting energy, and to send the electrical signal data to the data processing unit; The data processing unit is used for: The system receives electrical signal data sent by the signal acquisition unit, wherein the electrical signal data is data collected by the signal acquisition unit from the energy harvesting unit when the energy harvesting unit is collecting energy. The electrical signal data is input into the second calculation model in the data processing unit to obtain the second bolt preload at the second target moment when the electrical signal data is collected. The first bolt preload is calibrated using the second bolt preload to obtain the calibrated first bolt preload.

[0013] Furthermore, the bolt has a channel inside the screw, and the measuring coil and the impedance sensor are disposed in the channel, with the measuring coil surrounded by the sensing part.

[0014] Furthermore, the energy harvesting based on the change in magnetic properties includes: Based on the changes in magnetic properties, energy is collected using the measuring coil.

[0015] Thirdly, this application provides a real-time monitoring device for bolt preload self-sensing based on magnetostrictive effect. The monitoring device is used in a monitoring system, which includes a data processing unit, a bolt, an energy harvesting unit, an excitation unit, and an impedance measurement unit. The monitoring device includes: The data receiving module is used to receive impedance data sent by the impedance sensor in the impedance measurement unit. The impedance data is the data obtained by the impedance sensor measuring the impedance of the measuring coil in the impedance measurement unit under the action of an excitation signal. The excitation signal is output from the excitation unit to the measuring coil. When the preload of the bolt changes, the sensing part of the bolt generates a change in magnetic properties. The change in magnetic properties causes a change in the impedance of the measuring coil under the action of the excitation signal. The excitation unit and the impedance measurement unit are powered by the energy harvesting unit, which harvests energy based on the change in magnetic properties. The data processing module is used to input the impedance data into the first calculation model in the data processing unit to obtain the first bolt preload at the first target moment when the impedance data is collected.

[0016] Fourthly, this application provides a computer-readable storage medium storing computer program instructions thereon, which, when executed by a processor, implement the above-described method. The computer-readable storage medium may be volatile or non-volatile.

[0017] Fifthly, this application provides an electronic device, comprising: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above-described method when executing instructions stored in the memory.

[0018] Sixthly, this application provides a computer program product including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0019] In the embodiments of this specification, an energy harvesting unit capable of collecting energy based on changes in magnetic properties caused by variations in bolt preload is used to supply functions to an excitation unit and an impedance measurement unit in the monitoring system. This allows the impedance sensor in the impedance measurement unit to measure the impedance changes in the measurement coil based on the changes in magnetic properties when the excitation unit provides an excitation signal, thereby obtaining the first bolt preload corresponding to the impedance data. This system couples the energy harvesting and impedance measurement processes based on the changes in magnetic properties of the sensing part caused by variations in bolt preload, achieving self-sufficiency in the bolt preload monitoring process. This reduces the cost of bolt preload monitoring while improving its efficiency.

[0020] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure.

[0021] Other features and aspects of this disclosure will become clear from the following detailed description of exemplary embodiments with reference to the accompanying drawings. Attached Figure Description

[0022] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0023] Figure 1 The diagram above illustrates a schematic of a real-time monitoring system for self-sensing bolt preload based on the magnetostrictive effect. Figure 2 The diagram above illustrates a flowchart of a real-time monitoring method for bolt preload based on magnetostrictive effect. Figure 3 The diagram above illustrates a schematic of a real-time monitoring device for bolt preload based on the magnetostrictive effect.

[0024] The accompanying drawings have illustrated specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to specific embodiments. Detailed Implementation

[0025] Various exemplary embodiments, features, and aspects of this disclosure will now be described in detail with reference to the accompanying drawings. The same reference numerals in the drawings denote elements that have the same or similar functions. Although various aspects of the embodiments are shown in the drawings, they are not necessarily drawn to scale unless specifically indicated otherwise.

[0026] The term “exemplary” as used herein means “serving as an example, embodiment, or illustration.” Any embodiment illustrated herein as “exemplary” is not necessarily to be construed as superior to or better than other embodiments.

[0027] In this document, the term "and / or" is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent three cases: A exists alone, A and B exist simultaneously, and B exists alone. Furthermore, the term "at least one" in this document means any combination of at least two of any one or more elements. For example, including at least one of A, B, and C can mean including any one or more elements selected from the set consisting of A, B, and C.

[0028] Furthermore, to better illustrate this disclosure, numerous specific details are set forth in the following detailed description. Those skilled in the art will understand that this disclosure can be practiced without certain specific details. In some instances, methods, means, components, and circuits well known to those skilled in the art have not been described in detail in order to highlight the main points of this disclosure.

[0029] Figure 1 A schematic diagram of a real-time monitoring system for self-sensing bolt preload based on the magnetostrictive effect is shown, as follows: Figure 1 As shown, the monitoring system may include a bolt 11, an energy harvesting unit 12, an excitation unit 13, an impedance measurement unit 14, and a data processing unit 15.

[0030] In one possible implementation, the bolt 11 includes a sensing portion made of a magnetostrictive material, the sensing portion being configured to generate a change in magnetic properties when the preload of the bolt 11 changes; the energy harvesting unit 12 is used for: Energy is harvested based on the changes in the magnetic properties. Power is supplied to the impedance measurement unit 14 and the excitation unit 13; The excitation unit 13 is used to output an excitation signal to the measuring coil included in the impedance measuring unit 14. Based on the change in magnetic characteristics, the measuring coil undergoes an impedance change under the action of the excitation signal. The impedance measurement unit 14 further includes an impedance sensor, which is used to measure the impedance data of the measuring coil and send it to the data processing unit. The data processing unit 15 is used to obtain the first bolt preload at the first target moment when the impedance data is collected, based on the received impedance data.

[0031] Specifically, bolt 11 is the body for monitoring preload, and it may include a sensing part, which may be made of magnetostrictive materials such as ferromagnetic materials (such as iron, cobalt, nickel and their alloys, as well as rare earth alloys such as Terfenol-D).

[0032] In one implementation, the bolt 11 may consist entirely of the sensing portion, meaning the bolt 11 may be entirely made of ferromagnetic material. In another implementation, the sensing portion may be the bolt head and / or the smooth shank of the bolt 11, meaning the bolt 11 may be partially made of ferromagnetic material. In other implementations, the sensing portion may be a portion of the bolt 11 coated with ferromagnetic material. This specification does not impose specific limitations on the arrangement of the sensing portion in the bolt 11; it can be determined according to the actual situation.

[0033] The energy harvesting unit 12 is a unit that harvests energy by utilizing the external vibration or preload variation of the bolt 11, and it can be installed inside the bolt 11. The energy harvesting unit 12 can provide energy to other units in the monitoring system (such as the excitation unit 13). The energy harvesting unit 12 may include a self-powered sensor.

[0034] In one example, a microchannel can be reserved in the bolt stub and / or bolt head of bolt 11, and an energy harvesting unit 12 can be installed in this channel. The self-powered sensor in this energy harvesting unit 12 can collect energy generated by external vibration or preload changes of bolt 11 using piezoelectric, electromagnetic, or triboelectric methods. Specifically, when bolt 11 experiences preload changes or vibration, the magnetic properties of the sensing part change, and energy can be harvested through the induced electromotive force generated at both ends of the coil.

[0035] In addition to the self-powered sensor, the excitation unit 13, impedance measurement unit 14 (including a measurement coil and an impedance sensor), and data processing unit 15 can also be arranged within the channel. Specifically, the measurement coil can be located at the sensing part; for example, the measurement coil can be surrounded by the sensing part. In this way, by embedding the measurement coil, impedance sensor, etc., inside the bolt, the problems of complex installation and easy detachment of bolt monitoring devices can be solved. On the other hand, the impedance sensor can avoid external environmental interference and improve measurement stability.

[0036] Since both the energy harvesting unit 12 and the impedance measurement unit 14 need to use coils to sense changes in magnetic properties, in order to reduce the volume occupied by the monitoring system, the energy harvesting unit 12 can use a measurement coil to harvest energy, that is, the energy harvesting unit 12 and the impedance measurement unit 14 share the measurement coil.

[0037] In one example, the self-powered sensor and / or impedance sensor can be constructed using flexible materials to better adapt to the curved microchannels, and further, to more effectively detect impedance and electrical signal data. To improve the structural stability of the bolt 11 with microchannels, a three-dimensional model of the bolt 11 can be built for stress simulation to determine the location and shape of the microchannels in the bolt 11, thereby limiting the influence of the microchannels on the mechanical strength of the bolt to within 1%.

[0038] Excitation unit 13 is a unit that stably outputs an excitation signal of constant frequency, which can be a high-frequency sinusoidal signal. In one example, the excitation signal can be amplified before being output.

[0039] The impedance measurement unit 14 includes a measuring coil and an impedance sensor. The measuring coil can be a miniature coil, which can be disposed within a miniature channel. When the measuring coil outputs an excitation signal from the excitation unit 13, if the bolt experiences axial stress (i.e., a change in preload), the magnetic properties of the sensing part, such as permeability and magnetic flux, will change (i.e., a contramagnetic-strictive effect will occur), causing a change in the impedance of the measuring coil. The impedance sensor can be connected to the measuring coil to determine the impedance data of the strategy coil.

[0040] In one example, the impedance measurement unit 14 and the energy harvesting unit 12 can perform corresponding tasks at intervals, that is, energy harvesting and impedance measurement can be performed alternately. After energy harvesting for a period of time, impedance measurement is performed, and then the cycle of energy harvesting and impedance measurement begins again.

[0041] The data processing unit 15 is used to perform data calculations in the detection system. For example, a preload-impedance model (i.e., the first calculation model) can be established, and the impedance data obtained from the impedance sensor can be input into the model to determine the bolt preload (i.e., the first bolt preload).

[0042] When the energy harvesting unit 12 harvests energy solely by utilizing the preload variation of the bolt 11, in addition to impedance data, the electrical signal from the energy harvesting unit 12 during energy harvesting also reflects the parametric information of the preload variation. In one possible implementation, in addition to the bolt 11, energy harvesting unit 12, excitation unit 13, impedance measurement unit 14, and data processing unit 15, the monitoring system can also include a signal acquisition unit.

[0043] The signal acquisition unit is used to collect electrical signal data during the energy harvesting unit's energy harvesting process, and to send the electrical signal data to the data processing unit. In one implementation, the data processing unit is used to: The system receives electrical signal data sent by the signal acquisition unit, wherein the electrical signal data is data collected by the signal acquisition unit from the energy harvesting unit when the energy harvesting unit is collecting energy. The electrical signal data is input into the second calculation model in the data processing unit to obtain the second bolt preload at the second target moment when the electrical signal data is collected. The first bolt preload is calibrated using the second bolt preload to obtain the calibrated first bolt preload.

[0044] Specifically, the data processing unit can process the electrical signal data in a similar manner to the impedance data: by inputting the electrical signal data into the second calculation model specifically designed for processing electrical signal data in the data processing unit, the second bolt preload can be obtained. Since both impedance data and electrical signal data reflect changes in preload, to improve the accuracy of the first bolt preload calculation, the second bolt preload can be used to calibrate the first bolt preload, resulting in a calibrated first bolt preload.

[0045] In the embodiments of this specification, an energy harvesting unit capable of collecting energy based on changes in magnetic properties caused by variations in bolt preload is used to supply functions to an excitation unit and an impedance measurement unit in the monitoring system. This allows the impedance sensor in the impedance measurement unit to measure the impedance changes in the measurement coil based on the changes in magnetic properties when the excitation unit provides an excitation signal, thereby obtaining the first bolt preload corresponding to the impedance data. This system couples the energy harvesting and impedance measurement processes based on the changes in magnetic properties of the sensing part caused by variations in bolt preload, achieving self-sufficiency in the bolt preload monitoring process. This reduces the cost of bolt preload monitoring while improving its efficiency.

[0046] Figure 2 A flowchart is shown for a real-time monitoring method for bolt preload self-sensing based on magnetostrictive effect according to an embodiment of the present disclosure. This method can be applied to the data processing unit of a real-time monitoring system for bolt preload self-sensing based on magnetostrictive effect.

[0047] In some possible implementations, this real-time monitoring method for self-sensing bolt preload based on the magnetostrictive effect can be implemented by a processor calling computer-readable instructions stored in memory.

[0048] like Figure 2 As shown, the real-time monitoring method for bolt preload based on magnetostrictive effect may include: Step S21: Receive impedance data sent by the impedance sensor in the impedance measurement unit.

[0049] The impedance measurement unit is used to measure the impedance data in the coil.

[0050] As mentioned earlier, impedance data can be obtained by measuring the impedance of the measuring coil in the impedance measurement unit under the action of an excitation signal. This excitation signal can be output from the excitation unit to the measuring coil. When the preload of the bolt changes, the magnetic properties of the sensing part of the bolt change, and this change in magnetic properties causes the impedance of the measuring coil to change under the action of the excitation signal.

[0051] In addition, the excitation unit and impedance measurement unit are powered by the energy harvesting unit, which collects energy based on the changes in the magnetic properties of the sensing part.

[0052] Step S22: Input the impedance data into the first calculation model in the data processing unit to obtain the first bolt preload at the first target moment when the impedance data is collected.

[0053] The first target time is the moment when the impedance data of the measuring coil is acquired. The first bolt preload is the preload of the bolt at the first target time.

[0054] Specifically, before installing the bolts, a test bolt of the same material and structure as the bolts to be installed can be used to obtain impedance data of the test bolt under different preload forces. This data is then used to train the first calculation model so that after the installed bolts are put into use, the corresponding preload force (i.e., the first bolt preload force) can be obtained from the impedance data. In one possible implementation, the training process of the first calculation model includes: When the excitation unit outputs the excitation signal to the measuring coil, the sample impedance data of the measuring coil is obtained when different sample preloads are applied to the bolt; Construct multiple second data pairs, each second data pair containing the sample preload force and the sample impedance data corresponding to the sample preload force; The first computational model is trained using the second data pair to obtain the trained first computational model.

[0055] The sample preload is a known preload. The sample impedance data is the impedance data of the measuring coil corresponding to the sample preload.

[0056] After applying a known sample preload to the bolt, an excitation unit can output an excitation signal to the measuring coil, and the sample impedance data of the measuring coil can be obtained through the impedance measuring unit. This excitation signal must be the same as the excitation signal used during the bolt's operation. By applying different sample preloads, multiple second data pairs containing both sample preload and sample impedance data can be obtained.

[0057] Furthermore, the first calculation model can be trained using the second data pair to obtain a trained first calculation model. Using the trained first calculation model, the first bolt preload can be obtained.

[0058] In the embodiments of this specification, an energy harvesting unit capable of collecting energy based on changes in magnetic properties caused by variations in bolt preload is used to supply functions to an excitation unit and an impedance measurement unit in the monitoring system. This allows the impedance sensor in the impedance measurement unit to measure the impedance changes in the measurement coil based on the changes in magnetic properties when the excitation unit provides an excitation signal, thereby obtaining the first bolt preload corresponding to the impedance data. This system couples the energy harvesting and impedance measurement processes based on the changes in magnetic properties of the sensing part caused by variations in bolt preload, achieving self-sufficiency in the bolt preload monitoring process. This reduces the cost of bolt preload monitoring while improving its efficiency.

[0059] In addition to impedance data, the electrical signal data generated by the energy harvesting unit during the energy harvesting process can also reflect changes in bolt preload. In one possible implementation, the monitoring system includes a signal acquisition unit, and the monitoring method further includes: The system receives electrical signal data sent by the signal acquisition unit, wherein the electrical signal data is data collected by the signal acquisition unit from the energy harvesting unit when the energy harvesting unit is collecting energy. The electrical signal data is input into the second calculation model in the data processing unit to obtain the second bolt preload at the second target moment when the electrical signal data is collected. The first bolt preload is calibrated using the second bolt preload to obtain the calibrated first bolt preload.

[0060] The signal acquisition unit is the unit that acquires the electrical signals when the energy harvesting unit collects energy.

[0061] Specifically, when the energy harvesting unit collects energy corresponding to the electromotive force generated by the change in magnetic properties, it can use a signal acquisition unit to collect electrical signal data during the energy harvesting process and send the electrical signal data to the data processing unit. The data processing unit can then obtain the second bolt preload using a processing method similar to that used for impedance data.

[0062] The training process for the second calculation model to calculate the preload of the second bolt can be similar to the training process for the aforementioned first calculation model. In one possible implementation, the training process for the second calculation model includes: When the excitation unit outputs the excitation signal to the measuring coil, the sample electrical signal data of the signal acquisition unit are acquired when different sample preload forces are applied to the bolt. Construct multiple first data pairs, each first data pair containing the sample preload force and the sample electrical signal data corresponding to the sample preload force; The second computational model is trained using the first data pair to obtain the trained second computational model.

[0063] The sample preload is a known preload. The sample impedance data are the electrical signal data of the energy harvesting unit corresponding to the sample preload.

[0064] After applying a known sample preload to the bolt, a signal acquisition unit can be used to collect the electrical signal data obtained by the energy harvesting unit during energy collection. This excitation signal must be the same as the excitation signal used during the bolt's operation. By applying different sample preloads, multiple first data pairs containing both sample preload and sample electrical signal data can be obtained.

[0065] Furthermore, the second calculation model can be trained using the first data pair to obtain the trained second calculation model. Using the trained second calculation model, the second bolt preload can be obtained.

[0066] Furthermore, the preload of the second bolt can be used to calibrate the preload of the first bolt, avoiding inaccurate impedance data due to circuit problems during impedance acquisition, which in turn leads to inaccurate first bolt preload, thus improving the accuracy of the first bolt preload.

[0067] Clearly, the second bolt preload is different from the first bolt preload corresponding to the first target time; it is the bolt preload at the second target time when the electrical signal is acquired. Therefore, the second bolt preload needs to be processed before using it to calibrate the first bolt preload. In one possible implementation, the calibration operation using the second bolt preload to obtain the calibrated first bolt preload includes: Obtain the historical bolt preload force at multiple historical moments prior to the second target time; Construct a preload curve based on the second bolt preload and the historical bolt preload; The predicted bolt preload at the first target moment is obtained from the preload curve. The predicted bolt preload is used to calibrate the first bolt preload to obtain the calibrated first bolt preload.

[0068] The historical bolt preload is the bolt preload prior to the second target time, obtained through electrical signal data. The predicted bolt preload is the bolt preload predicted based on the existing bolt preload (i.e., the second bolt preload and the historical bolt preload) obtained from the electrical signal data.

[0069] After obtaining the historical bolt preload at multiple historical moments, a preload curve based on electrical signal data can be plotted. In this curve, the horizontal axis represents time, and the vertical axis represents preload. The curve includes the second bolt preload and the historical bolt preload, and it represents the trend of bolt preload based on the electrical signal data. Thus, the predicted bolt preload at the first target moment can be determined from the preload curve.

[0070] Furthermore, the predicted bolt preload can be used to calibrate the first bolt preload. When the difference between the predicted bolt preload and the first bolt preload is small (e.g., less than a preset value, which can be determined based on actual conditions, such as 5% of the first bolt preload), it indicates that the current first bolt preload is relatively accurate and no calibration is required. In one possible implementation, calibrating the first bolt preload using the predicted bolt preload to obtain the calibrated first bolt preload includes: If the difference between the predicted bolt preload and the first bolt preload is greater than a preset value, the predicted bolt preload is used to calibrate the first bolt preload to obtain the calibrated first bolt preload.

[0071] When the difference between the predicted bolt preload and the first bolt preload is significant, it indicates that the current first bolt preload needs calibration. If a bolt problem is known, this calibration can involve changing the first bolt preload to a specific value, which instructs monitoring personnel to investigate the cause of the large difference. If no bolt quality issue is known, the calibration can simply use the predicted bolt preload as the calibrated first bolt preload.

[0072] In the above process, the predicted bolt preload at the first target moment is obtained through the preload curve, which realizes the timing alignment between the first bolt preload based on impedance data and the second bolt preload based on electrical signal data, thereby improving the accuracy of the calibration operation.

[0073] In addition, this disclosure also provides a bolt preload self-sensing real-time monitoring device, electronic device, computer-readable storage medium, and program based on the magnetostrictive effect. All of the above can be used to implement any of the bolt preload self-sensing real-time monitoring methods based on the magnetostrictive effect provided in this disclosure. The corresponding technical solutions and descriptions are described in the corresponding descriptions in the method section and will not be repeated here.

[0074] Figure 3 A block diagram of a bolt preload self-sensing real-time monitoring device based on the magnetostrictive effect according to an embodiment of the present disclosure is shown. The bolt preload monitoring device can be a terminal device, a server, or other processing equipment. The terminal device can be a user equipment (UE), mobile device, user terminal, terminal, cellular phone, cordless phone, personal digital assistant (PDA), handheld device, computing device, vehicle-mounted device, wearable device, etc.

[0075] In some possible implementations, the real-time monitoring device for self-sensing bolt preload based on the magnetostrictive effect can be implemented by a processor calling computer-readable instructions stored in memory.

[0076] like Figure 3 As shown, the bolt preload self-sensing real-time monitoring device 30 based on magnetostrictive effect may include: The data receiving module 31 is used to receive impedance data sent by the impedance sensor in the impedance measurement unit. The impedance data is the data obtained by the impedance sensor measuring the impedance of the measuring coil in the impedance measurement unit under the action of an excitation signal. The excitation signal is output from the excitation unit to the measuring coil. When the preload of the bolt changes, the sensing part of the bolt generates a change in magnetic properties. The change in magnetic properties causes the impedance of the measuring coil to change under the action of the excitation signal. The excitation unit and the impedance measurement unit are powered by the energy harvesting unit, which harvests energy based on the change in magnetic properties. Data processing module 32 is used to input the impedance data into the first calculation model in the data processing unit to obtain the first bolt preload at the first target moment when the impedance data is collected.

[0077] This disclosure also proposes a computer-readable storage medium storing computer program instructions that, when executed by a processor, implement the above-described method. The computer-readable storage medium can be volatile or non-volatile.

[0078] This disclosure also proposes an electronic device, including: a processor; and a memory for storing processor-executable instructions; wherein the processor is configured to implement the above method when executing the instructions stored in the memory.

[0079] This disclosure also provides a computer program product, including computer-readable code, or a non-volatile computer-readable storage medium carrying computer-readable code, wherein when the computer-readable code is run in a processor of an electronic device, the processor in the electronic device performs the above-described method.

[0080] This invention is now complete.

[0081] In summary, in the embodiments of this specification, an energy harvesting unit capable of collecting energy based on changes in magnetic properties caused by variations in bolt preload is used to supply functions to the excitation unit and impedance measurement unit in the monitoring system. This allows the impedance sensor in the impedance measurement unit to measure the impedance changes in the measurement coil based on changes in magnetic properties when the excitation unit provides an excitation signal, thereby obtaining the first bolt preload corresponding to the impedance data. This system couples the energy harvesting and impedance measurement processes based on the changes in magnetic properties of the sensing part caused by variations in bolt preload, achieving self-sufficiency in the bolt preload monitoring process. This reduces the cost of bolt preload monitoring while improving its efficiency.

[0082] The optional embodiments of the present invention have been described in detail above with reference to the accompanying drawings. However, the embodiments of the present invention are not limited to the specific details in the above embodiments. Within the scope of the technical concept of the embodiments of the present invention, various simple modifications can be made to the technical solutions of the embodiments of the present invention, and these simple modifications all fall within the protection scope of the embodiments of the present invention.

[0083] It should also be noted that the various specific technical features described in the above embodiments can be combined in any suitable manner without contradiction. To avoid unnecessary repetition, the embodiments of the present invention will not describe the various possible combinations separately.

[0084] Those skilled in the art will understand that all or part of the steps in the methods of the above embodiments can be implemented by a program instructing related hardware. This program is stored in a storage medium and includes several instructions to cause a microcontroller, chip, or processor to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as a USB flash drive, a portable hard drive, a read-only memory (ROM), a random access memory (RAM), a magnetic disk, or an optical disk.

[0085] Furthermore, various different implementations of the present invention can be combined arbitrarily, as long as they do not violate the spirit of the present invention, they should also be regarded as the content disclosed in the present invention.

Claims

1. A real-time monitoring method for bolt preload based on magnetostrictive effect, characterized in that, The monitoring method is used in the data processing unit of a real-time monitoring system for self-sensing bolt preload based on magnetostrictive effect. The monitoring system further includes a bolt, an energy harvesting unit, an excitation unit, and an impedance measurement unit. The monitoring method includes: The impedance measurement unit receives impedance data sent by the impedance sensor. The impedance data is obtained by the impedance sensor measuring the impedance of the measuring coil in the impedance measurement unit under the action of an excitation signal. The excitation signal is output from the excitation unit to the measuring coil. When the preload of the bolt changes, the sensing part of the bolt generates a change in magnetic properties. The change in magnetic properties causes a change in the impedance of the measuring coil under the action of the excitation signal. The excitation unit and the impedance measurement unit are powered by the energy harvesting unit, which harvests energy based on the change in magnetic properties. The impedance data is input into the first calculation model in the data processing unit to obtain the first bolt preload at the first target moment when the impedance data is collected.

2. The monitoring method according to claim 1, characterized in that, The monitoring system includes a signal acquisition unit, and the monitoring method further includes: The system receives electrical signal data sent by the signal acquisition unit, wherein the electrical signal data is data collected by the signal acquisition unit from the energy harvesting unit when the energy harvesting unit is collecting energy. The electrical signal data is input into the second calculation model in the data processing unit to obtain the second bolt preload at the second target moment when the electrical signal data is collected. The first bolt preload is calibrated using the second bolt preload to obtain the calibrated first bolt preload.

3. The monitoring method according to claim 2, characterized in that, The step of calibrating the first bolt preload using the second bolt preload to obtain the calibrated first bolt preload includes: Obtain the historical bolt preload force at multiple historical moments prior to the second target time; Construct a preload curve based on the second bolt preload and the historical bolt preload; The predicted bolt preload at the first target moment is obtained from the preload curve. The predicted bolt preload is used to calibrate the first bolt preload to obtain the calibrated first bolt preload.

4. The monitoring method according to claim 3, characterized in that, The step of calibrating the first bolt preload using the predicted bolt preload to obtain the calibrated first bolt preload includes: If the difference between the predicted bolt preload and the first bolt preload is greater than a preset value, the predicted bolt preload is used to calibrate the first bolt preload to obtain the calibrated first bolt preload.

5. The monitoring method according to claim 2, characterized in that, The training process of the second computational model includes: When the excitation unit outputs the excitation signal to the measuring coil, the sample electrical signal data of the signal acquisition unit are acquired when different sample preload forces are applied to the bolt. Construct multiple first data pairs, each first data pair containing the sample preload force and the sample electrical signal data corresponding to the sample preload force; The second computational model is trained using the first data pair to obtain the trained second computational model.

6. The monitoring method according to claim 1, characterized in that, The training process of the first computational model includes: When the excitation unit outputs the excitation signal to the measuring coil, the sample impedance data of the measuring coil is obtained when different sample preloads are applied to the bolt; Construct multiple second data pairs, each second data pair containing the sample preload force and the sample impedance data corresponding to the sample preload force; The first computational model is trained using the second data pair to obtain the trained first computational model.

7. A real-time monitoring system for bolt preload based on magnetostrictive effect, characterized in that, The monitoring system includes bolts, an energy harvesting unit, an excitation unit, an impedance measurement unit, and a data processing unit. The bolt includes a sensing portion made of a magnetostrictive material, the sensing portion being used to generate a change in magnetic properties when the preload of the bolt changes; The energy harvesting unit is used for: Energy is harvested based on the changes in the magnetic properties. Power is supplied to the impedance measurement unit and the excitation unit; The excitation unit is used to output an excitation signal to the measuring coil included in the impedance measuring unit. Based on the change in magnetic characteristics, the measuring coil undergoes an impedance change under the action of the excitation signal. The impedance measurement unit further includes an impedance sensor, which is used to measure the impedance data of the measuring coil and send it to the data processing unit. The data processing unit is used to obtain the first bolt preload at the first target moment when the impedance data is collected, based on the received impedance data.

8. The monitoring system according to claim 7, characterized in that, The monitoring system includes a signal acquisition unit, which is used to collect electrical signal data of the energy collection unit while the energy collection unit collects energy, and send the electrical signal data to the data processing unit. The data processing unit is used for: The system receives electrical signal data sent by the signal acquisition unit, wherein the electrical signal data is data collected by the signal acquisition unit from the energy harvesting unit when the energy harvesting unit is collecting energy. The electrical signal data is input into the second calculation model in the data processing unit to obtain the second bolt preload at the second target moment when the electrical signal data is collected. The first bolt preload is calibrated using the second bolt preload to obtain the calibrated first bolt preload.

9. The monitoring system according to claim 7, characterized in that, The bolt has a channel inside the screw, and the measuring coil and the impedance sensor are disposed in the channel, with the measuring coil surrounded by the sensing part.

10. The monitoring system according to claim 7, characterized in that, The energy harvesting based on the change in magnetic properties includes: Based on the changes in magnetic properties, energy is collected using the measuring coil.

Images