JP2024111783A - Ultra-thin force sensor, monitoring system, monitoring method and application - Google Patents

Abstract

To provide an ultrathin force sensor, a monitoring system, a monitoring method and application to improve the reliability of pre-tension monitoring.SOLUTION: The present invention discloses an ultrathin force sensor, a monitoring system, a monitoring method and application. The ultrathin force sensor comprises a force washer, a plurality of strain gauges and a protective case. The force washer is provided with a structural hole for a to-be-measured part to penetrate through. The peripheral wall of the force washer is provided with a strain groove. The strain gauges are fixedly connected to the bottom of the strain groove in the circumferential direction. The strain gauges are electrically connected with a deformation monitoring circuit used for detecting the deformation quantity of the strain gauges. The protective case is externally fitted onto the peripheral wall of the force washer for fixed connection. The strain groove is filled with a potting body.SELECTED DRAWING: None

Description

This application relates to the technical field of pretension monitoring, and in particular to an ultra-thin force sensor, a monitoring system, a monitoring method, and applications.

Prestressed anchor structures, cable bridge structures, and bolted structures are widely used in civil engineering, transportation, mechanical engineering, and other fields. These types of structures are exposed to complex and harsh natural environments for long periods of time, and are subjected to increasingly heavy traffic loads, so the engineering community is paying close attention to their safety in long-term use.

In the engineering industry, cable tension or bolt pretension is usually tested by vibrating string sensors, ultrasonic methods, and vibration acceleration methods, but the above methods have many shortcomings in practical application.

Vibrating string force sensors are large and difficult to apply to situations where the space at anchor positions is narrow, such as the tension cables of cable-stayed bridges, the joints of prefabricated pipe galleries, and the cable clamp screws of suspension bridges. These sensors also have problems such as creep after pre-tensioning the steel string, the output signal being difficult to detect slight changes in force, and high power consumption for data collection and transmission, making it difficult to meet the demand for monitoring large amounts of screw fastening force, such as in cable bridge structures.

Both the vibration acceleration method and the ultrasonic method are detection methods that indirectly estimate the fastening force. The vibration acceleration method is often used to monitor the tension of cable-stayed bridges. On-site testing is convenient and quick, but the test location is usually close to the bridge surface, which makes it susceptible to the influence of vibration dampers, and the influence of low-order frequencies due to slack in the tension cable is large, so the difference between the test tension value and the actual value is often large.

The ultrasonic method estimates the current tightening force value of the screw by establishing the relationship between the propagation time of sound waves inside the screw and the axial force. This method has difficulty in eliminating problems such as measurement errors caused by creep of metal screw materials and insufficient durability of ultrasonic transducers. The effective monitoring time is usually less than one year, after which the data deviates significantly from the actual value. Monitoring using the ultrasonic method requires the use of special screws, which requires high processing accuracy and high sensor costs. Therefore, the ultrasonic method has been mainly used in scientific research tests and is difficult to widely apply in actual construction.

Therefore, there is a great need to develop a low-cost, high-quality, ultra-thin intelligent sensing force sensor and monitoring system.

This application provides an ultra-thin force sensor, a monitoring system, a monitoring method, and applications to increase the reliability of pretension monitoring.

This application is realized by adopting the following technical solutions:

This ultra-thin force sensor includes a force washer, several strain gauges, and a protective case, in which a structural hole is formed in the force washer for drilling a part to be measured, strain grooves are formed in the outer wall of the force washer, several strain gauges are fixedly connected to the bottom of the strain grooves in the circumferential direction, a deformation monitoring circuit is electrically connected to the strain gauges for detecting the amount of deformation of the strain gauges, the protective case is fitted onto the outer wall of the force washer and fixedly connected, and a potting material is filled in the strain grooves.

The force sense washer is provided with structural holes for drilling components that require pretension monitoring, allowing the force sense washer to function as a gasket for fasteners; strain grooves are provided on the outer wall of the force sense washer, and several strain gauges are distributed circumferentially and fixedly connected to the bottom of the strain groove, making it easy to monitor the deformation of the force sense washer using the strain gauge; a deformation monitoring circuit for detecting the deformation of the strain gauge is electrically connected to the strain gauge, converting the deformation into an electrical signal, and measuring the value and change of the electrical signal makes it easy to calculate the value and change of the pretension of the component to be measured; a protective case is fitted onto the outer wall of the force sense washer and fixedly connected, and a potting material is filled into the strain groove to isolate the strain gauge from the external environment, protect the strain gauge, and reduce the effect of the protective case on the elastic deformation of the force sense washer.

Optionally, an annular protrusion is provided on the surface of the force sense washer that is separated from the member contact surface. The surface of the force sense washer that comes into contact with the member is the member contact surface, and the annular protrusion is provided on the surface of the force sense washer that is separated from the member contact surface in order to reduce the possibility of eccentric load occurring between the part to be measured and the surface of the force sense washer that is separated from the member contact surface and to increase the uniformity with which the force sense washer receives the fastening force of the part to be measured.

Optionally, the axial dimension of the force sense washer is larger than the axial dimension of the protective case to reduce the effect of the protective case on the axial deformation of the force sense washer.

Optionally, the deformation monitoring circuitry
an input voltage control module including an adjustable power supply;
a resistance bridge module electrically connected to the input voltage control module and including a Wheatstone bridge consisting of a strain resistor R1 fixedly connected to a strain gauge;
an output voltage sensing module electrically connected to the resistive bridge module.

The input voltage control module includes an adjustable power supply and is used to input a specific value of input voltage to the resistance bridge module of the deformation monitoring circuit, the resistance bridge module includes a Wheatstone bridge consisting of a strain resistor R1, where the strain resistor R1 is the resistor to be measured and is fixedly connected to a strain gauge to adjust the resistance value of the strain resistor R1 according to the deformation amount of the force sense washer, the output voltage detection module is electrically connected to the resistance bridge module and is used to detect the value of the output voltage of the resistance bridge module so as to calculate the actual resistance value of the strain resistor R1, thereby realizing an electrical measurement of a non-electrical quantity, namely the deformation amount of the force sense washer.

This application is realized by adopting the following technical solutions:

A monitoring system using ultra-thin force sensors, comprising several ultra-thin force sensors, an adjustable power supply, a voltage detector sensor, a data analysis module, and a system control module, in which a wireless transmission unit is electrically connected to the adjustable power supply, the voltage detector sensor, the data analysis module, and the system control module, the system control module transmits a monitoring control signal to the adjustable power supply via the wireless transmission unit, the adjustable power supply provides an input voltage to the resistive bridge module of each ultra-thin force sensor, the voltage detector sensor generates stress monitoring data based on the output voltage of the resistive bridge module, the voltage detector sensor transmits the stress monitoring data to the data analysis module via the wireless transmission unit, and the data analysis module analyzes the stress monitoring data, generates feedback control information, and transmits it to the system control module via the wireless transmission unit.

In the monitoring system using the ultra-thin force sensor, the adjustable power supply, the voltage detector sensor, the data analysis module, and the system control module are all electrically connected to a wireless transmission unit in order to wirelessly transmit information between each device in the monitoring system. Among them, the system control module is used to send a monitoring control signal to the adjustable power supply to control the adjustable power supply to provide an input voltage to each ultra-thin force sensor. After the input voltage is processed by the resistive bridge module in the ultra-thin force sensor, the voltage detector sensor generates stress monitoring data by detecting the output voltage of the resistive bridge module. After the voltage detector sensor transmits the stress monitoring data to the data analysis module, the data analysis module analyzes the stress monitoring data to determine whether the pretension of the measurement target part at the location where each ultra-thin force sensor is located is normal, thereby determining the stress monitoring means that need to be adjusted, generating feedback control information and transmitting it to the system control module to adjust the monitoring control signal.

Optionally, the device further includes a warning module including an on-site alarm attached to the location where the ultra-thin force sensor is disposed, and the warning module further includes a remote alarm for transmitting a remote alarm signal. When it is monitored that the pretension of the measurement target part at the location where some of the ultra-thin force sensors are disposed is abnormal, an alarm function is realized by transmitting an on-site alarm and a remote alarm signal, thereby making it easy to warn nearby people to disperse and immediately inform relevant parties to eliminate hidden danger.

This application is realized by adopting the following technical solutions:

obtaining stress monitoring data corresponding to each ultra-thin force sensor, evaluating a warning stage for each fastener based on the stress monitoring data, and generating warning information based on the warning stage information;
Obtaining design drawings of the equipment to be monitored, determining performance parameters of each component and fastener based on the design drawings, and creating a mechanical information model;
Marking the stress monitoring data into a mechanical information model, and judging the risk information of each fastening point based on the mechanical information model;
A monitoring method using an ultra-thin force sensor includes a step of determining a corresponding monitoring frequency based on risk information of each fastening point, and generating feedback control information based on the monitoring frequency.

The pretension of the fasteners monitored by each ultra-thin force sensor is obtained, stress monitoring data is generated, the warning stage of each fastener is evaluated based on the stress monitoring data, and based on the warning stage information, whether or not a warning is necessary and what kind of warning signal to issue is determined, and warning information is generated, thereby issuing a warning signal when the pretension of the fastener is abnormal; the design drawings of the monitored equipment are obtained, and performance parameters of each component and fastener are determined based on the design drawings, thereby creating a mechanical information model, and then, it is easy to determine the mechanical performance of the monitored equipment in each area by the mechanical information model; the acquired stress monitoring data is marked on the mechanical information model, and based on the current pretension of each fastener and the performance parameters of each component, the risk level of each fastening point is determined, risk level information is generated; based on the current risk level information of different fastening points, the monitoring frequency for each fastening point is determined, and feedback control information is generated, thereby making it easy to adjust the stress monitoring plan for the monitored equipment, thereby increasing the reliability of pretension monitoring.

Optionally, acquiring stress monitoring data corresponding to each ultra-thin force sensor, evaluating a warning stage for each fastener based on the stress monitoring data, and generating warning information based on the warning stage information;
Obtaining model number information of a fastener, determining warning threshold intervals for each warning stage based on the model number information, and generating a threshold interval set;
obtaining each stress monitoring data, comparing each stress monitoring data with a corresponding set of threshold intervals, and determining a corresponding warning level;
matching a corresponding warning signal based on the warning stage of each fastener, and generating warning information based on the warning signal of each fastener.

By acquiring model number information of each fastener, performance parameters of each fastener are determined; based on the performance parameters of each fastener, warning threshold intervals corresponding to different danger levels are determined, each warning threshold interval corresponds to one warning stage; a threshold interval set is generated based on multiple warning threshold intervals of the same fastener; stress monitoring data corresponding to each fastener is acquired; a corresponding warning stage is determined by comparing the stress monitoring data with the threshold interval set of the fastener; based on the warning stage of each fastener, a type of warning signal that needs to be issued is determined; warning information is generated based on the warning signal of each fastener; and then an alarm of a warning module is controlled based on the warning information.

Optionally, in the step of acquiring each stress monitoring data,
oversampling the ultra-thin force sensor using a sensor signal processing circuit to obtain a sampling data set including a first numerical value number of monitoring data within each sampling period;
Averaging each of the monitoring data in the sampling data set to generate a plurality of sets of average values, subtracting the plurality of sets of average values from each of the monitoring data in the sampling data set to obtain low-wave data, and generating a low-wave data set based on each of the low-wave data;
A step of performing data statistics on the low-wave data set, calculating the number of low-wave data in each numerical segment, and comparing the number of low-wave data in each numerical segment with a noise removal threshold;
The method includes a step of calculating an average value of the monitoring data of all the numerical segments having a number of low-wave data greater than a noise removal threshold, and setting the average value as the stress monitoring data for the sampling period.

By oversampling the ultra-thin force sensor by the signal processing circuit of the sensor, the authenticity of the collected data is improved, and one sampling data set is obtained in each sampling period based on a predetermined sampling period and sampling frequency, where the number of monitoring data in each sampling data set is the number of first numerical values, and the average value of the corresponding multiple sets is generated by averaging each monitoring data in the sampling data set, and the average value of the multiple sets is subtracted from each monitoring data in the sampling data set to generate a low-wave data set, thereby reducing the range of the numerical values of each data in the data set and improving the accuracy of the data fluctuation analysis. During the data sampling process, noise waves are generated due to disturbances in each element of the signal processing circuit and other modules in the monitoring system, which affects the detection accuracy, so each data in the low-wave data set is statistically analyzed, and the number of low-wave data in each numerical segment is calculated, and if the number is smaller than a predetermined noise elimination threshold, the data in the numerical segment is considered to be a noise wave and is not involved in the averaging calculation, and if the number is larger than the predetermined noise elimination threshold, it is reserved, and the monitoring data corresponding to the low-wave data in the reserved numerical segment is calculated to be the stress monitoring data of the sampling period.

Optionally, prior to the step of oversampling the ultra-thin force sensor by the sensor signal processing circuit,
applying pretension to the part to be measured in stages and reading corresponding calibration measurement data;
Calculating an elastic modulus corresponding to the pretension section of this stage based on the calibration measurement data corresponding to the pretension of each stage and the calibration measurement data corresponding to the pretension of the upper stage;
The method further includes inputting each elastic modulus into a data analysis module to perform automatic calibration of the ultra-thin force sensor.

The ultra-thin force sensor of the present application uses a strain gauge to detect the deformation of the force washer and calculate the stress, specifically utilizing the properties of elastic deformation of the material. However, since the relationship between stress and strain in the elastic range of the material is not absolutely linear, and the deformation of the force washer may be hindered by other factors, it is necessary to calibrate the ultra-thin force sensor to improve the detection accuracy for the pretension of the part being measured. Pretension is applied to the part being measured in stages, and the voltage value detected by the ultra-thin force sensor after each stage of pretension is read and used as calibrated measurement data, and the Based on the calibration measurement data corresponding to the pretension and the calibration measurement data corresponding to the pretension of the upper stage, and based on the currently applied pretension value and the pretension value applied to the upper stage, the elastic coefficient corresponding to the pretension section of this stage is calculated, and the elastic coefficient corresponding to the pretension of each stage is input into the data analysis module to automatically calibrate the ultra-thin force sensor, which then facilitates the calculation of the measured stress monitoring data based on the elastic coefficient of the ultra-thin force sensor in different stress sections, thereby achieving the effect of improving the detection accuracy of the ultra-thin force sensor.

Optionally, after the step of inputting each elastic modulus into a data analysis module and performing automatic calibration of the ultra-thin force sensor,
applying a test pretension to the part to be measured, reading corresponding test measurement data, and determining whether a predetermined accuracy requirement is met based on the test measurement data;
If the test measurement data does not meet the predetermined accuracy requirement, setting an interpolation point in the pretension section whose accuracy is unacceptable, and determining a corresponding pretension subsection based on the interpolation point;
calculating a partial elastic modulus corresponding to each pretensioned subsection and calculating a difference in the partial elastic modulus corresponding to adjacent pretensioned subsections to generate an elasticity difference;
The method further includes a step of completing correction calibration if each elasticity difference is smaller than the corresponding elasticity difference threshold, and setting a new interpolation point in the corresponding pretension subsection and continuing correction calibration if any elasticity difference is larger than the elasticity difference threshold.

After the ultra-thin force sensor is automatically calibrated, the automatically calibrated force sensor can be further inspected and corrected by applying a test pretension to the part to be measured, reading the voltage value detected by the ultra-thin force sensor to determine whether the test measurement data and the test measurement data meet the specified accuracy requirements, and if the test measurement data does not meet the specified accuracy requirements, determining the pretension section whose accuracy is unacceptable, setting an interpolation point in the pretension section whose accuracy is unacceptable, and further determining a new pretension subsection based on the set interpolation point, calculating the partial elastic modulus corresponding to each pretension subsection, calculating the difference between the partial elastic modulus of each adjacent pretension subsection as the elasticity difference, and comparing each elasticity difference with a specified elasticity difference threshold. If all the elasticity differences are smaller than the elasticity difference threshold, it is considered that the elasticity coefficients between the adjacent pretension subsections are close, and the correction calibration is completed. If any of the elasticity differences is larger than the specified elasticity difference threshold, a new interpolation point is set in the corresponding pretension subsection, and the correction calibration work needs to be continued.

Optionally, marking the stress monitoring data into a mechanical information model and determining risk information for each fastening point based on the mechanical information model,
Marking the stress monitoring data into a mechanical information model, and judging the load data of each fastening point according to the force-bearing data of each member of the monitored equipment;
and calculating a load factor for each fastening point based on the load data and stress monitoring data for each fastening point to generate risk information.

By marking the stress monitoring data on the mechanical information model, the current fastening force of each fastening point in the monitored equipment is determined, the current load situation of each fastening point is determined based on the force-bearing data of each component of the monitored equipment to generate load data, the current load rate of each fastening point is calculated based on the load data of each fastening point and the corresponding stress monitoring data, the risk of each fastening point is evaluated to generate risk information, and then it becomes easy to set the monitoring frequency based on the different risk information of each fastening point, thereby enhancing the scientificity of stress monitoring for each fastening point.

1 is a schematic diagram of a structure of an ultra-thin force sensor in Example 1. FIG. FIG. 2 is a circuit diagram of a deformation monitoring circuit according to the first embodiment. 11 is a schematic diagram of the structure of an ultra-thin force sensor in Example 2. FIG. FIG. 11 is a schematic diagram of a monitoring system to which the ultra-thin force sensor in Example 3 is applied. 13 is a flowchart of a monitoring method to which the ultra-thin force sensor in Example 5 is applied. 13 is a flowchart of step S10 of the monitoring method using the ultra-thin force sensor. 13 is a flowchart of step S12 of the monitoring method to which the ultra-thin force sensor is applied. 13 is another flowchart of a monitoring method using the ultra-thin force sensor. 13 is another flowchart of a monitoring method using the ultra-thin force sensor. 13 is a flowchart of step S30 of the monitoring method using the ultra-thin force sensor. FIG. 13 is a circuit diagram of a deformation monitoring circuit in a sixth embodiment. 13 is another flowchart of a monitoring method using the ultra-thin force sensor. FIG. 13 is a schematic diagram of the device in Example 7.

This application discloses an ultra-thin force sensor, a monitoring system, a monitoring method, and applications.

The present application will be described in further detail below with reference to Figures 1 to 13.

Example 1
As shown in FIG. 1 , the present application discloses an ultra-thin force sensor 100 including a force sensor washer 1, several strain gauges 3, and a protective case 2, in which the force sensor washer 1 is used to attach the strain gauges 3 and can be a gasket of a fastener to be measured, the strain gauges 3 are fixedly connected to the force sensor washer 1, and a deformation monitoring circuit is electrically connected to detect the amount of deformation of the force sensor washer 1 so as to calculate the magnitude of the force received by the force sensor washer 1, and the protective case 2 is used to protect an electronic device attached to the force sensor washer 1.

The force sense washer 1 is disk-shaped, and a structural hole 11 for drilling the fastener to be measured is opened along the axial direction in the force sense washer 1. The inner diameter of the structural hole 11 is determined based on the diameter of the fastener to be measured. One surface of the force sense washer 1 is a member contact surface. The member contact surface of the force sense washer 1 is set flat and is used to contact the member that needs to be connected by the fastener. In order to reduce the possibility of eccentric load occurring between the fastener to be measured and the force sense washer 1 and to increase the uniformity with which the force sense washer 1 receives the fastening force of the fastener to be measured, an annular protrusion 13 is provided on the surface of the force sense washer 1 that is away from the member contact surface.

A strain groove 12 is opened on the outer peripheral wall of the force sense washer 1, and the bottom surface of the strain groove 12 is installed around the axis of the force sense washer 1 to form a cylindrical surface. Several strain gauges 3 are then uniformly and fixedly connected to the bottom of the strain groove 12 along the circumferential direction of the force sense washer 1 to detect the axial deformation amount of the force sense washer 1. When the force sense washer 1 receives the pretension of the fastener, it deforms in the axial direction. When the force sense washer 1 elastically deforms, the deformation amount of the force sense washer 1 and the pretension it receives have an approximately linear relationship. The strain gauges 3 can be used to detect the deformation amount of the force sense washer 1, so that the pretension that the force sense washer 1 receives from the fastener can be indirectly detected.

The protective case 2 has a circular ring shape, and several first connection holes 15 are uniformly opened in the circumferential direction of the force sense washer 1. In this embodiment, the first connection holes 15 are screw holes, and the number of the first connection holes 15 is four. The protective case 2 is fitted onto the force sense washer 1, and several second connection holes 21 are opened in the protective case 2, and the number of the second connection holes 21 is the same as the number of the first connection holes 15, and the second connection holes 21 are installed directly opposite the first connection holes 15. The protective case 2 is removably connected to the force sense washer 1 by a screw connector, and the force sense washer 1 receives the axial pretension of the fastener alone, and the axial dimension of the force sense washer 1 is larger than the axial dimension of the protective case 2, so as to reduce the effect of the axial pretension of the protective case 2 on the deformation amount of the force sense washer 1, thereby improving the accuracy of detection of the magnitude of the pretension.

The protective case 2 is further provided with a threading hole 22 for passing a cable of a deformation detection circuit connected to the strain gauge 3, and the strain groove 12 is filled with a potting body 4. In this embodiment, the potting body 4 is formed by solidifying the potting adhesive, and protects the strain gauge 3 and makes it easy to isolate the strain gauge 3 from the external environment to increase the service life of the strain gauge 3.

As shown in FIG. 2, the deformation detection circuit includes an input voltage control module 5, a resistive bridge module 6, and an output voltage detection module 7.

The input voltage control module 5 is used to input a specific value of input voltage to the deformation monitoring circuit, and the input voltage control module 5 includes an adjustable power supply, and the two output electrodes of the adjustable power supply are respectively connected to the two input electrodes of the resistance bridge module 6.

The resistance bridge module 6 is electrically connected to the input voltage control module 5, and the resistance bridge module 6 is used to adjust the ratio between the output voltage and the input voltage of the resistance bridge module 6 according to the deformation amount of the force sense washer 1. The resistance bridge module 6 includes a strain resistor R1, a second resistor R2, a third resistor R3, and a fourth resistor R4. One end of the strain resistor R1 is connected to the second resistor R2, one end of the second resistor R2 away from the strain resistor R1 is connected to the third resistor R3, one end of the third resistor R3 away from the second resistor R2 is connected to the fourth resistor R4, and one end of the fourth resistor R4 away from the third resistor R3 is connected to one end of the strain resistor R1 away from the second resistor R2, thereby forming a Wheatstone bridge, of which the strain resistor R1 is the resistor to be measured, and the strain resistor The resistor R1 is fixedly connected to the strain gauge 3, and the two input electrodes of the resistance bridge module 6 are the connection node between the strain resistor R1 and the fourth resistor R4, and the connection node between the second resistor R2 and the third resistor R3, respectively. Since resistance measurement using the Wheatstone bridge method is a conventional technology, in this embodiment, the principle of how the resistance value of the strain resistor R1 is measured by the resistance bridge module 6 and how the resistance value of the strain resistor R1 is converted into a voltage amount and measured is omitted. Here, the second resistor R2, the third resistor R3, and the fourth resistor R4 are connected in parallel to the strain resistor R1 in the ultra-thin force sensor 100 via long conductors, so that the second resistor R2, the third resistor R3, and the fourth resistor R4 can be easily installed in a position that is convenient for the operator to perform maintenance and debugging.

The output voltage detection module 7 is electrically connected to the resistive bridge module 6 and is used to detect the numerical value of the output voltage of the resistive bridge module 6. The output voltage detection module 7 includes a voltage detector, and the two input electrodes of the voltage detector are respectively connected to the two output electrodes of the resistive bridge module 6. The two output electrodes of the resistive bridge module 6 are the connection node between the strain resistor R1 and the second resistor R2, and the connection node between the third resistor R3 and the fourth resistor R4.

Example 2
As shown in FIG. 3, the present application discloses an ultra-thin force sensor 100, in which, in addition to the first embodiment, a deformation groove 14 is further provided on the inner wall of the structural hole 11 of the force sense washer 1, and the deformation groove 14 is provided in the circumferential direction along the axis of the force sense washer 1, thereby making it easy to increase the degree of deformation of the force sense washer 1 due to axial pretension and further increase the detection accuracy for the axial pretension.

Example 3
As shown in FIG. 4, the present application discloses a monitoring system using an ultra-thin force sensor 100, which includes several ultra-thin force sensors 100, an adjustable power supply, a voltage detector sensor, a data analysis module 9, and a system control module 8, in which a wireless transmission unit is electrically connected to the adjustable power supply, the voltage detector sensor, the data analysis module 9, and the system control module 8. In this embodiment, a LoRa communication chip is installed in the wireless transmission unit, and the monitoring system using the ultra-thin force sensor 100 further includes a LoRa communication repeater, which enables wireless communication connection to be realized between the adjustable power supply, the voltage detector sensor, the data analysis module 9, and the system control module 8 through LoRa wireless wide area network technology, and has the advantages of low power consumption and long signal transmission distance.

The system control module 8 transmits a monitoring control signal to the adjustable power supply via the wireless transmission unit, so that the adjustable power supply provides an input voltage to the resistive bridge module 6 of each ultra-thin force sensor 100, and the voltage detector generates stress monitoring data based on the output voltage of the resistive bridge module 6, where the monitoring control signal is a signal for controlling the time node and voltage parameters at which the adjustable power supply provides the input voltage.

After the voltage detector detects the output voltage and generates stress monitoring data, the voltage detector transmits the stress monitoring data to the data analysis module 9 via a wireless transmission unit so that the data analysis module 9 can analyze the stress monitoring data. After analyzing the stress monitoring data, the voltage detector generates feedback control information and transmits the feedback control information to the system control module 8 via the wireless transmission unit, thereby performing closed-loop control of the monitoring operation performed by the ultra-thin force sensor 100.

The monitoring system using the ultra-thin force sensor 100 further includes a warning module, which includes an on-site alarm and a remote alarm, of which the on-site alarm is attached to the location where the ultra-thin force sensor 100 is placed, and specifically can include one or more of a buzzer, a speaker, and a warning light, so that when the ultra-thin force sensor 100 monitors an abnormality in a fastener, it alerts nearby people to disperse with an audio signal and/or a light signal, or alerts a maintenance worker to the location of the abnormality in the fastener, and when the remote alarm monitors an abnormality in a fastener, it transmits a remote alarm signal to the maintenance worker to inform the maintenance worker of the location where the fastener with the current abnormality is placed.

Example 4
The present application discloses a monitoring method using an ultra-thin force sensor that can be used to monitor the stress of fasteners to improve the safety of bridges, buildings, pipe galleries, blowers, anchor structures, rails, mechanical equipment, etc. that are connected or reinforced by the fasteners to be measured, and in this embodiment, the monitoring method using the ultra-thin force sensor is stored in and executed by a data analysis module.

As shown in FIG. 5, the monitoring method using the ultra-thin force sensor specifically includes the following steps:

In S10, stress monitoring data corresponding to each ultra-thin force sensor is acquired, the warning stage of each fastener is evaluated based on the stress monitoring data, and warning information is generated based on the warning stage information.

In this embodiment, the stress monitoring data is the stress data of the corresponding fastener detected by the ultra-thin force sensor.

Specifically, the deformation amount of the force washer is detected by a deformation monitoring circuit built into the ultra-thin force sensor, and stress data of the fastener corresponding to the ultra-thin force sensor is further calculated. Based on the stress monitoring data corresponding to each fastener and the performance parameters of the corresponding fastener, the degree of deviation of the current stress data of each fastener from the corresponding stress data in the design drawing is evaluated, thereby determining the warning stage of each fastener, matching information on the type of warning signal that needs to be issued based on the warning stage of each fastener, further generating warning information, and facilitating controlling the warning module to issue a corresponding warning signal based on the warning information.

In addition, since there are resistors whose resistance values change with different temperatures, the data analysis module stores a temperature compensation algorithm for temperature compensating the resistance value data of each resistor in the deformation detection circuit, and the specific compensation values of each resistor at different temperatures can be obtained based on experiments or related technical manuals.

Now, referring to FIG. 6, step S10 includes the following:

In S11, the model number information of the fastener is obtained, and the warning threshold intervals for each warning stage are determined based on the model number information, and a threshold interval set is generated.

Specifically, obtain the model number information of the fastener, match the corresponding performance parameters from the technical file base based on the model number information of the fastener, determine the range of the fastening force value of each fastener under normal circumstances, set multiple warning threshold intervals for each fastener based on the performance parameters of each fastener, each warning threshold interval corresponds to one warning stage, and then facilitate determining the warning stage of the fastener based on the fastening force value of the fastener, and generate a threshold interval set based on the multiple warning threshold intervals of each fastener.

In S12, each stress monitoring data is acquired, each stress monitoring data is compared with a corresponding threshold interval set, and a corresponding warning stage is determined.

Specifically, after obtaining the current stress monitoring data of each fastener, the stress monitoring data of each fastener is compared with the corresponding threshold interval set to determine the current warning stage of each fastener, making it easy to know the current risk level of each fastener.

In S13, corresponding warning signals are matched based on the warning stage of each fastener, and warning information is generated based on the warning signal of each fastener.

In this embodiment, the warning signal is a signal for triggering the warning module to perform a specific warning action, and one warning signal correspondingly triggers one warning action of the warning module, such as sounding a siren, turning on a warning light, and sending a remote warning signal, and the warning information is information generated based on the warning signals of all fasteners in the monitoring system using the ultra-thin force sensor.

Specifically, by matching corresponding warning signals based on the warning stage of each fastener, generating warning information based on the warning signals of each fastener, and then synthesizing based on the current risk level information of each fastener, the warning module is triggered to execute corresponding alarm actions in an integrated manner.

Now, referring to FIG. 7, step S12 includes the following:

In S121, the sensor signal processing circuit oversamples the ultra-thin force sensor to obtain one sampling data set that includes the first number of monitoring data within each sampling period.

In this embodiment, the monitoring data is stress data measured by the ultra-thin force sensor, the sampling period is the period during which one stress monitoring operation is performed and the monitoring data is collected by the ultra-thin force sensor, and preferably the sampling period is 1S, and the sampling data set is a data set consisting of all the monitoring data collected within one sampling period, and one collected data set includes a first number of pieces of monitoring data, and preferably the first number is 200.

Specifically, the sensor signal processing circuit oversamples the ultra-thin force sensor to obtain sufficient stress data measured by the ultra-thin force sensor, and then facilitates improving the detection accuracy of the stress monitoring data. A first number of pieces of monitoring data are obtained within each sampling period to form a corresponding sampling data set.

In S122, each monitoring data in the sampling data set is averaged to generate an average value of the multiple sets, the average value of the multiple sets is subtracted from each monitoring data in the sampling data set to obtain low-motion data, and a low-motion data set is generated based on each low-motion data.

Specifically, a specific method for averaging each piece of monitoring data in a sampling data set is to sort the monitoring data in each sampling data set by the magnitude of the numerical value, set the first numerical value to A, set a removal ratio value B, preferably set the removal ratio value to 0.05, remove A*B maximum values and A*B minimum values of the monitoring data in the sampling data set, and then calculate the average value of the remaining monitoring data to obtain the average value of the multiple sets.

Specifically, after subtracting the average value of multiple sets from each monitoring data in the sampling dataset, low-vibration data is obtained, and a low-vibration dataset is generated based on each calculated low-vibration data. Subsequent data analysis is performed with the low-vibration dataset instead of the sampling dataset, which makes it easy to reduce the data fluctuation range in the dataset. In actual use, the average value of the sample dataset is about 20 μV, and the average value of the low-vibration dataset is about 1 μV, and the data fluctuation range in the dataset is reduced.

In S123, data statistics are performed on the low-vibration data set, the number of low-vibration data in each numerical segment is calculated, and the number of low-vibration data in each numerical segment is compared with a noise removal threshold.

In this embodiment, a numerical segment is a numerical interval that separates consecutive numerical values in order to discretize the data.

Specifically, during the data sampling process, noise waves may be generated due to disturbances in each element of the signal processing circuit and other modules of the monitoring system, which will affect the detection accuracy, so it is necessary to perform noise removal processing on the data. According to the data statistical requirements, a numerical range corresponding to each numerical segment is set, and a noise removal threshold is set. Preferably, the noise removal threshold value is 10. Data statistics is performed on the low-wave data set, and the number of low-wave data in each numerical segment is statistically calculated, where the low-wave data in the same numerical segment is considered to be almost the same. The number of low-wave data in each numerical segment is statistically calculated, and the number of low-wave data in each numerical segment is compared with the noise removal threshold. If the number of low-wave data in a numerical segment is smaller than the noise removal threshold, the data in the numerical segment is considered to be a noise wave, is not involved in the averaging calculation, and needs to be removed.

In S124, the average value of the monitoring data of all numerical segments whose number of low-vibration data is greater than the noise removal threshold is calculated and used as the stress monitoring data for that sampling period.

Specifically, the low-wave data in the numerical segments that are smaller than the noise removal threshold for the number of low-wave data are removed, and the average value of the monitoring data for all remaining numerical segments that are larger than the noise removal threshold for the number of low-wave data is calculated to obtain the stress monitoring data for that sampling period.

The ultra-thin force sensor of the present application uses a strain gauge to detect the deformation of the force washer and calculate the stress, specifically utilizing the elastic deformation properties of the material. However, since the relationship between stress and strain in the elastic range of the material is not absolutely linear, and the deformation of the force washer may be disturbed by other factors, it is necessary to calibrate the ultra-thin force sensor to improve the detection accuracy for the pretension of the part being measured.

Here, before the sensor signal processing circuit oversamples the ultra-thin force sensor, it is necessary to calibrate the ultra-thin force sensor, which, referring to FIG. 8, includes the following before step S121.

In S101, pretension is applied in stages to the part being measured, and the corresponding calibration measurement data is read.

In this embodiment, the applied pretension is divided into 10 stages, and the pretension of each stage is 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the rated maximum stress measurement value of the ultra-thin force sensor of the present application, respectively, and the calibration measurement data is the corresponding stress data read after applying pretension when calibrating the ultra-thin force sensor.

Specifically, pretension is applied to the part being measured in stages, and after one stage of pretension is applied, the corresponding stress data is read and used as the calibration measurement data.

In S102, the elastic modulus corresponding to the pretension section of this stage is calculated based on the calibration measurement data corresponding to the pretension of each stage and the calibration measurement data corresponding to the pretension of the upper stage.

In this embodiment, the pretension section of this stage is the section consisting of the currently applied pretension value and the pretension value applied in the previous stage, and the elastic modulus is the ratio between stress and strain.

Specifically, the pretension value applied at each stage is Y, the corresponding calibration measurement data is X, and _Kn is calculated according to the formula _Yn - _Yn-1 =( _Xn - _Xn-1 ) _xKn to be the elastic modulus corresponding to the pretension section at that stage, where the value of n is a non-zero natural number, the maximum value of n is the maximum stage of the applied pretension, _X0 is 0, and _Y0 is the calibrated pretension data corresponding to when no pretension is applied.

In S103, each elastic coefficient is input into a data analysis module, and automatic calibration of the ultra-thin force sensor is performed.

In this embodiment, the data analysis module is a model for automatically calibrating the ultra-thin force sensor based on the calculated elastic coefficient.

Specifically, each calculated elastic coefficient is input into a data analysis module to automatically calibrate the ultra-thin force sensor. In this embodiment, when the stress received by the ultra-thin force sensor is between 0% and 10% of the maximum stress measurement value, the elastic coefficient is _K1 , and when the stress received by the ultra-thin force sensor is between 10% and 20% of the maximum stress measurement value, the elastic coefficient is _K2 . By analogy, different elastic coefficients can be set in different stress intervals of the ultra-thin force sensor, making it easy to improve the measurement accuracy of calculating the pre-tension value.

After automatically calibrating the ultra-thin force sensor, the calibrated force sensor can be further inspected and corrected automatically.

Now, referring to FIG. 9, after step S103, the following is included:

In S104, a test pretension is applied to the part to be measured, the corresponding test measurement data is read, and it is determined whether or not the specified accuracy requirements are met based on the test measurement data.

In this embodiment, the test pretension is the force applied when performing a test operation on the ultra-thin force sensor after automatic calibration is completed, and the test measurement data is the stress data read corresponding to the test pretension applied when testing the ultra-thin force sensor after automatic calibration is completed.

Specifically, a test pretension is applied to the part to be measured, the corresponding test measurement data is read, the read test measurement data is compared with the specified accuracy requirements, and it is determined whether the test measurement data meets the specified accuracy requirements. If it does, it indicates that the automatic calibration has passed and that the ultra-thin force sensor can be used.

Specifically, the test pretension is applied in a stepwise manner, and the difference in test pretension between different steps is smaller than the difference between the pretension steps in the automatic calibration process.

In S105, if the test measurement data does not meet the specified accuracy requirements, an interpolation point is set in the pretension section where the accuracy fails, and the corresponding pretension subsection is determined based on the interpolation point.

Specifically, when performing a test operation on an ultra-thin force sensor, if the test measurement data does not meet the specified accuracy requirements, an interpolation point is set in the pretension section where the accuracy fails. For example, if the accuracy within the section of 5% to 10% of the rated maximum stress measurement value of the ultra-thin force sensor does not meet the specified accuracy requirements, 7.5% of the rated maximum stress measurement value is set as the interpolation point, and two pretension subsections are determined based on the interpolation point: the section of 5% to 7.5% of the rated maximum stress measurement value and the section of 7.5% to 10% of the rated maximum stress measurement value.

In S106, the partial elastic modulus corresponding to each pretension subsection is calculated, and the difference between the partial elastic modulus corresponding to adjacent pretension subsections is calculated to generate an elasticity difference.

In this embodiment, the partial elastic modulus is the elastic modulus corresponding to each pretensioned partial section.

Specifically, the partial elastic modulus corresponding to each pretensioned subsection is calculated, and the difference between the partial elastic modulus of each adjacent pretensioned subsection is calculated as the elasticity difference, which then makes it easy to determine the magnitude of difference in the partial elastic modulus between the two pretensioned subsections based on the elasticity difference.

In S107, if each elasticity difference is smaller than the corresponding elasticity difference threshold, the correction calibration is completed, and if any elasticity difference is larger than the elasticity difference threshold, a new interpolation point is set in the corresponding pretension subsection, and correction calibration is continued.

In this embodiment, the elasticity difference threshold is a threshold for comparing the elasticity difference to determine the magnitude of the partial elastic modulus difference within adjacent pretensioned subsections.

Specifically, each elasticity difference is compared with a predetermined elasticity difference threshold. If all elasticity differences are smaller than the elasticity difference threshold, the elastic coefficients between adjacent pretension subsections are considered to be close, and the correction calibration is completed. If any elasticity difference is larger than the predetermined elasticity difference threshold, a new interpolation point must be set in the corresponding pretension subsection, and the correction calibration work must be continued.

In S20, the design drawings of the equipment to be monitored are obtained, the performance parameters of each component and fastener are determined based on the design drawings, and a mechanical information model is created.

In this embodiment, the mechanical information model is a model constructed for the equipment to be monitored, and is used to indicate information such as the mechanical performance and force bearing conditions of each component in the equipment to be monitored, so as to determine the safety of the equipment to be monitored.

Specifically, to determine the performance parameters of each component and each fastener in the monitored equipment, the current design drawings of the monitored equipment are obtained, and a mechanical information model of the monitored equipment is created based on the design drawings and the performance parameters of each component and each fastener, where the mechanical information model can be constructed using conventional BIM software.

In S30, the stress monitoring data is marked in a mechanical information model, and risk information for each fastening point is determined based on the mechanical information model.

In this embodiment, risk information is information that evaluates the possibility of a failure event occurring at each fastening point and the severity of the failure event based on the load status of each fastening point.

Specifically, the risk of each fastening point is judged based on the current pretension of each fastener and the performance parameters of each component, and the acquired stress monitoring data is marked on the mechanical information model to generate risk information. Referring now to FIG. 10, step S30 specifically includes the following:

In S31, the stress monitoring data is marked in a mechanical information model, and the load data for each fastening point is determined based on the force-bearing data of each component of the equipment to be monitored.

Specifically, the stress monitoring data is marked on a mechanical information model, and force data for each component in the monitored equipment is acquired using sensors installed in the monitored equipment and input into the mechanical information model. In order to know the load conditions at each fastening point, the load data for each fastening point is calculated based on the force data and stress monitoring data for each component in the monitored equipment.

In S32, the load rate of each fastening point is calculated based on the load data and stress monitoring data of each fastening point, and risk information is generated.

Specifically, the mechanical performance parameters of each fastening point are determined from a mechanical information model, the current load rate of each fastening point is calculated based on the current load data of each fastening point and the corresponding stress monitoring data, the risk of each fastening point is evaluated to generate risk information, and then it is made easy to set the monitoring frequency based on the different risk information of each fastening point, thereby enhancing the scientificity of stress monitoring for each fastening point.

In S40, a corresponding monitoring frequency is determined based on the risk information for each fastening point, and feedback control information is generated based on the monitoring frequency.

In this embodiment, the monitoring frequency is the frequency at which stress monitoring is performed for a certain fastening point.

Specifically, the monitoring frequency for each fastening point is determined based on the current risk information of the different fastening points, and feedback control information is generated, which facilitates subsequent adjustment of the stress monitoring plan for the monitored equipment, where the higher the risk of a fastening point, the higher its monitoring frequency, and the generated feedback control information is used to control an increase in the frequency of performing stress monitoring operations for that fastening point, thereby increasing the reliability of pretension monitoring.

The numbering of each step in the above embodiment does not indicate the order of execution, and the order of execution of each process should be determined based on its function and inherent logic, and it should be understood that this does not imply any limitations on the implementation process of the embodiment of this application.

Example 5
In some cases, for example when the ultra-thin force sensor 100 is used to detect the fastening force of bolts used in buildings such as towers and bridges, once the ultra-thin force sensor 100 is installed, it will continue to perform monitoring tasks for decades to come, making it difficult to perform regular maintenance of the ultra-thin force sensor; however, the strain gauge 3, the strain resistor R1, the second resistor R2, the third resistor R3, and the fourth resistor R4 may have problems such as a decrease in accuracy of the measured resistance value due to oxidation and corrosion during long-term use, which will further affect the accuracy of the stress monitoring data calculated from the data measured by the resistance bridge module.

Based on the integration of the technical solutions of the second and fifth embodiments, referring to FIG. 11, a calibration resistor R5 and a debug switch S1 are further installed in the resistance bridge module 6, the calibration resistor R5 and the debug switch S1 are connected in series, and the calibration resistor R5 and the debug switch S1 are connected in series and connected in parallel to the bridge arm on which the distortion resistor R1 is disposed, the calibration resistor R5 is an adjustable resistor, and the debug switch S1 is used to control whether the calibration resistor R5 is connected to the bridge arm on which the distortion resistor R1 is disposed, and then the resistance bridge module 6 is debugged. If recalibration is required, it becomes easy to connect the calibration resistor R5 to the bridge arm in which the strain resistor R1 is located. When the calibration resistor R5 is connected to the bridge arm in which the strain resistor R1 is located, the total resistance of the bridge arm in which the strain resistor R1 is located is R6 = R1 x R5 ÷ (R1 + R5). Here, the calibration resistor R5 and the debug switch S1 are connected in parallel to the strain resistor R1 in the ultra-thin force sensor 100 via a long conductor, so that the calibration resistor R5 and the debug switch S1 can be easily installed in a position that is convenient for an operator to perform maintenance and debugging.

Referring to FIG. 12, the monitoring method using the ultra-thin force sensor further includes the following:

In S50, a strain calibration command is generated based on a predetermined strain calibration period and sent to the resistance bridge module.

In this embodiment, the strain calibration period is the period during which the strain resistor R1 in the strain gauge is periodically calibrated, and the strain calibration command is a command to control the closure of the debug switch S1 so as to connect the calibration resistor R5 to the bridge arm in which the strain resistor R1 is located and perform the strain calibration operation.

Specifically, the method includes periodically generating a strain calibration command according to a predetermined strain calibration period, sending the strain calibration command to the resistance bridge module to start the execution of the strain calibration operation, specifically controlling the closure of the debug switch S1 to connect the calibration resistor R5 to the bridge arm in which the strain resistor R1 is located, and controlling the voltage detector sensor to temporarily stop receiving stress monitoring data in order to prevent false alarms caused by the strain calibration operation.

In S60, the calibration resistor R5 is controlled based on a predetermined calibration resistance data set, and a calibration monitoring data set is obtained.

In this embodiment, the calibration resistance data set includes several resistance values, at least two, and is used to adjust the resistance value of the calibration resistor R5, and the calibration monitoring data set is several data obtained by measuring the total resistance of the bridge arm in which the strain resistor R1 is placed after the calibration resistor R5 is connected, and the number of data in the calibration monitoring data set corresponds to the calibration resistance data set.

Specifically, the resistance value of the calibration resistor R5 is adjusted based on a predetermined calibration resistance data set, and the total resistance of the bridge arm in which the strain resistor R1 is placed after the calibration resistor R5 is connected is detected by a voltage detector sensor.

In S70, a strain resistance value is calculated based on the calibration resistance data set and the corresponding calibration monitoring data set, and the strain resistor R1 is calibrated based on the strain resistance value.

In this embodiment, the strain resistance value is the actual value of strain resistance R1 calculated by strain resistor R1 during the strain calibration operation.

Specifically, the resistance value of the strain resistor R1 is unknown, the resistance value of the calibration resistor R5 is known, and the total resistance of the bridge arm in which the strain resistor R1 is placed can be measured after the calibration resistor R5 is connected. Therefore, the actual resistance value of the strain resistor R1 can be calculated as the strain resistance value using only two sets of calibration resistance data and calibration monitoring data. The strain resistance value is compared with the resistance value of the strain resistor R1 measured before the strain calibration operation to determine the deviation situation of the two data. If the deviation is large, the second resistor R2, the third resistor R3, and the fourth resistor R4 may be oxidized, corroded, or deteriorated, which may affect the detection accuracy of the resistance value of the strain resistor R1, and maintenance is considered necessary. If the deviation is small, the strain resistor R1 can be calibrated based on the measured strain resistance value.

In this embodiment, the resistance bridge module is provided with a circuit element capable of performing periodic calibration, which improves the monitoring accuracy of the ultra-thin force sensor after it is first used, and facilitates recalibration of the resistance bridge module periodically or according to actual needs in order to reduce the possibility of deterioration of the measurement accuracy due to creep, deformation due to thermal expansion and contraction, oxidation, corrosion, etc., caused by the ultra-thin force sensor or each resistor inside being subjected to force for a long period of time in an outdoor environment. Here, the recalibration of the resistance bridge module of important measurement points can provide a sensor sensitivity self-test function that is periodically or constantly performed and controlled by the back platform according to the actual needs of the user. According to practical data used in an outdoor environment for more than five years, the calibration accuracy of the gasket sensor can reach ±0.1% Fs, proving that the installation and use accuracy is better than ±1%, and that it has excellent long-term reliability and stability.

Example 6
A computer device that can be a server, and whose internal configuration diagram can be as shown in FIG. 13, includes a processor, a memory, a network interface, and a database, which are connected via a system bus. The processor of the computer device is used to provide calculation and control functions. The memory of the computer device includes a non-volatile storage medium and an internal memory. An operating system, a computer program, and a database are stored in the non-volatile storage medium. The internal memory provides an environment for the execution of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store data such as stress monitoring data, warning stage information, warning information, design drawings of the equipment to be monitored, performance parameters, mechanical information models, risk information, monitoring frequency, and feedback control information. The network interface of the computer device is used to connect and communicate with an external terminal via a network. When the computer program is executed by the processor, a monitoring method using an ultra-thin force sensor is realized.

In one embodiment, a computing device is provided that includes a memory, a processor, and a computer program stored in the memory and executable by the processor, the processor performing the following steps when it executes the computer program:
In S10, obtain stress monitoring data corresponding to the ultra-thin force sensor, evaluate a warning stage of each fastener based on the stress monitoring data, and generate warning information based on the warning stage information;
In S20, design drawings of the equipment to be monitored are obtained, performance parameters of each component and fastener are determined based on the design drawings, and a mechanical information model is created;
In S30, the stress monitoring data is marked into a mechanical information model, and risk information of each fastening point is determined based on the mechanical information model;
In S40, a corresponding monitoring frequency is determined based on the risk information of each fastening point, and feedback control information is generated based on the monitoring frequency.

In one embodiment, a computer readable storage medium having a computer program stored thereon is provided, the computer program achieving the following steps when executed by a processor:
In S10, obtain stress monitoring data corresponding to the ultra-thin force sensor, evaluate a warning stage of each fastener based on the stress monitoring data, and generate warning information based on the warning stage information;
In S20, design drawings of the equipment to be monitored are obtained, performance parameters of each component and fastener are determined based on the design drawings, and a mechanical information model is created;
In S30, the stress monitoring data is marked into a mechanical information model, and risk information of each fastening point is determined based on the mechanical information model;
In S40, a corresponding monitoring frequency is determined based on the risk information of each fastening point, and feedback control information is generated based on the monitoring frequency.

In any of the above embodiments, the ultra-thin force sensor can be applied to detect pretension of fasteners, where the fasteners include threaded connectors, pins, rivets, etc., and application scenarios for the fasteners include steel-framed buildings, transportation equipment, machinery, etc.

100...Ultra-thin force sensor, 1...force washer, 11...structural hole, 12...strain groove, 13...annular protrusion, 14...deformation groove, 15...first connection hole, 2...protective case, 21...second connection hole, 22...threading hole, 3...strain gauge, 4...potting body, 5...input voltage control module, 6...resistance bridge module, 7...output voltage detection module, 8...system control module, 9...data analysis module.

Claims (10)

An ultra-thin force sensor including a force sensor washer (1), several strain gauges (3), and a protective case (2), in which a structural hole (11) is opened in the force sensor washer (1) for drilling a part to be measured, strain grooves (12) are opened in the outer wall of the force sensor washer (1), several strain gauges (3) are fixedly connected to the bottom of the strain grooves (12) in the circumferential direction, a deformation monitoring circuit for detecting the deformation amount of the strain gauges (3) is electrically connected to the strain gauges (3), the protective case (2) is fitted onto the outer wall of the force sensor washer (1) and fixedly connected, and a potting material (4) is filled in the strain grooves (12). An annular protrusion (13) is provided on a surface of the force sense washer (1) that is away from the member contact surface, and the axial dimension of the force sense washer (1) is larger than the axial dimension of the protective case (2).
2. The ultra-thin force sensor according to claim 1. The deformation monitoring circuit is
an input voltage control module (5) including an adjustable power supply for inputting a specific value of an input voltage to the deformation monitoring circuit;
a resistive bridge module (6) electrically connected to the input voltage control module (5) and used to adjust the ratio between the output voltage and the input voltage of the resistive bridge module (6) in accordance with the deformation amount of the force sensor washer (1), the resistive bridge module (6) including a Wheatstone bridge consisting of a strain resistor R1 fixedly connected to the strain gauge (3);
an output voltage detection module (7) electrically connected to the resistive bridge module (6) for detecting a value of an output voltage of the resistive bridge module (6);
2. The ultra-thin force sensor according to claim 1. The device includes several ultra-thin force sensors (100), an adjustable power supply, an electroscopic sensor, a data analysis module (9), and a system control module (8), and a wireless transmission unit is electrically connected to the adjustable power supply, the electroscopic sensor, the data analysis module (9), and the system control module (8), and the system control module (8) transmits a monitoring control signal to the adjustable power supply through the wireless transmission unit, the adjustable power supply provides an input voltage to a resistive bridge module (6) of each ultra-thin force sensor (100), the electroscopic sensor generates stress monitoring data according to the output voltage of the resistive bridge module (6), the electroscopic sensor transmits the stress monitoring data to the data analysis module (9) through the wireless transmission unit, and the data analysis module (9) analyzes the stress monitoring data, generates feedback control information, and transmits it to the system control module (8) through the wireless transmission unit;
a warning module including a local alarm attached to the location where the ultra-thin force sensor (100) is disposed, the warning module further including a remote alarm for transmitting a remote alarm signal;
A monitoring system using an ultra-thin force sensor characterized by: obtaining stress monitoring data corresponding to each ultra-thin force sensor, evaluating a warning stage for each fastener based on the stress monitoring data, and generating warning information based on the warning stage information;
Obtaining design drawings of the equipment to be monitored, determining performance parameters of each component and fastener based on the design drawings, and creating a mechanical information model;
Marking the stress monitoring data into a mechanical information model, and judging the risk information of each fastening point based on the mechanical information model;
determining a corresponding monitoring frequency according to the risk information of each fastening point; and generating feedback control information according to the monitoring frequency;
A monitoring method using an ultra-thin force sensor. acquiring stress monitoring data corresponding to each ultra-thin force sensor, evaluating a warning stage for each fastener based on the stress monitoring data, and generating warning information based on the warning stage information;
Obtaining model number information of a fastener, determining warning threshold intervals for each warning stage based on the model number information, and generating a threshold interval set;
obtaining each stress monitoring data, comparing each stress monitoring data with a corresponding set of threshold intervals, and determining a corresponding warning level;
matching a corresponding warning signal based on the warning stage of each fastener, and generating warning information based on the warning signal of each fastener.
A monitoring method using the ultra-thin force sensor according to claim 5. In the step of acquiring each stress monitoring data,
oversampling the ultra-thin force sensor using a sensor signal processing circuit to obtain a sampling data set including a first numerical value number of monitoring data within each sampling period;
Averaging each of the monitoring data in the sampling data set to generate a plurality of sets of average values, subtracting the plurality of sets of average values from each of the monitoring data in the sampling data set to obtain low-wave data, and generating a low-wave data set based on each of the low-wave data;
A step of performing data statistics on the low-wave data set, calculating the number of low-wave data in each numerical segment, and comparing the number of low-wave data in each numerical segment with a noise removal threshold;
and calculating an average value of the monitoring data of all the numerical segments having a number of low-wave data greater than a noise removal threshold to set the average value as the stress monitoring data for the sampling period.
A monitoring method using the ultra-thin force sensor according to claim 6. Prior to the step of oversampling the ultra-thin force sensor by the sensor signal processing circuit,
applying pretension to the part to be measured in stages and reading corresponding calibration measurement data;
Calculating an elastic modulus corresponding to the pretension section of this stage based on the calibration measurement data corresponding to the pretension of each stage and the calibration measurement data corresponding to the pretension of the upper stage;
inputting each elastic modulus into a data analysis module to perform automatic calibration of the ultra-thin force sensor;
A monitoring method using the ultra-thin force sensor according to claim 7. After inputting each elastic modulus into a data analysis module and automatically calibrating the ultra-thin force sensor,
applying a test pretension to the part to be measured, reading corresponding test measurement data, and determining whether a predetermined accuracy requirement is met based on the test measurement data;
If the test measurement data does not meet the predetermined accuracy requirement, setting an interpolation point in the pretension section whose accuracy is unacceptable, and determining a corresponding pretension subsection based on the interpolation point;
calculating a partial elastic modulus corresponding to each pretensioned subsection and calculating a difference in the partial elastic modulus corresponding to adjacent pretensioned subsections to generate an elasticity difference;
If each elasticity difference is smaller than the corresponding elasticity difference threshold, the correction calibration is completed; if any elasticity difference is larger than the elasticity difference threshold, a new interpolation point is set in the corresponding pretension subsection, and correction calibration is continued.
A monitoring method using the ultra-thin force sensor according to claim 8. Marking the stress monitoring data into a mechanical information model and judging risk information of each fastening point based on the mechanical information model;
Marking the stress monitoring data into a mechanical information model, and judging the load data of each fastening point according to the force-bearing data of each member of the monitored equipment;
A monitoring method using an ultra-thin force sensor, comprising: a step of calculating a load rate of each fastening point based on the load data and stress monitoring data of each fastening point, and generating risk information;
generating and sending a strain calibration command to a resistive bridge module based on a predetermined strain calibration period;
controlling a calibration resistor R5 based on a predetermined calibration resistance data set to obtain a calibration monitoring data set;
calculating a strain resistance value based on the calibration resistance data set and the corresponding calibration monitoring data set; and calibrating the strain resistor R1 based on the strain resistance value.
A monitoring method using the ultra-thin force sensor according to claim 5.