A bolted joint structure health monitoring method

Abstract

The application provides a bolt connection structure health monitoring method, the method comprises the following steps: S1: arranging at least two piezoelectric sensors on both sides of a bolt connection surface to be monitored; S2: transmitting a guided wave signal through the first piezoelectric sensor, receiving the signal through the second piezoelectric sensor and recording the original waveform data; S3: analyzing the energy loss change of the received signal to determine the bolt connection state; S4: calculating the loosening area based on the root mean square difference value algorithm and generating an alarm signal; and S5: uploading the data to a cloud platform through remote data transmission for intelligent diagnosis. The application arranges a small number of piezoelectric sensors on key components of a power transmission and transformation line tower, excites a guided wave signal through voltage, analyzes the change of the guided wave propagation on the structure surface, realizes bolt loosening alarm, constructs a remote data transmission system, realizes in-depth analysis of bolt loosening by using big data technology, realizes positioning and theoretical analysis of bolt loosening, and provides support for operation and maintenance of the tower.

Description

Technical Field

[0001] This invention belongs to the field of monitoring technology, specifically relating to a method for monitoring the health of bolted connection structures. Background Technology

[0002] The main structure of power transmission line towers is a steel frame, connected by numerous bolts to form a single unit. Therefore, the effectiveness of bolt tightening directly affects the safety of the tower. Currently, maintenance personnel mainly conduct periodic on-site inspections of the towers to visually check for bolt abnormalities. However, given the wide distribution and large number of power transmission line towers, this maintenance method involves an enormous workload for personnel. Inspections in remote areas also pose safety risks to personnel. Visual inspections cannot detect early bolt loosening issues, and traditional bolt detection relies on manual patrols or offline equipment, resulting in low efficiency, incomplete coverage, and the inability to provide real-time early warnings. Therefore, there is an urgent need to develop online monitoring and intelligent diagnostic technology for the loosening of key bolts on power transmission line towers to achieve digital transformation of maintenance and improve the efficiency and intelligence level of power transmission line tower maintenance in my country. Summary of the Invention

[0003] This invention provides a method for health monitoring of bolted connection structures, the improvement of which is that the method includes...

[0004] S1: At least two piezoelectric sensors are arranged on both sides of the bolt connection surface to be monitored;

[0005] S2: The first piezoelectric sensor transmits a guided wave signal, and the second piezoelectric sensor receives the signal and records the original waveform data;

[0006] S3: Analyze the changes in energy loss of the received signal to determine the status of the bolt connection;

[0007] S4: Calculate the loose area based on the root mean square difference algorithm and generate an alarm signal;

[0008] S5: Uploads data to the cloud platform via remote data transmission for intelligent diagnosis.

[0009] Furthermore, the guided wave signal includes an ultrasonic pulse signal with a frequency range of 50kHz-2MHz, and the signal transmission interval is 0.1-5 seconds.

[0010] Furthermore, the guided wave signal includes

[0011] The first frequency band, 50-100kHz Lamb waves, is used to detect macroscopic structural deformation.

[0012] The second frequency band, 300-500kHz shear waves, is used to capture microscopic changes in the contact surface.

[0013] The third frequency band, 1-2 MHz, is used to evaluate surface stress distribution.

[0014] Furthermore, S3 includes

[0015] Under normal preload conditions, the original waveform data propagation matrix H0 is established, and the dynamic propagation matrix H is obtained during real-time monitoring. (t) Calculate the difference matrix ΔH (t) =H (t) -H0; for ΔH (t) Singular value decomposition is performed to extract principal mode variation features to determine the bolt connection status.

[0016] Furthermore, step S4 includes training the historical loosening data of the loosened area based on a machine learning model, optimizing the root mean square difference threshold and positioning accuracy, and determining the loosening of the loosened area.

[0017] Furthermore, the root mean square interpolation algorithm includes:

[0018] Calculate the root mean square value of the energy difference between the unloose state and the current state signal. When the root mean square difference exceeds a preset threshold, it is determined to be loose.

[0019] Furthermore, the location and distance of the loose bolt are determined based on the signal attenuation gradient between piezoelectric sensors.

[0020] Furthermore, when a serious looseness is detected, an alarm signal is generated, automatically triggering a shutdown command or notifying maintenance personnel.

[0021] Furthermore, step S5 includes linking the monitoring data with the wind turbine system, automatically generating maintenance work orders through remote data transmission in the wind turbine system, and pushing them to the cloud platform for intelligent diagnosis.

[0022] Furthermore, the piezoelectric sensor has an external waterproof housing, which is made of aerospace-grade aluminum and coated with an anti-salt spray coating.

[0023] Beneficial effects:

[0024] This invention utilizes a small number of piezoelectric sensors deployed on key components of power transmission line towers to generate guided wave signals using voltage. By analyzing the changes in the propagation of these guided waves on the structural surface, it enables bolt loosening alarms, establishes a remote data transmission system, and leverages big data technology to conduct in-depth analysis of bolt loosening, achieving bolt location and theorem analysis, thus providing support for tower operation and maintenance. This technology achieves full coverage and online monitoring of power transmission line tower bolts at a relatively low cost, demonstrating advanced technology and strong application value.

[0025] It should be understood that the above general description and the following specific embodiments are merely exemplary and illustrative, and do not limit the scope of the claims made in this application. Attached Figure Description

[0026] Figure 1 A flowchart of a bolted connection structure health monitoring method according to the present invention;

[0027] It should be understood that the accompanying drawings are not necessarily drawn to scale and present slightly simplified representations of various features illustrating the basic principles of this disclosure. Specific design features of the invention as disclosed herein, including, for example, particular dimensions, orientations, positions, and shapes, will be determined in part by the specifically intended application and usage environment.

[0028] In the figures, throughout the several figures, reference numerals refer to the same or equivalent parts of the invention. Detailed Implementation

[0029] Reference will now be made in detail to various embodiments of the invention, examples of which are illustrated in the accompanying drawings and described below. Although the invention will be described in conjunction with exemplary embodiments thereof, it should be understood that this specification is not intended to limit the invention to those exemplary embodiments. On the other hand, the invention is intended to cover not only the exemplary embodiments thereof, but also various alternatives, modifications, equivalents and other embodiments that may be included within the spirit and scope of the invention as defined by the appended claims.

[0030] Hereinafter, exemplary embodiments of the present invention will be described in detail with reference to the accompanying drawings. The specific structures and functions described in the exemplary embodiments of the present invention are for illustrative purposes only. Embodiments of the present invention can be implemented in various forms, and it should be understood that they should not be construed as limited to the exemplary embodiments described in the exemplary embodiments, but include all modifications, equivalents, or substitutions included within the spirit and scope of the present invention.

[0031] Throughout this specification, the technical terms used are for the purpose of describing various exemplary embodiments only and are not intended to be limiting. It will be further understood that the terms "comprising," "including," "having," etc., when used in the exemplary embodiments, specifically refer to the presence of the stated components, steps, operations, or elements, but do not exclude the presence or addition of one or more other components, steps, operations, or elements.

[0032] like Figure 1 As shown, this invention achieves real-time monitoring, location, and early warning of bolt loosening by arranging piezoelectric sensors on the bolt connection surface to transmit and receive guided wave signals, combined with root mean square difference algorithm and big data analysis. This invention features low cost, full coverage, and high reliability, significantly improving operation and maintenance efficiency and safety.

[0033] In an exemplary embodiment, a method for health monitoring of a bolted connection structure provided in this application includes...

[0034] S1: At least one pair of piezoelectric sensors are arranged on both sides of the bolt connection surface to be monitored;

[0035] At least two piezoelectric sensors are arranged on the surface of the flange of the bolted connection to be monitored, wherein the first sensor serves as the excitation end to emit guided wave signals and the second sensor serves as the receiving end to collect guided wave response signals.

[0036] For example: Six piezoelectric sensors are installed on the flange of the bolted connection structure of the wind turbine tower. Each group contains three piezoelectric plates arranged in a triangular pattern. Adjacent units are spaced 90° apart, and each unit covers a 45° sector area. Each sensor covers 12 bolts.

[0037] The piezoelectric sensor uses a flexible encapsulation material that can fit irregular surfaces, with an installation error of less than ±1mm. The piezoelectric sensor is fixed with epoxy resin adhesive and is calibrated with a reference signal after installation.

[0038] S2: The first piezoelectric sensor transmits a guided wave signal, and the second piezoelectric sensor receives the signal and records the original waveform data;

[0039] After generating an adjustable frequency band sinusoidal sweep excitation signal through the first piezoelectric sensor, the signal is converted and transmitted as a multi-band guided wave signal. The signal passing through the working frequency point of the bolt connection interface is collected by the second piezoelectric sensor and the original waveform data is recorded.

[0040] The guided wave signal includes an ultrasonic pulse signal with a frequency range of 50kHz-2MHz and a signal transmission interval of 0.1-5 seconds.

[0041] The first frequency band, 50-100kHz Lamb waves, is used to detect macroscopic structural deformation.

[0042] The second frequency band, 300-500kHz shear waves, is used to capture microscopic changes in the contact surface.

[0043] The third frequency band, 1-2 MHz, is used to evaluate surface stress distribution.

[0044] Specifically, after acquiring the reference raw waveform data signal, guided wave transmission is performed once every 1 second, with a sampling rate of not less than 10MS / s and a resolution of 16bit; the preset threshold is 10%-20% of the amplitude of the reference raw waveform data signal, and the threshold is dynamically adjusted to adapt to environmental noise.

[0045] S3: Analyze the changes in energy loss of the received signal to determine the status of the bolt connection;

[0046] Under normal preload conditions, the original waveform data propagation matrix H0 is established, and the dynamic propagation matrix H is obtained during real-time monitoring. (t) Calculate the difference matrix ΔH (t) =H (t) -H0; for ΔH (t) Singular value decomposition is performed to extract principal mode variation features to determine the bolt connection status.

[0047] S4: Calculate the loose area based on the root mean square difference algorithm and generate an alarm signal;

[0048] The loosening area is determined by training a machine learning model on historical loosening data, optimizing the root mean square difference threshold and positioning accuracy.

[0049] Calculate the root mean square value of the energy difference between the unloose state and the current state signal. When the root mean square difference exceeds a preset threshold, it is determined to be loose.

[0050] The loosened region is calculated using the Root Mean Square Difference (RMSD) algorithm. The RMSD formula is as follows:

[0051]

[0052] Among them, S ref As the reference signal, S current This is a real-time signal. Where x... i For the currently acquired signal, y i The original waveform data signal is the reference signal, where N is the number of sampling points.

[0053] The location and distance of the loose bolt are determined based on the signal attenuation gradient between piezoelectric sensors.

[0054] When a serious looseness is detected, an alarm signal is generated, which automatically triggers a shutdown command or notifies maintenance personnel.

[0055] In the above technical solution, if a bolt is loosened manually, the system will alarm within 3 seconds, and the positioning error is less than ±2 bolts.

[0056] S5: Uploads data to the cloud platform via remote data transmission for intelligent diagnosis.

[0057] By linking monitoring data with the wind turbine system, and through remote data transmission within the wind turbine system, maintenance work orders are automatically generated and pushed to the cloud platform for intelligent diagnosis.

[0058] The remote data transmission supports CAN bus, Ethernet or wireless communication protocols, and is configured with a positive isolation device to ensure data security.

[0059] For example, if the difference exceeds 15%, the system marks the loose area and uploads the data to the cloud platform via remote data transmission for intelligent diagnosis.

[0060] In the above technical solution, the piezoelectric sensor has an external waterproof housing, which is made of aviation aluminum and coated with an anti-salt spray coating, with a waterproof rating of IP67.

[0061] The embodiments of this application described above can be implemented in various hardware, software codes, or combinations thereof. For example, embodiments of this application may also represent program code executing the above methods in a data signal processor. This application may also relate to various functions performed by a computer processor, digital signal processor, microprocessor, or field-programmable gate array. The processor described above can be configured to perform specific tasks according to this application, which are accomplished by executing machine-readable software code or firmware code defining the specific methods disclosed in this application. The software code or firmware code can be developed to represent different programming languages ​​and different formats or forms. It can also represent software code compiled for different target platforms. However, the different code styles, types, and languages ​​of the software code performing tasks according to this application and other types of configuration code do not depart from the spirit and scope of this application.

[0062] The foregoing description of specific exemplary embodiments of the invention has been presented for purposes of illustration and description. It is not intended to exclude or limit the invention to the precise forms disclosed, and it will be apparent that many modifications and alterations are possible in light of the foregoing teachings. Exemplary embodiments were chosen and described to explain certain principles of the invention and their practical application, so that others skilled in the art can make or utilize various exemplary embodiments of the invention, and their various alternatives and modifications. The purpose is that the scope of the invention will be defined by the appended claims and their equivalents.

[0063] It is understood that the above embodiments are merely exemplary implementations used to illustrate the principles of the present invention, and the present invention is not limited thereto. For those skilled in the art, various modifications and improvements can be made without departing from the spirit and essence of the present invention, and these modifications and improvements are also considered to be within the scope of protection of the present invention.

Claims

1. A method for health monitoring of bolted connection structures, characterized in that, The method includes S1: At least two piezoelectric sensors are arranged on both sides of the bolt connection surface to be monitored; S2: The first piezoelectric sensor transmits a guided wave signal, and the second piezoelectric sensor receives the signal and records the original waveform data; S3: Analyze the changes in energy loss of the received signal to determine the status of the bolt connection; S4: Calculate the loose area based on the root mean square difference algorithm and generate an alarm signal; S5: Uploads data to the cloud platform via remote data transmission for intelligent diagnosis; In step S2, the guided wave signal emitted by the first piezoelectric sensor is a multi-band guided wave signal emitted after conversion of the tunable frequency band sinusoidal sweep frequency excitation signal; The signal received by the second piezoelectric sensor is the signal at the operating frequency point of the bolt connection interface. The guided wave signal includes an ultrasonic pulse signal with a frequency range of 50kHz-2MHz; The guided wave signal includes: The first frequency band, 50-100kHz Lamb waves, is used to detect macroscopic structural deformation. The second frequency band, 300-500kHz shear waves, is used to capture microscopic changes in the contact surface. The third frequency band, 1-2 MHz, is used to evaluate surface stress distribution.

2. The method for health monitoring of bolted connection structures according to claim 1, characterized in that, The signal transmission interval is 0.1-5 seconds.

3. The method for health monitoring of bolted connection structures according to claim 1, characterized in that, The S3 includes Under normal preload conditions, the original waveform data propagation matrix H0 is established, and the dynamic propagation matrix H is obtained during real-time monitoring. (t) Calculate the difference matrix ΔH (t) =H (t) -H0; for ΔH (t) Singular value decomposition is performed to extract principal mode variation features to determine the bolt connection status.

4. The method for health monitoring of bolted connection structures according to claim 1, characterized in that, Step S4 includes training a machine learning model on historical loosening data of the loosened area, optimizing the root mean square difference threshold and positioning accuracy, and determining the loosening of the loosened area.

5. The method for health monitoring of bolted connection structures according to claim 4, characterized in that, The root mean square difference algorithm includes: Calculate the root mean square value of the energy difference between the unloose state and the current state signal. When the root mean square difference exceeds a preset threshold, it is determined to be loose.

6. The method for health monitoring of bolted connection structures according to claim 5, characterized in that, The location and distance of the loose bolt are determined based on the signal attenuation gradient between piezoelectric sensors.

7. The method for health monitoring of bolted connection structures according to claim 6, characterized in that, When a serious looseness is detected, an alarm signal is generated, which automatically triggers a shutdown command or notifies maintenance personnel.

8. The method for health monitoring of bolted connection structures according to claim 1, characterized in that, Step S5 includes linking the monitoring data with the wind turbine system, automatically generating maintenance work orders through remote data transmission in the wind turbine system, and pushing them to the cloud platform for intelligent diagnosis.

9. The method for health monitoring of bolted connection structures according to claim 1, characterized in that, The piezoelectric sensor has an external waterproof housing, which is made of aviation aluminum and coated with an anti-salt spray coating.

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