A method for early warning of instability of a support and hanger by fusing stress and angle data

Abstract

The present application relates to the technical field of structural stability analysis, and particularly relates to a support and hanger instability early warning method fusing stress and angle data, comprising the following steps: synchronously collecting real-time axial stress and spatial pitch angle of a support and hanger node to generate a time series data pair; calculating stress change rate and angle change rate; constructing a stress-angle deviation index; setting a dynamic threshold according to a real-time thermal displacement speed of a pipeline; when the stress-angle deviation index is greater than the dynamic threshold for a duration exceeding an early warning time window T, determining that the constraint is invalid and triggering early warning. Compared with a traditional monitoring method relying only on stress or displacement, the present application can effectively capture early coupling instability signs such as structural stiffness degradation, gap slip and thermal expansion constraint disorder, and realize high-sensitivity response to dynamic structural behavior.

Description

Technical Field

[0001] This invention relates to the field of structural stability analysis technology, and in particular to a method for early warning of support instability that integrates stress and angle data. Background Technology

[0002] Pipe supports, as key constraint components in pressurized pipeline systems, are widely used in thermal power, nuclear power, petrochemical, and urban heating networks. Their main function is to provide necessary stiffness support and inertial constraints while ensuring the freedom of thermal displacement of the pipeline, preventing structural instability caused by vibration, impact, or thermal expansion. However, in actual operation, pipe supports often face the effects of thermo-mechanical coupling, multi-dimensional vibration, and long-term fatigue, making them highly susceptible to latent instability risks such as constraint stiffness degradation, slippage of connection gaps, and loosening of supports. If instability is not identified in time, it may lead to abnormal redistribution of pipeline stress, excessive displacement of fixed points, and even serious safety accidents such as valve pull-out and interface cracking.

[0003] Existing pipe support monitoring methods primarily rely on single-variable monitoring of stress or displacement. For example, strain gauges or displacement meters are installed on the pipe to monitor structural response parameters in real time, supplemented by fixed threshold judgment logic to trigger alarms. However, slippage, loosening, and other faults are often accompanied by nonlinear deviations between stress changes and attitude changes, making it difficult to comprehensively reflect potential instability states based solely on a single stress or angle change. During hot operation, the thermal displacement velocity of pipes varies greatly, and the static and dynamic response characteristics are significantly different. Traditional fixed threshold settings are prone to false alarms at low speeds and missed detections at high speeds. Summary of the Invention

[0004] This invention provides a support and hanger instability early warning method that integrates stress and angle data. This method integrates multi-source structural data, has dynamic threshold adjustment capability and time-domain evolution judgment mechanism, so as to improve the accuracy of identification and response effectiveness of potential instability behavior under complex working conditions.

[0005] A method for early warning of support instability that integrates stress and angle data includes the following steps:

[0006] S1: Synchronously acquire the real-time axial stress and spatial pitch angle of the support and hanger nodes to generate time series data pairs;

[0007] S2: Calculate the rate of change of stress and the rate of change of angle;

[0008] S3: Construction stress-angle divergence index;

[0009] S4: Set a dynamic threshold based on the real-time thermal displacement velocity of the pipeline;

[0010] S5: When the duration of the stress-angle divergence index being greater than the dynamic threshold exceeds the warning time window T, the constraint is determined to be in failure and a warning is triggered.

[0011] Optionally, S1 further includes integrating a strain sensing unit and an attitude sensing unit on the support rod body, controlling the dual sensing units to sample synchronously, aligning the sampling timestamps, and generating time series data pairs.

[0012] Optionally, the strain sensing unit uses a temperature-compensated strain gauge to collect the axial strain change, and calculates the real-time axial stress σ based on the axial strain change and the strain-stress conversion coefficient.

[0013] Optionally, the attitude sensing unit uses a tilt sensor to calculate the spatial pitch angle θ of the boom body through three-axis acceleration components.

[0014] Optionally, in S2:

[0015] The rate of change of stress is calculated as: R σ =Δσ / Δt;

[0016] The rate of change of angle is calculated as: R θ =Δθ / Δt;

[0017] R σ R represents the rate of change of stress. θ Δt represents the rate of change of angle, Δt is the preset time window, representing the length of the time interval from the current moment forward, Δσ is the stress change within the preset time window, and Δθ is the pitch angle change within the preset time window.

[0018] Optionally, the preset time window Δt is negatively correlated with the thermal displacement acceleration of the pipeline.

[0019] Optionally, the stress-angle divergence index is calculated as: β=|R σ -k·R θ |, where k is the dimensionless normalization coefficient and β represents the stress-angle divergence index.

[0020] Optionally, S4 specifically includes setting a dynamic threshold β for determining the divergence index based on the real-time thermal displacement velocity v. th (v), the dynamic threshold β th (v) A piecewise linear function structure is adopted to adapt to the different requirements for early warning sensitivity under different thermal conditions.

[0021] Optionally, the piecewise linear function structure specifically includes:

[0022] Low-speed section v < v1: When the thermal displacement speed is low, a linear function with a slope of α1 is used to calculate the dynamic threshold, expressing the high-sensitivity response ability to small displacement changes. At this time, the dynamic threshold increases proportionally with the speed from the static reference value β min and increases proportionally with the speed;

[0023] Medium-speed section v1 ≤ v < v2: In the medium thermal displacement speed range, the threshold slope is adjusted to a gentle α2 to achieve the transition from sensitive response to stable judgment and suppress frequent warnings. The threshold connection value β1 at the starting point of the medium-speed section is determined by the calculation result of the low-speed section to ensure the continuity of the function;

[0024] High-speed section v ≥ v2: When the thermal displacement speed reaches a high level, the dynamic threshold is fixed at the maximum limit value β max to form a platform value control mechanism;

[0025] v1 represents the demarcation point between low speed and medium speed, and v2 represents the demarcation point between medium speed and high speed.

[0026] Optionally, the warning duration window T is dynamically adjusted according to the current pipe vibration main frequency f and is defined as: where T is the warning time window and f is the pipe vibration main frequency.

[0027] Advantages of the present invention:

[0028] In the present invention, the axial stress change rate and the spatial pitch angle change rate are integrated to construct a "stress-angle deviation index" as the hanger instability judgment index. By introducing means such as linear fitting enhancement, sliding window difference, and physical quantity conversion proportional coefficient, the index has good interpretability and engineering stability. Compared with the traditional monitoring methods that only rely on stress or displacement judgment, it can effectively capture early coupling instability signs such as structural stiffness degradation, gap slip, and thermal expansion constraint disorder, and achieve a high-sensitivity response to dynamic structural behavior.

[0029] In the present invention, aiming at the problem of significant differences in the behavior characteristics of hangers under different thermal conditions, a dynamic threshold function with the thermal displacement speed as the adjustment factor is proposed, and a piecewise linear model is designed to distinguish three states: low-speed sensitivity, medium-speed stability, and high-speed tolerance, so as to achieve precise adjustment of the deviation index threshold. By setting the threshold change rate as a positive function relationship, it is ensured that no false judgment occurs during the stage of剧烈 thermal expansion, and at the same time, it quickly responds at the initial stage of微小 thermal disturbance, improving the adaptive ability and false alarm suppression ability under a wide range of operating conditions.

[0030] In the present invention, the slip time accumulation mechanism is combined with the main frequency adaptive time window to achieve real-time tracking and judgment of the continuous super-threshold behavior of the structure. By using the main frequency as the input source of the inverse function of the warning duration, the system quickly responds under high-frequency impacts and delays appropriately in low-frequency drifts (such as thermal slow changes), effectively avoiding false judgments. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in this invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only for this invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of the method flow according to an embodiment of the present invention. Detailed Implementation

[0033] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments. For some well-known technologies, those skilled in the art may also use other alternative methods to implement the invention. Moreover, the accompanying drawings are only for more specific description of the embodiments and are not intended to specifically limit the present invention.

[0034] like Figure 1 As shown, a method for early warning of support instability that integrates stress and angle data includes the following steps:

[0035] S1: Synchronously acquire the real-time axial stress and spatial pitch angle of the support and hanger nodes to generate time series data pairs.

[0036] S1 specifically includes:

[0037] Strain sensing unit and attitude sensing unit are integrated on the support rod body. The dual sensing units are controlled by a shared hardware trigger signal to achieve synchronous sampling and are aligned with millisecond-level timestamps to generate time series data pairs: {σ(t),θ(t)}.

[0038] in:

[0039] The strain sensing unit uses a temperature-compensated strain gauge to collect the axial strain change Δε and calculate the real-time axial stress value according to the following formula: σ=K·Δε, where σ is the real-time axial stress of the rod body, Δε is the axial micro-strain measured by the strain sensing unit, and K is the strain-stress conversion coefficient, which is obtained based on the material elastic modulus and the strain gauge bonding position.

[0040] The attitude sensing unit uses a three-MEMS tilt sensor to detect the three-axis acceleration components A. x A y A z The spatial pitch angle θ of the boom body is calculated as follows:

[0041]

[0042] Among them, Ax A y A z This represents the acceleration output components of the tilt sensor along the three axes, where θ is the spatial pitch angle of the boom relative to the horizontal plane.

[0043] To ensure the accuracy and timing consistency of stress and angle change rates, the dual-channel sensor uses a unified hardware trigger signal to achieve strict physical synchronization acquisition, and marks the acquisition results with a unified timestamp t at the millisecond level, for example:

[0044] At time t0, the stress value σ(t0) and the pitch angle θ(t0) are simultaneously collected and stored in the same data packet;

[0045] A continuous sampling frequency of 100Hz to 500Hz is recommended to effectively monitor vibration frequencies of 5Hz to 50Hz in pipe supports and piping systems. In large-scale projects such as thermal power plants, nuclear power plants, and petrochemical plants, piping systems experience low-frequency vibrations due to fluid excitation, thermal expansion, and mechanical interference. The main frequencies of these vibrations are typically concentrated in the 5Hz to 50Hz range, especially during hot starts and cold shutdowns. As constraint components of the piping, pipe supports and hangers also exhibit similar frequency distributions in their stress and attitude changes.

[0046] According to the Nyquist sampling theorem, and based on signal processing principles, to accurately reconstruct a continuous signal with frequency f, its sampling frequency must be at least 2f (Nyquist's theorem). To fully capture dynamic responses up to 50Hz, the minimum sampling frequency should be 100Hz. Considering the non-ideal nature of signals and filtering margins in practical engineering, it is usually necessary to set it to 2-10 times the original signal frequency. If the sampling frequency is too low, it may cause data aliasing distortion, i.e., high-frequency disturbances are misjudged as low-frequency changes, affecting the accuracy of stress change rate and angle change rate calculations, and consequently leading to false triggering or missed detection of the divergence index β.

[0047] The strain-stress conversion factor K can be obtained by following these steps:

[0048] Material property determination: First, refer to the elastic modulus E of the rod body based on its manufacturing material (such as carbon steel, stainless steel or alloy material). This value is a standard material mechanical property parameter, which can be obtained from the material handbook or verified through tensile testing.

[0049] Strain gauge placement confirmation: Determine the method of attaching the strain gauges to the suspension rod, including orientation (whether aligned with the axial direction), location (stress concentration area or near the neutral axis), and attachment quality. Ensure that the strain values ​​measured by the strain gauges are axial strain or principal strain in a known direction.

[0050] Preliminary coefficient estimation: Under ideal linear elastic conditions, strain and stress satisfy Hooke's law, and can be preliminarily estimated as: K = E; that is, the strain-stress conversion coefficient is approximately equal to the elastic modulus of the material.

[0051] Physical calibration test:

[0052] Under controlled load conditions (such as load applied by a boom or standard weights), record the strain value Δε output by the sensor;

[0053] Simultaneously use external mechanical testing equipment (such as an electronic universal testing machine) to determine the corresponding true stress σ;

[0054] Based on the measured data, the following relationship was fitted: If there are multiple sets of data, the least squares method is used to fit the average value to improve stability and versatility.

[0055] Temperature drift correction: For booms operating in thermally variable environments, repeated calibration is required under different temperature conditions to establish a temperature-strain correction curve. Temperature compensation processing is performed on the strain gauge output to ensure that the effectiveness of K does not fail due to environmental fluctuations.

[0056] S2: Calculate the rate of change of stress and the rate of change of angle.

[0057] In S2:

[0058] Stress change rate R σ =Δσ / Δt;

[0059] Rate of change of angle R θ =Δθ / Δt;

[0060] Δt is the preset time window length, representing the length of the time interval from the current time t0 forward. Δσ is the stress change within the preset time window, and Δθ is the pitch angle change within the preset time window.

[0061] Specifically, at the current time t0, backtracking from it, the stress data sequence {σ} within the time window [t0-Δt,t0] is extracted. i} and pitch angle data sequence {θ i}, calculate the rate of change of stress R σ and the rate of change of angle R θ The specific process is as follows:

[0062] 1. Calculation method for changes: A difference method enhanced by linear fitting is adopted to improve noise resistance and the accuracy of trend recognition. Specifically:

[0063]

[0064] If multiple intermediate data points are involved, a moving average or weighted filtering can be introduced on the basis of the difference to preprocess the data in order to weaken local interference.

[0065] 2. Dynamic time window setting:

[0066] The time window length Δt is set within the following range: 0.5s ≤ Δt ≤ 3s; and is negatively correlated with the pipe thermal displacement acceleration a.

[0067] In static or slowly changing conditions, the default setting is Δt = 2 seconds. When an increase in pipeline thermal displacement acceleration is detected (such as rapid steam start-up or thermal shock), Δt is automatically shortened to enhance the ability to capture rapid responses.

[0068] The time window Δt is set within the range of 0.5 to 3 seconds. This setting is primarily based on the dynamic response characteristics and signal processing accuracy requirements of the support system under different operating conditions. A shorter time window results in a more sensitive response to sudden changes (such as thermal shock, steam blasting, and equipment startup); a longer time window includes more data points in the rate of change calculation, thus providing stronger suppression of random noise. Therefore, a balance must be struck between the two: a lower limit of 0.5 seconds to ensure rapid response capability, and an upper limit of 3 seconds to improve anti-interference stability. Considering the system sampling frequency is 100Hz to 500Hz, the data volume corresponding to 0.5-3 seconds is 50 to 1500 points, which is sufficient to support reliable trend identification; less than 0.5 seconds will result in insufficient data, which is easily amplified by noise; more than 3 seconds will result in a large delay, making it easy to miss the early signs of rapid instability.

[0069] In static or slowly changing states, the default setting is Δt = 2 seconds: When the equipment is stopped, running steadily, or in a thermally static state, the stress and angle changes of the supports are slow and small. If an excessively short time window is selected at this time, it is easily misinterpreted as a valid signal by minor electrical noise or external interference. Setting Δt = 2 seconds extends the observation period, smooths out occasional disturbances, and improves the reliability of the judgment. The 2-second timeframe contains a sufficient number of data points, which helps to improve the ability to identify the overall changing trends of Δσ and Δθ, making the calculated rate of change more stable and reliable.

[0070] In thermal piping systems, pipes undergo linear expansion or contraction due to temperature increases or decreases, resulting in so-called "thermal displacement." When this displacement rate is rapid, i.e., when the thermal displacement acceleration increases, it is often accompanied by rapid stress redistribution and abrupt changes in posture. The constraint state of supports and hangers also changes drastically, making this a high-risk stage for system instability. In this situation, if the time window is too long, the calculated results of stress and angle change rates will be "smoothed," thus masking the characteristics of short-term dramatic changes and reducing early warning sensitivity. Therefore, it is necessary to shorten Δt to enhance the response capability of the change rate to rapid operating conditions. Therefore, the time window Δt is set to be negatively correlated with the pipe's thermal displacement acceleration 'a'.

[0071] When the thermal displacement acceleration is small, that is, when the pipeline displacement is slow and steady, the system is in a quasi-steady state, and the mechanical behavior of the supports and hangers is mainly fine-tuning.

[0072] It is appropriate to extend Δt to obtain more sampling data, improve the accuracy of the rate of change calculation, and suppress misjudgments caused by small disturbances.

[0073] When the thermal displacement acceleration is large, such as in scenarios like equipment start-up and shutdown, sudden load changes, or rapid steam impact, the stress and posture of the supports and hangers change abruptly in a very short time.

[0074] At this point, Δt should be shortened to ensure that the rate of change calculation can focus on the real mutations in the short term.

[0075] S3: Construction stress-angle divergence index.

[0076] The stress-angle divergence index β in S3 is |R σ -k·R θ | where k is the proportionality coefficient, which also serves to normalize the dimensions, representing the rate of change of angle R. θ Converted to stress change rate R σ Equivalent expressions with the same dimensions and scales allow for direct difference calculations between the two, thereby measuring the degree of deviation between them.

[0077] Under the cold calibration conditions of the pipeline, an axial test load F is applied to the supports and hangers in a stepped manner. n Simultaneously record the stress change Δσ under each load level. n With pitch angle change Δθ n To achieve dimensional matching between the rate of change of stress and the rate of change of angle, the stress change is first converted into a dimensionless strain rate form: By stress change Δσ n Dividing by the material's elastic modulus E, it is converted into an equivalent strain change, achieving dimensional unification with the angular change; then, through linear regression, the proportionality coefficient k between the strain change and the angular change is solved, minimizing the sum of squared residuals in the following formula:

[0078] The objective function minimizes the sum of squares of the fitting errors between the equivalent strain and the angle change at each load level. This expression represents finding the most suitable scaling factor k among multiple sets of experimental data so that the "equivalent strain caused by a unit angle change" most closely approximates the actual observed result. The least squares method is used to... With k·Δθ nThe squared deviations are summed to reflect the overall fitting error; by adjusting k, this total error is minimized to obtain a globally optimal solution. This is a typical linear regression fitting method, suitable for situations where there is a linear relationship between two variables but measurement errors exist. Here, stress data is converted into strain, and a linear mapping is established between this strain and the angular change.

[0079] The final regression equation slope k is expressed as:

[0080]

[0081] Where, Δσ n Δθ represents the stress change under the nth load level. n Let E represent the change in pitch angle under the nth load level, E be the elastic modulus of the boom or pipe material, N be the load step number, cov() represent the covariance, and var() represent the variance. Set a correlation requirement for this regression process: Rn 2 ≥0.85.

[0082] This expression is the analytical solution to the previous minimization process, directly providing the calculation of the proportionality coefficient k, which is equal to the covariance of strain and angle change divided by the variance of angle change. Covariance measures the degree to which two variables change simultaneously, while variance measures the dispersion of one of the variables. The ratio of the two is the slope of the optimal linear fit, which is the proportionality coefficient in the least squares sense. It is recommended that the calibration error range of k be controlled within ±10%, meaning that after adding different load disturbances or repeated loading experiments to the regression sample, the relative standard deviation of the obtained k value should not exceed 10%. If the error exceeds this range, the number of step loads should be increased, the loading time extended, or the sensor installation accuracy optimized to improve the stability and repeatability of the calibration.

[0083] S4: Set a dynamic threshold based on the real-time thermal displacement velocity of the pipeline.

[0084] The dynamic threshold setting in S4 includes setting a dynamic threshold function β for deviation index judgment based on the real-time monitored pipeline thermal displacement velocity v. th (v), which takes the form of a piecewise function, is defined as follows:

[0085]

[0086] Where: v is the thermal displacement velocity of the pipe, β min The threshold value under static operating conditions is taken as 1.2 times the maximum β value in the calibration conditions. max The maximum threshold under high-speed operating conditions is β. minThree times that, v1 = 0.5 mm / s represents the demarcation point between low speed and medium speed, v2 = 3.0 mm / s represents the demarcation point between medium speed and high speed, α1 = 0.8 s / mm represents the sensitivity coefficient of the low-speed section, α2 = 0.3 s / mm represents the sensitivity coefficient of the medium-speed section, β1 = β min +α1·v1 represents the threshold connection value at the starting point of the middle section.

[0087] Low-speed area (v < v1): Set a relatively high slope α1 to enhance the response sensitivity to the start-up process of small thermal displacements (such as steam pipe warming).

[0088] Medium-speed area (v1 ≤ v < v2): Adjust the slope to a gentler α2 to ensure a smooth transition and avoid frequent system warnings.

[0089] High-speed area (v ≥ v2): The threshold is fixed at β max , preventing the false triggering of the warning mechanism during normal or intense thermal expansion stages.

[0090] The measurement of the thermal displacement velocity can be achieved by installing a high-resolution laser sensor between the pipeline and the fixed support, measuring the change in the pipeline movement distance in real time, and calculating the thermal displacement velocity.

[0091] Or by the strain integration method: According to the thermal strain rate Integrate and convert, and the estimation formula for the thermal displacement velocity v is:

[0092] Among them, L is the expansion joint spacing, is the thermal strain rate, measured by a strain gauge.

[0093] Satisfy The dynamic threshold β th Monotonically increases with the increase of the thermal displacement velocity v, that is, the faster the thermal displacement velocity, the higher the corresponding threshold.

[0094] S5: When the duration of the stress-angle deviation index being greater than the dynamic threshold exceeds the warning time window T, it is determined that the constraint fails and a warning is triggered.

[0095] [[ID=4!]]In S5, when the hanger deviation index β exceeds the dynamic threshold β th (v), the slip time counter mechanism is used to accumulate the duration of continuous overrun, and then a stability warning response is triggered in stages. Specifically, it includes the following steps:

[0096] a) Initialize the counter: When the system starts, initialize the slip time counter to: τ = 0.

[0097] b) Cumulative criterion construction: During the real-time operation of the system, judge whether the current deviation index exceeds the dynamic threshold within each sampling period: Among them, τn This is the current cumulative overtime, τ n-1 I represents the cumulative value of the previous period, Δt represents the sampling interval, and I represents the cumulative value of the previous period. {·} This represents the indicator function, which takes a value of 1 if the condition is met, and 0 otherwise. β is the divergence index currently being calculated. th (v) represents the dynamic threshold determined by the thermal displacement velocity v.

[0098] If the current period β≤β th If (v), then I = 0, meaning no time is accumulated in this period; it can be set to automatically clear τ when it is continuously below the threshold to avoid "false accumulation" caused by sporadic fluctuations.

[0099] When the cumulative sliding time reaches the set threshold T, the support constraint state is determined to be invalid.

[0100] d) To adapt to the dynamic characteristics of pipelines under different operating conditions, the warning duration window T is dynamically adjusted according to the current dominant vibration frequency f of the pipeline, and is defined as follows: Where T is the warning time window and f is the dominant frequency of pipeline vibration. The dominant frequency component is obtained by performing a Fast Fourier Transform (FFT) on the real-time stress signal σ(t), as follows:

[0101] 1. Acquire real-time stress signals: Continuously acquire data on the axial stress on the hanger or pipe as it changes over time to form a continuous stress time series.

[0102] 2. Constructing a sliding analysis window: The real-time acquired stress data is divided into fixed-length time windows for spectral analysis. Each window can cover several seconds of historical data to reflect the dynamic characteristics of the current structure.

[0103] 3. Perform Fast Fourier Transform (FFT): Perform a frequency domain transformation on the stress signal within each time window to obtain the corresponding spectrum. This displays the energy distribution of different frequency components in the signal, i.e., the amplitude corresponding to each frequency.

[0104] 4. Dominant Frequency Identification: In the spectrum diagram, identify the frequency point with the largest amplitude. This frequency corresponds to the strongest energy and reflects the main vibration mode of the current pipeline or support system. This frequency is the dominant frequency component.

[0105] 5. Output main frequency: The identified main frequency is used as a representative dynamic characteristic value under the current operating condition and passed to the subsequent time window calculation module to adjust the size of the warning duration window.

[0106] Pipeline support instability is often closely related to the periodic vibration of the piping system, especially under high-frequency disturbances (such as water hammer, steam hammer, and mechanical resonance), where the structural response period is much shorter than that under steady-state conditions. Using a fixed time window can lead to the following problems:

[0107] An excessively long time window can mask short-cycle consecutive divergence events under high-frequency operating conditions, leading to false alarms or missed alarms.

[0108] A short time window can make the system overly sensitive to occasional minor disturbances under low-frequency operating conditions, leading to false alarms.

[0109] Therefore, it is necessary to dynamically adjust the early warning time window according to the dominant frequency f of the current pipeline vibration so that it matches the physical response period of the system.

[0110] The time window is set to the length of three cycles of the current main frequency, i.e., T = 3 / f, to avoid occasional false triggering. There may be isolated interference peaks within a single cycle, and it is necessary to observe whether β continues to exceed the limit within multiple consecutive cycles. Most structural dynamic responses reach their peak within 1-3 main cycles, which is the key time period for judging whether they have entered the instability zone.

[0111] The upper limit of 15 seconds is set to account for low-frequency operating conditions (such as the long-distance pipe warm-up phase), where the main frequency is as low as 0.1Hz. In this case, 3 / f = 30 seconds, which exceeds the reasonable range of maintenance response time. If the deviation continues for a long time, maintenance personnel may miss the opportunity to intervene due to the lag in response. Therefore, setting the maximum upper limit T ≤ 15 seconds is based on the following considerations:

[0112] Meets the response cycle requirements of actual alarm systems;

[0113] It takes into account both the operator's attention and reaction ability;

[0114] To prevent the slow trend from being mistaken for a normal, stable state of the system.

[0115] This time window calculation method takes into account the system behavior characteristics under different frequency operating conditions, reflecting the monitoring logic of "fast response-slow suppression":

[0116] Rapid convergence under high-frequency vibration conditions improves early warning timeliness;

[0117] Extend the observation window under low-frequency stable operating conditions to improve the robustness of judgment;

[0118] By setting a maximum limit, we can avoid misjudging system stability under extreme conditions.

[0119] This invention encompasses any substitutions, modifications, equivalent methods, and solutions made within the spirit and scope of this invention. To provide the public with a thorough understanding of this invention, specific details are described in detail in the following preferred embodiments; however, those skilled in the art will fully understand the invention even without these details. Furthermore, to avoid unnecessary misunderstanding of the essence of this invention, well-known methods, processes, procedures, components, and circuits are not described in detail.

[0120] The above description is only a preferred embodiment of the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for early warning of support instability that integrates stress and angle data, characterized in that, Includes the following steps: S1: Synchronously acquire the real-time axial stress and spatial pitch angle of the support and hanger nodes to generate time series data pairs; S2: Calculate the rate of change of stress Rate of change of angle ; S3: Construction stress-angle divergence index; The stress-angle divergence index is calculated as follows: ,in The dimensionless normalization coefficient, Indicates the stress-angle divergence index; S4: Set a dynamic threshold based on the real-time thermal displacement velocity of the pipeline; specifically, this includes setting a dynamic threshold based on the real-time thermal displacement velocity. Set a dynamic threshold for determining the divergence index. The dynamic threshold A piecewise linear function structure is adopted to adapt to the different requirements for early warning sensitivity under different thermal conditions; The dynamic threshold is defined as follows: ； in: For the thermal displacement velocity of the pipeline, The threshold value under static conditions is taken as 1.2 times the maximum β value in the calibration conditions. The maximum threshold under high-speed operating conditions is taken as... 3 times, This indicates the dividing point between low speed and medium speed. This indicates the dividing point between medium and high speeds. This represents the sensitivity coefficient in the low-speed range. This represents the sensitivity coefficient in the mid-speed range. The threshold transition value indicating the starting point of the medium-speed segment; S5: When the duration of the stress-angle divergence index being greater than the dynamic threshold exceeds the warning time window T, the constraint is determined to be in failure and a warning is triggered.

2. The method for early warning of support instability by integrating stress and angle data according to claim 1, characterized in that, S1 also includes integrating a strain sensing unit and an attitude sensing unit on the support rod body, controlling the dual sensing units to sample synchronously, aligning the sampling timestamps, and generating time series data pairs.

3. The method for early warning of support instability by integrating stress and angle data according to claim 2, characterized in that, The strain sensing unit employs a temperature-compensated strain gauge to acquire axial strain changes. Based on the axial strain changes and the strain-stress conversion coefficient, the real-time axial stress is calculated. .

4. The method for early warning of support instability by integrating stress and angle data according to claim 2, characterized in that, The attitude sensing unit uses a tilt sensor to calculate the spatial pitch angle of the boom body through three-axis acceleration components. .

5. The method for early warning of support instability by integrating stress and angle data according to claim 1, characterized in that, In S2: The rate of change of stress is calculated as follows: ; The rate of change of angle is calculated as follows: ; in, Indicates the rate of change of stress. Indicates the rate of change of angle. The preset time window represents the length of the time interval from the current moment backwards. This represents the stress change within a preset time window. This represents the change in pitch angle within a preset time window.

6. The method for early warning of support instability by integrating stress and angle data according to claim 5, characterized in that, The preset time window It is negatively correlated with the thermal displacement acceleration of the pipeline.

7. The method for early warning of support instability by integrating stress and angle data according to claim 1, characterized in that, The warning time window Based on the current dominant frequency of pipeline vibration Dynamic adjustment is defined as: ;in, As a warning time window, This is the dominant frequency of pipeline vibration.

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