Support hanger instability early warning method fusing stress and angle data

Abstract

The invention relates to the technical field of structural stability analysis, in particular to a support hanger instability early warning method fusing stress and angle data, which comprises the following steps: synchronously acquiring real-time axial stress and space pitch angle of a support hanger node, and generating a time sequence data pair; calculating a stress change rate and an angle change rate; constructing a stress-angle deviation index; setting a dynamic threshold value according to the real-time thermal displacement speed of the pipeline; and when the duration when the stress-angle deviation index is greater than the dynamic threshold exceeds the early warning time window T, determining that the constraint is invalid and triggering early warning. Compared with a traditional monitoring method which only depends on stress or displacement judgment, early coupling instability symptoms such as structural rigidity degradation, gap slippage and thermal expansion constraint imbalance can be effectively captured, and high-sensitivity response to dynamic structural behaviors is achieved.

Description

TECHNICAL FIELD

[0001] 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. BACKGROUND

[0002] As a key constraint component in the pressure pipeline system, the support and hanger is widely used in the fields of thermal power, nuclear power, petrochemical industry and urban heat supply, and mainly provides necessary stiffness support and inertia constraint to prevent structural instability caused by vibration, impact or thermal expansion on the premise of ensuring the thermal displacement freedom of the pipeline. However, in the actual operation process, the support and hanger often faces the influences of thermal coupling, multi-dimensional vibration and long-term fatigue, and is prone to hidden instability risks such as constraint stiffness degradation, connection gap sliding and support loosening. Once the 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-off and interface cracking.

[0003] The existing support and hanger monitoring method mainly relies on single-variable monitoring of stress or displacement. For example, strain gauges or displacement meters are arranged on the pipeline to monitor the structural response parameters in real time, and fixed threshold judgment logic is used to trigger alarm. However, the faults such as sliding and loosening are often accompanied by nonlinear deviation of stress change and posture change, and it is difficult to comprehensively reflect the potential instability state by relying on only single stress or angle change. The pipeline thermal displacement speed is different in the thermal operation process, and the static and dynamic response characteristics are obviously different, so the traditional fixed threshold setting is prone to false alarm at low speed and missed judgment at high speed. SUMMARY

[0004] The present application provides a support and hanger instability early warning method fusing stress and angle data, a support and hanger instability early warning method fusing multiple source structure data, having dynamic threshold adjustment capability and time domain evolution judgment mechanism, to improve the identification accuracy and response effectiveness of potential instability behavior under complex working conditions.

[0005] A support and hanger instability early warning method fusing stress and angle data, comprising the following steps:

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

[0007] S2: calculating stress change rate and angle change rate;

[0008] S3: constructing stress-angle deviation index;

[0009] S4: setting dynamic threshold according to real-time thermal displacement speed of the pipeline;

[0010] S5: determining that the constraint is invalid and triggering the early warning when the duration that the stress-angle deviation index is greater than the dynamic threshold value exceeds the early warning time window T.

[0011] Optionally, the S1 further includes integrating a strain sensing unit and a posture sensing unit on the boom body of the support and suspension device, controlling the two sensing units to synchronously sample, and aligning the sampling time stamps to generate time series data pairs.

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

[0013] Optionally, the posture sensing unit adopts an inclination sensor to solve a spatial pitch angle θ of the boom body through three-axis acceleration components.

[0014] Optionally, the S2 includes:

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

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

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

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

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

[0020] Optionally, the S4 specifically includes setting a dynamic threshold value β th (v) for the deviation index judgment according to a real-time thermal displacement speed v, and the dynamic threshold value β th (v) adopts a piecewise linear function structure to adapt to differentiated requirements for early warning sensitivity under different thermal working 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 alpha1 is used to calculate the dynamic threshold, which represents the high sensitivity response capability to small displacement changes, and the dynamic threshold is determined by the static reference value beta min The speed is increased proportionally;

[0023] Medium-speed section v1 <= v < v2: in the medium thermal displacement speed interval, the threshold slope is adjusted to alpha2 to realize the transition from sensitive response to stable judgment, and the threshold value beta1 at the start 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 to the maximum limit value beta max , forming a plateau value control mechanism;

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

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

[0027] The beneficial effects of the present application are:

[0028] The present application combines the axial stress change rate and the spatial pitch angle change rate to construct a "stress-angle deviation index" as a support hanger instability judgment index, and through the introduction of linear fitting enhancement, sliding window difference, physical quantity conversion coefficient and other means, the index has good interpretability and engineering stability, compared with the traditional monitoring method which only relies on stress or displacement judgment, the early coupling instability signs such as structural stiffness degradation, gap slip and thermal expansion constraint disorder can be effectively captured, and high sensitivity response to dynamic structure behavior can be realized.

[0029] The present application, aiming at the problem that the behavior characteristics of the support hanger are significantly different under different thermal conditions, proposes a dynamic threshold function with thermal displacement speed as the adjusting factor, and designs a segmented linear model to distinguish low-speed sensitivity, medium-speed stability and high-speed tolerance, so as to realize accurate adjustment of the threshold value of the deviation index, and by setting the threshold change rate as a positive function relationship, it is ensured that no false judgment is generated in the stage of severe thermal expansion, and rapid response is realized in the initial stage of small thermal disturbance, and the self-adaptive ability and false alarm suppression ability under extensive operation conditions are improved.

[0030] The present application combines the slip time accumulation mechanism with the main frequency adaptive time window to realize real-time tracking and judgment of continuous over-threshold behavior of the structure. By taking the main frequency as the inverse function input source of the warning duration, the system can quickly respond under high-frequency impact and appropriately delay in low-frequency drift (such as thermal slow change), effectively avoiding false judgment. 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 The acceleration output components of the tilt angle sensor along the three-axis directions are represented by σx, σy, and σz, and the spatial pitch angle of the boom relative to the horizontal plane is θ.

[0043] To ensure the accuracy and timing consistency of stress and angular rate, the dual-channel sensor uses a unified hardware trigger signal to achieve strict physical synchronous acquisition, and uses a unified timestamp t to mark the acquisition results at the millisecond level, for example:

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

[0045] The continuous sampling frequency is recommended to be set to 100 Hz to 500 Hz to meet the effective monitoring of the 5 Hz to 50 Hz vibration frequency in the pipe support and pipe system. In large projects such as thermal power plants, nuclear power plants, and petrochemical devices, low-frequency vibrations occur in the pipe system due to fluid excitation, thermal expansion, mechanical interference, etc. The main frequency is usually concentrated in the 5 Hz to 50 Hz interval, especially during the hot start and cold shutdown processes. The force and attitude change of the pipe support as a constraint component will also show similar frequency distribution.

[0046] According to the Nyquist sampling theorem, according to the signal processing principle, to accurately restore a continuous signal with a frequency of f, the sampling frequency must be at least 2f (Nyquist theorem). To completely capture the dynamic response of the highest 50 Hz, the sampling frequency should be at least 100 Hz. Considering the non-ideal nature of the signal in actual engineering and the filter margin, it is usually set 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 calculation accuracy of stress rate and angular rate, and thus leading to false triggering or missed judgment of the index β.

[0047] The process of obtaining the strain-stress conversion coefficient K can be carried out according to the following steps:

[0048] Material property determination: First, according to the manufacturing material of the boom body (such as carbon steel, stainless steel, or alloy material), consult its elastic modulus E, which is a standard material mechanics performance parameter, which can be obtained from the material manual or verified by tensile test.

[0049] Strain gauge arrangement confirmation: Determine the paste method of the strain gauge on the boom, including direction (whether aligned with the axial direction), position (stress concentration area or near the neutral axis), and paste quality. Ensure that the strain value measured by the strain gauge is the axial strain or the principal strain in the known direction.

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

[0051] Physical calibration test:

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

[0053] Synchronously use an external mechanical test device (such as an electronic universal testing machine) to measure the corresponding true stress σ;

[0054] According to the measured data, fit the relationship: If multiple sets of data, use the least squares method to fit the average value to improve stability and universality.

[0055] Temperature drift correction: for booms working in a hot variable environment, repeated calibration under different temperature conditions is required to establish a temperature-strain correction curve for temperature compensation of the strain gauge output, ensuring that the effectiveness of K does not fail due to environmental fluctuations.

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

[0057] In S2:

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

[0059] Angle change rate R θ = Δθ / Δt;

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

[0061] Specifically, at the current time t0, the stress data sequence {σ i} and the pitch angle data sequence {θ i} in the time window [t0-Δt, t0] are intercepted, and the stress change rate R σ and the angle change rate R θ are calculated, the specific process being as follows:

[0062] 1. Change amount calculation method: linear fitting enhanced difference form is used to improve noise resistance and change trend identification accuracy. Specifically:

[0063]

[0064] If there are multiple intermediate data points involved, a moving average or weighted filtering can be introduced on the basis of the difference to pre-process and weaken local interference.

[0065] 2. Dynamic time window setting:

[0066] The setting range of the time window length Δt is as follows: 0.5s≤Δt≤3s; and is negatively correlated with the pipeline thermal displacement acceleration a:

[0067] In the static or slowly changing state, Δt is set to 2 seconds by default, and when the pipeline thermal displacement acceleration is increased (such as when steam is rapidly started and stopped or thermal shock occurs), Δt is automatically shortened to enhance the ability to capture rapid responses.

[0068] The setting range of the time window Δt is 0.5 seconds to 3 seconds. This setting is mainly based on the dynamic response characteristics of the support and hanger system under different working conditions and the signal processing accuracy requirements. The shorter the time window, the more sensitive the response to sudden changes (such as thermal shock, steam door, and equipment start-up). The longer the time window, the more data points included in the change rate calculation, and the stronger the ability to suppress random noise. Therefore, a balance needs to be struck between the two, with 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 that the system sampling frequency is 100Hz to 500Hz, the data amount 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, which is prone to missing early warning signs of rapid instability.

[0069] In the static or slowly changing state, Δt is set to 2 seconds by default: When the equipment is shut down, running steadily, or in a hot static state, the stress and angle change of the support and hanger is slow and small. At this time, if a too short time window is selected, it is easy to be misjudged as an effective signal by small electrical noise or external interference. Setting Δt to 2s can expand the observation period, smooth occasional disturbances, and improve the reliability of the judgment. The 2-second time window contains enough data points, which helps to improve the overall trend identification capability of Δσ and Δθ, making the calculated change rate more stable and reliable.

[0070] In a thermal pipeline system, the pipeline linearly expands or contracts due to temperature rise or fall, forming so-called "thermal displacement"; when the rate of change of this displacement is rapid, i.e., the thermal displacement acceleration increases, it is often accompanied by rapid redistribution of forces and sudden changes in posture, and the constraint state of the support and hanger also changes dramatically, which is a high-risk period for system instability. At this time, if the time window is too long, the calculation results of the stress and angle change rate will be "smoothed", thus masking the characteristics of short-term dramatic changes and reducing the warning sensitivity. Therefore, Δt needs to be shortened to enhance the response capability of the change rate to rapid working conditions. Therefore, the time window Δt is set to be negatively correlated with the pipeline thermal displacement acceleration a.

[0071] When the thermal displacement acceleration is small, i.e. the pipeline displacement is slowly and steadily pushed forward, the system is in quasi-steady state, and the mechanical behavior of the support hanger is mainly fine-tuning;

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

[0073] When the thermal displacement acceleration is large, such as equipment start-stop, load changes, steam rapid impact, etc., the stress and posture of the support hanger will mutate in a very short time;

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

[0075] S3: Construct stress-angle deviation index.

[0076] The stress-angle deviation index β in S3 = |R σ -k·R θ |, where k is a proportional coefficient, which also plays a role in dimensionless normalization, converting the angle change rate R θ into an equivalent expression with the same dimension and scale as the stress change rate R σ , so that they can be directly calculated by difference, thereby measuring the degree of deviation between them.

[0077] Under the cold-state calibration working condition of the pipeline, a step-by-step increasing axial test load F n is applied to the support hanger, and the stress change Δσ n and the pitch angle change Δθ n under each load level are recorded simultaneously. To realize the dimensional matching between the stress change rate and the angle change rate, the stress change is first converted to a dimensionless strain rate form: By dividing the stress change Δσ n by the material elastic modulus E, it is converted into an equivalent strain change, realizing the dimensional unity with the angle change; and then through linear regression, the proportional coefficient k between the strain change and the angle change is solved, so that the residual sum of squares is minimized:

[0078] This minimization objective function minimizes the square sum of the fitting error between the equivalent strain change and the angle change under each load level. The expression indicates that by finding the most suitable proportional coefficient k in multiple sets of experimental data, the "equivalent strain change caused by unit angle change" is closest to the actual observation result. Using the least squares method, and k·Δθ nThe square of the deviation is accumulated, reflecting the overall fitting error; by adjusting k to minimize the total error, a global optimal solution is obtained. This is a typical linear regression fitting method, suitable for cases where there is a linear relationship between two variables but there is measurement error. Here, the stress data is converted to strain, and a linear mapping is established between the strain and the angle change.

[0079] The final regression equation slope k expression is:

[0080]

[0081] Where Δσ n represents the stress change under the nth load, Δθ n represents the change in pitch angle under the nth load, E is the elastic modulus of the boom or pipeline material, N is the number of load steps, cov() represents covariance, and var() represents variance. The correlation requirement for this regression process is set as follows: R 2 ≥ 0.85.

[0082] This expression is the analytical solution of the previous minimization process, which directly gives 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 of simultaneous change of two variables, and variance measures the dispersion of one variable itself. The ratio of the two is the slope of the optimal linear fitting, which is the proportionality coefficient in the least squares sense. The error range of k is recommended to be controlled within ±10%, that is, after adding different load disturbances or repeating the loading experiment in the regression sample, the relative standard deviation of the obtained k value is not more than 10%. If the error exceeds this range, the number of load steps should be increased, the loading time should be extended, or the sensor installation precision should be optimized to improve the stability and repeatability of the calibration.

[0083] S4: Set dynamic threshold according to real-time pipeline thermal displacement speed.

[0084] The dynamic threshold setting in S4 includes setting a dynamic threshold function β th (v) for the divergence index judgment according to the real-time monitored pipeline thermal displacement speed v, which adopts a piecewise function and is defined as follows:

[0085]

[0086] Where v is the pipeline thermal displacement speed, β min is the threshold value under static working conditions, which is 1.2 times the maximum β value in the calibration condition, β max is the maximum threshold value under high-speed working conditions, which is β min3 times, v1=0.5 mm / s represents the boundary between low speed and medium speed, v2=3.0 mm / s represents the boundary between medium speed and high speed, a1=0.8 s / mm represents the sensitivity coefficient of the low speed section, a2=0.3 s / mm represents the sensitivity coefficient of the medium speed section, b1=b2=0.5 mm represents the threshold value of the low speed section, and b1+b1·a1·v1 represents the threshold value of the medium section. min + a1·v1 represents the threshold value of the medium section.

[0087] Low speed area (v < v1): Set a higher slope a1 to enhance the response sensitivity to the process of starting a small thermal displacement (such as steam warming pipe);

[0088] Medium speed area (v1≤v < v2): The slope is adjusted to be slower a2 to ensure smooth transition and avoid frequent system early warning;

[0089] High speed area (v≥v2): The threshold is fixed as b max to prevent false triggering of the early warning mechanism during normal or severe thermal expansion.

[0090] The measurement of thermal displacement speed can be realized by installing a high-resolution laser sensor between the pipe and the fixed support to measure the change of pipe movement distance in real time and calculate the thermal displacement speed.

[0091] Or by strain integration method: according to the thermal strain rate The estimation formula of thermal displacement speed v is:

[0092] Wherein, L is the distance between expansion joints, is the thermal strain rate, which is measured by strain gauges.

[0093] Satisfies The dynamic threshold b th monotonically increases with the increase of thermal displacement speed v, that is, the faster the thermal displacement speed, the higher the corresponding threshold.

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

[0095] In S5, when the deviation index b th of the support hanger exceeds the dynamic threshold b

[0096] a) Initialize the counter: when the system starts, the slip time counter is initialized as: τ=0.

[0097] b) Accumulation criterion construction: in the real-time running process of the system, it is judged whether the current deviation index exceeds the dynamic threshold in each sampling period: Wherein, τn is the current accumulated overrun time, τ n-1 denotes the accumulated value of the previous cycle, Δt denotes the sampling interval time, I {·} denotes the indicator function, which takes the value 1 when the condition is met, otherwise 0, β is the current calculated deviation index, β th (v) denotes the dynamic threshold value determined by the thermal displacement velocity v.

[0098] If the current cycle β ≤ β th (v), then I = 0, i.e. the current cycle does not accumulate time; it can be set to automatically clear τ when it is continuously below the threshold value, to avoid "false accumulation" caused by sporadic fluctuations.

[0099] When the accumulated slip time reaches the set threshold T, it is determined that the restraint state of the support hanger is invalid.

[0100] d) To adapt to the dynamic characteristics of the pipeline under different working conditions, the warning duration window T is dynamically adjusted according to the current pipeline vibration main frequency f, defined as follows: where T is the warning time window, f is the main frequency of pipeline vibration, and the main frequency component is obtained by performing fast Fourier transform (FFT) on the real-time stress signal σ(t), as follows:

[0101] 1. Collect real-time stress signals: continuously collect the data of the axial stress on the hanger or pipeline changing with time, forming a continuous stress time series.

[0102] 2. Construct a sliding analysis window: divide the real-time collected stress data 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 frequency domain transform on the stress signal in each time window to obtain the corresponding frequency spectrum. Show the energy distribution of different frequency components in the signal, i.e. the amplitude of each frequency.

[0104] 4. Main frequency identification: in the frequency spectrum, identify the frequency point with the maximum amplitude, which corresponds to the strongest energy, reflecting the main vibration mode of the current pipeline or support hanger system. This frequency is the main frequency component.

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

[0106] The instability of support hanger is often closely related to the periodic vibration of the pipeline system, especially under high-frequency disturbance (such as water hammer, steam hammer, mechanical resonance), the response period of the structure is much smaller than that under stable working conditions. If a fixed time window is used, the following problems will occur:

[0107] The time window is too long: it will cover up the short-period continuous deviation events under high-frequency working conditions, leading to false negatives and false positives;

[0108] The time window is too short: under low-frequency working conditions, it will be too sensitive to occasional minor disturbances, and prone to false alarms.

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

[0110] Set the time window to three periods of the current main frequency, i.e. T = 3 / f to avoid accidental triggering. There may be isolated interference peaks in a single period, and it is necessary to observe whether β is continuously out of limits in multiple consecutive periods. Most structural dynamic responses reach their peaks within 1-3 main periods, which is the key time period for determining whether to enter the instability zone;

[0111] The upper limit is set to 15 seconds to consider low-frequency working conditions (such as long-distance warm-up pipeline stage), where the main frequency is as low as 0.1 Hz, and 3 / f = 30 seconds, which has exceeded the reasonable operation response time range. If the deviation lasts for a long time, the operator may miss the intervention opportunity due to delayed response. Therefore, setting the maximum upper limit T ≤ 15 seconds is based on the following considerations:

[0112] Meet the response period requirements of the actual alarm system;

[0113] Take into account the attention and reaction ability of the operator;

[0114] Prevent mistaking slow trends as normal stable state of the system.

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

[0116] Fast convergence under high-frequency vibration working conditions, improving the timeliness of early warning;

[0117] Under low-frequency stable working conditions, extend the observation window to improve the robustness of the judgment;

[0118] By setting the maximum limit, false judgments of system stability under extreme conditions are avoided.

[0119] The present application encompasses any alternatives, modifications, equivalent methods and solutions made to the essence and scope of the present application. In order to make the public have a thorough understanding of the present application, specific details are described in the following preferred embodiments of the present application, and the present application can also be fully understood without the description of these details to those skilled in the art. In addition, in order to avoid unnecessary confusion to the essence of the present application, well-known methods, processes, procedures, elements and circuits, etc. are not described in detail.

[0120] The above is only the preferred embodiment of the present application, and it should be pointed out that for ordinary skilled in the art, without departing from the principles of the present application, a number of improvements and refinements can also be made, which should be considered as the protection scope of the present application.

Claims

1. A method for early warning of support instability that integrates stress and angle data, characterized in that, It includes the following steps: S1: Synchronously collect the real-time axial stress and spatial pitch angle of the support hanger node to generate a time series data pair; S2: Calculate the stress change rate and the angle change rate; S3: Construct a stress-angle deviation index; S4: Set a dynamic threshold according to the real-time thermal displacement velocity of the pipeline; S5: When the duration for which the stress-angle deviation index is greater than the dynamic threshold exceeds the warning time window T, determine that the constraint fails and trigger a warning.

2. The method for early warning of support instability by integrating stress and angle data according to claim 1, characterized in that, In the above S1, it also includes integrating a strain sensing unit and an attitude sensing unit on the suspension rod body of the support hanger, controlling the synchronous sampling of the dual sensing units, and aligning the sampling timestamps to generate a time series data pair.

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 uses a temperature self-compensated strain gauge to collect the axial strain change amount, and calculates the real-time axial stress σ based on the axial strain change amount in combination with the strain-stress conversion coefficient.

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 an inclination sensor to calculate the spatial pitch angle θ of the suspension rod body through the 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 the above S2: The rate of change of stress is calculated as: R σ =Δσ / Δt; The rate of change of angle is calculated as: R θ =Δθ / Δt; Among them, 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.

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 Δt 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 5, characterized in that, 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.

8. The method for early warning of support instability by integrating stress and angle data according to claim 1, characterized in that, Specifically, S4 includes setting a dynamic threshold β for judging 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.

9. The method for early warning of support instability by integrating stress and angle data according to claim 8, characterized in that, The specific structure of the piecewise linear function includes: Low-speed range 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微小位移变化 (tiny displacement changes). At this time, the dynamic threshold increases proportionally with the static reference value β min and increases proportionally with the speed; Medium speed segment v1≤v<v2: In the medium thermal displacement velocity range, the threshold slope is adjusted to a gentle α2 to achieve the transition from sensitive response to stable judgment, suppress frequent warnings, and the threshold connection value β1 at the starting point of the medium speed segment is determined by the calculation result of the low speed segment to ensure the continuity of the function; High-speed segment v≥v2: When the thermal displacement velocity reaches a high level, the dynamic threshold is fixed at the maximum limit β. max This establishes a platform value control mechanism. Among them, v1 represents the demarcation point between low speed and medium speed, and v2 represents the demarcation point between medium speed and high speed.

10. The method for early warning of support instability by integrating stress and angle data according to claim 1, characterized in that, The warning duration window T is dynamically adjusted according to the current dominant frequency f of the pipeline vibration, and is defined as follows: Where T is the warning time window and f is the dominant frequency of pipeline vibration.

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