Structural strain monitoring method based on time sequence relative difference

Abstract

The invention discloses a structural strain monitoring method based on time sequence relative difference. The method comprises the following steps: acquiring original strain time sequence data; identifying and processing abnormal data points in the original strain time sequence data by adopting a statistical method based on a sliding window to obtain purified strain data; a time difference technology is adopted, and relative strain reflecting load change is obtained by calculating a difference value of strain monitoring data at a fixed time interval before and after; on-site deformation test data is combined, a load strain-deformation coupling curve is generated, and the structural health state is evaluated by analyzing the long-term evolution form change of the curve. According to the method, a mobile Z-score method and a time difference technology are organically combined, a synergistic effect is achieved, abnormal point purification is performed on original strain data, an extreme abnormal value can be prevented from interfering subsequent difference calculation, and a closed-loop solution from original data acquisition to quality analysis curve generation is formed in the whole process.

Description

Technical Field

[0001] This invention relates to the field of strain measurement, and in particular to a structural strain monitoring method based on time series relative difference. Background Technology

[0002] Strain measurement is a crucial process for assessing the behavior of materials and structures under stress, providing essential data for evaluating structural integrity and performance. Its applications are wide-ranging and vital, encompassing civil infrastructure (such as bridges, dams, and buildings), aerospace (such as aircraft frames), and renewable energy systems (such as wind turbines). The goal of structural health monitoring is to build a system similar to the human nervous system, capable of sensing structural "damage" or "lesions," thereby providing a basis for subsequent maintenance decisions. Strain data is highly valuable because it is highly sensitive to localized damage, revealing early signs before problems such as cracks or material fatigue become severe.

[0003] In any application requiring long-term continuous strain monitoring, whether the object is a bridge, a wind turbine, or other large structure, the quality of monitoring data generally faces three core challenges: temperature drift, zero-point drift, and data anomalies. These challenges stem from environmental factors, sensor characteristics, and external interference, collectively constituting the main obstacles to long-term monitoring data processing.

[0004] Temperature drift is one of the most challenging aspects of long-term monitoring. It is defined as any change in strain gauge resistance not caused by structural mechanical stress. Its physical mechanism is quite complex, stemming from the superposition of two effects: first, the resistivity of the strain gauge grid wire alloy itself changes with temperature; second, due to the coefficient of thermal expansion of the strain gauge material… Typically related to the coefficient of thermal expansion of the material being tested. Unlike other components, temperature changes cause relative expansion or contraction between the two. This mismatched deformation exerts a mechanical stretching or compression on the strain gauge, resulting in a "thermal output" or "apparent strain" that is not caused by external loads. In scenarios requiring long-term monitoring across seasons (e.g., from summer to winter), large temperature differences can cause significant drift, the magnitude of which may even completely mask the true structural strain signal, leading to data failure.

[0005] Zero-point drift refers to the slow, gradual shift of the sensor output baseline over time, even under constant temperature and no external load conditions. This phenomenon is primarily attributed to the long-term aging and creep effects of the sensor materials (such as the sensing grid and substrate) and the adhesives used to fix them to the structure. This drift accumulates with prolonged service life, severely impacting the absolute accuracy of measurements and the consistency of long-term data.

[0006] Outliers are data points that deviate significantly from other observations in a data sample. These anomalies frequently appear in the raw data stream of strain monitoring, and their sources are diverse.

[0007] To address these three core challenges, existing technologies have developed various methods, including:

[0008] Hardware compensation scheme: The most classic method for addressing temperature drift is to introduce a "compensator" into the Wheatstone bridge. This compensator is mounted on a material of the same material as the structure being measured but completely unloaded, and placed in the same temperature environment. Theoretically, the temperature drift it produces can cancel out the temperature drift of the working strain gauge. However, this method significantly increases the complexity, cost, and potential points of failure of the system. It requires the temperature fields of the compensator and the working strain gauge to be completely identical, which is extremely difficult to guarantee in practical applications involving large structures.

[0009] Software and Auxiliary Sensor Solutions: Another common approach is to install an additional temperature sensor to measure the ambient temperature in real time, and then compensate using a software algorithm based on a pre-calibrated temperature-strain relationship curve. This method also requires additional hardware, increasing the maintenance burden, and the universality and long-term effectiveness of the calibration curve are questionable. For zero-point drift, the conventional practice is to periodically recalibrate the sensor offline, but this is clearly impractical for facilities such as wind turbines or important bridges that require continuous operation.

[0010] Traditional outlier processing algorithms rely heavily on simple statistical thresholding (e.g., setting fixed upper and lower limits) or basic filtering algorithms (e.g., median filtering, Kalman filtering) for outliers. These methods have limited effectiveness when handling complex, non-stationary signals, struggle to adapt to changing anomaly patterns, and are insufficient for processing large-scale real-time data streams. While advanced deep learning methods such as Long Short-Term Memory (LSTM) networks exist, their high computational cost, dependence on large amounts of labeled training data, and model complexity limit their widespread application on resource-constrained structural health monitoring edge devices.

[0011] In summary, the current technological field lacks a comprehensive software solution that can simultaneously, efficiently, and cost-effectively address the three core issues of temperature drift, zero-point drift, and data anomalies. In particular, existing methods often focus on "correction" or "compensation" to obtain the "true value" of absolute strain, an approach that itself faces significant challenges. As infrastructure service life continues to extend, the market urgently needs a more intelligent, robust, and economical data processing method to improve the accuracy and reliability of long-term structural health monitoring. Summary of the Invention

[0012] To address the aforementioned technical problems, this invention provides a simple, stable, and reliable structural strain monitoring method based on time series relative difference.

[0013] The technical solution of this invention to solve the above-mentioned technical problems is: a structural strain monitoring method based on time series relative difference, comprising the following steps:

[0014] S1: Acquire raw strain time series data from strain sensors mounted on the structure;

[0015] S2: A sliding window-based statistical method is used to identify and process outlier data points in the original strain time series data to obtain purified strain data;

[0016] S3: For the strain data after purification, time difference technology is used to obtain the relative strain reflecting the load change by calculating the difference between strain monitoring data with a fixed time interval between them.

[0017] S4: By using relative strain, which reflects load changes, and combining it with on-site deformation test data, a load-strain-deformation coupling curve is generated. By analyzing the long-term evolution of the load-strain-deformation coupling curve, the health status of the structure is assessed.

[0018] In the above-mentioned structural strain monitoring method based on time series relative difference, the statistical method based on the sliding window in step S2 is the moving Z-score. The moving Z-score identifies outliers by calculating the relationship between data points and their local mean and local standard deviation within the sliding window.

[0019] In the aforementioned structural strain monitoring method based on time-series relative differences, step S2 involves using the moving Z-score, an online anomaly detection algorithm suitable for time series data. This algorithm evaluates the degree of local anomalies at data points using a sliding window. , Z-score value of movement Defined as:

[0020]

[0021] in, and They are The mean and standard deviation of the previous W data points, where W is the window size;

[0022] Set an anomaly threshold T. If the moving Z-score of a data point is greater than T, the data point is marked as an anomaly. For the identified anomaly data, a preceding segment replacement strategy is used for repair.

[0023] In the above-mentioned structural strain monitoring method based on time series relative difference, the preceding segment replacement strategy in step S2 is specifically as follows:

[0024] For a single outlier data point: directly replace the value of the outlier data point with the value of the preceding normal data point;

[0025] For consecutive abnormal segments: If an abnormal segment consisting of k consecutive data points is detected, the preceding normal data segment of the same length k is located, and then the entire abnormal segment is replaced with this normal data segment.

[0026] In the above-mentioned structural strain monitoring method based on time series relative difference, in step S3, the time after purification... Measured strain value The calculation formula is:

[0027]

[0028] in, It is the actual mechanical strain caused by structural stress; It is the apparent strain caused by temperature change; The zero-point drift is caused by sensor aging.

[0029] In the above-mentioned structural strain monitoring method based on time series relative difference, the process of obtaining the relative strain reflecting load changes in step S3 is as follows:

[0030] Select a fixed time interval ,for Backtracking search Historical cleanup data points before the specified time Then, a subtraction operation is performed to obtain the relative strain reflecting the load change. :

[0031]

[0032] We can obtain:

[0033]

[0034] Based on the slowly varying characteristics of drift signals, when When the selection is short enough, assume:

[0035]

[0036]

[0037] Therefore, the drift term is effectively canceled out during the subtraction process, and the formula simplifies to:

[0038] .

[0039] In the above-mentioned structural strain monitoring method based on time series relative difference, in step S3, The value of is greater than the characteristic time for the structure to complete one cycle under typical operating conditions, and less than the thermal response time constant caused by significant temperature drift of the strain sensor due to changes in ambient temperature.

[0040] In the above-mentioned structural strain monitoring method based on time series relative difference, step S4 involves continuously monitoring whether the long-term evolution of the load-strain-deformation coupling curve changes, thereby determining whether the structural stiffness has decreased, thus achieving early warning and quantitative assessment of structural damage.

[0041] The beneficial effects of this invention are as follows:

[0042] 1. For transient abnormal data caused by human touch, circuit failure, etc., this invention first employs a moving Z-score algorithm to identify and eliminate data from the original data stream. This algorithm calculates the deviation of the current data point from the local statistical features (mean and standard deviation) within the window in real time by sliding a window across the data stream. Compared with the traditional global thresholding method, the moving Z-score can adapt to local dynamic changes in the data and has better robustness to non-stationary time series. Compared with deep learning models, it is computationally extremely efficient, requires no training process, and is very suitable for real-time processing of large-scale data at the data acquisition front end, significantly improving data quality and processing efficiency.

[0043] 2. After anomalies are processed, this invention calculates the difference between monitoring data collected at fixed time intervals before and after the anomaly to obtain the relative strain reflecting load changes. Since temperature drift and zero-point drift are slow-changing processes, their drift amounts are approximately constant within short time intervals, thus effectively canceling each other out during the subtraction operation. This method eliminates the need for any additional temperature sensors or compensation devices, fundamentally simplifying the monitoring system and reducing initial costs, wiring complexity, and long-term maintenance costs. It transforms a complex physical compensation problem into a simple mathematical calculation problem, making it particularly suitable for long-term monitoring environments with significant temperature differences, and simultaneously eliminating both main types of slow drift.

[0044] 3. This invention organically combines the moving Z-score method with temporal difference technology, forming a logically clear and highly efficient data purification process that first removes outliers and then eliminates drift. This process design has a synergistic effect; by first purifying the raw data for outliers, it can prevent extreme outliers from interfering with subsequent difference calculations, thereby obtaining more stable and reliable relative values. The entire process forms a closed-loop solution from raw data acquisition to the generation of high-quality analysis curves, which is particularly suitable for applications with extremely high requirements for the reliability of long-term continuous monitoring data.

[0045] 4. This invention specifies a fixed time interval. The selection principle, namely It must satisfy two constraints: it must be greater than the characteristic time for the structure to complete one full operating condition (such as wind turbine pitch control or vehicle crossing a bridge) to capture the complete mechanical response process; at the same time, it must be much smaller than the thermal response time constant of the structure to ensure that the drift is approximately constant within Δt. This mechanism directly links the algorithm parameters to the physical characteristics (operating condition cycle, thermodynamic characteristics) of the monitored object, providing theoretical support for the effectiveness of the method and making it portable and configurable, allowing for optimization and adjustment according to different monitoring objects and environments. Attached Figure Description

[0046] Figure 1 This is the overall flowchart of the present invention.

[0047] Figure 2 This is a schematic diagram illustrating the principle of moving the Z-score.

[0048] Figure 3 The waveform before correction of abnormal data points.

[0049] Figure 4 The waveform diagram after correction of abnormal data points.

[0050] Figure 5 This is the strain coupling diagram before processing using time-difference technology.

[0051] Figure 6 This is the strain coupling diagram processed using time-difference technology.

[0052] Figure 7 This is a curve showing the structural damage evolution process. Detailed Implementation

[0053] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0054] like Figure 1 As shown, a structural strain monitoring method based on time series relative difference includes the following steps:

[0055] S1: Acquire raw strain time series data from strain sensors mounted on the structure.

[0056] S2: A sliding window-based statistical method is used to identify and process outlier data points in the original strain time series data to obtain purified strain data.

[0057] like Figure 2 As shown, the statistical method based on the sliding window is the moving Z-score. The moving Z-score identifies outliers by calculating the relationship between data points and their local means and local standard deviations within the sliding window. The moving Z-score is an online anomaly detection algorithm suitable for time series data. It uses a sliding window to evaluate the degree of local anomalies in data points; for data points in a time series... , Z-score value of movement Defined as:

[0058]

[0059] in, and They are The mean and standard deviation of the previous W data points; the moving Z-score visually represents the standard deviation by which the current data point deviates from its "recent historical" mean.

[0060] W represents the window size, a key parameter that determines the locality of the statistical baseline. Smaller windows are sensitive to short-term fluctuations, while larger windows are smoother but may miss transient anomalies. The window size should be determined based on the sampling frequency to cover a representative time period.

[0061] An anomaly threshold T is set, which is based on the "3-sigma" principle of normal distribution, that is, about 99.7% of the data should fall within ±3 standard deviations of the mean; if the moving Z-score of a data point is greater than T, the data point is marked as an anomaly; for the identified anomalous data, a preceding segment replacement strategy is used for repair.

[0062] The specific strategy for replacing preceding segments is as follows:

[0063] For a single outlier data point: directly replace the value of the outlier data point with the value of the preceding normal data point;

[0064] For consecutive abnormal segments: If an abnormal segment consisting of k consecutive data points is detected, the preceding normal data segment of the same length k is located, and then the entire abnormal segment is replaced with this normal data segment.

[0065] In structural monitoring data that typically has a high sampling rate, the value changes between adjacent data points are continuous and minute. Therefore, replacing abnormal data points with the value of the immediately preceding normal data point is based on the reasonable assumption that "the signal value should remain stable if no anomaly occurs." This method can preserve the local dynamic characteristics and temporal continuity of the data to the greatest extent possible, avoiding the introduction of artificial abrupt changes, which is crucial for subsequent difference calculations that rely on the temporal correlation of the data. This method only requires backtracking one or a few data points, with minimal computational cost, fully meeting the requirements of real-time processing. At the same time, it strictly adheres to the causality of time series, using only historical data for correction, avoiding the forward-looking bias that may arise from using future data (such as the next point in linear interpolation).

[0066] By immediately correcting outliers, outliers can be prevented from entering the sliding window of subsequent moving Z-score calculations, thus avoiding contamination of the mean and standard deviation within the window and ensuring the accuracy of subsequent outlier detection.

[0067] like Figure 3 As shown, a significant spike anomaly exists in the original data. By using preceding segment replacement, this anomalous data point is replaced by the value of its preceding normal data point, and the corrected data waveform is as follows. Figure 4 As shown, the local smoothness and continuity have been restored.

[0068] S3: For the strain data after purification, time difference technology is used to obtain the relative strain reflecting the load change by calculating the difference between strain monitoring data with a fixed time interval between them.

[0069] The moment after purification Measured strain value The calculation formula is:

[0070]

[0071] in, It is the actual mechanical strain caused by structural stress; It is the apparent strain caused by temperature change; The zero-point drift is caused by sensor aging.

[0072] The process of obtaining the relative strain that reflects the change in load is as follows:

[0073] Select a fixed time interval ,for Backtracking search Historical cleanup data points before the specified time Then, a subtraction operation is performed to obtain the relative strain reflecting the load change. :

[0074]

[0075] We can obtain:

[0076]

[0077] Based on the slowly varying characteristics of drift signals, when When the selection is short enough, assume:

[0078]

[0079]

[0080] Therefore, the drift term is effectively canceled out during the subtraction process, and the formula simplifies to:

[0081] .

[0082] Thus, the relative strain reflecting the change in load is obtained. This mainly reflects the fact that The actual mechanical response changes of the structure over a time period are largely unaffected by slow drift factors, such as... Figure 5 , Figure 6 As shown.

[0083] The selection of [the appropriate element] is crucial to this invention, as it directly affects the drift elimination effect and the integrity of mechanical information capture. The value of must be balanced between two mutually restrictive conditions:

[0084] Lower limit constraint: greater than the operating cycle, that is, greater than the characteristic time for the structure to complete one typical operating condition during operation. It must be long enough to encompass one or more complete typical operating cycles of the monitored structure. For example, for wind turbines, It should include a complete cycle of significant load changes such as yaw, pitch, or wind speed fluctuations. If If the time is too short, the difference operation may only capture a local segment of the working condition, resulting in the relative value failing to reflect the complete mechanical behavior.

[0085] Upper limit constraint: less than the time of significant temperature drift, that is, less than the thermal response time constant of the strain sensor caused by the change of ambient temperature. The timeframe must be short enough to ensure that the apparent strain increment caused by temperature change is negligible within that time period. Large structures typically have large thermal masses and long thermal time constants, resulting in relatively slow temperature responses. Empirical and experimental data indicate that, on a 15-minute timescale, changes in ambient temperature are generally insufficient to cause significant strain gauge drift.

[0086] Handling extreme operating conditions: Taking wind turbine monitoring as an example, when events such as emergency shutdowns or extreme gusts occur, the duration may exceed [a certain period of time]. In extreme operating conditions, a single difference may not capture the full picture of the event. However, this does not affect the long-term effectiveness of this method. Because if extreme conditions cause damage to the structure (such as plastic deformation or crack propagation), it will inevitably lead to permanent changes in the structure's fundamental physical properties, such as stiffness. This change will be continuously reflected in the load-strain-deformation coupling curves of all subsequent normal operating conditions; for example, the slope of the curve will permanently change. Therefore, by monitoring the evolution of this curve shape over a long period, the cumulative impact of extreme conditions on structural health can still be effectively identified.

[0087] S4: By utilizing relative strain, which reflects load changes, and combining it with on-site deformation test data, a load-strain-deformation coupling curve is generated. Through analysis of the long-term evolution of this curve (such as slope, linearity, and hysteresis loop area), early warning and quantitative assessment of structural damage can be achieved, thus evaluating the structural health status. Figure 7 As shown, Figure 7 The typical load-strain-deformation coupling curves of the structure at three different service time points (10 days, 6 months, and 1 year) are shown. Figure 7 As can be seen, the curve's shape undergoes a significant evolution with prolonged service life: on the one hand, the overall slope of the curve gradually decreases, directly reflecting the degradation of structural stiffness; on the other hand, the area enclosed by the hysteresis loop formed by the curve increases significantly over time, representing increased energy dissipation due to damage mechanisms such as microcrack propagation during load cycles. Therefore, long-term monitoring of this curve's shape evolution allows for a clear tracking of the structure's progression from an initial healthy state (10 days) to gradual damage accumulation (one year), thereby enabling a quantitative assessment of the structure's health status.

Claims

1. A structural strain monitoring method based on time series relative difference, characterized in that, Includes the following steps: S1: Acquire raw strain time series data from strain sensors mounted on the structure; S2: A sliding window-based statistical method is used to identify and process outlier data points in the original strain time series data to obtain purified strain data; S3: For the strain data after purification, time difference technology is used to obtain the relative strain reflecting the load change by calculating the difference between strain monitoring data with a fixed time interval between them. S4: By using relative strain, which reflects load changes, and combining it with on-site deformation test data, a load-strain-deformation coupling curve is generated. By analyzing the long-term evolution of the load-strain-deformation coupling curve, the health status of the structure is assessed.

2. The structural strain monitoring method based on time series relative difference according to claim 1, characterized in that, In step S2, the statistical method based on the sliding window is the moving Z-score. The moving Z-score identifies outliers by calculating the relationship between data points and their local mean and local standard deviation within the sliding window.

3. The structural strain monitoring method based on time series relative difference according to claim 2, characterized in that, In step S2, the moving Z-score is an online anomaly detection algorithm suitable for time series data. It uses a sliding window to evaluate the degree of local anomalies in data points. , Z-score value of movement Defined as: ； in, and They are The mean and standard deviation of the previous W data points, where W is the window size; Set an anomaly threshold T. If the moving Z-score of a data point is greater than T, the data point is marked as an anomaly. For the identified anomaly data, a preceding segment replacement strategy is used for repair.

4. The structural strain monitoring method based on time series relative difference according to claim 3, characterized in that, In step S2, the preceding segment replacement strategy is specifically as follows: For a single outlier data point: directly replace the value of the outlier data point with the value of the preceding normal data point; For consecutive abnormal segments: If an abnormal segment consisting of k consecutive data points is detected, the preceding normal data segment of the same length k is located and then the entire abnormal segment is replaced with this normal data segment.

5. The structural strain monitoring method based on time series relative difference according to claim 1, characterized in that, In step S3, the time after purification Measured strain value The calculation formula is: ； in, It is the actual mechanical strain caused by structural stress; It is the apparent strain caused by temperature change; The zero-point drift is caused by sensor aging.

6. The structural strain monitoring method based on time series relative difference according to claim 5, characterized in that, In step S3, the process of obtaining the relative strain reflecting the load change is as follows: Select a fixed time interval ,for Backtracking search Historical cleanup data points before the specified time Then, a subtraction operation is performed to obtain the relative strain reflecting the load change. : ； We can obtain: ； Based on the slowly varying characteristics of drift signals, when When the selection is short enough, assume: ； ； Therefore, the drift term is effectively canceled out during the subtraction process, and the formula simplifies to: 。 7. The structural strain monitoring method based on time series relative difference according to claim 6, characterized in that, In step S3 The value of is greater than the characteristic time for the structure to complete one cycle under typical operating conditions, and less than the thermal response time constant caused by significant temperature drift of the strain sensor due to changes in ambient temperature.

8. The structural strain monitoring method based on time series relative difference according to claim 1, characterized in that, In step S4, by continuously monitoring whether the long-term evolution of the load-strain-deformation coupling curve changes, it can be determined whether the structural stiffness has decreased, thereby achieving early warning and quantitative assessment of structural damage.

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