Optical cable hardware fatigue damage detection method and system
By constructing a wind field and structure coupled observation baseline and using various signal processing techniques, the phase drift problem in complex environments during fatigue damage detection of optical cable fittings was solved, achieving stable identification of crack propagation direction and adaptive control of the detection system, thus improving the accuracy and safety of the detection.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANDONG RUINENG NEW ENERGY CO LTD
- Filing Date
- 2025-11-11
- Publication Date
- 2026-06-26
Smart Images
Figure CN121207749B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of power transmission line condition monitoring technology, specifically to a method and system for detecting fatigue damage to optical cable fittings. Background Technology
[0002] Fatigue damage detection of optical cable fittings refers to a detection process that monitors and analyzes the stress changes, microcrack propagation, and material performance degradation processes of various connection and fixing components (such as suspension fittings, tension fittings, connecting clamps, pre-twisted wires, etc.) under long-term stress operation, thereby identifying potential fatigue damage states. This process typically relies on multi-source signal acquisition and feature analysis technology from intelligent sensing systems. These systems consist of distributed strain sensing units, acoustic emission arrays, vibration response acquisition devices, and fiber optic grating sensor networks, enabling simultaneous sensing of stress changes and micro-damage evolution at different spatial locations. Through signal fusion, correction, and identification by the intelligent sensing system, the dynamic response of the fittings under combined loads such as wind vibration, temperature difference, mechanical tension, and environmental corrosion can be recorded in real time. Combining time-frequency analysis, damage feature extraction, and pattern recognition algorithms, the presence of fatigue damage signs such as microcracks, loosening, or plastic deformation in the fittings can be accurately determined, and their remaining life and safety margin can be assessed, thus providing intelligent decision-making basis for preventive maintenance and safe operation of optical cable lines.
[0003] The existing technology has the following shortcomings:
[0004] In existing technologies, acoustic emission detection of fatigue damage in optical cable fittings typically relies on a single sensing channel to locate and identify the crack propagation process, assuming a stable acoustic wave propagation path and continuous signal phase. However, in complex operating environments, especially when the optical cable line is located in a non-uniform wind field, the turbulent structure of the airflow, wind shearing effects, and gust impacts generate high-frequency micro-vibration excitation sources in multiple directions on the fitting surface. These excitation sources act on the fitting structure at different times and spatial locations, causing the acoustic emission signal to propagate along different paths within the fitting and undergo multi-path reflection. Due to the anisotropy and geometric inhomogeneity of the metallic material, the reflected beams superimpose and interfere with each other, resulting in beam overlap and phase drift in the signal waveform.
[0005] This dynamic phase drift disrupts the original temporal consistency and propagation direction characteristics of the acoustic emission signal, causing deviations in feature extraction and localization calculations in the detection system. Consequently, it fails to accurately identify the crack propagation direction and the true location of the damage. Under continuous wind-induced vibration conditions, this error accumulates gradually, causing spatial misalignment and trend reversal in fatigue damage identification results. This results in a significant discrepancy between the crack evolution path output by the monitoring system and the actual damage state. This problem has not yet been effectively solved in existing technologies. Once it occurs, it will directly lead to distorted fatigue life predictions, and may even result in critical components being misjudged as being in a normal state just before they are about to fracture, posing an extremely high safety risk.
[0006] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention
[0007] The purpose of this invention is to provide a method and system for detecting fatigue damage in optical cable fittings, so as to solve the problems in the background art.
[0008] To achieve the above objectives, the present invention provides the following technical solution: a method for detecting fatigue damage in optical cable fittings, comprising the following steps:
[0009] S1. Construct a wind field and structure coupled observation baseline, obtain the full-time vibration response signal of optical cable hardware under non-uniform wind field environment, generate phase consistency distribution map, and establish the correspondence between multipath reflection source group and time correlation sequence as a traceable reference for phase analysis.
[0010] S2, based on the phase consistency distribution map, performs causal beam unmixing processing, calculates arrival time difference density and performs curvature spectrum separation analysis on each sound wave propagation path, and extracts crack propagation direction data with direction discrimination characteristics;
[0011] S3, based on crack propagation direction data, constructs a self-consistent phase regression model, maps multipath reflection signals to the geometric boundary region of optical cable fittings, and forms a closed-loop verification sequence containing path mapping errors, which is used to evaluate the spatial deviation caused by phase drift;
[0012] S4. Using the closed-loop verification sequence, the reflection path is clipped and the phase monotonicity constraint is applied to construct the reconstructed acoustic wave propagation topology and restore the real evolution process of the crack propagation trajectory in the spatiotemporal domain.
[0013] S5 performs a dynamic control process within the acoustic wave propagation topology. Through a synergistic mechanism of polarized exposure rotation scanning, reversible time grid rearrangement, and phase conjugate suppression window migration, it suppresses residual interference signals, achieving stable output for crack propagation direction identification and adaptive closed-loop control of the detection system.
[0014] Preferably, step S1 includes:
[0015] In the process of constructing the wind field and structure coupled observation baseline, the first step is to select the overhead optical cable line segment in the area of non-uniform wind field action, and to perform point modeling of optical cable hardware based on the topographic wind flow characteristics to obtain structural geometry and material parameters.
[0016] Wind field disturbance data is acquired and mapped to hardware nodes. High-resolution wind measurement radar and fiber optic sensing devices are used to collect wind speed vector and shear intensity data to construct a spatially coupled input field.
[0017] By combining measured signals and simulation data, time-domain alignment and spectral calibration are performed to generate a complete vibration vector sequence, and the energy integration path under wind-induced response is extracted as the input signal source.
[0018] Based on the degree of vibration energy coupling between paths, a phase consistency distribution map is constructed. Based on the matching results of phase change points and energy jump points, a mapping relationship between propagation paths and time series is established, forming a corresponding set of multi-path reflection source groups and time-related sequences.
[0019] Preferably, step S2 includes:
[0020] Based on the phase consistency distribution map, a group of propagation paths with high phase coordination is extracted, and the paths are reordered according to their initial response time to establish a causal propagation chain between the paths.
[0021] Based on the causal propagation chain, the propagation path is densified by time delay. Through interpolation expansion and phase extension, the propagation time difference is refined to a continuous time series, and a propagation time label with physical meaning is constructed.
[0022] Based on the dense time series, curvature spectrum separation analysis is carried out to extract the curvature change characteristics of the path in the three-dimensional structure, identify curvature abrupt change points and directional trends, and form a joint spatiotemporal feature matrix.
[0023] By integrating the causal sequence of the integrated path, the propagation time difference, and the curvature direction trend, the crack propagation direction chain is reconstructed and mapped to the fitting geometric model, outputting a crack propagation direction dataset.
[0024] Preferably, in the process of constructing the crack propagation direction chain, a propagation consistency weight model is established based on the time series similarity and curvature orientation consistency between propagation paths, and paths with weights higher than a preset threshold are fused and reconstructed to generate a directional continuous chain.
[0025] Preferably, step S3 includes:
[0026] Using the crack propagation direction dataset as input, the time series of the path, the spatial coordinate transformation trend, and the curvature direction label are aligned with the three-dimensional geometric structure model of the optical cable fitting. A spatial projection reference surface is established and point-by-point phase integration is performed to realize the mapping of the propagation path to the geometric boundary.
[0027] A self-consistent phase regression process is performed based on the mapped path set. The propagation start phase is used as the initial condition to construct a phase correlation model between multiple paths. High-density fitting nodes are added to characterize the phase change trend and maintain the consistency of the propagation direction.
[0028] Based on the phase regression model, the projection results of each path are compared with the fitted phase surface to generate a spatial mapping error distribution and form a closed-loop verification sequence arranged in the order of the paths. The time series constraints smooth the error fluctuations and guide the error change trend.
[0029] The closed-loop verification sequence is mapped back to the surface of the geometric structure to form an error heat map. The spatial correspondence between the error concentration area and the crack propagation path is identified, and the offset direction and drift rate information are extracted for evaluation of spatial deviation.
[0030] Preferably, step S4 includes:
[0031] Based on the closed-loop verification sequence, the path segments with high error values are extracted as the initial screening objects for mirror paths. In combination with the structural geometric symmetry and error heat map, non-physical mirror paths are identified and trimmed.
[0032] Apply phase monotonicity constraints to the retained path segments, calculate the phase difference between continuous nodes of the path, identify monotonicity-breaking segments, and combine structural boundaries and propagation energy density to truncate invalid path segments to retain monotonic continuous segments.
[0033] Based on the results of mirror path elimination and monotonicity filtering, a propagation continuity graph is constructed, shared node paths are connected and mapped to the optical cable hardware structure model, the propagation direction and time sequence are marked, and a propagation topology network is constructed.
[0034] The path sequence is matched with the crack propagation direction data, and the crack propagation trajectory is reconstructed based on the time label and spatial coordinates to generate a crack evolution path set for subsequent fatigue life assessment and damage display.
[0035] Preferably, the identification of mirror paths is based on the error polarity reversal feature in the error heatmap. The pruning operation prioritizes the removal of path pairs with opposite propagation directions and highly overlapping time series to improve the accuracy of physical validity in the path set.
[0036] Preferably, step S5 includes:
[0037] In the acoustic wave propagation topology, polarized exposure rotation scanning is performed on the propagation path nodes to adjust the propagation direction vector to compensate for local interference caused by structural asymmetry, thereby obtaining the direction-sensitive spectral surface and optimizing the propagation channel;
[0038] Based on the path sequence optimized by direction, a reversible temporal grid rearrangement operation is performed to construct an ideal propagation time model and fine-tune the actual time labels to maintain consistency between the propagation order and the response behavior.
[0039] Based on the time rearrangement results, a phase conjugate suppression window migration mechanism is applied to the topology to slide and cover the residual interference hotspot region and dynamically adjust the suppression intensity to weaken the influence of secondary signal superposition.
[0040] The propagation path response sequence is compared synchronously to construct a candidate set of main crack propagation directions. The path result with the highest directional stability is output, and the identification process is adjusted by combining the propagation response trend feedback to achieve closed-loop control.
[0041] Preferably, the polarization exposure rotation scanning uses a step angle method to excite the multi-angle response state of each propagation path, suppressing the real-time adjustment of the coverage range based on the path direction gradient and phase perturbation intensity during window migration, so that the propagation path maintains phase continuity and direction consistency in the response sequence.
[0042] A fatigue damage detection system for optical cable fittings includes a coupling sensing module, a pointing extraction module, a phase regression module, a topology reconstruction module, and a steady-state adjustment module.
[0043] The coupled sensing module constructs a wind field and structure coupled observation baseline, acquires the full-time vibration response signal of optical cable hardware under non-uniform wind field environment, generates a phase consistency distribution map, and establishes the correspondence between multipath reflection source group and time correlation sequence as a traceable reference for phase analysis.
[0044] The pointing extraction module, based on the phase consistency distribution map, performs causal beam unmixing processing, calculates arrival time difference density and performs curvature spectrum separation analysis on each sound wave propagation path, and extracts crack propagation pointing data with direction discrimination characteristics;
[0045] The phase regression module, based on crack propagation direction data, constructs a self-consistent phase regression model, maps multipath reflection signals to the geometric boundary region of optical cable fittings, and forms a closed-loop verification sequence containing path mapping errors, which is used to evaluate the spatial deviation caused by phase drift.
[0046] The topology reconstruction module uses a closed-loop verification sequence to perform mirror path clipping on the reflection path and applies phase monotonicity constraints to construct the reconstructed acoustic wave propagation topology, thus restoring the true evolution process of the crack propagation trajectory in the spatiotemporal domain.
[0047] The steady-state adjustment module performs a dynamic control process within the acoustic wave propagation topology. Through a synergistic mechanism of polarization exposure rotation scanning, reversible time grid rearrangement, and phase conjugate suppression window migration, it suppresses residual interference signals, achieving stable output for crack propagation direction identification and adaptive closed-loop control of the detection system.
[0048] The technical effects and advantages provided by the present invention in the above technical solution are as follows:
[0049] This invention establishes a traceable mapping relationship between the propagation path and the time series by constructing a coupled observation baseline of wind field and structural response, thus solving the problem of uncertain signal propagation paths in complex environments. By introducing causal beam unmixing and curvature spectrum separation analysis, the ability to identify crack propagation directions is enhanced. Through a self-consistent phase regression model and a closed-loop error verification mechanism, precise quantification and correction of spatial deviations in the propagation path are achieved. Furthermore, through mirror path clipping and monotonicity constraints, the true propagation topology is reconstructed, effectively restoring the crack evolution trajectory. Finally, through collaborative mechanisms such as rotating exposure, time rearrangement, and phase conjugate suppression window migration, the influence of residual interference on signal judgment is significantly reduced, resulting in highly stable crack propagation direction outputs with structural adaptability. Attached Figure Description
[0050] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.
[0051] Figure 1 This is a flowchart of a method for detecting fatigue damage in optical cable fittings according to the present invention.
[0052] Figure 2 This is a schematic diagram of a fatigue damage detection system for optical cable fittings according to the present invention. Detailed Implementation
[0053] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.
[0054] This invention provides, for example Figure 1 The method for detecting fatigue damage in optical cable fittings, as shown, includes the following steps:
[0055] S1. Construct a wind field and structure coupled observation baseline, obtain the full-time vibration response signal of optical cable hardware under non-uniform wind field environment, generate phase consistency distribution map, and establish the correspondence between multipath reflection source group and time correlation sequence as a traceable reference for phase analysis.
[0056] To accurately identify and establish a phase baseline for the complex vibration behavior of optical cable fittings under non-uniform wind fields, it is necessary to construct a coupled observation baseline between the wind field and the structure, extract the response characteristics of the fitting structure under complex disturbances, and provide a high-precision tracing reference for subsequent multipath reflection identification and phase analysis. The specific steps are as follows:
[0057] A section of overhead optical cable located in a typical non-uniform wind field was selected. Taking into account topographic relief, wind direction variability, and local flow field disturbance intensity, the optical cable hardware was modeled. Target hardware types included suspension connectors, tension clamps, and pre-twisted wire joints, whose geometry and material properties were fully simulated during modeling. Through on-site wind field parameter acquisition, including instantaneous wind speed vectors, wind pressure distribution, turbulent energy density, and shear gradient intensity, a three-dimensional spatial wind field real-time scan was performed using a high spatiotemporal resolution laser wind radar and distributed fiber optic sensing. Wind field boundary layer disturbances were mapped to key nodes on the hardware surface, achieving spatially coupled directional input of wind pressure and structural response. This input not only reflects the normal pulsating force distribution on the structural surface but also includes tangential micro-perturbations induced by shear displacement, with the data format being the resultant force spectrum of each node within a specific time step.
[0058] Based on the established wind field coupled input field, the dynamic response of optical cable fittings under unsteady wind loads was obtained by fusing measured vibration response data with modeling and simulation. The measured part employed multi-point mounted piezoelectric ceramic sheets and fiber Bragg grating arrays, distributed across the high strain gradient region and geometric transition region of the fitting surface, to capture transient strain and vibration velocity information at different frequency bands. The simulation part reconstructed the acceleration vectors, nodal displacement trajectories, and stress cloud diagrams of structural units at different time steps through dynamic response calculations based on finite element and fluid-structure interaction mechanisms. The measured signals and simulation results were time-domain aligned and amplitude-frequency calibrated, generating a complete vibration vector sequence set in each response cycle, covering low-frequency swaying, high-frequency pulsation, and random vortex-induced vibration coupling modes throughout the entire wind disturbance cycle. Energy integration paths with strong spatial correlation were extracted, providing an input data source for subsequent signal phase consistency evaluation.
[0059] Based on wind-induced response vector sequence sets, a phase consistency distribution map based on the time-path joint dimension is constructed by comparing the degree of vibration energy coupling between paths within a continuous response period. The phase stability between response paths is measured using the relative response amplitude integral and response peak time delay as basic indicators, thereby screening out propagation path groups exhibiting high phase coordination in non-uniform wind fields. For response segments with short-period abrupt changes and anisotropic interference, corrections are made using response signal sliding superposition and time delay compensation methods to improve the steady-state continuity of the phase map. The final generated phase consistency map is in two-dimensional matrix form, where each pixel represents the phase consistency level between a certain path and the reference path within a certain time period, used to indicate the relative stability of the multipath reflection interference region.
[0060] Based on the phase consistency distribution map, path groups marked with high consistency are time-series mapped and matched one-to-one with the wind-induced response time series, forming a correlation structure between multipath reflection source groups and response time periods. Specifically, by locating the phase abrupt change start point and energy jump point on the response signal of each path group, its propagation entry point and reflection interface in the three-dimensional structural boundary are established. Combined with the propagation distance and direction offset of each reflection path in the structural geometric model, the start and end intervals of path reflection pairs are defined. Finally, a set of time-correlated sequences containing path numbers, start and end times, and spatial propagation vectors is obtained, providing a complete traceability reference framework for subsequent multipath demixing, propagation model reconstruction, and damage direction determination.
[0061] S2, based on the phase consistency distribution map, performs causal beam unmixing processing, calculates arrival time difference density and performs curvature spectrum separation analysis on each sound wave propagation path, and extracts crack propagation direction data with direction discrimination characteristics;
[0062] After completing the observation of the vibration response of the optical cable fitting structure under non-uniform wind field and constructing the phase consistency distribution map, in order to accurately identify the crack propagation direction in multipath acoustic signals, it is necessary to conduct causal relationship-driven beam unmixing based on the high phase consistency propagation path revealed by the map, and to refine the analysis of the propagation path time difference and spatial characteristics in order to extract directional data that can be used to determine the crack evolution direction. The specific steps are as follows:
[0063] Using the constructed phase consistency distribution map as input, propagation path groups marked with high phase coherence are clustered and extracted to form a stable propagation path set. Based on this, the temporal sequence of propagation characteristics of reflection paths in the optical cable hardware structure is utilized to dynamically track the starting response point and phase transition time of each path, clarifying the causal order of each propagation path in the temporal dimension. The high-consistency path groups are reordered according to the response time of their propagation starting points, constructing a progressive temporal hierarchy. By analyzing the response sequence and energy transition positions between adjacent paths, multiple propagation pairs with causal constraints are further identified, and causal propagation chains are constructed to constrain path selection in the subsequent unmixing process.
[0064] Based on the path ordering results provided by the causal propagation chain, the time delay characteristics of each path during propagation within the structure are densified. Addressing the issues of insufficient sampling accuracy and sparse time difference distribution between adjacent paths in traditional propagation paths, fine-grained sub-intervals are introduced between the timestamps of each path through interpolation expansion and phase difference extension, refining the propagation time difference from the original integer period unit to a sub-period scale. In this process, combined with the time-correlated sequence set constructed in the previous stage, the propagation time of each propagation path between the start and end points is segmented and marked, and the intervals are dynamically adjusted according to the trend of structural micro-vibration period changes under actual wind field interference, ensuring that the path time difference densification results have real physical meaning. After time difference densification, a set of continuous time series that can accurately reflect the differences in propagation delay of each reflection path is obtained, laying the foundation for further extraction of path directionality.
[0065] Based on the refined propagation time series, curvature spectrum separation analysis of the structural propagation path is conducted. This step focuses on the actual propagation trajectory of the reflection path within the geometric region of the structural surface. Using the spatial coordinates of the structural nodes involved in the path propagation, the curvature change of the path trajectory in three-dimensional space is calculated and transformed into a curvature change spectrum. The analysis emphasizes identifying key features such as curvature abrupt change points, curvature monotonic segments, and curvature periodic segments to distinguish whether the path exhibits strong spatial turning characteristics. By comparing the curvature spectrum distribution characteristics of different propagation paths laterally, path clusters with obvious directional characteristics in the propagation direction are extracted. Simultaneously, by combining the time-dense sequence, the curvature pointing trends of different paths are compared within the same propagation period, forming a time-space joint feature matrix to enhance the dimension of directional analysis. This step ultimately outputs a set of data records containing propagation path number, propagation time period, structural geometric segment, and curvature trend labels, serving as the structural basis for extracting the crack propagation direction.
[0066] This study generates crack propagation direction data by integrating the causal sequence, dense time series, and curvature direction trend of the propagation path. This data is no longer limited to traditional point-based localization results but instead presents the evolution direction of cracks within the hardware structure as a linear propagation trend. Specifically, by superimposing the temporal sequence and curvature direction, multiple candidate crack paths with stable directional trends are identified. A propagation consistency weight model is constructed among these paths to fuse and reconstruct paths with similar time series and curvature directions, forming directional continuous chains. Each directional chain is then spatially mapped and embedded into the three-dimensional geometric model of the hardware, ultimately outputting a crack propagation direction dataset. This dataset serves as the input for subsequent steps in crack trajectory reconstruction and damage localization, ensuring high stability and reliability of the direction determination results.
[0067] S3, based on crack propagation direction data, constructs a self-consistent phase regression model, maps multipath reflection signals to the geometric boundary region of optical cable fittings, and forms a closed-loop verification sequence containing path mapping errors, which is used to evaluate the spatial deviation caused by phase drift;
[0068] After extracting the crack propagation direction, in order to map the multipath propagation behavior of sound waves onto the geometric structure and verify the errors, a phase regression mechanism driven by propagation direction data needs to be constructed. This mechanism will fit the mapping relationship between the path and the structural surface at high resolution and form a closed-loop error sequence containing phase mismatch characteristics, which will be used to quantify the spatial identification bias caused by phase drift. The specific steps are as follows:
[0069] The obtained crack propagation direction dataset is used as the main guiding variable for the construction input, which includes the time series of the path, the spatial coordinate transformation trend, and the curvature direction label. To ensure that this direction data maintains physical continuity during geometric mapping, it needs to be aligned with the full-scale 3D geometric structure model of the optical cable fitting. In the specific implementation, based on the boundary lines of each structural unit, corner nodes, and connection transition areas defined in the fitting model, a set of spatial projection reference surfaces is set to receive the data mapping input from the propagation path. During the path projection process, a point-by-point phase integration mechanism is introduced to track the phase transition position of each propagation path in the propagation time series, project it segmentally to the corresponding position of the geometric boundary, and maintain the consistency of the projection vector according to the propagation direction. This processing realizes the cross-domain mapping from the time-path space to the structure-phase space, providing the input basis for subsequent phase regression.
[0070] Based on the set of paths with completed projection mapping, a self-consistent phase regression process is performed. In this step, a phase variation correlation model among multiple paths is established based on the projection distribution formed by the paths on the structural surface. The regression construction method uses the initial phase value of the propagation path as the initial condition and gradually expands towards the terminal direction. By comparing the phase transition characteristics of different paths in the same region, a phase similarity network between paths is formed. To enhance the local accuracy of the fitting, phase density points are added at structural locations where the propagation path turns, reflects, or overlaps. High-density fitting nodes are introduced to finely characterize the phase change trend. During the construction process, the consistency of the propagation direction is maintained, and the propagation time and spatial paths are linked and constrained to prevent misfitting of path intersections. After the regression is completed, a multidimensional phase fitting surface covering the structural surface is obtained, where each surface represents the phase response distribution law of a propagation path on the structural surface.
[0071] Based on the self-consistent phase regression model, the difference between each projected path and its fitted phase surface is compared point-by-point to generate a spatial mapping error distribution map. During error calculation, the focus is on high curvature regions, phase abrupt change points, and propagation turning regions within the path segments, identifying areas with significant phase fitting deviations and marking them as potential drift sources. A closed-loop verification sequence is then established. This sequence consists of a set of error scalar values arranged in path order, each corresponding to the phase mismatch degree of a specific projected segment of the propagation path on the structural surface. To improve the stability of the error sequence, temporal constraints are introduced, smoothing the temporal error fluctuations of adjacent propagation paths and guiding the error change trend according to the propagation logic direction of the path. This closed-loop sequence not only reflects the local phase shift degree of each path but also reveals the cumulative effect of phase error propagation along the path, realistically characterizing the mismatch mode of the propagating signal in complex geometric structures.
[0072] A closed-loop verification sequence is used to assess the spatial deviation of phase drift in the overall propagation structure. First, the closed-loop error sequence is mapped back to its corresponding position in the structural model, forming a set of surface error heatmaps to visualize the mismatch density of each propagation path on the actual geometric boundary. Based on these heatmaps, the spatial correspondence between high-density error regions and crack propagation paths is analyzed to identify key nodes where propagation direction shifts, deflections, or misjudgments occur. Furthermore, by tracking the slope changes of the error trend line, the rate and direction of error growth during path propagation are obtained to determine whether there is a risk of systematic drift. Finally, an error identification model covering the entire structural surface, relating to propagation paths, and with temporal constraints is formed, providing a quantifiable spatial correction basis for subsequent crack trajectory reconstruction and propagation path trimming.
[0073] S4. Using the closed-loop verification sequence, the reflection path is clipped and the phase monotonicity constraint is applied to construct the reconstructed acoustic wave propagation topology and restore the real evolution process of the crack propagation trajectory in the spatiotemporal domain.
[0074] After constructing the closed-loop verification sequence, to further eliminate invalid propagation paths and reconstruct the propagation logic of sound waves in complex structures, it is necessary to filter, constrain, and reconstruct the sound wave paths based on the error identification results, thereby forming a clear propagation topology and restoring the true evolution process of the crack propagation trajectory in the spatiotemporal domain. The specific steps are as follows:
[0075] The generated closed-loop verification sequence is used as the criterion for path optimization. Path segments with error values exceeding a preset threshold are extracted as initial mirror path screening targets. These mirror paths typically arise from structural geometric symmetry or propagation repetition caused by multi-path reflection superposition, and often lack physical realism in actual sound wave propagation. During the screening process, the error heatmap of each path is first spatially partitioned to locate areas of concentrated error density, and potential mirror path pairs are extracted based on the symmetry features in the geometric model. Subsequently, the propagation direction, propagation time, and phase transition curves of the two paths in the mirror pair are cross-compared. If there is a high degree of overlap but the error distribution shows a polarity reversal, it is determined to be a non-physical mirror path and included in the pruning candidate set. By performing path elimination operations on this set, path segments with real propagation potential are retained, laying the foundation for subsequent propagation topology reconstruction.
[0076] After completing the mirror path pruning, to further improve the physical rationality and directional consistency of the propagation path, a phase monotonicity constraint is applied to the retained path segments. This constraint is established based on the phase value change trend along the propagation path, requiring that the phase change within the path segment maintains a single direction of increase or decrease, and that there should be no multiple reversals or fluctuations. In specific implementation, the phase difference is calculated sequentially for each continuous node of the propagation path, forming a phase change sequence. Local reversal points, extreme points, and non-monotonic segments are located in the phase sequence, and monotonicity-damaging segments are extracted using sliding window identification and trend line fitting techniques. For the damaging segments, the structural geometric boundaries and propagation energy density are considered to assess whether they originate from abnormal structural reflections or incidental interference. If determined to be caused by invalid reflections, a path truncation operation is performed, retaining monotonic continuous segments for topological connection. This constraint process significantly improves the consistency of the path in both the time and propagation directions, ensuring the traceability of sound wave propagation behavior.
[0077] Based on the path set after mirror path elimination and phase monotonicity filtering, the acoustic wave propagation topology is reconstructed. In this step, the retained path segments are first ordered chronologically to construct a propagation continuity diagram between paths. Adjacent paths are connected through shared nodes or propagation direction connection points to form complete propagation links. Subsequently, the propagation links are mapped back to the three-dimensional geometry of the optical cable fittings, identifying the structural boundaries, corners, connection points, and reflection interfaces traversed by the paths, and constructing a propagation spectrum of the path segments in the structural coordinate system. The propagation spectrum is further annotated with the path start point, end point, propagation direction, phase sequence, and propagation time period to achieve a spatiotemporally linked propagation structure representation. To improve the temporal integrity of the topology, multiple propagation links undergo phase synchronization processing to maintain consistency in their propagation timeline, thus forming a complete acoustic wave propagation topology network that accurately reflects the propagation behavior of acoustic waves from the excitation source to the termination interface in complex structures.
[0078] Based on the constructed propagation topology, the spatiotemporal evolution of crack propagation trajectories within the structure is reconstructed. This process uses the path sequence within the topology as the main thread, matching it point-by-point with previously extracted crack propagation direction data to confirm the spatial overlap and directional consistency between the crack propagation path and the acoustic wave propagation path. By mapping path nodes to the structural surface, crack propagation trajectory curves are constructed, and a dynamic evolution sequence of crack propagation is built based on the path's timestamp information. In the time dimension, crack propagation speed, staged growth trends, and propagation stagnation points are identified; in the spatial dimension, the crack propagation direction, spatial distribution density, and possible intersection areas are determined. Ultimately, a set of crack evolution paths is obtained, with propagation time as the horizontal axis and spatial coordinates as the vertical axis. This not only displays the entire crack development process but also serves as crucial foundational data for subsequent fatigue life prediction and damage visualization.
[0079] S5 performs a dynamic control process in the acoustic wave propagation topology. Through the synergistic mechanism of polarization exposure rotation scanning, reversible time grid rearrangement, and phase conjugate suppression window migration, it suppresses residual interference signals, achieves stable output for crack propagation direction identification, and enables adaptive closed-loop control of the detection system.
[0080] After reconstructing the acoustic wave propagation topology and spatiotemporally restoring the crack trajectory, a dynamic control mechanism is introduced to ensure stable output during crack propagation direction identification under complex interference environments. This mechanism uses multiple physical processes to collaboratively control the cumulative effect of residual interference signals and achieves temporal reconstruction and response balance of the propagation process within the propagation structure, thus achieving closed-loop adaptive adjustment of the detection process. The specific steps are as follows:
[0081] Based on the reconstructed acoustic wave propagation topology, polarization exposure rotation scanning is performed on all path nodes involved in the propagation. This process aims to identify residual interference regions in the propagation path caused by local structural anisotropy or boundary asymmetry, and dynamically adjust the vector alignment between the signal incident and response directions. In practice, a model of the angle distribution between the path propagation direction and the structural normal is first established. Rotation vector retargeting is then implemented at the path propagation nodes to simulate the response sensitivity changes of local structural units to multi-angle polarization incident states. By implementing a step-by-step rotation angle excitation process on each propagation path, the changes in the corresponding response intensity and phase continuity are measured to obtain the local response direction-sensitive spectrum. The directional segments with the largest phase perturbation and significant response shift are identified in the spectrum, and anti-polarization rotation compensation is applied to their propagation directions during path adjustment to suppress minor reflection perturbations caused by non-ideal boundaries, thereby forming a continuous and stable propagation response channel between the path start and end points.
[0082] Based on polarization rotation scanning, a reversible time raster rearrangement operation is performed on the time series of each propagation path in the topology to overcome response distortion caused by nonlinear propagation time distortion during path reconstruction. Specifically, the time-labeled sequence of each propagation path is mapped to a unified propagation time reference axis, and an ideal propagation time model is constructed based on the actual propagation distance and propagation medium characteristics. Differential analysis is performed between the actual time labels and the ideal model to identify propagation rate anomalies and response peak misalignment segments within the path. For the identified time-distorted segments, a locally reversible raster structure is constructed to decompose the time series into multiple adjustable sub-segments, and time label fine-tuning is performed within each sub-segment. During the rearrangement process, the causal order between path segments remains unchanged, and the directional phase response obtained after polarization rotation is combined to correct the time label rearrangement logic in real time, ensuring consistency between propagation order and response behavior. This results in all propagation paths in the topology exhibiting a propagation state with minimal phase drift error within a unified time frame.
[0083] Combining the stability of the propagation path direction and the consistency of the time sequence after the first two steps, a phase conjugate suppression window migration mechanism is applied to the structural propagation topology map to further weaken the interference effect of residual interference kernels in local areas. This mechanism, based on identified residual interference hotspots, conjugate maps the local path response signal to its theoretical phase sequence and establishes a conjugate suppression window within the mapped region. The suppression window is mobile, meaning its position in the propagation topology map can slide forward or backward along the path propagation direction, dynamically covering the phase reflection peak superposition areas that occur at different propagation stages. During each migration, the coverage and intensity of the suppression window are adjusted according to the path direction gradient and phase superposition intensity, ensuring priority suppression in multi-path convergence areas while preserving the original response characteristics in single propagation segments. Through multiple rounds of suppression window migration iterations, secondary signal superposition caused by path overlap, non-ideal reflections, and direction shifts is gradually eliminated, ultimately forming a propagation topology environment with low interference and high phase consistency.
[0084] After completing the three coordinated operations of polarization rotation, time rearrangement, and phase suppression, the path sequence and response state in the entire acoustic wave propagation topology are dynamically evaluated. Based on the minimization trend of residual interference signals in the propagation link, a stable output of the crack propagation direction identification result is implemented. Specifically, the final response phase sequence of each propagation path is synchronously compared to identify path clusters exhibiting high consistency in directional response within a unified time period, thus constructing a candidate set of main crack propagation directions. From this candidate set, the path with the highest directional consistency, strongest phase stability, largest response intensity, and longest duration in the time grid is selected as the main propagation direction. After mapping the main propagation direction back to the structural coordinate space, the final crack propagation direction identification result is obtained. Combined with the propagation response change trend in the topology diagram, adaptive adjustments are made to the phase jitter, time drift, and spatial response discontinuity during the identification process, achieving stable output of the direction identification result across different detection rounds. Meanwhile, based on the synchronous feedback between the output direction and the structural response, the system automatically adjusts the subsequent propagation path selection and response tracking strategy to construct a complete identification closed-loop process, ensuring that the detection behavior has continuous adaptability and stable detection performance under the background of complex wind field disturbances and structural vibrations.
[0085] This invention establishes a traceable mapping relationship between the propagation path and the time series by constructing a coupled observation baseline of wind field and structural response, thus solving the problem of uncertain signal propagation paths in complex environments. By introducing causal beam unmixing and curvature spectrum separation analysis, the ability to identify crack propagation directions is enhanced. Through a self-consistent phase regression model and a closed-loop error verification mechanism, precise quantification and correction of spatial deviations in the propagation path are achieved. Furthermore, through mirror path clipping and monotonicity constraints, the true propagation topology is reconstructed, effectively restoring the crack evolution trajectory. Finally, through collaborative mechanisms such as rotating exposure, time rearrangement, and phase conjugate suppression window migration, the influence of residual interference on signal judgment is significantly reduced, resulting in highly stable crack propagation direction outputs with structural adaptability.
[0086] This invention provides, for example Figure 2 The optical cable fitting fatigue damage detection system shown includes a coupling sensing module, a pointing extraction module, a phase regression module, a topology reconstruction module, and a steady-state adjustment module.
[0087] The coupled sensing module constructs a wind field and structure coupled observation baseline, acquires the full-time vibration response signal of optical cable hardware under non-uniform wind field environment, generates a phase consistency distribution map, and establishes the correspondence between multipath reflection source group and time correlation sequence as a traceable reference for phase analysis.
[0088] The pointing extraction module, based on the phase consistency distribution map, performs causal beam unmixing processing, calculates arrival time difference density and performs curvature spectrum separation analysis on each sound wave propagation path, and extracts crack propagation pointing data with direction discrimination characteristics;
[0089] The phase regression module, based on crack propagation direction data, constructs a self-consistent phase regression model, maps multipath reflection signals to the geometric boundary region of optical cable fittings, and forms a closed-loop verification sequence containing path mapping errors, which is used to evaluate the spatial deviation caused by phase drift.
[0090] The topology reconstruction module uses a closed-loop verification sequence to perform mirror path clipping on the reflection path and applies phase monotonicity constraints to construct the reconstructed acoustic wave propagation topology, thus restoring the true evolution process of the crack propagation trajectory in the spatiotemporal domain.
[0091] The steady-state adjustment module performs a dynamic control process within the acoustic wave propagation topology. Through a synergistic mechanism of polarization exposure rotation scanning, reversible time grid rearrangement, and phase conjugate suppression window migration, it suppresses residual interference signals, achieving stable output for crack propagation direction identification and adaptive closed-loop control of the detection system.
[0092] The present invention provides a method for detecting fatigue damage to optical cable fittings, which is implemented by the aforementioned optical cable fitting fatigue damage detection system. For details of the specific method and process of the optical cable fitting fatigue damage detection system, please refer to the embodiment of the above-mentioned method for detecting fatigue damage to optical cable fittings, which will not be repeated here.
[0093] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.
Claims
1. A method for detecting fatigue damage in optical cable fittings, characterized in that, Includes the following steps: S1. Construct a wind field and structure coupled observation baseline, obtain the full-time vibration response signal of optical cable hardware under non-uniform wind field environment, generate phase consistency distribution map, and establish the correspondence between multipath reflection source group and time correlation sequence as a traceable reference for phase analysis. S2, based on the phase consistency distribution map, performs causal beam unmixing processing, calculates arrival time difference density and performs curvature spectrum separation analysis for each sound wave propagation path, and extracts crack propagation direction data; S3, based on crack propagation direction data, constructs a self-consistent phase regression model, maps multipath reflection signals to the geometric boundary region of optical cable fittings, and forms a closed-loop verification sequence to evaluate the spatial deviation caused by phase drift; S4. Using the closed-loop verification sequence, the reflection path is clipped and the phase monotonicity constraint is applied to construct the reconstructed acoustic wave propagation topology and restore the real evolution process of the crack propagation trajectory in the spatiotemporal domain. S5 performs a dynamic control process within the acoustic wave propagation topology, suppressing residual interference signals through a synergistic mechanism of polarization exposure rotation scanning, reversible time grid rearrangement, and phase conjugate suppression window migration.
2. The method for detecting fatigue damage of optical cable fittings according to claim 1, characterized in that, Step S1 includes: In the process of constructing the wind field and structure coupled observation baseline, the first step is to select the overhead optical cable line segment in the area of non-uniform wind field action, and to perform point modeling of optical cable hardware based on the topographic wind flow characteristics to obtain structural geometry and material parameters. Wind field disturbance data is acquired and mapped to hardware nodes. High-resolution wind measurement radar and fiber optic sensing devices are used to collect wind speed vector and shear intensity data to construct a spatially coupled input field. By combining measured signals and simulation data, time-domain alignment and spectral calibration are performed to generate a complete vibration vector sequence, and the energy integration path under wind-induced response is extracted as the input signal source. Based on the degree of vibration energy coupling between paths, a phase consistency distribution map is constructed. Based on the matching results of phase change points and energy jump points, a mapping relationship between propagation paths and time series is established, forming a corresponding set of multi-path reflection source groups and time-related sequences.
3. The method for detecting fatigue damage of optical cable fittings according to claim 2, characterized in that, Step S2 includes: Based on the phase consistency distribution map, a group of propagation paths with high phase coordination is extracted, and the paths are reordered according to their initial response time to establish a causal propagation chain between the paths. Based on the causal propagation chain, the propagation path is densified by time delay. Through interpolation expansion and phase extension, the propagation time difference is refined to a continuous time series, and a propagation time label with physical meaning is constructed. Based on the dense time series, curvature spectrum separation analysis is carried out to extract the curvature change characteristics of the path in the three-dimensional structure, identify curvature abrupt change points and directional trends, and form a joint spatiotemporal feature matrix. By integrating the causal sequence of the integrated path, the propagation time difference, and the curvature direction trend, the crack propagation direction chain is reconstructed and mapped to the fitting geometric model, outputting a crack propagation direction dataset.
4. The method for detecting fatigue damage of optical cable fittings according to claim 3, characterized in that, In the process of constructing the crack propagation direction chain, a propagation consistency weight model is established based on the time series similarity and curvature direction consistency between propagation paths. Paths with weights higher than a preset threshold are fused and reconstructed to generate a directional continuous chain.
5. The method for detecting fatigue damage of optical cable fittings according to claim 3, characterized in that, Step S3 includes: Using the crack propagation direction dataset as input, the time series of the path, the spatial coordinate transformation trend, and the curvature direction label are aligned with the three-dimensional geometric structure model of the optical cable fitting. A spatial projection reference surface is established and point-by-point phase integration is performed to realize the mapping of the propagation path to the geometric boundary. A self-consistent phase regression process is performed based on the mapped path set. The propagation start phase is used as the initial condition to construct a phase correlation model between multiple paths. High-density fitting nodes are added to characterize the phase change trend and maintain the consistency of the propagation direction. Based on the phase regression model, the projection results of each path are compared with the fitted phase surface to generate a spatial mapping error distribution and form a closed-loop verification sequence arranged in the order of the paths. The time series constraints smooth the error fluctuations and guide the error change trend. The closed-loop verification sequence is mapped back to the surface of the geometric structure to form an error heat map. The spatial correspondence between the error concentration area and the crack propagation path is identified, and the offset direction and drift rate information are extracted for evaluation of spatial deviation.
6. The method for detecting fatigue damage of optical cable fittings according to claim 4, characterized in that, Step S4 includes: Based on the closed-loop verification sequence, the path segments with high error values are extracted as the initial screening objects for mirror paths. In combination with the structural geometric symmetry and error heat map, non-physical mirror paths are identified and trimmed. Apply phase monotonicity constraints to the retained path segments, calculate the phase difference between continuous nodes of the path, identify monotonicity-breaking segments, and combine structural boundaries and propagation energy density to truncate invalid path segments to retain monotonic continuous segments. Based on the results of mirror path elimination and monotonicity filtering, a propagation continuity graph is constructed, shared node paths are connected and mapped to the optical cable hardware structure model, the propagation direction and time sequence are marked, and a propagation topology network is constructed. The path sequence is matched with the crack propagation direction data, and the crack propagation trajectory is reconstructed based on the time label and spatial coordinates to generate a crack evolution path set for subsequent fatigue life assessment and damage display.
7. The method for detecting fatigue damage of optical cable fittings according to claim 6, characterized in that, The identification of mirror paths is based on the error polarity reversal feature in the error heatmap. The pruning operation prioritizes the removal of path pairs with opposite propagation directions and highly overlapping time series to improve the accuracy of physical validity in the path set.
8. The method for detecting fatigue damage of optical cable fittings according to claim 6, characterized in that, Step S5 includes: In the acoustic wave propagation topology, polarized exposure rotation scanning is performed on the propagation path nodes to adjust the propagation direction vector to compensate for local interference caused by structural asymmetry, thereby obtaining the direction-sensitive spectral surface and optimizing the propagation channel; Based on the path sequence optimized by direction, a reversible temporal grid rearrangement operation is performed to construct an ideal propagation time model and fine-tune the actual time labels to maintain consistency between the propagation order and the response behavior. Based on the time rearrangement results, a phase conjugate suppression window migration mechanism is applied to the topology to slide and cover the residual interference hotspot region and dynamically adjust the suppression intensity to weaken the influence of secondary signal superposition. The propagation path response sequence is compared synchronously to construct a candidate set of main crack propagation directions. The path result with the highest directional stability is output, and the identification process is adjusted by combining the propagation response trend feedback to achieve closed-loop control.
9. The method for detecting fatigue damage of optical cable fittings according to claim 8, characterized in that, The polarization exposure rotation scanning method uses a step angle approach to excite the multi-angle response state of each propagation path, suppressing the window migration process by adjusting the coverage range in real time according to the path direction gradient and phase perturbation intensity, so that the propagation path maintains phase continuity and direction consistency in the response sequence.
10. A fatigue damage detection system for optical cable fittings, used to implement the fatigue damage detection method for optical cable fittings as described in any one of claims 1-9, characterized in that, It includes a coupled sensing module, a pointer extraction module, a phase regression module, a topology reconstruction module, and a steady-state adjustment module: The coupled sensing module constructs a wind field and structure coupled observation baseline, acquires the full-time vibration response signal of optical cable hardware under non-uniform wind field environment, generates a phase consistency distribution map, and establishes the correspondence between multipath reflection source group and time correlation sequence as a traceable reference for phase analysis. The pointing extraction module, based on the phase consistency distribution map, performs causal beam unmixing processing, calculates arrival time difference density and performs curvature spectrum separation analysis on each sound wave propagation path, and extracts crack propagation pointing data with direction discrimination characteristics; The phase regression module, based on crack propagation direction data, constructs a self-consistent phase regression model, maps multipath reflection signals to the geometric boundary region of optical cable fittings, and forms a closed-loop verification sequence containing path mapping errors, which is used to evaluate the spatial deviation caused by phase drift. The topology reconstruction module uses a closed-loop verification sequence to perform mirror path clipping on the reflection path and applies phase monotonicity constraints to construct the reconstructed acoustic wave propagation topology, thus restoring the true evolution process of the crack propagation trajectory in the spatiotemporal domain. The steady-state adjustment module performs a dynamic control process within the acoustic wave propagation topology. Through a synergistic mechanism of polarization exposure rotation scanning, reversible time grid rearrangement, and phase conjugate suppression window migration, it suppresses residual interference signals, achieving stable output for crack propagation direction identification and adaptive closed-loop control of the detection system.