A connector terminal crimping fastening evaluation test method and a connector terminal

Abstract

The application relates to a connector terminal crimping fastening evaluation test method and a connector terminal in the field of information technology, and the method comprises the following steps: obtaining an initial stress distribution diagram, simulating a temperature cycle sequence, determining a temperature-induced deformation vector, superimposing a random vibration spectrum according to the deformation vector to obtain a composite vibration acceleration spectrum, calculating a cumulative fatigue damage index, judging a potential loosening position set, extracting a displacement deviation sequence from the position set, adopting a time sequence analysis algorithm to obtain a connection fastening degradation curve, performing trend fitting on the degradation curve to determine a performance attenuation threshold point, and collecting multiple rounds of simulation data to adopt a data fusion method to obtain a comprehensive durability evaluation report. The application combines multi-physical field coupling and data fusion technology, realizes full-chain prediction from stress distribution to performance attenuation, and significantly improves the reliability evaluation precision of crimping points in a complex environment.

Description

Technical Field

[0001] This invention relates to the field of information technology, and in particular to a method for evaluating the tightness of connector terminal crimping and a connector terminal. Background Technology

[0002] In the field of electrical connections, terminal crimping is a key technology for ensuring stable connections between wires and connectors, and its quality directly affects the safe operation and long-term stability of the entire electrical system. The importance of this process is self-evident, especially in high-reliability scenarios such as automotive, aerospace, and industrial equipment, where the performance of crimped points often becomes a potential source of system failure, necessitating scientific methods to assess their durability.

[0003] However, current methods for detecting and evaluating crimping quality have significant limitations. Many traditional methods focus only on performance under initial conditions, ignoring the complex environmental challenges faced by crimped joints in actual use. These methods struggle to capture the performance degradation that crimped connections may experience during long-term operation, especially under the combined influence of multiple external factors. The evaluation results are often out of touch with real-world conditions and lack the ability to predict future risks. A deeper technical challenge lies in the fact that crimped joints are affected by a combination of forces in the actual environment, with temperature changes and mechanical vibration being two core factors. Temperature changes cause internal stress in the crimping material due to thermal expansion and contraction. This stress accumulation further weakens the tightness of the connection and can even lead to poor contact. Furthermore, mechanical vibration exacerbates this instability, causing minor slippage or wear at the crimped joint during repeated vibrations, ultimately compromising the integrity of the connection.

[0004] For example, in the engine compartment of a car, terminal crimping points not only have to withstand frequent temperature changes but also the continuous bumps and vibrations of a moving vehicle. This dual effect often leads to gradual loosening of the connection and even the risk of open circuits. Therefore, how to realistically reproduce the impact of these multiple environmental factors on crimping points in a laboratory environment and quickly identify the patterns of performance degradation has become a key issue in improving the reliability of the crimping process. Summary of the Invention

[0005] This invention provides a method for evaluating the tightness of connector terminal crimping and a connector terminal, mainly comprising:

[0006] The process includes obtaining an initial stress distribution map, simulating a temperature cycling sequence to determine the temperature-induced deformation vector, obtaining a composite vibration acceleration spectrum by superimposing the deformation vector with a random vibration spectrum, calculating the cumulative fatigue damage index to determine the potential loosening location set, extracting a displacement deviation sequence from the location set, using a time-series analysis algorithm to obtain a connection fastness degradation curve, performing trend fitting on the degradation curve to determine the performance degradation threshold point, and summarizing multiple rounds of simulation data using a data fusion method to obtain a comprehensive durability assessment report. Further, obtaining the initial stress distribution map and simulating a temperature cycling sequence to determine the temperature-induced deformation vector includes obtaining the coefficient of thermal expansion and elastic modulus from a preset material property database to construct a three-dimensional mesh model of the pressing points, using the finite difference method to discretize the three-dimensional mesh model to obtain the initial stress distribution, extracting stress coefficients based on the initial stress distribution to update the mesh model node data, generating a stress distribution diagram through the updated mesh model, obtaining a temperature cycling sequence based on the initial stress distribution map to determine the gradual thermal load application parameters, applying a gradual thermal load to the temperature cycling sequence using an iterative calculation method to determine whether the thermal load exceeds a preset threshold; if it exceeds the preset threshold, adjusting the mesh node displacement to obtain the adjusted node position data, and determining the temperature-induced deformation vector based on the node position data. Furthermore, the step of obtaining a composite vibration acceleration spectrum based on the deformation vector superimposed with a random vibration spectrum, calculating the cumulative fatigue damage index, and determining the potential loosening location set includes obtaining the deformation node coordinates based on the temperature-induced deformation vector, superimposing a random vibration spectrum signal on the deformation node coordinates, using Fourier transform to convert the time-domain signal into a frequency-domain response, determining whether the frequency-domain response meets preset conditions to obtain the composite vibration acceleration spectrum result, obtaining the cumulative fatigue damage index based on the composite vibration acceleration spectrum, comparing the index with the material durability limit, and marking a high-risk area if the index is greater than the limit, and determining the potential loosening location for the high-risk area to determine the loosening location set result. Furthermore, the step of extracting the displacement deviation sequence from the location set and obtaining the connection fastness degradation curve using a time-series analysis algorithm includes: extracting the displacement deviation sequence from the potential loosening location set; processing the displacement deviation sequence using a time-series analysis algorithm to obtain a data sequence; generating the connection fastness degradation curve based on the data sequence; judging the trend of fastness change through the degradation curve; summarizing multiple rounds of simulation data from performance threshold points to form an initial dataset; integrating the temperature influence factor and the vibration influence factor using a data fusion method to obtain a fused dataset; judging the attenuation trend line and determining the durability index based on the fused dataset; and outputting a comprehensive durability assessment report based on the durability index.Furthermore, the step of determining the performance degradation threshold point by trend fitting of the degradation curve includes obtaining the connection fastness degradation curve and extracting trend sequence data from it; processing the trend sequence data using a fitting method to obtain a slope value; if the absolute value of the slope value is higher than a warning threshold, a degradation acceleration alarm is triggered; determining the performance degradation threshold point from the slope value based on the degradation acceleration alarm; constructing a three-dimensional mesh model of the pressing point by obtaining the coefficient of thermal expansion and elastic modulus from a preset material property database; using the finite difference method to discretize the three-dimensional mesh model to obtain the initial stress distribution; and extracting stress coefficients based on the initial stress distribution to update the mesh model node data. Furthermore, the comprehensive durability assessment report is obtained by summarizing the multi-round simulation data using a data fusion method. This includes generating a stress distribution diagram using the updated mesh model, determining the gradual thermal load application parameters based on the temperature cycle sequence obtained from the initial stress distribution diagram, applying the gradual thermal load to the temperature cycle sequence using an iterative calculation method to determine whether the thermal load exceeds a preset threshold. If it exceeds the preset threshold, the mesh node displacement is adjusted to obtain the adjusted node position data. The temperature-induced deformation vector is determined based on the node position data. The coordinates of the deformed nodes are obtained based on the temperature-induced deformation vector. The random vibration spectrum signal is superimposed on the coordinates of the deformed nodes. Furthermore, the step of obtaining the initial stress distribution map, simulating the temperature cycle sequence, and determining the temperature-induced deformation vector includes using Fourier transform to convert the time-domain signal into a frequency-domain response, determining whether the frequency-domain response meets preset conditions, obtaining a composite vibration acceleration spectrum, obtaining a cumulative fatigue damage index based on the composite vibration acceleration spectrum, comparing the index with the material durability limit, marking high-risk areas if the index is greater than the limit, determining potential loosening locations for the high-risk areas, determining a set of loosening locations, extracting a displacement deviation sequence from the set of potential loosening locations, processing the displacement deviation sequence using a time-series analysis algorithm to obtain a data sequence, and generating a connection fastness degradation curve based on the data sequence. Furthermore, the step of determining the potential loosening location set by calculating the cumulative fatigue damage index based on the composite vibration acceleration spectrum obtained by superimposing the deformation vector and random vibration spectrum includes determining the fastening state change trend through the degradation curve, obtaining the connection fastening degradation curve and extracting trend sequence data from it, processing the trend sequence data using a fitting method to obtain a slope value, triggering a degradation acceleration alarm if the absolute value of the slope value is higher than a warning threshold, determining the performance degradation threshold point from the slope value based on the degradation acceleration alarm, summarizing multiple rounds of simulation data from the performance threshold point to form an initial dataset, integrating the temperature influence factor and the vibration influence factor using a data fusion method to obtain a fused dataset, and determining the attenuation trend line and durability index based on the fused dataset.Furthermore, the step of extracting the displacement deviation sequence from the location set and obtaining the connection fastness degradation curve using a time-series analysis algorithm includes: outputting a comprehensive durability assessment report through the durability index; constructing a three-dimensional mesh model of the pressing points by obtaining the coefficient of thermal expansion and elastic modulus from a preset material property database; performing discrete calculations on the three-dimensional mesh model using the finite difference method to obtain the initial stress distribution; extracting stress coefficients based on the initial stress distribution to update the mesh model node data; generating a stress distribution diagram through the updated mesh model; obtaining a temperature cycle sequence based on the initial stress distribution diagram to determine the gradual heat load application parameters; and applying a gradual heat load to the temperature cycle sequence using an iterative calculation method to determine whether the heat load exceeds a preset threshold. Furthermore, the step of determining the performance degradation threshold point by trend fitting of the degradation curve includes adjusting the grid node displacement to obtain adjusted node position data if the threshold is exceeded, determining the temperature-induced deformation vector based on the node position data, obtaining the coordinates of the deformed node based on the temperature-induced deformation vector, superimposing a random vibration spectrum signal on the deformed node coordinates, using Fourier transform to convert the time-domain signal into a frequency-domain response, judging whether the frequency-domain response meets the preset conditions to obtain a composite vibration acceleration spectrum result, obtaining the cumulative fatigue damage index based on the composite vibration acceleration spectrum, comparing the index with the material durability limit, and marking high-risk areas if the index is greater than the limit, determining potential loosening locations for the high-risk areas, and determining the loosening location set result.

[0007] A connector terminal includes a connector terminal, wherein the connector terminal is subjected to a fastening evaluation test method for the crimping process.

[0008] The technical solutions provided by the embodiments of the present invention may include the following beneficial effects:

[0009] This invention discloses a connection tightness assessment test for crimping processes. Addressing the unique business scenario of assessing the fastness degradation and durability of crimped joint connections, it integrates multiple influencing factors such as temperature cycling, vibration loading, and fatigue damage, forming a logically interconnected comprehensive problem: how to accurately predict the degradation trend of crimped joint performance and assess its durability under complex environments. This invention constructs a three-dimensional mesh model using the finite difference method, extracts material properties to simulate the initial stress distribution, and combines temperature-induced deformation and random vibration spectrum analysis to obtain the composite vibration acceleration spectrum and cumulative fatigue damage index, marking high-risk areas. Furthermore, through time-series analysis and trend fitting, it predicts the fastness degradation curve and triggers accelerated degradation alarms. Finally, it integrates multiple rounds of simulation data to generate a comprehensive durability assessment report. The core innovation of this invention lies in combining multi-physics coupling and data fusion technology, achieving full-chain prediction from stress distribution to performance degradation, significantly improving the reliability assessment accuracy of crimped joints in complex environments, and providing a scientific basis for engineering applications. Attached Figure Description

[0010] Figure 1 This is a flowchart of a test method for evaluating the tightness of connector terminal crimping according to the present invention.

[0011] Figure 2 This is a schematic diagram of the framework of step S102 in this embodiment. Detailed Implementation

[0012] To further understand the content of this invention, a detailed description of the invention is provided in conjunction with the accompanying drawings and embodiments. The invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only for explaining the relevant invention and are not intended to limit the invention. Furthermore, it should be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0013] like Figures 1-2 This embodiment of a method for evaluating the tightness of connector terminal crimping can specifically include:

[0014] Step S101: Construct a three-dimensional mesh model of the press joint using the finite difference method, extract the coefficient of thermal expansion and contraction and the elastic modulus from the preset material property database, and obtain the initial stress distribution map.

[0015] A three-dimensional mesh model of the pressing points is constructed by retrieving the coefficient of thermal expansion and the modulus of elasticity from a preset material property database. The initial stress distribution is obtained by discretizing the three-dimensional mesh model using the finite difference method. Stress coefficients are extracted based on the initial stress distribution to update the node data of the mesh model. A stress distribution diagram is then generated from the updated mesh model.

[0016] In one implementation, the three-dimensional mesh model of the crimp point is constructed using the finite difference method to achieve discretization.

[0017] Specifically, the crimping area is divided into regular cubic cells, each representing a local material volume. A difference equation is used to approximate the displacement and strain relationship caused by temperature changes. This method effectively captures the geometric irregularities within the crimping point, such as the contact interface between the wire and the terminal.

[0018] In one possible implementation, when extracting the coefficient of thermal expansion and the modulus of elasticity from a pre-set material property database, the system first identifies the material type used for the crimping point, such as copper alloy or aluminum, and then calls the corresponding parameter values. The coefficient of thermal expansion is used to quantify the degree of material volume expansion when the temperature rises, while the modulus of elasticity reflects the material's ability to resist deformation. The combination of the two forms the initial condition input.

[0019] For example, assuming the crimping point is in an electrical wiring connection scenario, after the operator imports the 3D model into the analysis software, the program automatically reads the parameters from the database and assigns them to each grid node.

[0020] It should be noted that this extraction process ensures that the model reflects the actual material behavior and avoids calculation errors due to missing parameters. Furthermore, the initial stress distribution map is generated iteratively based on the above parameters.

[0021] In one embodiment, the equilibrium equation is solved by finite difference approximation, the stress components of each grid cell are calculated step by step, and finally a visual cloud map is output to show the stress concentration areas on the surface and inside of the press point.

[0022] Specifically, in automotive wiring harness crimping applications, the model can adjust the mesh density to accommodate different crimping depths. When the coefficient of thermal expansion is high, stress peaks appear at the interface edges, while a higher modulus of elasticity results in a more uniform overall stress distribution. This setup supports various electrical connection scenarios, including high-voltage cable terminal crimping and low-voltage signal line crimping. For example, in automotive wiring harness crimping applications, the 3D mesh model of the crimp point is first input with geometric parameters, and then the mesh density is adjusted in increments of 0.5 mm to 2 mm. When the coefficient of thermal expansion is higher than 15 x 10⁻⁶ degrees Celsius, stress peaks appear at the interface edges. When the modulus of elasticity exceeds 200 gigapascals, the overall stress distribution is more uniform. This model supports both high-voltage cable terminal crimping and low-voltage signal line crimping.

[0023] Understandably, the finite difference method is used here to transform a continuous physical field into discrete calculation points, and to obtain the initial distribution state by transmitting stress information through the difference between adjacent elements.

[0024] Preferably, the database update mechanism allows users to add new material parameters to expand the model's applicability.

[0025] For example, in power equipment maintenance scenarios, the generated stress distribution map can directly guide the optimization of the crimping process, reducing the risk of loosening due to thermal cycling. The entire process remains within the scope of electrical connections, achieving stress prediction through parameter extraction and mesh construction. In one implementation, the above steps can be repeatedly applied to different crimping shapes, adapting to circular or square terminal structures by changing the mesh boundary conditions, ensuring the versatility of the technical solution.

[0026] Step S102: Simulate the temperature cycle sequence based on the initial stress distribution diagram, apply a gradual thermal load using an iterative calculation method, and adjust the mesh node displacement if the thermal load exceeds a preset threshold to determine the temperature-induced deformation vector.

[0027] The temperature cycling sequence is obtained based on the initial stress distribution diagram. The parameters for applying the gradual thermal load are determined. An iterative calculation method is used to apply the gradual thermal load to the temperature cycling sequence. Specifically, the initial load is 0, and the intensity is increased by 10% in each stage. Convergence is achieved when the displacement change is less than 0.01. It is then determined whether the thermal load exceeds a preset threshold of 50. If it does, the mesh node displacements are adjusted according to the displacement increment. The adjusted node position data is obtained. The temperature-induced deformation vector is determined based on the node position data.

[0028] In one implementation, when simulating a temperature cycling sequence based on an initial stress distribution map, stress data of the structural components is first read as a starting condition, and then temperature change parameters are gradually introduced to construct the cycling sequence. This approach can cover the stress evolution process under different temperature ranges.

[0029] Specifically, the process of applying a gradual thermal load using an iterative calculation method needs to be carried out in stages. In the initial stage, a base thermal load value is set, and then in each iteration, the load intensity is gradually increased according to a predetermined increment until the target temperature point in the cyclic sequence is reached.

[0030] For example, in the scenario of thermal stress analysis at the crimping point, assuming the component is a metal terminal structure, the initial stress distribution map shows that there is a concentrated stress area at the crimping end. At this time, the iterative calculation will apply a thermal load starting from room temperature, increasing a certain temperature gradient at each step, and calculating the resulting thermal expansion effect.

[0031] It should be noted that if the thermal load exceeds a preset threshold, the mesh node displacements are adjusted. This adjustment mechanism ensures the stability of the simulation results. The threshold is typically set based on the material's yield strength. When the load causes local stresses to approach this limit, the system reallocates node positions to release some constraints. In one possible implementation, the adjustment is achieved by moving adjacent mesh nodes to compensate for displacement, thereby avoiding mesh distortion.

[0032] In one embodiment, when determining the temperature-induced deformation vector, the iterative nodal displacement data are integrated into a vector form for output. This vector contains displacement components in each direction and is used for subsequent structural deformation assessment.

[0033] For example, in stress analysis of press joints, this vector can directly reflect the deformation trend of the contact area caused by temperature cycling, and can be used to assess local stress concentration.

[0034] Preferably, the above process can be verified through multiple cycles to improve the reliability of the results. The entire process is limited to the field of structural thermodynamic analysis and is applicable to the assessment of the temperature effects on various metal or composite material components.

[0035] Step S103: Obtain the coordinates of the deformation nodes from the temperature-induced deformation vector, superimpose the random vibration spectrum on these coordinates, and convert the time-domain signal into a frequency-domain response through Fourier transform to obtain the composite vibration acceleration spectrum.

[0036] Specifically, the coordinates of the deformed nodes are obtained from the temperature-induced deformation vector. These coordinates are then superimposed on the random vibration displacement time series. The input format is a discrete time series of node acceleration with a sampling rate of 1000Hz and a length of 1024 points. The time-domain signal x(t) is converted into the frequency-domain response X(f) through Fourier transform to obtain the composite vibration acceleration spectrum.

[0037] The coordinates of the deformed nodes are obtained based on the temperature-induced deformation vector. A random vibration spectrum signal is then superimposed on these coordinates. A Fourier transform is used to convert the time-domain signal into a frequency-domain response. The maximum acceleration spectral density value of the frequency-domain response is calculated; if it is less than 5 g² / Hz in the range of 10 to 100 Hz, the condition is met. The composite vibration acceleration spectrum is then obtained.

[0038] In one implementation, when obtaining the coordinates of deformed nodes from the temperature-induced deformation vector, the displacement components in each direction of the vector are first read, and then these components are mapped onto the finite element mesh of the structural component to form updated node position data.

[0039] Specifically, this process is applied to metal beam structures. The initial vector contains thermal expansion displacement information at the beam ends, and the precise three-dimensional position is obtained through coordinate transformation.

[0040] In one possible implementation, superimposing random vibration spectra on these coordinates requires first defining the amplitude and frequency range of the vibration spectrum. Random vibration spectra are typically described by a power spectral density function, containing multiple frequency components.

[0041] For example, in the analysis of aero-engine blades, the nodal coordinates after blade deformation are used as a reference, and vibration excitation signals are added point by point to ensure that the superimposed data reflects the nodal motion state under the combined action of temperature and vibration.

[0042] It should be noted that when converting the time-domain signal to a frequency-domain response using Fourier transform, the input is an array of nodal displacement time series recorded every 0.1 seconds. The discrete Fourier transform method is used to process the superimposed time series data. This transform decomposes the change of nodal displacement over time into amplitude and phase information at different frequencies.

[0043] In one embodiment, for a vibration test scenario of a metal beam, the transformation process starts with the collected time-domain acceleration data and gradually calculates the response value at each frequency point.

[0044] Preferably, the process of obtaining the composite vibration acceleration spectrum integrates the frequency domain response results into a single-spectrum curve. The composite spectrum contains the distribution of vibration peaks under the influence of temperature deformation, which is used for subsequent analysis. In one embodiment, for temperature cycling tests of composite material components, this spectral data directly outputs the acceleration amplitude at each node.

[0045] For example, in the coupled thermal-vibration analysis of mechanical components, the above steps can be repeatedly applied to blade models under different temperature gradients. The vibration spectrum parameters superimposed after coordinate extraction are adjusted according to the actual working conditions, and the transformed frequency domain response covers the low-frequency to high-frequency range, ensuring that the composite acceleration spectrum accurately reflects the dynamic characteristics of the nodes.

[0046] Step S104: Calculate the cumulative fatigue damage index based on the composite vibration acceleration spectrum. If the damage index is greater than the material durability limit, mark the high-risk area and determine the potential loosening location set.

[0047] The cumulative fatigue damage index is obtained based on the composite vibration acceleration spectrum. This index is then compared to the material durability limit. If the index exceeds the limit, a high-risk area is marked. Potential loosening locations are identified for these high-risk areas. A set of loosening locations is then determined.

[0048] In one implementation, the composite vibration acceleration spectrum is first input into the cumulative fatigue damage calculation module. Based on the amplitude information of each frequency point in the spectrum, the module extracts the corresponding acceleration response sequence one by one, and gradually accumulates the damage contribution of each cycle according to the material fatigue characteristic curve to form the overall cumulative fatigue damage index.

[0049] In one implementation, the cumulative fatigue damage calculation module takes a composite vibration acceleration spectrum as input and outputs an overall cumulative fatigue damage index. This module first analyzes the amplitude at each frequency point in the spectrum to generate a corresponding acceleration response sequence; then, it uses the rainflow method to extract the number of cycles for each segment; finally, it determines the allowable number of cycles based on the material fatigue characteristic curve, calculates the damage ratio segment by segment, and accumulates them to form an index. For example, at a frequency of 10 Hz and an amplitude of 2g, after extracting 50 cycles, the accumulated damage is 0.01.

[0050] Specifically, this process is applied to metal beam structures. Initial spectral data includes the peak distribution at the beam ends under the combined effects of temperature and vibration. First, the peak values ​​are divided into five intervals based on amplitude, and the equivalent cycle number n_i is calculated by integrating each interval. Next, the lifespan N_i is matched using the material's SN curve parameters. Finally, the damage index D is calculated as the sum of each n_i divided by N_i.

[0051] For example, in the analysis of aero-engine blades, the above calculations are performed on the root node of the blade. The spectral data is divided into a low-frequency thermal deformation-dominated segment and a high-frequency vibration-dominated segment. The contribution of each segment is calculated separately and then added together to ensure that the index reflects the cumulative effect under real operating conditions.

[0052] It should be noted that the material durability limit is used as a threshold benchmark and is directly compared with the obtained index. If the index exceeds the limit, the corresponding node is marked as a high-risk area.

[0053] In one possible implementation, the marking results of high-risk areas are further passed to the location determination module, which clusters the marked nodes based on the finite element mesh coordinates and identifies the loosening tendency set formed by adjacent high-risk nodes.

[0054] Preferably, this judgment process is also applicable to temperature cycling tests of composite material components, by adjusting the spectral input parameters to cover loosening predictions under different gradients.

[0055] Specifically, the output of the potential loosening location set is presented as a list of node numbers, with each location in the list associated with the degree to which its damage index exceeds the limit, which is used to guide the subsequent maintenance priority ranking.

[0056] Specifically, S104 outputs a list of node numbers and the degree of damage index exceeding limits. S105 calls this list to extract the displacement deviation of the corresponding nodes, with deviation values ​​ranging from 0.5 to 2.0 mm. S106 performs trend fitting based on the exceeding limits index in the list, and outputs the risk ranking results after inputting the list data, which are used to guide the subsequent maintenance priority ranking.

[0057] Understandably, the entire process can be repeatedly applied to different load combinations in the thermal vibration coupling analysis of mechanical components to verify the consistency of the judgment results.

[0058] Step S105: Extract the displacement deviation sequence from the potential loosening locations, process the sequence data using a time series analysis algorithm, and obtain the connection tightness degradation curve.

[0059] Displacement deviation sequences are extracted from potential loosening locations. These sequences are then processed using a differential integrated moving average autoregressive model. The model first checks the stationarity of the sequence and determines its order, then inputs the displacement deviation sequence and outputs a predicted data sequence. A connection tightness degradation curve is generated based on this data sequence. The trend of changes in the tightness state is determined using this degradation curve.

[0060] In one implementation, the process of extracting a displacement deviation sequence from a set of potential loose locations first requires identifying monitoring points on the equipment's connecting components. These points are typically located in fastened areas subjected to repeated loads. After collecting displacement data at each location using sensors, a set of points with deviations exceeding a threshold is selected to form the initial sequence data.

[0061] Specifically, the construction of the displacement deviation sequence relies on the comparison of measurements over a continuous time period, arranging the displacement differences between adjacent moments in chronological order to reflect the subtle changing trends of the connected components. This sequence can capture early abnormal signals of the fastening state.

[0062] In one possible implementation, when using time series analysis algorithms to process sequence data, it is necessary to first perform a stationarity test on the sequence to remove trend components and seasonal fluctuations. The algorithm then uses an autoregressive model to fit historical deviation patterns and gradually predicts the future direction of deviation evolution.

[0063] It should be noted that the core of the time series analysis algorithm lies in establishing a correlation model between deviation and time. It inputs the sequence data into the differential integrated moving average process, calculates the rate of change of the tightness index at each time point, and thus generates the connection tightness degradation curve.

[0064] For example, in the monitoring of large mechanical equipment operation, the curve shows an initial flattening followed by an accelerated decline, which intuitively shows the process of the fastening force gradually weakening.

[0065] Preferably, the curve output can be combined with multiple sets of sequence data for cross-validation to improve the reliability of the results.

[0066] Understandably, the entire process, from location selection to curve generation, remains within the scope of mechanical connection monitoring and is applicable to tightness assessment under different equipment load conditions. Through these steps, the system can promptly identify potential loosening risks.

[0067] Step S106: Perform trend fitting on the connection fastness degradation curve. If the absolute value of the fitting slope is higher than the warning threshold, trigger the degradation acceleration alarm and determine the performance degradation threshold point.

[0068] The connection tightness degradation curve is obtained, and trend sequence data is extracted from it. A fitting method is used to process the trend sequence data to obtain a slope value. If the absolute value of the slope value is higher than a warning threshold, a degradation acceleration alarm is triggered. Based on the degradation acceleration alarm, a performance degradation threshold point is determined from the slope value.

[0069] In one implementation, when performing trend fitting on the connection fastening degradation curve, the system first acquires a sequence of previously generated curve data, which reflects the displacement deviation changes of the fastening components during operation. The fitting process employs linear regression, using time as the independent variable and the deviation value as the dependent variable. The optimal fitted line is calculated using the least squares method, and the slope of this line directly represents the average rate of fastening degradation. Specifically, the fitting operation is continuously executed in the monitoring system. The fitting parameters are updated whenever new data points are added to the sequence, ensuring that the slope value tracks the evolution of the component's state in real time. In the scenario of monitoring connections in large lifting equipment, the slope of the curve is relatively small in the initial stage, and the fitted line gradually shows a significant downward trend as usage time increases. It should be noted that after calculating the absolute value of the slope, the system compares it with a preset warning threshold. If the absolute value exceeds the threshold, an accelerated degradation alarm is immediately triggered, which is displayed through the equipment control interface and recorded in the log. In one possible implementation, the determination of the performance degradation threshold point depends on the intersection of the fitted line and the set degradation limit; the system automatically marks this time point as a warning reference.

[0070] Preferably, the threshold point calculation considers multiple sets of historical curve data to improve the stability of the judgment. For example, in the fastening monitoring of a conveyor belt drive device, when the absolute value of the fitted slope reaches the warning level, the alarm is triggered, and the operator can promptly arrange an inspection to prevent the connection failure from spreading. It is understood that the entire process, from curve fitting to alarm output, remains within the scope of mechanical connection monitoring and is suitable for tracking the fastening status under different load conditions. Through the above steps, the system can achieve quantitative early warning of the degradation process.

[0071] Step S107: Summarize the simulation data from multiple rounds from the performance degradation threshold point, and use the data fusion method to integrate the temperature and vibration influence factors to obtain a comprehensive durability assessment report.

[0072] An initial dataset is formed by aggregating multiple rounds of simulation data from performance degradation threshold points. A weighted average data fusion method is used to integrate the temperature influence factor T and the vibration influence factor V, outputting a fused dataset F = 0.6T + 0.4V. A degradation trend line is fitted based on the fused dataset, and a durability index is calculated. A comprehensive durability assessment report is generated using the durability index.

[0073] In one implementation, the performance degradation threshold is determined by monitoring changes in key parameters during device operation. These thresholds mark the locations where performance begins to decline significantly. When summarizing data from multiple rounds of simulation, the results of each round need to be arranged in a time series and common degradation features need to be extracted.

[0074] Specifically, the data fusion method first normalizes the temperature influence factor, then combines it with the vibration influence factor according to weights, which are adjusted based on historical operating records, to achieve a joint assessment of the impact of temperature and vibration.

[0075] In one possible implementation, the temperature influence factor represents the degree of material expansion or contraction of the equipment under different thermal environments, while the vibration influence factor reflects the fatigue accumulation of mechanical components under cyclic loads. The two are combined to form a comprehensive index for predicting the overall durability level.

[0076] For example, the fusion process uses a weighted average algorithm. Inputs are a temperature factor sequence and a vibration factor sequence. The temperature factor weights range from 0.6 to 0.7, determined by linear regression of historical data. The average temperature factor is calculated and multiplied by its corresponding weight, then the peak vibration factor is multiplied by a weight of 0.3 to 0.4, resulting in a final durability score. For example, with input temperature factors of 1.2 and 1.5, the average value (1.35) multiplied by 0.65 yields 0.8775, the peak vibration value (2.0) multiplied by 0.35 yields 0.7, and the total score is 1.5775.

[0077] It should be noted that the comprehensive durability assessment report is based on the integrated index output, including the equipment's remaining life estimate and maintenance recommendations. The report format is fixed as a table with text descriptions, which is convenient for engineers to use directly.

[0078] In one embodiment, for high temperature and high vibration scenarios, dense sampling points near the threshold point are selected first when summarizing the simulation data to ensure that the fusion result accurately reflects the attenuation trend under extreme conditions.

[0079] Preferably, the report generation step maps the fusion metrics to durability level ranges, such as excellent, medium, or attention-required levels, thereby supporting decision-making in multiple scenarios.

[0080] The above description is merely a preferred embodiment of the present invention and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention is not limited to the specific combination of the above-described technical features, but also includes other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of the present invention. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in the present invention.

Claims

1. A method for evaluating the tightness of connector terminal crimping, characterized in that, The process includes obtaining an initial stress distribution map, simulating a temperature cycle sequence to determine the temperature-induced deformation vector, obtaining a composite vibration acceleration spectrum based on the deformation vector superimposed with a random vibration spectrum, calculating the cumulative fatigue damage index to determine the potential loosening location set, extracting the displacement deviation sequence from the location set, using a time-series analysis algorithm to obtain the connection fastness degradation curve, performing trend fitting on the degradation curve to determine the performance degradation threshold point, and summarizing multiple rounds of simulation data to obtain a comprehensive durability assessment report using a data fusion method.

2. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The process of obtaining the initial stress distribution map, simulating the temperature cycling sequence, and determining the temperature-induced deformation vector includes: acquiring the coefficient of thermal expansion and elastic modulus from a preset material property database to construct a three-dimensional mesh model of the pressing points; using the finite difference method to discretize the three-dimensional mesh model to obtain the initial stress distribution; extracting stress coefficients based on the initial stress distribution to update the mesh model node data; generating a stress distribution map using the updated mesh model; obtaining the temperature cycling sequence based on the initial stress distribution map to determine the gradual heat load application parameters; applying the gradual heat load to the temperature cycling sequence using an iterative calculation method to determine whether the heat load exceeds a preset threshold; if it exceeds the preset threshold, adjusting the mesh node displacements to obtain the adjusted node position data; and determining the temperature-induced deformation vector based on the node position data.

3. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The step of determining the potential loosening location set by obtaining a composite vibration acceleration spectrum based on the deformation vector superimposed with a random vibration spectrum, calculating the cumulative fatigue damage index, includes obtaining the coordinates of the deformation nodes based on the temperature-induced deformation vector, superimposing a random vibration spectrum signal on the deformation node coordinates, using Fourier transform to convert the time-domain signal into a frequency-domain response, determining whether the frequency-domain response meets preset conditions to obtain the composite vibration acceleration spectrum result, obtaining the cumulative fatigue damage index based on the composite vibration acceleration spectrum, comparing the index with the material durability limit, and marking a high-risk area if the index is greater than the limit, determining the potential loosening location for the high-risk area, and determining the loosening location set result.

4. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The process of extracting displacement deviation sequences from a set of locations and using a time-series analysis algorithm to obtain a connection fastness degradation curve includes: extracting displacement deviation sequences from a set of potential loose locations; processing the displacement deviation sequences using a time-series analysis algorithm to obtain a data sequence; generating a connection fastness degradation curve based on the data sequence; determining the trend of fastness changes through the degradation curve; summarizing multiple rounds of simulation data from performance threshold points to form an initial dataset; integrating the temperature influence factor and the vibration influence factor using a data fusion method to obtain a fused dataset; determining the attenuation trend line and durability index based on the fused dataset; and outputting a comprehensive durability assessment report based on the durability index.

5. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The process of determining the performance degradation threshold point by trend fitting of the degradation curve includes: acquiring the connection fastness degradation curve and extracting trend sequence data from it; processing the trend sequence data using a fitting method to obtain a slope value; triggering a degradation acceleration alarm if the absolute value of the slope value is higher than a warning threshold; determining the performance degradation threshold point from the slope value based on the degradation acceleration alarm; constructing a three-dimensional mesh model of the pressing point by obtaining the coefficient of thermal expansion and elastic modulus from a preset material property database; using the finite difference method to discretize the three-dimensional mesh model to obtain the initial stress distribution; and extracting stress coefficients based on the initial stress distribution to update the mesh model node data.

6. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The aggregated multi-round simulation data is used to obtain a comprehensive durability assessment report through data fusion. This includes generating a stress distribution diagram using the updated mesh model, determining the gradual thermal load application parameters based on the temperature cycle sequence obtained from the initial stress distribution diagram, applying the gradual thermal load to the temperature cycle sequence using an iterative calculation method to determine whether the thermal load exceeds a preset threshold. If it exceeds the preset threshold, the mesh node displacement is adjusted to obtain the adjusted node position data. The temperature-induced deformation vector is determined based on the node position data. The coordinates of the deformed nodes are obtained based on the temperature-induced deformation vector. The random vibration spectrum signal is superimposed on the coordinates of the deformed nodes.

7. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The process of obtaining the initial stress distribution map, simulating the temperature cycle sequence, and determining the temperature-induced deformation vector includes: using Fourier transform to convert the time-domain signal into a frequency-domain response; determining whether the frequency-domain response meets preset conditions to obtain a composite vibration acceleration spectrum; obtaining a cumulative fatigue damage index based on the composite vibration acceleration spectrum; comparing the index with the material durability limit; marking high-risk areas if the index is greater than the limit; determining potential loosening locations for the high-risk areas; extracting a displacement deviation sequence from the potential loosening location set; processing the displacement deviation sequence using a time-series analysis algorithm to obtain a data sequence; and generating a connection fastness degradation curve based on the data sequence.

8. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The step of determining the potential loosening location set by calculating the cumulative fatigue damage index based on the composite vibration acceleration spectrum obtained by superimposing the deformation vector and random vibration spectrum includes: judging the trend of fastening status change through the degradation curve; obtaining the connection fastening degradation curve and extracting trend sequence data from it; processing the trend sequence data using a fitting method to obtain a slope value; if the absolute value of the slope value is higher than the warning threshold, triggering a degradation acceleration alarm; determining the performance degradation threshold point from the slope value based on the degradation acceleration alarm; summarizing multiple rounds of simulation data from the performance threshold point to form an initial dataset; integrating the temperature influence factor and the vibration influence factor using a data fusion method to obtain a fused dataset; and judging the degradation trend line and determining the durability index based on the fused dataset.

9. The method for evaluating the tightness of connector terminal crimping as described in claim 1, characterized in that, The process of extracting displacement deviation sequences from the location set and obtaining connection fastness degradation curves using time-series analysis algorithms includes: outputting a comprehensive durability assessment report through the durability index; constructing a three-dimensional mesh model of the pressing points by obtaining the coefficient of thermal expansion and elastic modulus from a preset material property database; performing discrete calculations on the three-dimensional mesh model using the finite difference method to obtain the initial stress distribution; extracting stress coefficients based on the initial stress distribution to update the mesh model node data; generating a stress distribution diagram through the updated mesh model; obtaining a temperature cycle sequence based on the initial stress distribution diagram to determine the gradual heat load application parameters; and applying a gradual heat load to the temperature cycle sequence using an iterative calculation method to determine whether the heat load exceeds a preset threshold.

10. A connector terminal, characterized in that, The connector terminal is included, and the fastening evaluation test of the crimping process is performed using a fastening evaluation test method for the connector terminal crimping according to any one of claims 1-9.

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