A method for rapid testing performance of a connector under multiple stress environments

Abstract

This invention relates to a rapid performance testing method for connectors under multiple stress environments in the field of next-generation information technology. The method includes: analyzing the potential laws governing contact performance degradation based on dynamic behavior patterns and characteristic data of current surge effects, and determining key time points for contact pressure changes; analyzing the impact trend of multiple stress environments on the long-term performance of the connector based on abnormal fluctuation signals using a pre-established failure behavior prediction model, and obtaining predicted performance degradation results; collecting a new batch of operating data of the connector under optimized conditions through improved testing conditions, analyzing the comprehensive effect of dynamic factor analysis, and determining the final scheme for rapid performance testing; and, based on the final scheme, integrating the output results of real-time monitoring technology and multiple stress environment simulation to construct a dynamic evaluation system for connector performance, and obtaining a comprehensive performance test report.

Description

Technical Field

[0001] This invention relates to the field of next-generation information technology, and in particular to a rapid performance testing method for connectors under multiple stress environments. Background Technology

[0002] Connectors, as indispensable components in modern electronic devices, play a crucial role in various fields such as communications, automotive, and aerospace. Their stability and reliability directly affect the safety and efficiency of the entire system. Especially under high-frequency use and high-load environments, the durability and contact performance of connectors become key to ensuring the normal operation of equipment.

[0003] However, current methods for connector performance testing and inspection have significant shortcomings, failing to meet the demands of rapid iteration and high quality. Existing testing methods often neglect the influence of dynamic, multi-factor effects when dealing with complex usage environments. Traditional methods tend to focus on testing under single conditions, lacking a comprehensive consideration of the simultaneous effects of multiple environmental stresses, leading to test results that are out of sync with actual usage scenarios. Furthermore, manual inspection methods are inefficient and susceptible to human error in large-scale production, making it difficult to guarantee the comprehensiveness and consistency of testing. These limitations prevent the timely detection of potential connector failure risks, impacting the overall reliability of the product.

[0004] Against this backdrop, the field of connector performance testing faces two core challenges. Firstly, simulating connector wear and failure behavior under real-world, complex environments in a short timeframe is a pressing issue. Especially under the combined effects of multiple stresses such as vibration, temperature variations, and current surges, the degradation patterns of contact performance are difficult to accurately capture. Secondly, this performance degradation under complex environments further exacerbates the need for real-time monitoring technology. Because of the lack of effective online detection methods, subtle changes in contact pressure are often overlooked, making it impossible to provide timely warnings of potential failure risks. These two problems are interconnected and jointly constrain the improvement of testing efficiency and prediction accuracy.

[0005] Therefore, how to achieve rapid testing and real-time monitoring of connector performance under multiple stress environments has become a key issue in improving connector reliability and testing efficiency. Summary of the Invention

[0006] This invention provides a rapid performance testing method for connectors under multiple stress environments, mainly including:

[0007] By constructing a multi-stress environment simulation system, operational data of the connector under the combined effects of vibration, temperature change, and current surge are acquired. The collected raw signals are preprocessed to obtain a preliminary environmental stress response dataset. Based on this dataset, time-frequency analysis is used to extract characteristic parameters of the effects of vibration stress and temperature change, determining the dynamic behavior pattern of the connector under multi-stress conditions. For this dynamic behavior pattern, combined with characteristic data of the current surge effect, the potential law of contact performance degradation is analyzed, and key time points of contact pressure change are identified. If the contact pressure change exceeds a preset threshold range, the connector's operating status is continuously tracked using real-time monitoring technology to acquire abnormal fluctuation signals and determine potential risks. The failure behavior triggering conditions are determined. Based on abnormal fluctuation signals, a pre-established failure behavior prediction model is used to analyze the impact trend of multiple stress environments on the long-term performance of the connector, and the performance degradation prediction results are obtained. Based on the performance degradation prediction results, the parameter configuration of the complex environment simulation is adjusted to optimize the test conditions for the effects of vibration stress and temperature changes, and the improvement in test efficiency is judged. Through the improved test conditions, a new batch of operating data of the connector in the optimized environment is collected, the comprehensive effect of dynamic factor analysis is analyzed, and the final solution for rapid performance testing is determined. Based on the final solution, the output results of real-time monitoring technology and multiple stress environment simulation are integrated to construct a dynamic evaluation system for connector performance and obtain a comprehensive performance test report.

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

[0009] This invention discloses a rapid performance testing method for connectors under multiple stress environments. The method constructs a multi-stress environment simulation system to acquire operational data of the connector under the combined effects of vibration, temperature changes, and current surges. Time-frequency analysis is used to extract characteristic parameters and determine the connector's dynamic behavior patterns. Combined with the current surge effect, the contact performance degradation law is analyzed to identify critical time points. When the contact pressure change exceeds a threshold, abnormal fluctuation signals are tracked using real-time monitoring technology. Based on a pre-established failure behavior prediction model, the impact of the multi-stress environment is analyzed to obtain performance degradation prediction results. By optimizing test conditions and improving test efficiency, a dynamic evaluation system for connector performance is finally constructed. This invention achieves rapid and comprehensive evaluation of connector performance under complex environments, providing an important basis for product reliability design and life prediction. Attached Figure Description

[0010] Figure 1 This is a flowchart of a rapid performance testing method for connectors under multiple stress environments according to the present invention.

[0011] Figure 2This is a schematic diagram of a rapid performance testing method for connectors under multiple stress environments according to the present invention. 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 specific embodiments described herein are for illustrative purposes only and are not intended to limit the invention. It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings.

[0013] like Figure 1-2 This embodiment of a rapid performance testing method for connectors under multiple stress environments may specifically include the following steps:

[0014] Step S101: By constructing a multi-stress environment simulation system, the operating data of the connector under the combined action of vibration, temperature change and current impact are obtained. The collected raw signals are preprocessed to obtain a preliminary environmental stress response dataset.

[0015] By constructing a multi-stress environment simulation system, operational data of the connector under the combined effects of vibration, temperature change, and current impact are obtained, resulting in an original signal set. Noise filtering is performed on this original signal set; Fourier transform is used to convert the signals from the time domain to the frequency domain to remove high-frequency noise components, yielding a filtered signal sequence. Normalization is then performed on the filtered signal sequence, mapping the signal values ​​to a uniform scale to determine stress response characteristic values. If the stress response characteristic values ​​exceed a preset threshold, environmental factor data obtained from vibration environment control, temperature change adjustment, and current impact application are integrated to obtain a preliminary environmental stress response dataset.

[0016] For example, in one implementation, environmental testing of the connector is achieved by constructing a multi-stress environment simulation system. This system includes a vibration platform, a temperature control chamber, and a current generator, these components operating in coordination via a central control unit.

[0017] Specifically, the vibration platform employs an electromagnetic vibrator capable of generating vibration signals in the frequency range of 10Hz to 2000Hz; the temperature control chamber uses heating and cooling elements to simulate temperature changes from -40℃ to 150℃; and the current generator provides pulsed current surges with amplitudes up to 100A. During system construction, the hardware framework is assembled first, followed by the integration of a sensor network to monitor the connector's real-time response. This construction method ensures that multiple stresses act on the connector simultaneously, such as in the testing scenario of pluggable connectors in avionics, simulating vibrations and temperature fluctuations combined with current loads during flight, thereby obtaining comprehensive operational data. Furthermore, when acquiring operational data of the connector under the combined effects of vibration, temperature changes, and current surges, multi-channel sensors are used to collect raw signals.

[0018] For example, vibration sensors such as accelerometers are mounted on the connector surface to record vibration amplitude and frequency; temperature sensors such as thermocouples are placed inside the connector to monitor temperature distribution; and current sensors detect the current waveform passing through the connector. During the simulation, the system first sets stress parameters, such as a vibration intensity of 5g, a temperature cycling rate of 5℃ / min, and a current impact duration of 1ms, and then initiates synchronous application. The data acquisition module records signals in real time at a 1000Hz sampling rate, ensuring that indicators such as connector deformation, resistance changes, and contact reliability under multiple stresses are captured. This method is suitable for connector testing in industrial control systems, emphasizing data integrity to reflect the actual working environment.

[0019] It should be noted that the acquired raw signals undergo preprocessing to remove noise and outliers. The specific process includes first applying a low-pass filter to remove high-frequency interference, for example, using a Butterworth filter with a cutoff frequency of 500Hz; then performing normalization to adjust the signal amplitude to the range of 0 to 1; and finally, performing anomaly detection by using a threshold method to remove data points exceeding a preset range, such as considering vibration signals exceeding 10g as anomalies. This preprocessing step ensures signal quality and improves the accuracy of subsequent analysis.

[0020] In one possible implementation, for testing automotive electronic connectors, preprocessing also includes time-domain to frequency-domain conversion, using Fourier transform to extract characteristic frequencies, thereby obtaining a preliminary environmental stress response dataset.

[0021] Preferably, the resulting preliminary environmental stress response dataset is organized in matrix form, with each row corresponding to a data vector at a given time point, including fields such as vibration acceleration, temperature value, and peak current. This dataset supports further statistical analysis, such as calculating the average response value or standard deviation, to evaluate the connector's durability.

[0022] In one embodiment, the dataset size is 10,000 rows, covering the response of a complete stress cycle, suitable for data-driven fault prediction models.

[0023] In another embodiment, for example, the system adjusts stress parameters for high-voltage cable connectors, such as increasing the current surge amplitude to 200A, while maintaining vibration and temperature settings. This variant demonstrates the versatility of the solution, achieved by modifying control software parameters without hardware changes, thus extending to connector testing in the energy transmission field.

[0024] Understandably, the advantage of this multi-stress simulation system lies in its ability to simulate the combined effects of real-world environments, such as observing the amplified effect of temperature on connector material fatigue in vibration-dominated scenarios. This approach ensures the reliability of the dataset through data preprocessing, supporting the optimization of connector designs.

[0025] Specifically, the filter selection during preprocessing is based on signal characteristics. For example, for noisy current signals, median filtering is used instead of low-pass filtering to preserve peak features. This fine-tuning enhances the representativeness of the dataset and plays a role in connector reliability assessment.

[0026] In one embodiment, the entire process, from system construction to dataset generation, forms a closed loop. For example, it involves simulating a one-hour stress cycle, acquiring data, preprocessing it immediately after acquisition, and outputting a dataset for storage or transmission. This loop is suitable for batch testing in a laboratory environment, improving efficiency. Furthermore, the response dataset obtained through this method can reveal potential failure modes of connectors under multiple stresses, such as loosening due to vibration and insulation degradation due to temperature variations, thereby guiding material improvements.

[0027] Step S102: Based on the preliminary environmental stress response dataset, time-frequency analysis technology is used to extract characteristic parameters of the influence of vibration stress and temperature change, and to determine the dynamic behavior mode of the connector under multiple stress environments.

[0028] Using the environmental stress response dataset, vibration signals are processed using short-time Fourier transform (SFT). The SFT involves windowing and weighting the signal before performing the Fourier transform, resulting in a time-spectrum graph representing the impact of vibration stress. Based on the time-spectrum graph, energy integrals for each frequency band are calculated to obtain the frequency domain energy distribution under temperature changes, thus determining stress interaction and fusion characteristics. For these stress interaction and fusion characteristics, wavelet transform is used to extract multiple environmental response parameters. The wavelet transform decomposes the feature signal using continuous wavelet coefficients, obtaining a preliminary classification of dynamic behavior patterns. From this preliminary classification of dynamic behavior patterns, the signal time-domain decomposition results are fused. These results are obtained by calculating the time-domain autocorrelation function of the vibration signal to determine the connector reliability assessment threshold. If the connector reliability assessment threshold exceeds a preset range, adjustments are made through behavior pattern classification. This behavior pattern classification is based on the preliminary classification of dynamic behavior patterns, performing clustering and grouping to determine the connector's dynamic behavior patterns under multiple stress environments.

[0029] For example, in one implementation, a preliminary environmental stress response dataset is first obtained, which is acquired by sensors in a connector testing environment and includes vibration signals and temperature change data.

[0030] Specifically, these data originate from experimental setups simulating multiple stress conditions, such as applying periodic vibrations and gradual temperature changes to electronic connectors in a laboratory to record their response signals. The acquisition of this dataset ensures the reliability of subsequent analysis and provides a foundation for extracting characteristic parameters. Furthermore, these datasets are processed using time-frequency analysis techniques. Time-frequency analysis is a method that analyzes signals simultaneously in the time and frequency domains, enabling the capture of the dynamic characteristics of non-stationary signals.

[0031] For example, by using short-time Fourier transform, vibration signals can be decomposed into spectra of different time windows, thereby identifying the impact of vibration stress at a specific moment.

[0032] It should be noted that this technique helps reveal local characteristics of signals, avoiding the limitation of traditional Fourier transforms that ignore time information. In the connector field, this helps analyze how vibration causes mechanical fatigue or changes in contact resistance.

[0033] In one possible implementation, characteristic parameters of the impact of vibration stress are extracted. The specific process includes peak detection and energy calculation on the time-spectrum graph. First, the power spectral density of the signal in each frequency band is calculated, and then the peak frequency and amplitude are extracted as features. These parameters reflect the impact of vibration stress on the connector structure; for example, high-frequency vibration may cause the propagation of microcracks.

[0034] For example, for an avionics connector, under vibration frequencies ranging from 50 Hz to 200 Hz, the extracted characteristic parameters show that energy is concentrated in the low-frequency range, indicating that stress mainly affects the connector's fixed components. This extraction allows for the quantification of the contribution of vibration to dynamic behavior.

[0035] Preferably, characteristic parameters of the effect of temperature change are extracted simultaneously. Temperature change affects the material properties of the connector, such as dimensional deformation caused by thermal expansion. The extraction process involves phase information analysis in time-frequency analysis, such as calculating the instantaneous frequency shift of the signal to quantify the modulation effect of the temperature gradient on the vibration response.

[0036] In one embodiment, for connectors in an industrial control system, characteristic parameters show an increased frequency drift as the temperature rises from 20°C to 80°C, indicating that temperature enhances the resonance effect of vibration. This extraction ensures a comprehensive consideration of multiple stresses. Based on the extracted characteristic parameters, the dynamic behavior pattern of the connector under multiple stress environments is determined.

[0037] Specifically, clustering algorithms are used to group feature parameters to form behavioral patterns, such as stable patterns, decay patterns, or fault precursor patterns.

[0038] For example, vibration and temperature characteristics are combined into a vector, and then K-means clustering is used to identify patterns. In one embodiment, for a communication device connector, the analysis shows that under the combined effects of high vibration and high temperature, the behavior pattern shifts towards attenuation, manifested as a 20% increase in signal attenuation, which helps predict potential failures. Further extending this approach, in another implementation, different connector types, such as fiber optic connectors, are considered. In this scenario, time-frequency analysis focuses on fluctuations in the optical signal, extracting phase noise caused by vibration and wavelength drift caused by temperature as characteristic parameters. These parameters are used to determine dynamic behavior patterns, such as intermittent interruptions in vibration-dominated environments and gradual attenuation in temperature-dominated environments. This diverse implementation demonstrates the versatility of the technology.

[0039] Understandably, this method can also be applied to waterproof connector testing. The extraction process is similar, but it emphasizes the interaction between humidity and temperature, for example, by calculating the cross spectrum through time-frequency analysis to extract composite features. The resulting patterns reveal the dynamic changes in sealing performance under multiple stresses, providing a basis for design optimization.

[0040] In one embodiment, the entire process is integrated into a software platform. First, a dataset is input, then time-frequency transformation and feature extraction are automatically performed, and finally, a behavioral pattern graph is output. This integration improves efficiency and enables real-time monitoring of the connector's status.

[0041] For example, in a specific test case, the initial dataset includes 1000 sampling points with a vibration amplitude of 5g and a temperature range of -40°C to 85°C. Time-frequency analysis reveals a vibration peak at 150Hz and a temperature-induced shift of 2Hz, thus identifying it as a high-risk dynamic mode. This guides maintenance strategies in practical applications. Through these steps, this technical solution achieves an accurate description of the connector's dynamic behavior, supporting reliability assessments across multiple scenarios.

[0042] Step S103: Based on the dynamic behavior pattern and combined with the characteristic data of the current impact effect, analyze the potential law of contact performance decay and determine the key time nodes of contact pressure change.

[0043] By collecting dynamic behavior pattern data, characteristic data are obtained from the current impact effect to obtain a contact performance degradation index. For this index, a linear regression algorithm is used to fit the dynamic behavior pattern as the independent variable and the contact performance degradation index as the dependent variable to determine the performance degradation law. Based on this degradation law, the impact effect characteristics are integrated to determine the contact pressure change curve. From the contact pressure change curve, the potential law analysis results are obtained to identify key time nodes. For these key time nodes, the criteria for judging pressure changes are determined based on the critical time of each node.

[0044] For example, in one implementation, dynamic behavior patterns refer to the real-time changing characteristics exhibited by components in an electrical contact system during operation, such as contact point position shifts or vibration amplitudes, which can be monitored in real time by sensors.

[0045] Specifically, by using accelerometers and displacement sensors installed on the contact device, dynamic data of the components as current flows is collected, thereby forming a sequence of behavioral patterns. This pattern helps to identify precursors to abnormal behavior; for example, when the current load increases, minute vibrations at the contact points may be amplified into potential fault signals.

[0046] It's important to note that the analysis of dynamic behavioral patterns is based on time-series data processing. First, the collected data is filtered to remove noise interference. Then, key features such as frequency and amplitude are extracted to construct pattern vectors for subsequent pattern analysis. Furthermore, to incorporate the characteristic data of the current surge effect, it's necessary to first understand the current surge effect, which refers to the instantaneous thermal effects and mechanical stress changes caused by sudden high-current pulses in electrical systems. The characteristic data includes the peak current at the time of the surge, its duration, the resulting temperature rise, and voltage fluctuations. This data is acquired using high-speed sampling instruments such as oscilloscopes.

[0047] In one possible implementation, such as in high-voltage switchgear, when a current surge occurs, the system records the integral value of the surge waveform and the amount of energy released; these characteristic data are quantized into vector form. The specific process is as follows: First, the sampling frequency is set to several thousand times per second to capture the surge transient; then, the peak current I_max and the surge energy E=∫I²dt are calculated, where the integral covers the surge duration; next, temperature characteristics are extracted, the contact point temperature rise ΔT is measured using a thermistor, and the thermal shock intensity is calculated in conjunction with the material's heat capacity. These steps ensure the integrity of the characteristic data, providing reliable input for analyzing contact performance degradation. In this way, the characteristic data not only reflects the direct impact of the surge but also reveals potential cumulative damage mechanisms, such as repeated surges potentially leading to thickening of the oxide layer on the contact surface, thus affecting conductivity.

[0048] For example, when analyzing the potential patterns of contact performance degradation, dynamic behavior patterns can be combined with current surge characteristic data for data fusion processing.

[0049] Specifically, a decay model is first constructed, assuming that performance decay follows an exponential law, i.e., decay rate R(t) = R0 * e^(k*t), where R0 is the initial decay rate and k is the decay coefficient, affected by the impact frequency. During the analysis, multiple sets of historical data are collected; for example, in a relay contact system, performance indicators such as changes in contact resistance are recorded after hundreds of current impacts. Then, statistical methods such as regression analysis are used to fit the data points to identify patterns, such as discovering that the decay rate accelerates when the accumulated impact energy exceeds a threshold. Furthermore, this analysis can incorporate machine learning methods, such as using support vector machines to classify feature vectors and distinguish between normal and abnormal decay patterns.

[0050] It should be noted that the discovery of potential patterns emphasizes causal relationships. For example, the thermal effect of current surges leads to changes in the microstructure of materials, thereby causing macroscopic performance degradation. Through multiple iterative analyses, the system can identify patterns such as "after more than 50 surges, the degradation rate increases by 20%." This detailed process ensures the accuracy of the analysis and provides predictive basis for electrical equipment maintenance, such as helping to replace contact components in advance to avoid failures.

[0051] Preferably, when determining the critical time points of contact pressure changes, a time series monitoring mechanism is introduced based on the aforementioned analysis results. The specific process includes: first, continuously sampling the contact pressure data and recording the real-time value P(t) using a pressure sensor; then, calculating the pressure change rate dP / dt based on the attenuation law, and setting a threshold such that when the change rate exceeds 5%, it is marked as a critical point.

[0052] For example, in a circuit breaker contact system, if analysis shows that the attenuation rate accelerates after t=100 hours, the node can be identified by monitoring the inflection point of the pressure curve, such as by using a sliding window algorithm to detect rate mutation points.

[0053] In one embodiment, dynamic behavior pattern data is further integrated, and when the vibration amplitude is synchronized with the pressure drop, a node is identified as a moment of significant pressure change. This judgment helps in real-time early warning; for example, in high-voltage transmission equipment, early identification of nodes can prevent power outages caused by contact failures. In another embodiment, the extended application of dynamic behavior patterns can be implemented in different electrical contact scenarios, such as in low-voltage distribution boxes, where pattern analysis focuses on micro-dynamic changes under daily loads, rather than high-voltage surges.

[0054] Specifically, behavioral data is collected in real time through an embedded sensor network and combined with current surge characteristics to form a comprehensive evaluation framework. The versatility of this framework lies in its adaptability to various contact materials, such as copper or silver alloys, ensuring consistency in the analysis process. Furthermore, the extraction of characteristic data of the current surge effect can be optimized into an automated process.

[0055] In one possible implementation, the system employs a digital signal processor to calculate feature values ​​in real time. For example, a peak detection algorithm automatically identifies impact events and quantifies their effects. This approach improves data processing efficiency and, in practical applications such as substation equipment monitoring, can quickly generate reports and support attenuation pattern analysis.

[0056] For example, the analysis of potential patterns in contact performance degradation can be deepened through multi-dimensional data fusion. The specific process involves integrating temperature, pressure, and resistance data to form a multivariate model; then, correlation analysis is used to identify dominant factors, such as calculating the Pearson coefficient between impact energy and attenuation rate. Results showing a high correlation prioritize impact control. This analysis reveals the underlying mechanisms of these patterns, such as the formation of microcracks on the contact surface under repeated impacts, leading to exponential performance degradation. In this way, the technical solution demonstrates practical value in electrical reliability assessment and can extend equipment lifespan.

[0057] It should be noted that the key time points for judging changes in contact pressure can be combined with a threshold alarm mechanism.

[0058] In one embodiment, when the analysis pattern indicates that the decay is entering an acceleration phase, the system marks the node with a timestamp and triggers a maintenance reminder.

[0059] For example, in industrial relays, node determination is based on cumulative impact data, with an accuracy rate of over 90%, ensuring safe operation.

[0060] Preferably, the entire analysis process forms a closed loop from data acquisition to node determination. In one implementation, for example in a smart grid contact system, dynamic patterns and impact characteristics are first extracted, then attenuation patterns are analyzed, and finally, time node reports are output. This integrated approach enhances the flexibility of the solution and is applicable to various electrical scenarios.

[0061] It is understandable that, through the above implementation methods, the technical solution achieves comprehensive monitoring of contact performance. For example, in practical applications, it can effectively reduce the occurrence of unexpected failures and provide reliable technical support.

[0062] Step S104: If the contact pressure change exceeds the preset threshold range, the operating status of the connector is continuously tracked through real-time monitoring technology to obtain abnormal fluctuation signals and determine the triggering conditions for potential failure behavior.

[0063] Based on the fluctuation points, the real-time monitoring technology is used to continuously track the connector's operating status, obtaining an abnormal fluctuation signal sequence during the tracking process. The degree of matching between the abnormal fluctuation signal sequence and the potential failure behavior is determined using this sequence, resulting in a candidate set of triggering conditions. For this candidate set, the failure risk assessment and monitoring data analysis are integrated, and the triggering conditions for the potential failure behavior are determined from the fusion result.

[0064] For example, in one implementation, the system activates a real-time monitoring mechanism when the contact pressure of the connector changes beyond a preset threshold range. This mechanism utilizes a pressure sensor mounted on the connector to continuously collect data, with the threshold range set, for example, ±10% of the normal pressure, to detect potential problems. In this way, the stable operation of the connector in industrial equipment such as power transmission systems is ensured.

[0065] Specifically, real-time monitoring technology involves using a wireless sensor network to continuously track the connector's operational status. These sensors acquire pressure data once per second and analyze fluctuations via an embedded processor.

[0066] It's important to note that operational status tracking involves monitoring multi-dimensional parameters such as pressure, temperature, and vibration at the contact points, forming a comprehensive state vector to capture subtle changes. Furthermore, once abnormal fluctuation signals are detected, the system records their characteristics, such as amplitude and frequency. Abnormal fluctuation signals refer to situations where pressure values ​​suddenly jump above a threshold, such as pressure instability caused by external vibration in high-voltage power connectors. These features are extracted using signal processing algorithms, such as Fourier transform, to identify signal patterns.

[0067] In one possible implementation, the triggering conditions for potential failure behavior are determined by comparing abnormal signals with historical data.

[0068] For example, a pressure change rate exceeding 5% per minute is considered a trigger condition. This involves building a failure model that is trained on historical failure cases using machine learning methods to predict potential disconnection or corrosion risks. In the industrial connector field, this approach is applicable to substation equipment, ensuring early intervention.

[0069] Preferably, in another embodiment, the real-time monitoring technology can be integrated with an IoT platform to perform group tracking of multiple connectors. When abnormal fluctuation signals are acquired, the data is processed locally using edge computing devices to reduce latency.

[0070] For example, in a cable connection system, when the pressure exceeds a threshold, the system automatically generates an alarm log, recording the trigger time and environmental factors such as humidity.

[0071] Understandably, the process of determining the triggering conditions for potential failure behavior includes adaptive threshold adjustment. Depending on the connector's operating environment, such as in a humid industrial setting, the threshold can be dynamically adjusted by ±15% to accommodate variations. This adaptive strategy improves monitoring accuracy and avoids false alarms.

[0072] For example, in the context of power equipment connectors, if the contact pressure exceeds a threshold due to thermal expansion, the system tracks and acquires fluctuation signals in real time, such as a sudden increase in pressure from 100 kPa to 120 kPa. Then, the triggering conditions are analyzed, including calculating the duration of the change. If it exceeds 10 seconds, it is identified as a potential failure, such as a risk of poor contact. This detailed analysis helps maintenance personnel replace components promptly, enhancing system reliability. Furthermore, the versatility of this technical solution is reflected in connector applications of varying scales.

[0073] In one embodiment, for small electronic connectors, monitoring focuses on micro-pressure changes, using high-precision sensors to acquire signals. Triggering conditions are based on statistical thresholds, such as the mean plus three standard deviations, to determine the likelihood of failure.

[0074] It should be noted that the acquisition of abnormal fluctuation signals can also be combined with filtering techniques to remove noise and ensure signal purity.

[0075] For example, using a Kalman filter to process the raw data and output a smooth fluctuation curve allows for more accurate determination of triggering conditions, such as marking a potential failure when the signal peak exceeds a preset level. In one implementation, the overall effect of this process is to improve connector lifespan prediction. Through continuous tracking and signal analysis, preventative maintenance can be achieved in industrial applications such as manufacturing production lines, reducing the occurrence of unexpected failures.

[0076] Step S105: Based on the abnormal fluctuation signal, use the pre-established failure behavior prediction model to analyze the influence trend of multiple stress environment on the long-term performance of the connector and obtain the prediction result of performance degradation.

[0077] A signal fluctuation dataset is obtained by acquiring real-time signal sequences from the connector's operating environment using an abnormal fluctuation signal acquisition device. A pre-established failure behavior prediction model is then used to input the signal fluctuation dataset into a support vector machine (SVM) algorithm. This algorithm classifies failure modes and constructs a model by minimizing structural risk. The model parameters include a kernel function and a penalty factor, which determine the initial failure feature vector. Based on multi-stress environment simulation, stress combinations such as temperature, humidity, and vibration are applied to the initial failure feature vector. The environmental stress influence matrix is ​​obtained by multiplying the stress values ​​with the feature vector. The long-term performance change trend of the connector is analyzed based on this environmental stress influence matrix. If the trend indicates accelerated degradation, performance indicator monitoring data is integrated, and the ratio of accelerated degradation to monitoring data is calculated to obtain a degradation acceleration coefficient. Using this degradation acceleration coefficient, the performance degradation result is predicted, resulting in a predicted curve for the long-term performance degradation of the connector.

[0078] For example, in one implementation, abnormal fluctuation signals refer to abnormal vibrations or electrical signal changes generated by the connector under multiple stress environments, such as signal deviations caused by the combined effects of temperature, humidity, vibration, and mechanical stress. These signals are acquired in real time by sensors.

[0079] Specifically, multi-stress environments include a combination of factors such as high temperature, low temperature, humidity, and mechanical vibration. These factors can accelerate material fatigue or increase contact resistance in connectors, thereby affecting long-term performance.

[0080] It should be noted that the pre-established failure behavior prediction model is trained based on historical data. This model uses machine learning algorithms to integrate signal features and predict the failure probability of the connector under sustained stress. In this way, the cumulative impact trend of the environment on performance can be analyzed.

[0081] For example, the process of building a failure behavior prediction model first involves a data collection phase. In the field of aerospace connectors, such as connectors in aircraft engine nacelles, they are exposed to high temperatures and vibrations. When collecting abnormal fluctuation signals, vibration and temperature sensors are used to record data, such as signal peaks when the vibration frequency exceeds a normal threshold. These signals are then converted into feature vectors, including parameters such as amplitude, frequency, and duration. Model training employs supervised learning methods, using historical failure cases as labels to train the model to learn the correspondence between signals and failure behaviors. For example...

[0082] In one possible implementation, the model uses a neural network structure. The input layer receives signal features, the hidden layers process the interactive effects of environmental variables, and the output layer generates a failure probability distribution. This setup ensures the model can capture the nonlinear effects of multiple stresses, thus providing a foundation for subsequent analysis. The entire process emphasizes the accuracy of data preprocessing, such as filtering to remove noise, to improve the model's prediction accuracy. Furthermore, when using this model to analyze the impact of multiple stress environments on the long-term performance of connectors, real-time abnormal fluctuation signals are first input. The model simulates stress accumulation effects, such as material expansion due to high temperatures, combined with loosening caused by vibration, to calculate the gradual change curve of performance indicators such as contact resistance. The specific analysis process includes decomposing multiple stresses into individual factors and then evaluating their interactions.

[0083] For example, increased humidity amplifies the corrosive effects of vibration. The model quantifies this trend using weighting coefficients, generating a performance degradation curve. This curve shows the time series from the initial state to potential failure, helping to predict the lifespan of connectors in aerospace equipment.

[0084] In one embodiment, the model can adjust parameters to adapt to specific stress combinations for connectors in different aviation scenarios, such as wings or cabin equipment.

[0085] Preferably, time series analysis is introduced, and the model tracks the long-term fluctuation pattern of the signal to predict the performance degradation rate.

[0086] For example, if the vibration signal shows periodic anomalies, the model will infer accelerated mechanical fatigue, and the output attenuation result will be a 20% increase in resistance value within 6 months. This approach demonstrates the versatility of the technical solution, covering multiple sub-scenarios within the same field.

[0087] Understandably, the predicted performance degradation can be used to make maintenance decisions.

[0088] For example, if the prediction results show a steep degradation trend, it is recommended to replace the connector in advance to avoid aviation equipment failure. The objective effect of this process is to improve the accuracy of connector reliability assessment. Through model analysis, it achieves quantitative prediction of long-term performance, rather than relying on experience-based judgment.

[0089] Specifically, in another implementation, the model can integrate a feedback mechanism to update parameters based on newly acquired signals.

[0090] For example, in an aviation testing environment, if actual performance does not degrade after the initial prediction, the model adjusts the weights to optimize future analyses. This flexibility enhances the applicability of the approach and ensures accurate predictions under multiple stresses.

[0091] In one embodiment, for the application of connectors under continuous vibration stress, the analysis process emphasizes the spectral analysis of the signal, decomposes the fluctuation into frequency domain components, and the model evaluates the cumulative damage of material strain accordingly, thereby deriving a quantitative indicator of performance degradation, such as the percentage of remaining life.

[0092] It should be noted that the core is the interactive analysis of multiple stress environments. The model simulates the combination of different stress levels, such as the synergistic effect of high temperature and humidity, which leads to accelerated aging of insulation materials. The prediction results are presented as a decay path in the form of a curve.

[0093] For example, in the maintenance of aviation connectors, the application of this method can cover the entire process from signal acquisition to result output, ensuring logical coherence and providing technical support for the claims.

[0094] Step S106: Based on the predicted performance degradation results, adjust the parameter configuration of the complex environment simulation, optimize the test conditions for the effects of vibration stress and temperature changes, and determine the improvement in test efficiency after the improvement.

[0095] The simulation parameter adjustment basis is obtained from the performance degradation prediction. This simulation parameter adjustment is then used to optimize the vibration impact configuration, resulting in a temperature effect change dataset after vibration impact optimization. For this temperature effect change dataset, the vibration impact optimization is processed by collecting multiple stress combination data to determine the stress impact analysis results of the test condition configuration. Based on the stress impact analysis results, the optimization effect targeted by the prediction results is fused, and the efficiency magnitude judgment index of the configuration parameter improvement is obtained by multiplying the stress value and the effect vector. Based on the efficiency magnitude judgment index, if the improvement trend exceeds a preset threshold, the improved test efficiency improvement magnitude is output.

[0096] For example, in one implementation, the parameter configuration of the complex environment simulation is first adjusted in response to the predicted performance degradation.

[0097] Specifically, complex environment simulation refers to the creation of multiple stress condition combinations in aerospace connector testing, including controlled settings for factors such as vibration and temperature. These parameter configurations are based on attenuation curve data from predicted results, such as modifying the vibration frequency and temperature gradient values ​​of the simulated equipment by analyzing predicted resistance increase trends.

[0098] It should be noted that the adjustment process involves iterative calibration of parameters. For example, the initial parameters are set to a standard vibration intensity, and then specific values ​​are gradually decreased or increased based on the decay rate shown in the prediction results to match the stress distribution in the actual aviation environment. This approach ensures that the simulation more closely approximates the actual exposure conditions of the connector within the aircraft cabin. Furthermore, when optimizing the test conditions for the effects of vibration stress, a focus is placed on refining the vibration parameters.

[0099] For example, in the testing of aerospace connectors such as engine connection components, the effect of vibration stress refers to the accumulation of material fatigue caused by mechanical vibration. The optimization process involves decomposing the vibration waveform into amplitude and periodic components, and then adjusting the vibration mode of the test platform based on the prediction results, for example, changing continuous vibration to an intermittent mode to reduce unnecessary stress superposition.

[0100] Understandably, this optimization is achieved through updating parameter configurations, such as setting the vibration duration to a fraction of the predicted decay period, thereby enabling the test to more specifically simulate the long-term effects of wing vibration environments.

[0101] In one possible implementation, the test conditions for the effect of temperature changes are optimized, emphasizing the dynamic adjustment of the temperature curve.

[0102] Specifically, temperature changes involve the impact of thermal expansion and contraction on connector contacts. Based on predicted performance degradation, such as the predicted material aging rate, the temperature range of the simulated chamber is adjusted, for example, shifted downwards from the high-temperature peak, to avoid excessively accelerating degradation.

[0103] Preferably, this optimization integrates the output data of the prediction model and quantifies the interaction between temperature and vibration through weight allocation, for example, prioritizing the reduction of temperature fluctuation amplitude when the prediction shows that high temperature dominates the decay.

[0104] In one embodiment, a comparative analysis method is used to determine the extent of the improvement in testing efficiency.

[0105] It should be noted that the improvement in testing efficiency refers to a quantitative indicator of the reduction in testing cycle or the improvement in accuracy after optimization. The specific process includes recording the time consumption and result deviation under the original testing conditions, then rerunning the simulation after adjusting the parameters, and calculating the difference value.

[0106] For example, in the testing scenario of aviation equipment connectors, the original test may take several weeks, while after optimization, by refining vibration and temperature parameters, the cycle can be compressed, and the magnitude can be evaluated as a percentage, such as the specific percentage reduction in time. Furthermore, the entire adjustment and optimization logic process starts from the input of the predicted results and gradually proceeds to parameter updates and condition resets.

[0107] For example, in cabin connector testing, key indicators of the predicted results, such as attenuation thresholds, are first extracted and then mapped to simulation parameters, such as vibration intensity coefficients, which are linearly adjusted according to the thresholds. This seamless transition ensures continuity from analysis to application. In another implementation, for different aerospace sub-scenarios, such as flight control system connectors, the adjustment process can introduce hierarchical parameter configuration.

[0108] Specifically, hierarchical configuration separates vibration and temperature into independent levels, optimizing individual factors first and then integrating their interactions. Using this method, when assessing efficiency improvements, the contribution of each level can be evaluated separately, such as the independent amplitude of vibration optimization.

[0109] Understandably, the judgment process emphasizes data-driven analysis, such as collecting statistical indicators of test data before and after optimization, like changes in simulated accuracy, to derive the numerical range of the improvement. This approach is consistent across the aerospace connector industry, covering various test configurations. Preferably...

[0110] In one embodiment, efficiency is determined through multiple rounds of iteration. Furthermore, the iteration involves a feedback loop, such as fine-tuning parameters based on the initial optimization results and re-evaluating the magnitude of the adjustment to ensure its gradual nature.

[0111] Step S107: Under the improved test conditions, collect a new batch of operating data of the connector in the optimized environment, analyze the comprehensive effect of dynamic factor analysis, and determine the final solution for rapid performance testing.

[0112] By improving the test conditions, operational data of the connectors under optimized conditions is collected to obtain dynamic factor change sequences. The completeness of these sequences is determined through data point continuity checks. Based on the dynamic factor change sequences and sequence completeness, the overall effect is analyzed, and the stability of the effect is judged using a preset threshold. If the stability exceeds the threshold, a set of performance indicators is obtained. For this set of performance indicators, a rapid testing framework is generated by integrating environmental variable control and data verification mechanisms. The framework's applicable scope is determined through boundary verification of the indicator set. From the framework's applicable scope, combined with iterative optimization, it is expanded to connector load simulation services, thus determining the final rapid performance testing solution.

[0113] For example, in one implementation, improved test conditions are used to enhance the accuracy of connector performance evaluation.

[0114] Specifically, test conditions may include adjusting the ambient temperature to a specified range, such as controlling the temperature between -40°C and 85°C in simulated industrial applications, to reflect the connector's performance under extreme conditions. Furthermore, humidity levels are optimized to 40% to 80%, and controlled vibration simulation is introduced to mimic dynamic disturbances encountered in actual operation. These improvements ensure the stability of the test environment, thus providing a reliable foundation for subsequent data acquisition.

[0115] It should be noted that these conditions are adjusted based on the thermal expansion coefficient and mechanical durability of the connector material, for example, using temperature chambers and vibration tables for precise control. This setup effectively simulates the working environment of connectors on electronic device assembly lines, improving the representativeness of the tests.

[0116] For example, when collecting a new batch of operational data from the connector in an optimized environment, a sensor network is first deployed to monitor key parameters.

[0117] In one possible implementation, current sensors and voltage probes are used to record the connector's electrical performance data in real time, for example, by acquiring data points once per second to capture transient fluctuations.

[0118] Preferably, mechanical stress sensors are used to measure tension and torque at the connection points, ensuring multi-dimensional data coverage. For example, on a connector test bench, multiple connectors are run in an optimized environment for several hours, collecting data including current values, voltage drops, and temperature change curves. This data is stored in digital format for easy subsequent analysis. This acquisition process emphasizes data integrity and real-time performance, supporting pre-mass production verification in the connector quality control field. Furthermore, analyzing the comprehensive effects of dynamic factor analysis involves detailed processing across multiple steps. Dynamic factors can be understood as variables affecting connector performance, such as vibration frequency, temperature gradient, and load changes. These factors interact during operation, leading to performance degradation.

[0119] Specifically, the analysis process first categorizes the collected data, for example, by correlating vibration data with temperature data, and constructing a multivariate model to assess the overall impact.

[0120] In one embodiment, statistical methods are used to calculate the weights of each factor, such as determining the contribution of vibration to electrical impedance through correlation analysis, and then the overall effect is quantified, such as calculating the performance degradation rate.

[0121] It's important to note that this analytical framework is based on causal reasoning. For example, if the vibration frequency exceeds 10Hz, it might amplify the effect of temperature on connector contact resistance, leading to a 20% increase in resistance. Extending this further, in scenarios where connectors are used in communication devices, this analysis reveals how dynamic factors can cause signal interruption risks, thus guiding design improvements. The entire process ensures the accuracy of the analysis results through iterative verification, such as repeatedly testing different batches of data to confirm consistent effects. This comprehensive analysis not only reveals the limitations of single factors but also highlights the crucial role of interaction effects, enabling more comprehensive performance predictions in connector reliability assessments.

[0122] Understandably, based on the above analysis, the final solution for rapid performance testing is further determined. In one implementation, the solution integrates improvement conditions and data analysis results to form a standardized protocol.

[0123] For example, the test duration can be shortened to within 30 minutes, and connector durability can be quickly assessed through preset dynamic factor simulations.

[0124] Preferably, the scheme includes a threshold setting; if the resistance change exceeds 5%, the connector is deemed unqualified. This rapid testing is suitable for in-line inspection on connector production lines, reducing the time consumption of traditional long-term testing. In another embodiment, for different types of connectors, such as RF connectors, improved test conditions can focus on electromagnetic interference simulation.

[0125] Specifically, an electromagnetic shielding chamber is introduced to optimize the environment, and signal attenuation is monitored during data acquisition. When analyzing dynamic factors, the interaction between electromagnetic waves and mechanical vibrations is emphasized, such as calculating the impact of peak interference on transmission efficiency. In this way, a signal integrity check step can be added when determining a rapid testing scheme to ensure its versatility. Furthermore, the results demonstrate that this method enables efficient evaluation in the field of connector testing.

[0126] For example, in practical applications, after adopting this solution, the testing cycle was shortened by 40% while maintaining an accuracy rate of over 95%.

[0127] For example, for waterproof connectors, environmental optimization includes water pressure simulation, with data collection focusing on sealing performance indicators. When analyzing dynamic factors, the combined effects of water pressure and temperature are examined in detail, such as how temperature fluctuations affect seal deformation when water pressure increases. The impact is quantified by calculating the deformation rate, enabling the development of rapid testing protocols, such as high-pressure short-time immersion tests.

[0128] In one possible implementation, the application of connectors in automotive electronic systems can be improved through a combination of vibration and thermal cycling. Data acquisition utilizes wireless sensors to avoid interference, the analysis process involves time-series models to assess long-term dynamic effects, and the final solution emphasizes modular testing for easy integration into the production process.

[0129] It should be noted that these embodiments are all limited to the field of connector performance testing to ensure the specificity of the technical solutions. The descriptions of various scenarios demonstrate the flexibility of the method, such as adjusting analysis parameters in high-frequency connectors to adapt to different frequency ranges. Further extending this approach, in batch testing, the solution can include automated script-controlled data acquisition and analysis, improving throughput without sacrificing accuracy.

[0130] Step S108: Based on the final solution, integrate the output results of real-time monitoring technology and multi-stress environment simulation to construct a dynamic evaluation system for connector performance and obtain a comprehensive performance test report.

[0131] Real-time monitoring technology is used to collect operational data of the connector under multiple stress environments, resulting in an initial set of operational data. Based on this initial set and the output results of the multiple stress environment simulation, data fusion processing is employed to match and weighted average the two sets, resulting in a fused performance dataset. A dynamic evaluation system is constructed for this fused performance dataset, determining the connector's load test indicators under simulated environmental parameters by comparing the performance dataset with preset benchmark values. If the load test indicators exceed a preset threshold, a fault mode identification requirement is identified, and durability indicators are calculated by matching the load test indicators with a known fault database. A report generation template is obtained from the durability indicator calculation results, and a comprehensive performance test report is generated by filling the durability indicator calculation results into the report generation template.

[0132] For example, in one implementation, real-time monitoring technology is applied to the performance evaluation of connectors, starting by deploying a sensor network to collect operational data of the connectors.

[0133] Specifically, these sensors include temperature sensors, vibration sensors, and humidity sensors, which are installed on key parts of the connector, such as the interface and housing, to capture changes in environmental variables in real time.

[0134] For example, in electrical connectors on industrial automated production lines, temperature sensors monitor the connector's thermal response under high-temperature operation, vibration sensors record the stress caused by mechanical vibration, and humidity sensors detect the impact of moisture intrusion on insulation performance. This monitoring technology ensures continuous data acquisition, transmitting data in real time to a central processing unit via a wireless transmission module, avoiding the limitations of traditional intermittent monitoring.

[0135] It's important to note that the core of real-time monitoring lies in the data synchronization mechanism, which utilizes timestamp algorithms to ensure the temporal consistency of data from multiple sensors, thus providing a reliable foundation for subsequent integration. In actual business operations, this technology can help identify early fault signals in connectors under dynamic loads, such as triggering alarms when vibration exceeds a threshold, achieving the goal of preventative maintenance. Furthermore, the output results of the multi-stress environment simulation are generated through a dedicated simulation platform that simulates the behavior of connectors under various stresses.

[0136] For example.

[0137] In one possible implementation, the simulation process first defines stress parameters, such as a temperature range from -40°C to 85°C, humidity from 20% to 95%, and vibration frequency from 10Hz to 2000Hz. These parameters are based on industry standard settings, for example, for the application scenarios of electrical connectors in automated equipment. The simulation platform uses finite element analysis methods to calculate the stress distribution of the connector material, for example, by simulating deformation caused by thermal expansion through input temperature gradients.

[0138] Specifically, the simulation process involves building a three-dimensional model of the connector and then applying multiple stress combinations, such as simultaneously applying high temperature and high humidity, to assess the risk of corrosion.

[0139] In one embodiment, the simulation output includes stress distribution maps and failure probability predictions, which are stored in the form of numerical vectors to quantify the durability of the connector.

[0140] Understandably, this simulation technology addresses the high cost of actual testing by reproducing complex scenarios in a virtual environment, thereby providing high-fidelity output data. In business applications, this helps optimize connector design; for example, if vibration-induced contact problems are detected in the simulation, material selection can be adjusted to improve reliability. Based on the aforementioned real-time monitoring and simulation output, building a dynamic evaluation system for connector performance involves data integration and model training processes.

[0141] Specifically, the real-time monitoring data is first fused with the simulation results. For example, a data matching algorithm is used to match the actual monitored temperature with the simulated thermal stress distribution.

[0142] In one embodiment, the evaluation system employs a machine learning framework, where a feature extraction module extracts key indicators from the fused data, such as the rate of change of connection impedance and fatigue life estimates. Furthermore, the model training process includes supervised learning using historical datasets as input, for example, training a predictive model using a neural network algorithm. This model takes a multidimensional stress vector as input and outputs a performance degradation curve.

[0143] It should be noted that the core innovation of dynamic evaluation lies in the real-time update mechanism, which re-integrates new data and adjusts evaluation parameters at fixed time intervals (such as 1 hour) to achieve continuous tracking of connector performance.

[0144] For example, in connector testing within industrial automation, this system can simulate the risk of production line disruptions and predict the remaining lifespan of connectors under multiple stresses. In this way, the evaluation system not only covers static performance but also extends to dynamic response, providing a more comprehensive performance insight. In practical applications, this brings technical benefits such as reducing unexpected failure rates and improving overall system stability.

[0145] Preferably, when constructing a dynamic evaluation system, multiple verification steps can be introduced to enhance accuracy.

[0146] For example, in one implementation, the system includes a feedback loop module that compares the deviation between real-time monitoring data and simulation predictions. If the deviation exceeds a preset threshold, it triggers the optimization adjustment of simulation parameters.

[0147] Specifically, the feedback process involves calculating the mean squared error and iteratively updating the simulation model using a gradient descent method. This approach ensures the robustness of the evaluation and is particularly useful in business scenarios involving connector performance testing, such as when actual vibration data does not match the simulation, allowing the system to automatically calibrate to reflect the real industrial environment.

[0148] It should be noted that this dynamic adjustment avoids the limitations of static models and achieves adaptive evaluation. In another embodiment, the dynamic evaluation system is applied to specific connector types, such as the performance evaluation of high-voltage electrical connectors in automated control systems. In this scenario, the integration process first standardizes the data format, for example, converting monitored voltage fluctuation data into a unified vector, and then combines it with simulated electromagnetic interference stress.

[0149] For example, the evaluation model calculates a comprehensive performance index, which is derived by weighted summation of multiple indicators, such as pressure resistance (40%), mechanical strength (30%), and environmental adaptability (30%). This index provides quantitative evidence to support the decision-making process. Furthermore, a comprehensive performance test report is obtained through the output module of the evaluation system.

[0150] Specifically, the report generation process includes summarizing the evaluation results and forming a structured document, which may include a summary, data charts, and conclusions.

[0151] In one possible implementation, the report automatically generates performance graphs, showing the connector's response curves under different stress levels.

[0152] Understandably, the report also includes a risk assessment section, such as listing potential failure modes and their probabilities. This type of report is used in industrial automation for quality control, for example, to guide connector maintenance plans.

[0153] In one embodiment, the performance test report is extended to multi-device scenarios, such as evaluating the overall performance of a group of connectors on a production line. Here, the report integrates group data, calculates average durability metrics, and provides optimization recommendations, such as replacing specific batches of connectors to improve system efficiency.

[0154] Preferably, the report generation process supports custom formats, such as adding detailed simulation logs according to user needs. This flexibility enhances the versatility of the technology and facilitates collaboration among different teams in connector testing. In practice, the logical flow of the entire process from monitoring to reporting ensures consistency, such as adjusting monitoring parameters based on simulation output to form a closed loop. Through these steps, the technical solution demonstrates its applicability in the performance evaluation of connectors in industrial automation.

[0155] The above description is merely a preferred embodiment of this application and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in this application is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the concept of this application. For example, technical solutions formed by substituting the above features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A rapid performance testing method for connectors under multiple stress environments, characterized in that, The method includes the following steps: Step S101: By constructing a multi-stress environment simulation system, the operating data of the connector under the combined action of vibration, temperature change and current impact are obtained. The collected raw signals are preprocessed to obtain a preliminary environmental stress response dataset. Step S102: Based on the preliminary environmental stress response dataset, time-frequency analysis technology is used to extract characteristic parameters of the influence of vibration stress and temperature change, and to determine the dynamic behavior mode of the connector under multiple stress environments. Step S102 includes: Using the environmental stress response dataset, the vibration signal is processed by short-time Fourier transform. The short-time Fourier transform divides the signal into windows and weights it before performing Fourier transform, resulting in a time-spectrum diagram representing the impact of vibration stress. Based on the time-frequency spectrum diagram, calculate the energy integral of each frequency band from the time-frequency spectrum diagram, obtain the frequency domain energy distribution under the action of temperature change, and determine the stress interaction fusion characteristics; To address the stress interaction and fusion characteristics, wavelet transform is used to extract multiple environmental response parameters. The wavelet transform decomposes the feature signal through continuous wavelet coefficients to obtain a preliminary classification of dynamic behavior patterns. From the preliminary classification of the dynamic behavior patterns, the signal time-domain decomposition results are integrated. These results are obtained by calculating the time-domain autocorrelation function of the vibration signal to determine the connector reliability assessment threshold. If the connector reliability assessment threshold exceeds the preset range, the behavior pattern classification is adjusted. The behavior pattern classification is based on the preliminary classification of dynamic behavior patterns to perform clustering and grouping, and to determine the dynamic behavior pattern of the connector under multiple stress environments. Step S103: Based on the dynamic behavior pattern and combined with the characteristic data of the current impact effect, analyze the potential law of contact performance decay and determine the key time nodes of contact pressure change. Step S104: If the contact pressure change exceeds the preset threshold range, the operating status of the connector is continuously tracked through real-time monitoring technology to obtain abnormal fluctuation signals and determine the triggering conditions for potential failure behavior. Step S105: Based on the abnormal fluctuation signal, use the pre-established failure behavior prediction model to analyze the influence trend of multiple stress environment on the long-term performance of the connector and obtain the prediction result of performance degradation. Step S106: Based on the predicted results of performance degradation, adjust the parameter configuration of the complex environment simulation, optimize the test conditions for the effects of vibration stress and temperature changes, and determine the improvement in test efficiency after the improvement. Step S107: Under the improved test conditions, collect a new batch of operating data of the connector in the optimized environment, analyze the comprehensive effect of dynamic factor analysis, and determine the final solution for rapid performance testing. Step S108: Based on the final solution, integrate the output results of real-time monitoring technology and multi-stress environment simulation to construct a dynamic evaluation system for connector performance and obtain a comprehensive performance test report.

2. The rapid performance testing method for connectors under multiple stress environments according to claim 1, characterized in that, Step S101 includes: By constructing a multi-stress environment simulation system, we can obtain the operating data of the connector under the combined effects of vibration, temperature change and current impact, and obtain the original signal set. Noise filtering is performed on the original signal set by using Fourier transform to convert the signal from the time domain to the frequency domain to remove high-frequency noise components, thereby obtaining a filtered signal sequence. Normalization is performed on the filtered signal sequence to determine stress response characteristic values ​​by mapping the signal values ​​to a uniform scale; If the stress response characteristic value exceeds the preset threshold, environmental factor data obtained from vibration environment control, temperature change regulation, and current impact application are integrated to obtain a preliminary environmental stress response dataset.

3. The rapid performance testing method for connectors under multiple stress environments according to claim 1, characterized in that, Step S103 includes: By collecting dynamic behavior pattern data, characteristic data are obtained from the current impact effect to obtain contact performance degradation index. For the aforementioned contact performance degradation index, a linear regression algorithm is used to fit the behavior pattern dynamics as the independent variable and the contact performance degradation index as the dependent variable to determine the performance degradation law. Based on the performance degradation law and the impact effect characteristics, the contact pressure change curve is determined. From the contact pressure change curve, the results of potential pattern analysis are obtained, and key time nodes are identified; For the aforementioned key time points, the criteria for judging pressure changes are determined by combining the key time points of the nodes.

4. The rapid performance testing method for connectors under multiple stress environments according to claim 1, characterized in that, Step S104 includes: Based on the fluctuation points, the real-time monitoring technology is used to continuously track the operating status of the connector, and abnormal fluctuation signal sequences are obtained from the continuous tracking process; By analyzing the abnormal fluctuation signal sequence, the degree of matching between the abnormal fluctuation signal sequence and the potential failure behavior is determined, thereby obtaining a candidate set of triggering conditions. For the candidate set of triggering conditions, the failure risk assessment and monitoring data are integrated and analyzed to determine the triggering conditions for potential failure behaviors from the integrated results.

5. A rapid performance testing method for connectors under multiple stress environments according to any one of claims 1-4, characterized in that, Step S105 includes: By using an abnormal fluctuation signal acquisition device, real-time signal sequences are obtained from the connector's operating environment to obtain a signal fluctuation dataset. A pre-established failure behavior prediction model is used to input the signal fluctuation dataset into a support vector machine algorithm. This algorithm classifies failure modes and builds a model by minimizing structural risk. The model parameters include a kernel function and a penalty factor to determine the initial failure feature vector. Based on the multi-stress environment simulation, stress combinations such as temperature, humidity and vibration are applied to the preliminary failure characteristic vector. By superimposing the stress values ​​and multiplying the characteristic vector, the environmental stress influence matrix is ​​obtained. For the environmental stress influence matrix, the long-term performance change trend of the connector is analyzed. If the change trend shows accelerated degradation, the performance index monitoring data is integrated, and the degradation acceleration coefficient is obtained by calculating the ratio of accelerated degradation to the monitoring data. By using the attenuation acceleration coefficient, the performance degradation result is predicted, and a prediction curve for the long-term performance degradation of the connector is obtained.

6. A rapid performance testing method for connectors under multiple stress environments according to any one of claims 1-4, characterized in that, Step S106 includes: The basis for adjusting the simulation parameters is obtained from the performance degradation prediction. The simulation parameter adjustment is used to configure the vibration impact optimization, and the temperature effect change dataset after vibration impact optimization is obtained. For the aforementioned temperature-induced variation dataset, the vibration impact is optimized by collecting and processing multiple stress combination data, and the stress impact analysis results of the test condition configuration are determined. Based on the stress influence analysis results, the optimization effect of the fusion prediction results is used to obtain the efficiency magnitude judgment index of the configuration parameter improvement by multiplying the stress value and the effect vector. Based on the efficiency magnitude judgment index, if the judgment trend exceeds the preset threshold, the improved test efficiency magnitude is output.

7. A rapid performance testing method for connectors under multiple stress environments according to any one of claims 1-4, characterized in that, Step S107 includes: By improving the test conditions, the connector's operating data in the optimized environment is collected to obtain the dynamic factor change sequence and determine the sequence integrity. The sequence integrity is obtained through data point continuity checks. Based on the dynamic factor change sequence and sequence completeness, the overall effect is analyzed, and the stability of the effect is judged by a preset threshold. If the stability of the effect exceeds the threshold, a set of performance indicators is obtained. For the set of performance metrics, an environmental variable control and data verification mechanism are integrated to generate a rapid testing framework, and the scope of application of the framework is obtained. The scope of application of the framework is determined by the boundary verification of the set of metrics. Based on the scope of application of the aforementioned framework and combined with iterative optimization of the solution, it was expanded to connector load simulation services to determine the final solution for rapid performance testing.

8. A rapid performance testing method for connectors under multiple stress environments according to any one of claims 1-4, characterized in that, Step S108 includes: The initial set of the operating data is obtained by collecting the connector's operating data under multiple stress environments through real-time monitoring technology; Based on the initial set and the output results of the multi-stress environment simulation, data fusion processing is used to match and weighted average the two to obtain the fused performance dataset. For the fused performance dataset, a dynamic evaluation system is constructed, and the load test index of the connector under environmental simulation parameters is determined by comparing the performance dataset with the preset benchmark value. If the load test index exceeds the preset threshold, it is determined that there is a fault mode identification requirement, and the durability index calculation result is obtained by matching the load test index with the known fault database. A report generation template is obtained from the durability index calculation results, and a comprehensive performance test report is generated by filling the durability index calculation results into the report generation template.

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