A connector performance detection method

Abstract

The present application relates to the technical field of electronic connector performance monitoring, and particularly relates to a connector performance detection method, aiming to solve the problem that the method of realizing performance early warning through dynamic correlation analysis of vibration signals and contact resistance in the prior art is not applicable to L-shaped pin connector without a sheath. The present application synchronously collects micro-strain signals and contact resistance signals, divides and analyzes time periods in combination with plugging events, quantitatively evaluates the degree of plastic deformation accumulation and signal response delay characteristics, fuses to generate a performance degradation coefficient and performs trend analysis, effectively solves the problem that the prior art cannot quantitatively evaluate the correlation between mechanical deformation and electrical performance, and has the advantages of realizing early warning, improving monitoring accuracy and reducing maintenance cost.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of electronic connector performance monitoring, and specifically relates to a connector performance detection method. BACKGROUND

[0002] The existing L-shaped pin connector is prone to mechanical and electrical performance degradation during long-term use, leading to connector failure. Specifically, due to the special structure of the L-shaped pin, repeated insertion and removal will cause stress concentration at the root of the pin, i.e. the L-shaped structure, resulting in cumulative deformation and eventually affecting the electrical performance of the connector.

[0003] In the prior art, the method of performance warning through dynamic correlation analysis of vibration signals and contact resistance is not applicable to L-shaped pin connectors without a sheath. This is because the connector without a sheath lacks obvious vibration characteristics, and the vibration analysis method cannot directly reflect the coupling relationship between the mechanical deformation of the pin and the contact stability. Therefore, a new method is needed to effectively monitor and warn the performance degradation of the L-shaped pin connector. SUMMARY

[0004] (I) Technical problem to be solved

[0005] To solve the above problems, the present application proposes a connector performance detection method, which aims to solve the problem that the method of performance warning through dynamic correlation analysis of vibration signals and contact resistance in the prior art is not applicable to L-shaped pin connectors without a sheath.

[0006] (II) Technical solution

[0007] The connector performance detection method of the present application has the following technical solution:

[0008] Strain gauges are used in the stress concentration area of the pin root to synchronously collect the micro-strain signal of the pin and the contact resistance signal of the connector;

[0009] Detect the insertion and removal action of the connector and record the time point of the insertion and removal event, and divide the independent analysis period based on the insertion and removal event as the dividing point;

[0010] In each independent analysis period, perform the first operation and the second operation:

[0011] The first operation calculates the deformation characteristic value reflecting the degree of plastic deformation accumulation of the pin based on the micro-strain signal;

[0012] The second operation extracts the time shift value corresponding to the maximum peak of the cross-correlation function of the micro-strain signal and the contact resistance signal as the hysteresis characteristic value by calculating the cross-correlation function of the micro-strain signal and the contact resistance signal;

[0013] The performance degradation coefficient is generated by fusing the deformation characteristic value and the hysteresis characteristic value.

[0014] The trend separation processing is performed on the continuous performance degradation coefficient sequence, and the performance warning is triggered according to the deviation degree of the separated trend component from the current coefficient and the deterioration rate of the trend component.

[0015] Further, the application further proposes that the calculation process of the deformation characteristic value comprises:

[0016] The micro-strain data sequence in the time window set after the plug-in event is extracted, the square of the difference between the strain value of each sampling point and the material yield strain threshold is calculated, and the square of the difference is time-weighted and accumulated in the time window to obtain the plastic deformation energy value.

[0017] Further, the application further proposes that the acquisition process of the hysteresis characteristic value comprises:

[0018] The micro-strain signal is matched and calculated with the time-shifted contact resistance signal to determine the time shift that makes the similarity of the two signals reach the maximum, and the time shift is taken as the hysteresis characteristic value reflecting the response delay degree.

[0019] Further, the application further proposes that the generation of the performance degradation coefficient satisfies the following relationship:

[0020] The performance degradation coefficient monotonically increases with the increase of the deformation characteristic value;

[0021] The performance degradation coefficient monotonically increases with the increase of the hysteresis characteristic value.

[0022] Further, the application further proposes that the specific generation method of the performance degradation coefficient is:

[0023] The deformation characteristic value is logarithmically scaled;

[0024] The hysteresis characteristic value is divided by a preset observation time for normalization;

[0025] The scaled deformation characteristic value is multiplied by the normalized hysteresis characteristic value.

[0026] Further, the application further proposes that the trend separation processing adopts a filtering algorithm that decomposes a time sequence into a long-term trend component and a short-term fluctuation component.

[0027] The warning triggering condition is met:

[0028] The absolute deviation of the current performance degradation coefficient from the trend component exceeds a first threshold value;

[0029] The unit time change rate of the trend component exceeds a second threshold value;

[0030] The early warning trigger condition is that the weighted sum exceeds a threshold value.

[0031] Further, the application also proposes to integrate a temperature sensor beside the strain gauge and introduce a temperature compensation mechanism:

[0032] According to the real-time temperature, the yield strain threshold of the material is dynamically adjusted;

[0033] The deformation characteristic value is calibrated using a temperature-dependent correction coefficient.

[0034] Further, the application also proposes that the micro-strain signal and the contact resistance signal are collected using a sampling frequency of >=1kHz;

[0035] The two signals are time-aligned by a hardware synchronization clock.

[0036] Further, the application also proposes a computing device comprising:

[0037] At least one processor; and a memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the above method.

[0038] Further, the application also proposes a non-transitory machine-readable storage medium storing executable instructions that, when executed, cause a machine to perform the above method.

[0039] (Three) beneficial effects

[0040] Compared with the prior art, the beneficial effects of the present application are:

[0041] (1) In the present application, the micro-strain signal and the contact resistance signal are collected synchronously, combined with the division of the analysis period of the plug-in event, the cumulative degree of plastic deformation and the signal response delay characteristics are quantitatively evaluated, the performance degradation coefficient is fused and generated, and trend analysis is performed, effectively solving the problem that the prior art cannot quantitatively evaluate the correlation between mechanical deformation and electrical performance, having the advantages of realizing early warning, improving monitoring accuracy and reducing maintenance cost. BRIEF DESCRIPTION OF DRAWINGS

[0042] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the drawings needed in the embodiments or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can also be obtained without creative labor on the basis of these drawings.

[0043] Figure 1 It is a schematic diagram of the overall logical structure of the connector performance detection method;

[0044] Figure 2A waveform schematic diagram of a micro-strain signal and a contact resistance signal;

[0045] Figure 3 A waveform schematic diagram of a plastic deformation characteristic value;

[0046] Figure 4 A schematic diagram of a framework structure of an execution device.

[0047] 1, processor, 2, memory, 3, communication interface, 4, communication bus. DETAILED DESCRIPTION

[0048] The L-shaped pin connector is difficult to monitor the mechanical performance due to the absence of a sheath structure, and the traditional vibration analysis method cannot capture the deformation characteristics of the stress concentration area at the root. The coupling effect exists between the cumulative plastic deformation caused by repeated insertion and removal and the change of contact resistance, but the existing technology lacks monitoring means for the absence of a sheath structure, and it is difficult to establish a correlation model between mechanical deformation and electrical performance degradation. For example, in the field of smart home appliances, power connectors that are frequently plugged in are prone to root fatigue fracture and poor contact, and traditional contact resistance detection cannot provide early warning during the deformation accumulation stage.

[0049] The inventor found that the plastic deformation of the stress concentration area at the pin root is the root cause of performance degradation, but existing strain detection is not analyzed in relation to the insertion and removal action. Through experiments, it is found that the change of contact resistance has a time lag relative to mechanical deformation, which provides a breakthrough for establishing a coupling model. Further research shows that dividing independent time periods with insertion and removal events can eliminate historical data interference, and cross-correlation analysis can effectively quantify the timing relationship between deformation and resistance. Finally, a complete monitoring scheme is formed through synchronous acquisition of strain-resistance signals, time period segmentation, feature fusion and trend analysis.

[0050] Embodiment 1

[0051] As shown in Figures 1-3 , the present application proposes a connector performance detection method, which specifically includes the following steps:

[0052] S100, using a strain gauge at the root stress concentration area of the pin, synchronously collecting the micro-strain signal of the pin and the contact resistance signal of the connector;

[0053] S200, detecting the insertion and removal action of the connector and recording the time point of the occurrence of the insertion and removal event, and dividing independent analysis time periods with the insertion and removal event as the dividing point;

[0054] S300, performing a first operation and a second operation in each independent analysis time period:

[0055] The first operation is to calculate a deformation characteristic value reflecting the degree of accumulation of plastic deformation of the pin based on the micro-strain signal;

[0056] Second operation, by calculating the cross-correlation function of the micro-strain signal and the contact resistance signal, the time shift amount corresponding to the maximum peak is extracted as the hysteresis characteristic value;

[0057] S400, the deformation characteristic value and the hysteresis characteristic value are fused to generate a performance degradation coefficient;

[0058] S500, trend separation processing is performed on the continuous performance degradation coefficient sequence, and a performance warning is triggered according to the deviation degree of the separated trend component from the current coefficient and the deterioration rate of the trend component.

[0059] The root stress concentration area refers to the inside surface of the L-shaped bending part of the pin, which bears the maximum bending stress. The patch position can be determined by finite element stress analysis to capture the plastic deformation starting point. The plug-in event demarcation point is determined by detecting the plug-in displacement mutation signal, for example, using a Hall sensor to monitor the plug-in state to achieve accurate segmentation of the action event.

[0060] The deformation characteristic value is obtained by calculating the square deviation of the strain signal and the material yield threshold, for example, using a time-weighted accumulation algorithm to quantify the irreversible accumulation of plastic deformation. The hysteresis characteristic value is calculated by the peak shift amount of the cross-correlation function, for example, using a sliding window cross-correlation algorithm to capture the time delay of the strain signal change to the resistance response.

[0061] The performance degradation coefficient fusion adopts nonlinear operation, for example, multiplying the log-scaled deformation characteristic value with the normalized hysteresis characteristic value to enhance the sensitivity of abnormal working conditions. The trend separation processing adopts a time series decomposition algorithm, for example, Hodrick-Prescott filtering, to separate the long-term trend and short-term fluctuation components.

[0062] The strain patch is attached to the inside surface of the pin root bending, and the micro-strain signal is collected in real time to reflect the material deformation state. When the plug-in action is triggered, the Hall sensor records the event timestamp, and the data between the adjacent two plug-ins is divided into independent analysis periods. In each period, first, the micro-strain data in the stable stage after plugging is extracted, the square difference between the strain value of each sampling point and the material yield threshold is calculated, and the deformation characteristic value is obtained by time exponential decay weighted accumulation. The value increases with the accumulation of plastic deformation.

[0063] Synchronously, the micro-strain signal and the contact resistance signal are cross-correlated, and the time shift amount corresponding to the maximum similarity is found as the hysteresis characteristic value. The two characteristic values are multiplied after log scaling and normalization to generate a coefficient that comprehensively reflects the mechanical and electrical performance degradation. The trend of the coefficient sequence in multiple continuous periods is decomposed, and the long-term trend component is extracted. When the absolute deviation of the current coefficient from the trend value exceeds the threshold and the trend change rate continues to rise, a warning signal is triggered.

[0064] Compared with the prior art, the traditional method relies on vibration signal analysis to detect the loosening state of the sheathed connector, and cannot be applied to the L-shaped pin without a sheath. The scheme directly monitors the micro-strain signal of the stress concentration area at the root, combines with the hysteresis response analysis of the contact resistance, and establishes a direct correlation model between deformation accumulation and contact performance degradation.

[0065] The contact resistance threshold method in the prior art can only detect the final stage of contact failure, while the performance degradation coefficient fusion mechanism of the scheme can identify abnormalities at the initial stage of plastic deformation, realizing early warning. In addition, the time period processing based on the plug-in event avoids historical data interference, and compared with continuous time series analysis, it can more accurately reflect the influence of single plug-in action.

[0066] The application can monitor the mechanical deformation accumulation process of the sheathed L-shaped pin connector in real time, accurately quantify the coupling relationship between deformation and contact resistance change. In the scene of intelligent household appliance power connector, it can give an early warning when the pin appears a bending deformation invisible to the naked eye, avoiding equipment failure caused by poor contact. The method separates the long-term degradation trend and short-term interference signal, effectively reduces the false alarm rate, and at the same time, uses nonlinear feature fusion to improve the detection sensitivity of early performance degradation.

[0067] The application further proposes a technical scheme of using a strain gauge at the stress concentration area of the pin root, synchronously collecting the micro-strain signal of the pin and the contact resistance signal of the connector, detecting the plug-in action of the connector and recording the time point of the plug-in event, dividing independent analysis periods as the dividing point of the plug-in event, and executing the first operation and the second operation in each independent analysis period.

[0068] Among them, setting a time window refers to intercepting a fixed length of data segment from the end of the plug-in event, which can be realized by using 5 seconds as the length of the time window. The window is used to isolate the deformation process caused by single plug-in action and exclude historical cumulative effect interference.

[0069] The difference square refers to subtracting the strain value of each sampling point from the material yield strain threshold and taking the square operation, which can be realized by using the yield strain threshold 0.35% of hard beryllium copper as the reference value. The operation zeros the elastic deformation data and amplifies the plastic deformation contribution. Time weighted accumulation refers to integrating and accumulating the difference square results of all sampling points in the window in time sequence, which can be realized by using the sampling time interval Δt as the integration step. The accumulation process converts the instantaneous plastic deformation into cumulative energy.

[0070] When the plug-in action is triggered, the system automatically intercepts the micro-strain data sequence in the next 5 seconds. For each sampling point, the difference between the strain measurement value and the preset material yield threshold is calculated. When the strain value exceeds the yield threshold, the difference square operation produces a positive output, otherwise the output is zero.

[0071] The plastic deformation energy value is obtained by multiplying the calculation results of all sampling points by the sampling time interval and summing them. This energy value directly reflects the damage accumulation state of the material at the root of the insert by quantifying the degree of irreversible deformation caused by a single insertion and extraction. For example, when the insert undergoes slight plastic bending, strain data exceeding the yield threshold continuously appear within the time window, and the sum of its squares increases linearly with the duration of deformation, thus establishing a correspondence between deformation and energy value. The specific expression of the plastic deformation energy formula is as follows: in Indicates the first The real-time strain values ​​at each sampling point, measured in microstrain (με), are acquired using strain gauges mounted at the base of the L-shaped pin. It represents the absolute value of strain and treats tensile and compressive deformation uniformly. This represents the yield strain threshold of a material, determined by a tensile test. For example, it is 3500 με for hard beryllium copper, 1000 με for semi-hard phosphor bronze, and 600 με for pure copper. This indicates the sampling interval, which is 1ms in this embodiment for 1kHz sampling. This represents the total number of sampling points within the time window; in this embodiment, the window is 5 seconds. , This represents the amplitude component of the plastic strain. The output is zero when the absolute value of the strain does not exceed the yield threshold.

[0072] This application further proposes a process for obtaining hysteresis characteristics, including performing similarity matching calculations between the micro-strain signal and the time-shifted contact resistance signal, determining the time shift that maximizes the similarity between the two signals, and using the time shift as a hysteresis characteristic that reflects the degree of response delay.

[0073] Micro-strain signals refer to the deformation data of the stress concentration area at the root of the insert acquired by strain gauges. Specifically, they can be implemented by using a resistive strain gauge combined with a Wheatstone bridge circuit to characterize the degree of mechanical deformation of the insert.

[0074] The time-shifted contact resistance signal refers to the signal sequence after shifting the contact resistance signal along the time axis. Specifically, it can be achieved by using a sliding window algorithm to perform cyclic displacement operations on the contact resistance signal, which is used to simulate the signal alignment state under different time shift conditions.

[0075] Similarity matching calculation refers to quantifying the degree of correlation between two signals in the time domain. Specifically, it can be implemented using a normalized cross-correlation function or a dynamic time warping algorithm to find the optimal time relationship between mechanical deformation and electrical performance changes.

[0076] Specifically, when the pin deforms, there is a physical conduction delay in the change of the contact resistance. By dynamically adjusting the time shift of the contact resistance signal, the cross-correlation function value of the micro-strain signal is calculated, and when the cross-correlation function reaches the maximum peak value, the corresponding shift value is the hysteresis characteristic value. This process simulates the complete conduction path from the occurrence of mechanical deformation to the initiation of contact resistance change. For example, when the root of the pin plastically bends, the contact surface offset needs to go through a material stress relaxation process to cause the contact resistance to rise, and at this time the hysteresis characteristic value represents the time span of the relaxation process. The hysteresis characteristic The specific expression of the cross-correlation function is obtained by searching for the peak value of the cross-correlation function: wherein represents the time shift, the search range is set to ±50 ms, and the step is 1 ms, represents the analysis window length, in the embodiment , represents the micro-strain value of the time , represents the contact resistance value after shifting, in mΩ, represents the maximum delay time, in the embodiment , covering the material stress relaxation process, represents the signal energy normalization factor, used to eliminate the influence of amplitude drift, and in the above formula the search range of is limited, excluding non-physical delay noise. When , it means that the deformation and the resistance change are synchronized, that is, the contact is good, and when , it means that there is a delay in the conduction of mechanical deformation to electrical response, indicating the risk of pin loosening.

[0077] The traditional method directly calculates the correlation coefficient of micro-strain and contact resistance to evaluate the correlation, but cannot distinguish the dynamic delay effect between signals. The present scheme introduces a search mechanism for time shift, accurately captures the physical process time span of the conduction of mechanical deformation to electrical performance degradation, and solves the misjudgment problem caused by ignoring time delay in the prior art. For example, when the pin elastically deforms, the contact resistance may fluctuate due to instantaneous pressure changes, but there is no actual delay effect, and at this time the hysteresis characteristic value output by the present scheme tends to zero, which can effectively distinguish between transient interference and real performance degradation.

[0078] The present application can accurately quantify the response delay time between mechanical deformation and contact resistance change, overcoming the limitations of traditional signal correlation analysis methods in dynamic delay scenarios. The hysteresis characteristic value provides a quantitative index in the time sequence dimension for evaluating the potential impact of pin deformation accumulation on electrical performance. For example, after multiple insertions and extractions, the continuous increase of the hysteresis characteristic value indicates that the deformation conduction efficiency is decreasing, which can provide an early warning of the risk of contact failure.

[0079] The application further proposes that the generation of the performance degradation coefficient satisfies the following relationship: the performance degradation coefficient monotonically increases with the increase of the deformation characteristic value; and the performance degradation coefficient monotonically increases with the increase of the hysteresis characteristic value.

[0080] The deformation characteristic value is a quantitative index reflecting the cumulative degree of plastic deformation of the pin, and can be specifically realized by time-weighted accumulation of the square of the difference between the micro-strain data in a set time window after the plugging event and the yield strain threshold of the material. The deformation characteristic value is used to convert discrete strain signals into continuous energy values representing material fatigue damage. The hysteresis characteristic value is a quantitative index of the response delay between the micro-strain signal and the contact resistance signal, and can be specifically realized by the time shift corresponding to the maximum peak of the cross-correlation function. The hysteresis characteristic value is used to capture the time lag effect of mechanical deformation on electrical performance changes.

[0081] When plastic deformation occurs at the root of the pin, the deformation characteristic value quantifies the irreversible damage of the material microstructure by calculating the deviation of the strain data from the yield threshold. When oxidation or wear occurs at the contact interface, the hysteresis characteristic value represents the deterioration of the mechanical-electrical response correlation by measuring the time offset between the strain signal and the resistance signal. The performance degradation coefficient forms a two-dimensional degradation evaluation mechanism by establishing a monotonically increasing relationship with the two types of characteristic values: the coefficient will inevitably increase when plastic deformation intensifies, and the coefficient will also increase when response delay worsens. Both failure modes are forced to be mapped to the one-way change trend of the coefficient. Specifically, the expression of the performance degradation coefficient is: wherein is the natural logarithm of the plastic deformation energy, which compresses the numerical magnitude, enhances the sensitivity to early damage, and represents the cumulative severity of deformation, is the time shift normalized to eliminate the influence of observation time, and represents the degree of response delay. When , it represents a normal state; when , it represents a moderate risk and needs continuous monitoring; and when , it represents a high risk and needs immediate maintenance.

[0082] The application further proposes that the specific generation method of the performance degradation coefficient is: logarithmic scaling of the deformation characteristic value; normalization of the hysteresis characteristic value by dividing by a preset observation time; and multiplication of the scaled deformation characteristic value and the normalized hysteresis characteristic value.

[0083] The logarithmic scaling processing refers to a nonlinear transformation on the plastic deformation energy, and specifically, a natural logarithm or a common logarithm can be used to compress the numerical value of the deformation eigenvalue, for balancing the damage accumulation rate difference in different stages. The normalization refers to eliminating the influence of the observation time length on the lagging eigenvalue, and specifically, the time shift amount can be divided by the total observation time to convert into a proportional coefficient, for realizing the data comparability in different use cycles. The multiplication refers to coupling operation of the two types of features, and specifically, an algebraic product can be used to fuse the mechanical deformation and the electrical response parameters, for strengthening the sensitivity in the synchronous degradation scenario.

[0084] After the logarithmic transformation of the deformation eigenvalue, the growth trend of the early micro plastic deformation is amplified, and the numerical magnitude of the late large deformation is suppressed, so that the damage accumulation process is linearized. The lagging eigenvalue is processed by time normalization, which converts the time shift amount into a lagging coefficient independent of the observation time length, avoiding the data deviation caused by different detection periods. The multiplication of the processed two types of features makes the performance degradation coefficient reflect the irreversible degree of mechanical damage and the time mismatch degree of electromechanical response at the same time, and when both of them deteriorate synchronously, the product operation will significantly amplify the degradation signal.

[0085] The application further proposes a filtering algorithm for separating the trend by decomposing the time series into a long-term trend component and a short-term fluctuation component, and sets the early warning trigger condition to simultaneously satisfy that the absolute deviation of the current performance degradation coefficient and the trend component exceeds a first threshold value, and the unit time change rate of the trend component exceeds a second threshold value.

[0086] The filtering algorithm specifically refers to the Hodrick-Prescott filtering algorithm for solving the trend component by optimizing the objective function, and specifically, a configuration with a smoothing parameter λ>200 can be used to achieve this, and this parameter setting can effectively preserve the step-type inflection point characteristics in the performance degradation process.

[0087] The first threshold value refers to the critical value of the deviation degree of the performance degradation coefficient and the trend component, and specifically, it can be obtained by training the historical failure data, for identifying the instantaneous contact failure event.

[0088] The second threshold value refers to the critical value of the change rate of the trend component, and specifically, an absolute value judgment mechanism is used to realize it, which only responds to the positive change rate to exclude the interference caused by performance recovery.

[0089] Specifically, the technical scheme decomposes the performance degradation coefficient sequence into a trend component and a fluctuation component by the Hodrick-Prescott filtering algorithm. In the filtering process, the rigidity of the trend component is controlled by the smoothing parameter λ, and when λ is set to be greater than 200, the algorithm can accurately capture the trend jump caused by the sudden crack of the pin. Specifically, the expression of the Hodrick-Prescott filtering algorithm is: wherein denotes a sequence of performance degradation coefficients, denotes a long-term trend component of performance degradation, denotes the rigidity degree of control trend, and In the present embodiment retains the trend jump caused by burst cracks, denotes the total number of sampling points of performance degradation coefficients within the analysis period, denotes a sampling point. Early warning trigger condition: wherein denotes a threshold value trained by historical failure data, in the present embodiment , denotes a weight coefficient, in the present embodiment avoids delaying the alarm of instantaneous failure due to slow degradation, denotes the performance degradation coefficient of the sampling point, dimensionless, denotes a discrete time index in the time series, is a residual term, i.e., a first threshold value, for ensuring that the system responds quickly to transient abnormalities, such as poor contact caused by pin vibration, is a trend derivative term, i.e., a second threshold value, for monitoring the slope change of the trend component. When the residual term is > 0.3, it indicates that a transient failure is detected, such as pin vibration, and when the trend derivative term is > 0.6, it indicates that accelerated degradation is identified, such as root crack propagation.

[0090] In the early warning trigger mechanism, the residual condition detects transient contact failure caused by pin vibration by calculating the absolute deviation of the current coefficient from the trend component; the trend deterioration condition identifies the accelerated degradation phenomenon in the later stage of fatigue accumulation by monitoring the change rate per unit time of the trend component. The logical and relationship of the two conditions is designed so that the system triggers an early warning only when the residual is out of limit and the trend continues to deteriorate, avoiding misjudgment caused by a single condition.

[0091] The present application further proposes a technical solution of integrating a temperature sensor beside the strain gauge and introducing a temperature compensation mechanism, which dynamically adjusts the yield strain threshold of the material according to the real-time temperature and simultaneously calibrates the deformation characteristic value using a temperature-dependent correction coefficient.

[0092] Among them, the temperature sensor refers to a temperature sensing element arranged in the stress concentration area of the pin root, which can be realized by using a micro thermocouple or a thin film resistance temperature detector, and is used for synchronously collecting temperature change data of the working area of the pin.

[0093] The temperature compensation mechanism refers to a systematic method of dynamically correcting the deformation detection process through temperature data, which specifically includes two related links of yield strain threshold adjustment and deformation characteristic value calibration, and is used for eliminating the coupling effect of temperature on the mechanical properties of the material and the output of the sensor.

[0094] The material yield strain threshold refers to the critical strain value of plastic deformation of a metal material, which is dynamically calculated by using an exponential function model based on the thermodynamic constitutive relationship of the material, and is used to accurately reflect the material softening phenomenon caused by temperature rise. The temperature-related correction coefficient refers to a proportional factor related to the thermal expansion effect, which is calculated by using an inverse proportional function constructed by the thermal expansion coefficient, and is used to suppress the strain signal baseline drift caused by temperature gradient.

[0095] The temperature sensor and the strain gauge are installed in a coplanar integrated manner to ensure the spatial consistency of the sensing areas of the two. When the ambient temperature changes, the temperature sensor outputs the temperature data at the root of the pin in real time, and the control system dynamically calculates the material yield strain threshold according to the current temperature value. The threshold is updated by an exponential decay function model, so that the yield strain criterion is reduced in a high-temperature environment and the criterion value is increased in a low-temperature environment. The calibration of the deformation characteristic value is achieved by multiplying the original strain data by the temperature-related correction coefficient, which eliminates the strain signal offset caused by the thermal expansion and contraction of the material. The calibrated deformation characteristic value only reflects the plastic deformation caused by mechanical stress, avoiding the misjudgment of the elastic deformation caused by temperature as permanent damage. The present application further proposes a technical solution that the micro-strain signal and the contact resistance signal are collected at a sampling frequency of not less than 1 kHz, and the two signals are time-aligned by a hardware synchronous clock.

[0096] The sampling frequency of not less than 1 kHz refers to the ability of the signal acquisition device to obtain at least 1000 data points per second, which can be realized by using a high-speed analog-to-digital converter in combination with an anti-aliasing filter. This frequency setting can completely capture the transient mechanical response characteristics of the stress concentration area at the root of the pin.

[0097] The hardware synchronous clock refers to a physical clock source that provides a unified time base for a multi-channel signal acquisition system, which can be realized by using a global clock tree of a field programmable gate array or a GPS synchronous pulse generator. This synchronization mechanism ensures that the data collected by different sensors are strictly aligned in the time axis.

[0098] Specifically, during the insertion and extraction of the pin, the micro-strain signal generated in the stress concentration area at the root of the pin contains millisecond-level dynamic variation characteristics, and a sampling frequency of not less than 1 kHz can effectively avoid aliasing distortion of high-frequency signal components. The contact resistance signal presents rapid fluctuation characteristics during the insertion and extraction action, and the hardware clock for synchronous acquisition directly controls the sampling trigger time of each channel through physical circuit, eliminating the time jitter caused by software scheduling. The time alignment accuracy of the two signals is controlled within a single sampling interval, providing an accurate time domain reference for subsequent calculation of the cross-correlation function, so that the dynamic coupling relationship between the micro-strain signal and the contact resistance signal can be accurately quantified.

[0099] The traditional method uses a sampling frequency lower than 500Hz, resulting in the loss of high-frequency mechanical deformation characteristics, and the time deviation of milliseconds in the mode of software timestamp synchronization. The scheme solves the technical obstacle that the mechanical deformation and electrical performance correlation model of the unshielded L-shaped pin connector is difficult to establish due to the lack of obvious vibration characteristics through the cooperation of high-frequency sampling and hardware clock.

[0100] Through the above technical scheme, the application realizes high-precision synchronous acquisition of micro-strain signals and contact resistance signals, effectively avoids the distortion problem of dynamic correlation analysis caused by insufficient sampling rate or time reference deviation, and provides a reliable data basis for performance degradation monitoring of the unshielded L-shaped pin connector.

[0101] Embodiment 2

[0102] The embodiment of the application provides a computer readable storage medium.

[0103] The computer readable storage medium provided by the embodiment of the application stores a computer program, and the computer program is executed by a processor to realize the steps of any one of the connector performance detection methods.

[0104] The computer readable storage medium can include: a U disk, a mobile hard disk, a read-only memory (Read-Only Memory, ROM), a random access memory (Random Access Memory, RAM), a magnetic disk or an optical disk, and various storage program codes.

[0105] For the computer readable storage medium provided by the embodiment of the application, refer to the above method embodiment, and the application will not be repeated here.

[0106] The embodiments in the specification are described in a progressive manner, and each embodiment focuses on the difference from other embodiments, and the same and similar parts of each embodiment can be referred to. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the related parts are described in the method part.

[0107] The skilled person can further realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be realized by electronic hardware, computer software or a combination of the two. In order to clearly show the interchangeability of hardware and software, the components and steps of each example have been described in the above description. Whether the functions are realized in hardware or software depends on the specific application and design constraints of the technical solution. The skilled person can use different methods to realize the described functions for each specific application, but such implementation should not be considered beyond the scope of the application.

[0108] The steps of a method or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module can reside in random access memory (RAM), flash memory, read-only memory (ROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0109] Embodiment 3

[0110] The embodiment of the present application provides an execution device.

[0111] Please refer to Figure 4 , Figure 4 The structure diagram of an execution device provided by the present application can include:

[0112] The memory is used for storing a computer program.

[0113] The processor is used for implementing the steps of any one of the connector performance detection methods when executing the computer program.

[0114] As shown in Figure 4 , the constituent structure diagram of the execution device can include a processor 1, a memory 2, a communication interface 3 and a communication bus 4. The processor 1, the memory 2 and the communication interface 3 can complete mutual communication through the communication bus 4.

[0115] In the embodiment of the present application, the processor 1 can be a central processing unit (CPU), an application specific integrated circuit, a digital signal processor, a field programmable gate array or other programmable logic devices.

[0116] The processor 1 can call the program stored in the memory 2, and specifically, the processor 1 can execute the operations in the embodiments of the button switch fault detection method.

[0117] The memory 2 is used for storing one or more programs, and the program can include program codes including computer operation instructions. In the embodiment of the present application, the memory 2 at least stores programs for implementing the following functions:

[0118] A strain gauge is used in the stress concentration area of the root of the pin, and the micro-strain signal of the pin and the contact resistance signal of the connector are synchronously collected;

[0119] The plugging action of the connector is detected, and the time point of the plugging event is recorded. The plugging event is used as a demarcation point to divide an independent analysis period.

[0120] The first operation and the second operation are performed in each independent analysis period:

[0121] The first operation is to calculate a deformation characteristic value reflecting the degree of cumulative plastic deformation of the pin based on the micro-strain signal;

[0122] The second operation is to extract a time shift corresponding to a maximum peak of a cross-correlation function of the micro-strain signal and the contact resistance signal as a hysteresis characteristic value by calculating the cross-correlation function;

[0123] The deformation characteristic value and the hysteresis characteristic value are fused to generate a performance degradation coefficient;

[0124] The trend separation processing is performed on a continuous performance degradation coefficient sequence, and a performance warning is triggered according to the deviation degree of the separated trend component from the current coefficient and the deterioration rate of the trend component.

[0125] In a possible implementation, the memory 2 can include a program storage area and a data storage area, where the program storage area can store an operating system, and application programs required by at least one function, etc.; and the data storage area can store data created in use.

[0126] In addition, the memory 2 can include a high-speed random access memory, and can also include a non-volatile memory, for example, at least one magnetic disk storage device or other volatile solid-state storage device.

[0127] The communication interface 3 can be an interface of a communication module, used to connect with other devices or systems.

[0128] Of course, it needs to be explained that, Figure 4 The structure shown does not constitute a limitation on the execution device in the embodiments of the application, and in actual application, the execution device can include more or fewer components than Figure 4 those shown, or combine certain components.

[0129] The preferred embodiments of the application are described above, which do not limit the concept and scope of the application. Various modifications and improvements made by those skilled in the art to the technical solutions of the application without departing from the design concept of the application shall fall within the protection scope of the application, and the technical content of the application claimed for protection has been fully recorded in the claims.

[0130] The above-described embodiments are merely preferred embodiments of the application, which do not limit the concept and scope of the application. Various modifications and improvements made by those skilled in the art to the technical solutions of the application without departing from the design concept of the application shall fall within the protection scope of the application, and the technical content of the application claimed for protection has been fully recorded in the claims.

Claims

1. A connector performance detection method characterized by, The method comprises the following steps: Strain gauges are used in the stress concentration area of the pin root to synchronously collect the micro-strain signal of the pin and the contact resistance signal of the connector; The plugging action of the connector is detected and the time point of the plugging event is recorded, and the independent analysis period is divided according to the plugging event as the demarcation point; In each independent analysis period, the first operation and the second operation are performed: The first operation is to calculate the deformation characteristic value reflecting the cumulative degree of plastic deformation of the pin based on the micro-strain signal; The second operation is to extract the time shift amount corresponding to the maximum peak of the cross-correlation function of the micro-strain signal and the contact resistance signal as the hysteresis characteristic value by calculating the cross-correlation function of the micro-strain signal and the contact resistance signal; The performance degradation coefficient is generated by fusing the deformation characteristic value and the hysteresis characteristic value; The trend separation processing is performed on the continuous performance degradation coefficient sequence, and the performance warning is triggered according to the deviation degree of the separated trend component and the current coefficient and the deterioration rate of the trend component; The calculation process of the deformation characteristic value comprises: The micro-strain data sequence in the set time window after the plugging event is extracted, the square of the difference between the strain value of each sampling point and the material yield strain threshold is calculated, and the time weighted accumulation of the square of the difference in the time window is performed to obtain the plastic deformation energy value; The acquisition process of the hysteresis characteristic value comprises: The similarity matching calculation is performed on the micro-strain signal and the time-shifted contact resistance signal to determine the time shift amount that makes the similarity of the two signals reach the maximum, and the time shift amount is taken as the hysteresis characteristic value reflecting the response delay degree; The specific generation method of the performance degradation coefficient is: The deformation characteristic value is logarithmically scaled; The hysteresis characteristic value is divided by the preset observation time for normalization; The scaled deformation characteristic value is multiplied by the normalized hysteresis characteristic value.

2. The method of claim 1, wherein, The generation of the performance degradation coefficient satisfies the following relationship: The performance degradation coefficient monotonically increases with the increase of the deformation characteristic value; The performance degradation coefficient monotonically increases with the increase of the hysteresis characteristic value.

3. The method of claim 1, wherein, The trend separation processing adopts a filtering algorithm that decomposes a time series into a long-term trend component and a short-term fluctuation component; The warning triggering condition is met when: The absolute deviation of the current performance degradation coefficient and the trend component exceeds the first threshold value; The unit time change rate of the trend component exceeds the second threshold value; The weighted sum exceeds the threshold value.

4. The method of claim 1, wherein, Further comprising: A temperature sensor is integrated beside the strain gauge, and a temperature compensation mechanism is introduced: The material yield strain threshold is dynamically adjusted according to the real-time temperature; The deformation characteristic value is calibrated using a temperature-dependent correction coefficient.

5. The method of claim 4, wherein, The micro-strain signal and the contact resistance signal are collected at a sampling frequency greater than or equal to 1 kHz; The micro-strain signal and the contact resistance signal are collected by hardware synchronization clock to realize time alignment.

6. A computing device, comprising: The method comprises the following steps: At least one processor; And A memory storing instructions that, when executed by the at least one processor, cause the at least one processor to perform the method of any one of claims 1-5.

7. A non-transitory machine-readable storage medium, comprising: It stores executable instructions that, when executed, cause the machine to perform the method of any one of claims 1-5.

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