CN-122024971-A - Method and system for analyzing endurance experimental data of memory alloy piece

CN122024971ACN 122024971 ACN122024971 ACN 122024971ACN-122024971-A

Abstract

The invention discloses a method and a system for analyzing endurance test data of a memory alloy part, which relate to the technical field of data processing, and aim at solving the problem that the existing endurance test data of the memory alloy has poor alloy performance detection effect caused by environmental noise interference and signal scattering, through adopting a differential signal processing strategy to perform noise reduction optimization on multidimensional performance response data, extracting and correcting characteristic factors representing deformation recovery, residual deformation and energy dissipation, the combination logic of the characteristic factors is dynamically judged by combining with the identification rule set based on the application scene to determine a specific performance degradation mode, and then a corresponding residual service cycle prediction model is constructed according to different degradation modes, the reliability range is calculated, and finally, the performance optimization strategy is generated by matching, so that the full-flow closed-loop analysis from data preprocessing, characteristic extraction to degradation mode identification, service life prediction and strategy generation is realized, and the performance prediction precision and reliability evaluation efficiency of the memory alloy piece under the complex working condition are remarkably improved.

Inventors

CHEN YONGHUI
HOU XIAOHUA

Assignees

浙江正特股份有限公司

Dates

Publication Date: 20260512
Application Date: 20260226

Claims (10)

1. The method for analyzing the endurance test data of the memory alloy piece is characterized by comprising the following steps of: Acquiring various performance response data generated by the memory alloy piece in a durability experiment, adopting a differential signal processing strategy to optimize the various performance response data, and extracting a plurality of characteristic information reflecting the performance state change of the memory alloy piece from the optimized performance response data, wherein the characteristic information comprises a recovery factor representing deformation recovery capacity, an accumulation factor representing residual deformation and an evolution factor representing energy dissipation; determining a specific performance degradation pattern of the memory alloy member from a combination of a plurality of the characteristic information, the specific performance degradation pattern including a change in an energy dissipation mechanism or an increase in plastic deformation; Predicting the residual service period of the memory alloy piece based on the specific performance degradation mode, and determining the reliability range of the residual service period; And determining a corresponding performance optimization strategy according to the specific performance degradation mode, the residual service period and the reliability range.
2. The method of claim 1, wherein said optimizing said performance response data using differential signal processing strategy to obtain a plurality of characteristic information reflecting a change in performance state of said memory alloy member comprises: performing time-frequency analysis on the performance response data to distinguish performance degradation characteristic signals from environmental noise signals; Performing amplitude enhancement on the identified performance degradation characteristic signals according to a preset gain function; Attenuating the amplitude of the identified ambient noise signal according to a predetermined suppression function, and And synthesizing the characteristic signal after gain and the noise signal after suppression to obtain optimized performance response data.
3. The method for analyzing endurance test data of a memory alloy member according to claim 1, wherein the recovery factors of the deformation recovery capability include a deformation recovery rate and a deformation recovery trend, the accumulation factors of the residual deformation include a residual deformation accumulation rate and a residual deformation accumulation trend, the evolution factors of the energy dissipation include an evolution rate and an evolution trend of the energy dissipation, and the step of determining the specific performance degradation mode of the memory alloy member according to the combination of the plurality of characteristic information includes: receiving application scene information of a memory alloy piece to be evaluated, wherein the application scene information comprises furniture product types and component working environments; According to the application scene information, retrieving an identification rule set matched with the application scene information from a preset scene rule base, wherein the identification rule set defines a judgment threshold and a combination logic of a recovery factor of deformation recovery capacity, a cumulative factor of residual deformation and an evolution factor of energy dissipation aiming at the application scene; And according to the identification rule set, carrying out combination judgment on the recovery factor of the deformation recovery capability, the accumulation factor of the residual deformation and the evolution factor of the energy dissipation so as to identify a specific performance degradation mode of the memory alloy piece, wherein the specific performance degradation mode comprises a change of an energy dissipation mechanism or an increase of plastic deformation.
4. A method of analyzing experimental data for the durability of a memory alloy member according to claim 3, wherein the step of identifying the specific performance degradation pattern of the memory alloy member by performing a combined judgment of the recovery factor of the deformation recovery capability, the cumulative factor of the residual deformation amount, and the evolution factor of the energy dissipation according to the identification rule set comprises: judging whether the accumulation factor of the residual deformation meets a first judging condition which is defined in the identification rule set and used for representing irreversible structural damage of the material; if the first determination condition is satisfied, identifying the specific performance degradation mode as an increase in plastic deformation; If the first judging condition is not met, judging whether a recovery factor of the deformation recovery capability and an evolution factor of the energy dissipation meet a second judging condition which is defined in the identification rule set and represents an abnormal energy consumption mechanism of the material; If the second judging condition is met, executing screening logic according to whether the residual deformation accumulation rate meets the stability condition defined in the identification rule set or not, otherwise, judging that the alloy performance state is normal; When the stability condition is met, judging the current state as a false signal caused by environmental interference; When the stability condition is not met, the particular performance degradation mode is identified as a change in energy dissipation mechanism.
5. The method according to claim 3 or 4, wherein the steps of predicting a remaining use period of the memory alloy member based on the specific performance degradation pattern, and determining a reliability range of the remaining use period include: when the specific performance degradation mode is identified as an increase in plastic deformation, acquiring a current residual deformation amount and a cumulative factor of the residual deformation amount; determining a residual service period according to a preset failure residual deformation threshold, the current residual deformation and an accumulation factor of the residual deformation; When the particular performance degradation pattern is identified as a change in energy dissipation mechanism and the cumulative factor of residual deformation does not exhibit an accelerated increase, then halting prediction of remaining usage periods and generating an environmental disturbance cue; and calculating a confidence interval corresponding to the residual using period based on the fluctuation range of the residual deformation accumulation rate in a preset historical data window.
6. The method according to claim 5, wherein the step of determining the remaining service period according to a preset failure residual deformation threshold, the current residual deformation, and a cumulative factor of the residual deformation comprises: Judging the growth mode of the current residual deformation according to the residual deformation accumulation trend contained in the residual deformation accumulation factor; When the growth mode is determined to be accelerated growth, constructing an exponential growth prediction model based on the current residual deformation, the residual deformation accumulation rate and acceleration parameters; taking the preset failure residual deformation threshold as the input of the exponential growth prediction model, and performing inverse solution to obtain the residual service period; When the growth mode is determined to be uniformly growing, a remaining usage period is determined based on a ratio of a difference between the current residual deformation and the failure residual deformation threshold to a current average accumulation rate contained in an accumulation factor of the residual deformation.
7. The method according to claim 5, wherein the step of calculating the confidence interval corresponding to the remaining use period based on the fluctuation range of the residual deformation accumulation rate within a preset history window includes: Determining a historical data window for statistical analysis, wherein the window comprises residual deformation accumulation rates corresponding to a preset number of continuous cycles; Taking standard deviation of all residual deformation accumulation rates in the window as a quantization index for representing the fluctuation range of the residual deformation accumulation rates; And taking the residual use period as a central predicted value, and determining an upper limit and a lower limit of the confidence interval based on the quantization index and a preset confidence level.
8. The method according to claim 1, wherein the step of determining the corresponding performance optimization strategy based on the specific performance degradation pattern, remaining usage period, and reliability range comprises: Determining a target type of an optimization strategy according to the specific performance degradation mode, wherein the target type comprises a material improvement strategy or a structure improvement strategy for delaying structural degradation, or an environment interference isolation strategy or an environment interference compensation strategy; Determining the priority of the optimization strategy based on the comparison result of the residual use period and a preset life safety threshold value and the scale of the credibility range; and matching and generating at least one specific performance optimization strategy from a preset optimization strategy knowledge base according to the target type and the priority.
9. A method of analyzing endurance test data of a memory alloy member according to claim 1,2 or 3, wherein the step of extracting a plurality of characteristic information reflecting a change in performance state of the memory alloy member from the optimized performance response data comprises: Extracting deformation recovery rate, residual deformation and load-displacement data of each test cycle from the optimized performance response data; Periodically obtaining an output value of the displacement sensor in a zero load state as a current zero offset, and correcting all the extracted deformation recovery rate and residual deformation in real time by utilizing the zero offset to obtain corrected deformation recovery rate and corrected residual deformation; Respectively calculating corresponding deformation recovery rate and deformation recovery trend based on the corrected deformation recovery rate; Calculating residual deformation accumulation rates and residual deformation accumulation trends in continuous cycles based on the corrected residual deformation respectively; The hysteresis loop area is calculated by numerical integration based on the load-displacement data of each cycle, and the evolution rate and evolution trend of the energy dissipation are determined based on the hysteresis loop area change data of the continuous cycles.
10. A memory alloy piece durability test data analysis system, adapted to a memory alloy piece durability test data analysis method according to any one of claims 1 to 9, comprising: the data acquisition module is used for acquiring various performance response data generated by the memory alloy piece in the durability experiment; The data preprocessing module is used for optimizing a plurality of performance response data by adopting a differential signal processing strategy, and extracting a plurality of characteristic information reflecting the performance state change of the memory alloy piece from the optimized performance response data; a degradation mode determining module for determining a specific performance degradation mode of the memory alloy piece according to the combination form of a plurality of the characteristic information; A life prediction module for predicting the remaining service period of the memory alloy piece based on the specific performance degradation mode and determining the credibility range of the remaining service period; And the strategy generation module is used for determining a corresponding performance optimization strategy according to the specific performance degradation mode, the residual use period and the credibility range.

Description

Method and system for analyzing endurance experimental data of memory alloy piece Technical Field The invention relates to the technical field of data processing, in particular to a method and a system for analyzing endurance test data of a memory alloy piece. Background In modern industrial production, accurate assessment of material properties is a key element in ensuring product quality and safety in use. In particular, in the field of furniture manufacturing and the like, which have special requirements on material characteristics, the memory alloy piece is widely applied to movable parts such as a liftable leisure table bracket, a folding camping table and chair and the like which need flexible deformation and reliable support due to the unique shape memory and super elasticity. The durability of these memory alloy members is directly related to the safety and service life of the product, and therefore it is important to evaluate the durability of the memory alloy members accurately and efficiently. However, in actual performance of endurance experiments on memory alloy members, repeated loading and unloading cycles are typically performed at different ambient pressures, which can generate a large amount of experimental data such as deformation recovery rate and load-displacement response. These data tend to exhibit significant divergence, and even with the same batch of material, there is little fluctuation in the data under similar test conditions. When the traditional analysis method processes the multi-aspect and nonlinear data, the efficiency is low, clear and consistent rules are difficult to extract from the data, so that great deviation exists in judgment of material performance degradation trend and residual service life, and finally the accuracy of product reliability design and quality control is affected. The above information disclosed in the background section is only for enhancement of understanding of the background of the application and therefore it may contain information that does not form the prior art that is already known to a person of ordinary skill in the art. Disclosure of Invention Aiming at the problem that the existing memory alloy durability experimental data has poor alloy performance detection effect caused by environmental noise interference and signal scattering, the invention provides a memory alloy piece durability experimental data analysis method and system, which adopts a differential signal processing strategy to perform noise reduction optimization on multidimensional performance response data, extracts and corrects characteristic factors representing deformation recovery, residual deformation and energy dissipation, combines a recognition rule set based on application scenes to dynamically judge the combination logic of the characteristic factors to determine a specific performance degradation mode, further constructs corresponding residual service cycle prediction models according to different degradation modes and calculates a reliability range, finally matches and generates a performance optimization strategy, and realizes the whole-flow closed-loop analysis from data preprocessing, characteristic extraction to degradation mode recognition, life prediction and strategy generation, thereby remarkably improving the performance prediction precision and reliability evaluation efficiency of the memory alloy piece under complex working conditions. In a first aspect, the technical scheme provided in the embodiment of the invention is that the method for analyzing the endurance test data of the memory alloy piece comprises the following steps: Acquiring various performance response data generated by the memory alloy piece in a durability experiment, adopting a differential signal processing strategy to optimize the various performance response data, and extracting a plurality of characteristic information reflecting the performance state change of the memory alloy piece from the optimized performance response data, wherein the characteristic information comprises a recovery factor representing deformation recovery capacity, an accumulation factor representing residual deformation and an evolution factor representing energy dissipation; determining a specific performance degradation pattern of the memory alloy member from a combination of a plurality of the characteristic information, the specific performance degradation pattern including a change in an energy dissipation mechanism or an increase in plastic deformation; Predicting the residual service period of the memory alloy piece based on the specific performance degradation mode, and determining the reliability range of the residual service period; And determining a corresponding performance optimization strategy according to the specific performance degradation mode, the residual service period and the reliability range. Preferably, the step of optimizing the plurality of performance response data using a diff