Fatigue life testing method, device, equipment and storage medium for shaft parts

Abstract

The present invention relates to the technical field of data processing of shaft parts, and provides a fatigue life test method, device, equipment and storage medium for shaft parts, including using distributed optical fiber sensors to collect strain difference data and temperature difference data at various positions in shaft parts; performing non-contact deformation measurement on the surface of shaft parts to obtain surface displacement field and surface strain distribution data; establishing stress amplitude change curves and temperature change curves based on the above data, and analyzing the stress change amplitude and temperature change amplitude, and then inputting them into a preset fatigue life prediction model for prediction, and outputting the fatigue life of shaft parts. Through the combined application of distributed optical fiber sensors and digital image correlation technology, the internal strain and temperature changes of shaft parts are comprehensively measured and analyzed, so as to ensure the reliability of test results under complex stress states and temperature field environments, and improve the problem of insufficient test accuracy under multi-axis stress states or complex temperature field environments.

Description

Technical Field

[0001] The present application relates to the technical field of shaft parts data processing, and in particular to fatigue life testing methods, devices, equipment and storage media for shaft parts. Background Art

[0002] Shaft parts are widely used in mechanical manufacturing and engineering fields, and are indispensable in automobiles, aviation, machine tools and other equipment. With the development of industry, higher requirements are placed on the performance and life of shaft parts, especially in fatigue life testing under complex stress conditions, which has become the focus of industry attention.

[0003] Among the related technical means, the fatigue life test of shaft parts is mainly carried out through the cyclic loading test method, that is, cyclic stress is applied on the test bench to simulate the actual working conditions. The strain and crack propagation of shaft parts are monitored by sensors such as strain gauges to evaluate their fatigue life. This method can more accurately reflect the fatigue behavior of shaft parts under specific stress.

[0004] Regarding the above technical solutions, although the above methods can effectively evaluate the fatigue life of shaft parts under specific conditions, in multi-axial stress states or complex temperature field environments, the existing solutions mainly rely on the measurement of surface strain and cannot fully capture the internal strain distribution and temperature effects, and there is a problem of insufficient test accuracy. Summary of the invention

[0005] In order to improve the problem of insufficient testing accuracy under multi-axial stress states or complex temperature field environments, the present application provides fatigue life testing methods, devices, equipment and storage media for shaft parts, which can more comprehensively and accurately analyze the fatigue behavior of shaft parts under different working conditions.

[0006] The present invention provides a fatigue life test method for shaft parts. The shaft parts to be tested are fixed in a fatigue life test device, and a distributed optical fiber sensor is preset on the fatigue test device. The fatigue life test method comprises: using the distributed optical fiber sensor to collect strain difference data and temperature difference data at various positions in the shaft parts; performing non-contact deformation measurement on the surface of the shaft parts by digital image correlation technology to obtain surface displacement field and surface strain distribution data; establishing a stress amplitude change curve based on the surface displacement field, the surface strain distribution data and the strain difference data, and analyzing the stress change amplitude based on the stress amplitude change curve; establishing a temperature change curve based on the surface displacement field, the surface strain distribution data and the temperature difference data, and analyzing the temperature change amplitude based on the temperature change curve; integrating the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, inputting the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and outputting the fatigue life of the shaft parts.

[0007] As a preferred solution, the step of using the distributed optical fiber sensor to collect strain difference data and temperature difference data at each position in the shaft parts includes: before the test starts, using the distributed optical fiber sensor to collect initial strain data and initial temperature data of the shaft parts under static load conditions; starting the fatigue test device to test the shaft parts, and using the distributed optical fiber sensor to collect real-time strain data and real-time temperature data of each shaft segment in the shaft parts in real time; performing multi-level filtering and trend decomposition on the real-time strain data through fast Fourier transform to obtain a strain sequence; performing time-frequency analysis on the strain sequence using wavelet transform to obtain local strain offset parameters and global strain balance parameters, and comparing the local strain offset parameters with the global strain balance parameters. The global strain balance parameters are correlated and compared to obtain strain correction reference data, and the amplitude and baseline of the strain sequence are calibrated according to the strain correction reference data to obtain processed strain data; the real-time temperature data are differentially analyzed by applying thermodynamic equations to obtain fluctuating temperature dynamic data, and the fluctuating temperature dynamic data are enveloped by Hilbert transform to obtain temperature amplitude characteristic parameters and temperature frequency domain characteristic parameters; the temperature frequency domain characteristic parameters are weightedly corrected based on the temperature amplitude characteristic parameters to obtain processed temperature data; the processed strain data are compared with the initial strain data to obtain strain difference data; the processed temperature data are compared with the initial temperature data to obtain temperature difference data.

[0008] As a preferred solution, the step of performing non-contact deformation measurement on the surface of the shaft-like parts by digital image correlation technology to obtain surface displacement field and surface strain distribution data includes: during the fatigue test, using a high-speed camera to synchronously shoot the speckle pattern on the surface of the shaft-like parts from multiple angles to obtain multi-view image data; aligning the multi-view image data with the processed strain data and the processed temperature data with the recording time as a reference, and performing spatial matching according to the sensor installation position and the shooting angle parameters to obtain a synchronous data set corresponding to time and space synchronization; performing grayscale equalization and edge invalid area cropping on the image in the synchronous data set by an image processing algorithm to obtain processed image data, using a digital image correlation method to perform multiple iterative searches on the processed image data and a preset initial speckle pattern to obtain feature point displacements, and calculating a surface displacement field based on the feature point displacements, wherein the initial speckle pattern is a speckle pattern randomly sprayed on the surface of the shaft-like parts before the test starts; and performing differential solutions for adjacent point displacements according to the surface displacement field to obtain surface strain distribution data.

[0009] As a preferred solution, the step of performing multiple iterative searches on the processed image data using a digital image correlation method to obtain feature point displacements, and calculating the surface displacement field according to the feature point displacements comprises: identifying feature points of a speckle pattern of the processed image data using a feature extraction algorithm, tracking the feature points of the speckle pattern with the feature points of the initial speckle pattern using a point-by-point matching algorithm, calculating the displacement between the feature points of the speckle pattern and the feature points of the initial speckle pattern, obtaining feature point displacements, and calculating the surface displacement field according to the feature point displacements; the calculation formula for calculating the surface displacement field according to the feature point displacements is as follows:

[0010]

[0011]

[0012] in, and Respectively expressed in Direction and The displacement component in the direction; and represents the first The coordinates of the feature points, and represents the coordinates of the corresponding feature points in the processed image data, is the number of feature points involved in the calculation.

[0013] As a preferred embodiment, the steps of establishing a stress amplitude variation curve based on the surface displacement field, the surface strain distribution data and the strain difference data, and analyzing the stress variation amplitude based on the stress amplitude variation curve include: analyzing the surface displacement field and the surface strain distribution data by a finite element analysis method to obtain stress field data, and establishing a stress amplitude variation curve based on the stress field data and the strain difference data; performing fitting analysis on the stress amplitude variation curve using a curve fitting algorithm to obtain fitting parameters and residuals; and calculating the stress variation amplitude based on the fitting parameters and the residuals.

[0014] As a preferred scheme, the steps of establishing a temperature change curve based on the surface displacement field, the surface strain distribution data and the temperature difference data, and analyzing the temperature change amplitude based on the temperature change curve include: establishing a transient temperature field model of shaft parts using the surface displacement field, the surface strain distribution data and the temperature difference data; calculating the temperature distribution data of each shaft segment in the shaft parts according to the transient temperature field model, establishing a temperature change curve based on the temperature distribution data, performing frequency domain analysis on the temperature change curve by means of a statistical analysis method, and extracting the temperature change amplitude characteristics based on the analysis results.

[0015] As a preferred embodiment, the step of integrating the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, inputting the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and outputting the fatigue life of shaft parts includes: integrating the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, performing feature decomposition on the comprehensive amplitude data by a hierarchical clustering analysis method to obtain key feature parameters, performing multivariate statistical analysis on the key feature parameters by a correlation coefficient calculation method to obtain correlation parameters between stress and temperature, and using the correlation parameters as input data; inputting the input data into a preset fatigue life prediction model, in the fatigue life prediction model, performing calculation and analysis on the input data by numerical simulation to obtain fatigue life prediction values, and outputting the fatigue life prediction values ​​as the fatigue life of the shaft parts.

[0016] The present application also provides a fatigue life test device for shaft parts, wherein the shaft parts to be tested are fixed in the fatigue life test device, and a distributed optical fiber sensor is preset on the fatigue test device. The fatigue life test device includes: an acquisition module, which is used to use the distributed optical fiber sensor to collect strain difference data and temperature difference data at various positions in the shaft parts; a measurement module, which is used to perform non-contact deformation measurement on the surface of the shaft parts through digital image correlation technology to obtain surface displacement field and surface strain distribution data; a first analysis module, which is used to establish a stress amplitude change curve based on the surface displacement field, the surface strain distribution data and the strain difference data, and analyze the stress change amplitude based on the stress amplitude change curve; a second analysis module, which is used to establish a temperature change curve based on the surface displacement field, the surface strain distribution data and the temperature difference data, and analyze the temperature change amplitude based on the temperature change curve; a prediction module, which is used to integrate the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, input the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and output the fatigue life of the shaft parts.

[0017] The present application also provides an electronic device, including a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements any one of the above-mentioned fatigue life testing methods for shaft parts.

[0018] The present application also provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed by a processor, the processor executes the fatigue life test method for shaft parts as described in any one of the above.

[0019] Compared with the prior art, the present application has the following beneficial effects: high test accuracy. Through the combined application of distributed optical fiber sensors and digital image correlation technology, the internal strain and temperature changes of shaft parts are comprehensively measured and analyzed, avoiding the limitation of traditional methods that rely only on surface strain data. It not only improves the accuracy of the test, but also provides a more accurate fatigue life prediction through the analysis of the comprehensive stress and temperature change amplitude, and realizes that in a complex stress state and temperature field environment, the fatigue behavior data of shaft parts under actual working conditions can be effectively obtained, ensuring the reliability of the test results, and improving the problem of insufficient test accuracy in a multi-axis stress state or a complex temperature field environment. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] The structures, proportions, sizes, etc. illustrated in the drawings of this specification are only used to match the contents disclosed in the specification so as to facilitate understanding and reading by persons familiar with this technology. They are not used to limit the conditions under which the present invention can be implemented, and therefore have no substantive technical significance. Any structural modification, change in proportion or adjustment of size, without affecting the effects and purposes that can be achieved by the present invention, should still fall within the scope of the technical contents disclosed by the present invention.

[0022] Figure 1 It is a schematic flow chart of a fatigue life testing method for shaft parts provided by an embodiment of the present invention;

[0023] Figure 2 It is a schematic block diagram of the structure of a fatigue life testing device for shaft parts provided by an embodiment of the present invention;

[0024] Figure 3 It is a schematic block diagram of the structure of an electronic device provided by an embodiment of the present invention.

[0025] Description of reference numerals:

[0026] 10. Fatigue life testing device for shaft parts; 11. Acquisition module; 12. Measurement module; 13. First analysis module; 14. Second analysis module; 15. Prediction module; 20. Electronic device; 21. Memory; 22. Processor. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0028] The flowcharts shown in the accompanying drawings are only examples and do not necessarily include all the contents and operations / steps, nor must they be executed in the order described. For example, some operations / steps may be decomposed, combined or partially merged, so the actual execution order may change according to actual conditions.

[0029] It should also be understood that the terms used in this application specification are only for the purpose of describing specific embodiments and are not intended to limit the application. As used in this application specification and the appended claims, unless the context clearly indicates otherwise, the singular forms "a", "an" and "the" are intended to include plural forms.

[0030] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0031] The technical solution of the present invention is further described below with reference to the accompanying drawings and through specific implementation methods.

[0032] Embodiment 1:

[0033] Fix the shaft parts to be tested in the fatigue life test device, and preset the distributed optical fiber sensor on the fatigue test device, such as Figure 1 As shown, an embodiment of the present application provides a fatigue life test method for shaft parts, and the fatigue life test method includes steps S100 to S500.

[0034] Step S100: using distributed optical fiber sensors to collect strain difference data and temperature difference data at various positions in shaft parts.

[0035] In this step, by installing distributed fiber optic sensors inside and outside the shaft parts, the advantages of the sensor array are utilized to collect strain difference data and temperature difference data at different positions of the shaft parts; specifically, the fiber optic sensors can provide continuous strain and temperature measurements over the entire length of the shaft parts, ensuring accurate data collection at each position.

[0036] For example, during the test, the fiber optic sensor recorded the strain changes of shaft parts under cyclic stress and simultaneously recorded the temperature changes, providing detailed basic data for subsequent data analysis.

[0037] Step S200: Perform non-contact deformation measurement on the surface of shaft parts by digital image correlation technology to obtain surface displacement field and surface strain distribution data.

[0038] In this step, a high-resolution camera and dedicated software are used to implement digital image correlation technology to perform non-contact deformation measurement on the surface of shaft parts. Specifically, by taking images of the surface of shaft parts under different stress states, the surface displacement field and surface strain distribution data are calculated using an image matching algorithm.

[0039] For example, in one experiment, digital image correlation technology accurately measured the surface deformation of shaft parts under specific stress, thereby obtaining the surface displacement field and surface strain distribution, providing reliable data support for accurate analysis of the stress conditions of shaft parts.

[0040] Step S300: establishing a stress amplitude variation curve based on the surface displacement field, the surface strain distribution data and the strain difference data, and analyzing the stress variation amplitude based on the stress amplitude variation curve.

[0041] In this step, the surface displacement field, surface strain distribution data and strain difference data are integrated, and the stress amplitude variation curve is established using data processing software; specifically, by numerically fitting and analyzing these data, the variation law of the stress amplitude is obtained.

[0042] For example, when testing a batch of shaft parts, the stress amplitude change curve was successfully drawn through comprehensive processing of the surface displacement field, surface strain distribution data and strain difference data, and the stress change amplitude was analyzed and obtained.

[0043] Step S400: establishing a temperature change curve based on the surface displacement field, the surface strain distribution data and the temperature difference data, and analyzing the temperature change amplitude based on the temperature change curve.

[0044] In this step, the surface displacement field, surface strain distribution data and temperature difference data are combined, and the temperature change curve is established using the same data processing method; specifically, the temperature change law under stress is determined by comprehensive processing of the above data.

[0045] For example, during the experiment, the temperature change curve was successfully established using surface displacement field, surface strain distribution data and temperature difference data, and the temperature change amplitude was accurately analyzed.

[0046] Step S500: Integrate the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, input the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and output the fatigue life of the shaft parts.

[0047] In this step, the stress change amplitude and temperature change amplitude data obtained from the previous analysis are integrated to generate comprehensive amplitude data; specifically, by inputting the comprehensive amplitude data into a preset fatigue life prediction model, the model calculates and predicts based on these data to output the fatigue life of shaft parts.

[0048] For example, when testing a batch of new shaft parts, the fatigue life of these parts under complex stress and temperature conditions was successfully predicted by using comprehensive analysis data of stress change amplitude and temperature change amplitude.

[0049] In this embodiment, first, the shaft parts to be tested are fixed in a fatigue life test device, and a distributed optical fiber sensor is preset on the fatigue test device. Then, the strain difference data and temperature difference data of each position in the shaft parts are collected by the distributed optical fiber sensor. Next, the surface of the shaft parts is non-contactly deformed by using digital image correlation technology to obtain the surface displacement field and surface strain distribution data. Based on these data, the stress amplitude change curve and the temperature change curve are established, and the stress change amplitude and the temperature change amplitude are analyzed. Finally, the stress change amplitude and the temperature change amplitude are integrated into comprehensive amplitude data, input into the preset fatigue life prediction model for prediction, and the fatigue life of the shaft parts is output; the comprehensive measurement and analysis of the internal and external strain and temperature changes of the shaft parts are realized, avoiding the limitation of relying solely on surface strain data. At the same time, the use of distributed optical fiber sensors and digital image correlation technology improves the accuracy and reliability of the test, making the fatigue life prediction of shaft parts more accurate in complex stress states and temperature fields, and improving the problem of insufficient test accuracy in multi-axial stress states or complex temperature fields.

[0050] Embodiment 2:

[0051] In step S100, before the test starts, the initial strain data and initial temperature data of the shaft parts under static load conditions are collected using distributed optical fiber sensors.

[0052] Distributed fiber optic sensors are placed at key locations on shaft parts and used to perform initial measurements of strain and temperature under static load conditions. Specifically, the sensors can provide accurate initial strain and temperature baseline data for comparison and analysis in subsequent tests.

[0053] For example, during the initial measurement of a shaft-type part, the sensor records the strain and temperature values ​​at different positions under static load, providing a basis for subsequent real-time data processing.

[0054] The fatigue test device is started to test the shaft parts, and the distributed optical fiber sensors are used to collect the real-time strain data and real-time temperature data of each shaft segment in the shaft parts.

[0055] By starting the fatigue test device to apply cyclic stress, the strain and temperature data of shaft parts under different stress states are collected in real time; specifically, the distributed fiber optic sensor can continuously collect the strain and temperature changes at each position under dynamic loading conditions.

[0056] For example, during fatigue testing, sensors record the strain changes and temperature changes in different shaft segments during each cycle in real time, ensuring that all important data is captured.

[0057] The real-time strain data is subjected to multi-level filtering and trend decomposition through fast Fourier transform to obtain the strain sequence.

[0058] By performing fast Fourier transform on the collected real-time strain data, multi-level filtering technology is applied to decompose strain signals of different frequency bands; specifically, after trend decomposition processing, a strain sequence reflecting the law of strain change is generated.

[0059] For example, during the processing, the strain change pattern of shaft parts in complex stress environments was successfully extracted through fast Fourier transform and multi-stage filtering, and a detailed strain sequence was generated.

[0060] Wavelet transform is used to perform time-frequency analysis on the strain sequence to obtain local strain offset parameters and global strain balance parameters. The local strain offset parameters are correlated and compared with the global strain balance parameters to obtain strain correction benchmark data. The strain sequence is calibrated for amplitude and baseline based on the strain correction benchmark data to obtain processed strain data.

[0061] By applying wavelet transform to the strain sequence and performing time-frequency domain analysis, local strain offset parameters and global strain balance parameters are extracted; specifically, these parameters are compared in terms of correlation to generate strain correction benchmark data, and the strain sequence is calibrated for amplitude and baseline.

[0062] For example, using wavelet transform technology, the difference between local strain and global strain of a certain shaft part at a specific frequency was successfully analyzed. After making corresponding corrections, more accurate strain data was obtained.

[0063] Thermodynamic equations are applied to perform differential analysis on real-time temperature data to obtain fluctuating temperature dynamic data. Envelope analysis is performed on the fluctuating temperature dynamic data through Hilbert transform to obtain temperature amplitude characteristic parameters and temperature frequency domain characteristic parameters.

[0064] By applying thermodynamic equations, the differential changes of real-time temperature data are analyzed and the dynamic data of fluctuating temperature are extracted. Specifically, the Hilbert transform is used to perform envelope analysis on the fluctuating temperature to obtain the temperature amplitude and frequency domain characteristic parameters.

[0065] For example, the combined application of thermodynamic equations and Hilbert transform successfully captured and analyzed the temperature fluctuation characteristics of shaft parts under fatigue loading and obtained important temperature parameters.

[0066] The temperature frequency domain characteristic parameters are weighted and corrected based on the temperature amplitude characteristic parameters to obtain processed temperature data.

[0067] By weighting the temperature amplitude characteristic parameters and the temperature frequency domain characteristic parameters, the deviation in the temperature data is corrected; specifically, the corrected temperature data is more accurate and can better reflect the actual temperature change.

[0068] For example, by performing weighted correction on temperature data, the impact of ambient temperature fluctuations is eliminated and more accurate temperature measurement results are obtained.

[0069] The processed strain data is compared with the initial strain data to obtain strain difference data; the processed temperature data is compared with the initial temperature data to obtain temperature difference data.

[0070] By comparing the real-time processed strain and temperature data with the initial reference data, strain difference data and temperature difference data are calculated; specifically, these difference data are used to further analyze the fatigue characteristics of shaft parts.

[0071] For example, through comparative analysis, the strain and temperature change patterns of shaft parts under actual working conditions were clarified, and accurate differential data were obtained.

[0072] In step S200, non-contact deformation measurement of the surface of shaft parts is performed by digital image correlation technology, and the step of obtaining surface displacement field and surface strain distribution data includes: during the fatigue test, using a high-speed camera to synchronously shoot the speckle pattern on the surface of the shaft parts from multiple angles to obtain multi-view image data.

[0073] By using a high-speed camera to perform multi-angle shooting during the fatigue test, the speckle pattern on the surface of the shaft parts is captured; specifically, synchronous image data at multiple angles is obtained to improve the accuracy of the measurement.

[0074] For example, a high-speed camera captures multiple-angle images of the surface of shaft parts at each loading cycle to ensure that all subtle deformation features are recorded.

[0075] The multi-view image data are aligned with the processed strain data and the processed temperature data using the recording time as a reference, and spatial matching is performed according to the sensor installation position and shooting angle parameters to obtain a synchronized data set corresponding to time and space synchronization.

[0076] The strain and temperature data are integrated by temporally aligning the multi-view image data and spatially matching them according to the sensor positions and shooting angles; specifically, a comprehensive dataset that is synchronized in time and space is generated.

[0077] For example, during the experiment, real-time strain and temperature data were successfully integrated by accurately aligning multi-view images in space and time, obtaining a high-precision synchronized data set.

[0078] The image processing algorithm is used to perform grayscale equalization and edge invalid area cropping on the images in the synchronous data set to obtain the processed image data. The digital image correlation method is used to perform multiple iterative searches on the processed image data and the preset initial speckle pattern to obtain the characteristic point displacement, and the surface displacement field is calculated based on the characteristic point displacement. The initial speckle pattern is the speckle pattern randomly sprayed on the surface of the shaft parts before the test begins.

[0079] The image data is gray-scale balanced and cropped through image processing algorithms to eliminate invalid areas and optimize image quality. Specifically, digital image correlation technology is used to perform iterative searches, extract feature point displacements, and calculate surface displacement fields.

[0080] For example, in actual operation, the image processing algorithm successfully removed noise and invalid areas in the surface images of shaft parts, improving the clarity and measurement accuracy of the speckle pattern.

[0081] The displacements of adjacent points are differentiated according to the surface displacement field to obtain the surface strain distribution data.

[0082] By performing differential operations on the surface displacement field and calculating the displacement change rate of adjacent points, the surface strain distribution data is obtained; specifically, the displacement field data is processed using a numerical differentiation method to obtain the strain distribution information.

[0083] For example, during the test, by performing differential calculations on the displacements between multiple feature points, the strain distribution data on the surface of shaft parts under different stress states was successfully obtained.

[0084] The steps of performing multiple iterative searches on the processed image data using a digital image correlation method to obtain a displacement of a feature point, and calculating a surface displacement field based on the displacement of the feature point include: using a feature extraction algorithm to identify feature points of a speckle pattern of the processed image data, tracking the feature points of the speckle pattern with feature points of an initial speckle pattern through a point-by-point matching algorithm, calculating the displacement between the feature points of the speckle pattern and the feature points of the initial speckle pattern, obtaining the displacement of the feature points, and calculating the surface displacement field based on the displacement of the feature points.

[0085] The feature points of the speckle pattern are identified from the processed image data through a feature extraction algorithm; specifically, a point-by-point matching algorithm is used to track the displacement of the feature points and calculate the displacement between the feature points, ultimately obtaining the surface displacement field.

[0086] For example, during the test, the feature extraction algorithm was used to identify the characteristic points of the speckle pattern on the surface of shaft parts. Through the matching algorithm, the displacement of the characteristic points was successfully tracked and the surface displacement field was calculated.

[0087] The calculation formula for calculating the surface displacement field based on the displacement of the characteristic points is as follows:

[0088]

[0089]

[0090] in, and Respectively expressed in Direction and The displacement component in the direction; and represents the first The coordinates of the feature points, and represents the coordinates of the corresponding feature points in the processed image data, is the number of feature points involved in the calculation.

[0091] The surface displacement field is calculated according to the displacement of the feature points through the above formula; specifically, the feature point data is processed using statistical methods to obtain the overall displacement field distribution.

[0092] For example, in actual operation, the displacement field on the surface of shaft parts was successfully calculated by performing statistical analysis on multiple feature point data.

[0093] In step S300, the surface displacement field and the surface strain distribution data are analyzed by a finite element analysis method to obtain stress field data, and a stress amplitude variation curve is established based on the stress field data and the strain difference data.

[0094] The surface displacement field and surface strain distribution data are numerically simulated by finite element analysis method to obtain stress field data; specifically, the data is processed by simulation software to generate a stress distribution model.

[0095] For example, when testing a batch of shaft parts, finite element analysis was used to successfully simulate the stress distribution of the shaft parts under different stress conditions, and a stress amplitude change curve was established.

[0096] The curve fitting algorithm is used to fit and analyze the stress amplitude variation curve to obtain the fitting parameters and residuals.

[0097] Through the curve fitting algorithm, the stress amplitude variation curve is numerically fitted to extract key fitting parameters and residuals; specifically, mathematical tools such as the least squares method are used to accurately fit the curve data.

[0098] For example, during the test, the stress change curve was processed using a curve fitting algorithm, and the fitting parameters and residuals were successfully extracted, ensuring the accuracy of data analysis.

[0099] The stress variation amplitude is calculated based on the fitting parameters and residuals.

[0100] The stress change amplitude is calculated by fitting parameters and residuals; specifically, the fitting result is processed by mathematical formula to obtain the stress change amplitude.

[0101] For example, during the data analysis phase, the stress variation amplitude of shaft parts in fatigue tests was successfully obtained by calculating fitting parameters and residuals.

[0102] In step S400, a transient temperature field model of shaft parts is established using the surface displacement field, surface strain distribution data and temperature difference data.

[0103] A transient temperature field model is established by integrating the surface displacement field, surface strain distribution data and temperature difference data. Specifically, the finite element method is used to simulate the data to simulate the transient temperature distribution of shaft parts.

[0104] For example, in the experiment, through data integration and simulation software, a transient temperature field model of shaft parts was successfully established, simulating its temperature distribution under different temperature conditions.

[0105] According to the transient temperature field model, the temperature distribution data of each shaft section in the shaft parts are calculated, and the temperature change curve is established based on the temperature distribution data. The temperature change curve is analyzed in the frequency domain by using the statistical analysis method, and the temperature change amplitude characteristics are extracted based on the analysis results.

[0106] The temperature distribution data of each shaft section is calculated through the transient temperature field model, and the temperature change curve is established accordingly; specifically, the temperature curve is analyzed in the frequency domain using the statistical analysis method to extract the temperature change amplitude characteristics.

[0107] For example, during the test, the temperature change amplitude characteristics were successfully extracted through frequency domain analysis of temperature data, providing reliable data support for subsequent analysis.

[0108] In step S500, the stress change amplitude and the temperature change amplitude are integrated to obtain comprehensive amplitude data, and the comprehensive amplitude data is feature decomposed by a hierarchical clustering analysis method to obtain key characteristic parameters. The key characteristic parameters are subjected to multivariate statistical analysis using a correlation coefficient calculation method to obtain correlation parameters between stress and temperature, and the correlation parameters are used as input data.

[0109] The comprehensive amplitude data are generated by integrating the stress change amplitude and the temperature change amplitude. Specifically, the data are feature decomposed using hierarchical clustering analysis to extract key characteristic parameters, and the correlation parameters between stress and temperature are calculated through multivariate statistical analysis.

[0110] For example, in the data processing stage, through hierarchical clustering analysis and statistical analysis, the key characteristic parameters of the comprehensive amplitude data were successfully extracted, and the correlation between stress and temperature was analyzed, providing an important basis for fatigue life prediction.

[0111] The input data is input into a preset fatigue life prediction model. In the fatigue life prediction model, the input data is calculated and analyzed through numerical simulation to obtain a fatigue life prediction value, which is used as the fatigue life output of the shaft parts.

[0112] By inputting the above correlation parameters into a preset fatigue life prediction model, numerical simulation and calculation analysis are performed; specifically, the input data is processed using numerical simulation technology to obtain fatigue life prediction values.

[0113] For example, in practical applications, the fatigue life of shaft parts was successfully predicted through numerical simulation of the fatigue life prediction model, ensuring the reliability and scientific nature of the test results.

[0114] In this embodiment, by combining distributed optical fiber sensors and digital image correlation technology, all-round monitoring and analysis of shaft parts in fatigue life testing is achieved. Specifically, by collecting initial strain data and initial temperature data before the test begins, and collecting strain data and temperature data of each shaft segment in real time during the test, the comprehensiveness and accuracy of the data are ensured. The real-time data undergoes complex processing steps such as fast Fourier transform, wavelet transform, differential analysis and Hilbert transform to obtain processed strain data and temperature data respectively. These data are further integrated to obtain strain difference data and temperature difference data, thereby providing a solid foundation for subsequent fatigue life prediction.

[0115] Using high-speed cameras and image processing algorithms, digital image correlation technology is applied to non-contact deformation measurement of the surface of shaft parts, and high-precision surface displacement field and surface strain distribution data are obtained. Through finite element analysis method and curve fitting algorithm, the surface displacement field and strain distribution data are deeply analyzed, the stress amplitude change curve is established and the stress change amplitude is extracted. At the same time, based on the transient temperature field model and frequency domain analysis method, the temperature change amplitude characteristics are analyzed to ensure a comprehensive understanding of shaft parts in complex stress and temperature environments.

[0116] By integrating the stress change amplitude and temperature change amplitude, comprehensive amplitude data is generated, and through hierarchical cluster analysis and multivariate statistical analysis, key characteristic parameters are extracted, and the correlation parameters between stress and temperature are calculated. Finally, the correlation parameters are input into the fatigue life prediction model, and the fatigue life prediction value of shaft parts is obtained through numerical simulation. The overall solution effectively improves the accuracy and reliability of fatigue life testing, and provides a scientific basis and technical guarantee for the reliable operation of mechanical equipment. This method not only improves the effectiveness of the test, but also provides valuable data support for the design and optimization of shaft parts, and has broad application prospects.

[0117] Embodiment 3:

[0118] like Figure 2 As shown, the present application also provides a fatigue life testing device 10 for shaft parts, in which the shaft parts to be tested are fixed in the fatigue life testing device, and the distributed optical fiber sensors are preset on the fatigue testing device. The fatigue life testing device includes an acquisition module 11, a measurement module 12, a first analysis module 13, a second analysis module 14 and a prediction module 15.

[0119] The acquisition module 11 is mainly used to collect strain difference data and temperature difference data at various positions in shaft parts using distributed optical fiber sensors.

[0120] The measurement module 12 is mainly used to perform non-contact deformation measurement on the surface of shaft parts through digital image correlation technology to obtain surface displacement field and surface strain distribution data.

[0121] The first analysis module 13 is mainly used to establish a stress amplitude variation curve based on the surface displacement field, the surface strain distribution data and the strain difference data, and to analyze the stress variation amplitude based on the stress amplitude variation curve.

[0122] The second analysis module 14 is mainly used to establish a temperature change curve based on the surface displacement field, the surface strain distribution data and the temperature difference data, and analyze the temperature change amplitude based on the temperature change curve.

[0123] The prediction module 15 is mainly used to integrate the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, input the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and output the fatigue life of shaft parts.

[0124] In this embodiment, by presetting the distributed optical fiber sensor on the fatigue test device, the strain difference data and temperature difference data of each position in the shaft parts can be collected in real time. The acquisition module 11 is responsible for collecting these data and transmitting them to the measurement module 12. The measurement module 12 performs non-contact deformation measurement on the surface of the shaft parts through digital image correlation technology to obtain high-precision surface displacement field and surface strain distribution data. The first analysis module 13 establishes a stress amplitude change curve by integrating the surface displacement field, surface strain distribution data and strain difference data, and analyzes the stress change amplitude. The second analysis module 14 establishes a temperature change curve based on the temperature difference data and analyzes the temperature change amplitude. Finally, the prediction module 15 integrates the stress change amplitude and the temperature change amplitude to generate comprehensive amplitude data, and inputs it into the preset fatigue life prediction model to output the fatigue life of the shaft parts. The overall device realizes efficient and accurate fatigue life testing through modular design, ensuring the comprehensiveness and accuracy of data collection and analysis.

[0125] It should be noted that technicians in the relevant technical field can clearly understand that for the convenience and simplicity of description, the specific working process of the above-described device and each module can refer to the corresponding process in the aforementioned fatigue life test method embodiment of shaft parts, and will not be repeated here.

[0126] Embodiment 4:

[0127] like Figure 3 As shown, the present application also provides an electronic device 20, including a memory 21 and a processor 22, wherein the memory 21 stores a computer program that can be run on the processor 22, and when the processor 22 executes the computer program, the fatigue life test method for shaft parts of Example 1 is implemented.

[0128] In this embodiment, the fatigue life test method for shaft parts described in Example 1 can be run on the processor 22 through the computer program stored in the memory 21. Specifically, when the processor 22 executes the computer program, it can control the distributed optical fiber sensor to collect strain difference data and temperature difference data of shaft parts, and obtain surface displacement field and surface strain distribution data through digital image correlation technology. The processor 22 further uses these data to establish a stress amplitude change curve and a temperature change curve, analyzes the stress change amplitude and the temperature change amplitude, and integrates them to generate comprehensive amplitude data. Finally, the processor 22 inputs the comprehensive amplitude data into the fatigue life prediction model to predict the fatigue life of shaft parts. In this way, the electronic device 20 realizes automated and precise fatigue life testing, providing users with a convenient and efficient testing method.

[0129] Embodiment 5:

[0130] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the processor executes the fatigue life test method for shaft parts as in Example 1.

[0131] In this embodiment, the processor can execute the fatigue life test method for shaft parts described in Example 1 through a computer program stored on a computer-readable storage medium. Specifically, when the processor runs the computer program, it first uses a distributed optical fiber sensor to collect strain difference data and temperature difference data of shaft parts, and then obtains surface displacement field and surface strain distribution data through digital image correlation technology. Then, the processor establishes a stress amplitude change curve and a temperature change curve based on these data, analyzes the stress change amplitude and the temperature change amplitude, and integrates them to generate comprehensive amplitude data. Finally, the processor inputs the comprehensive amplitude data into the fatigue life prediction model to predict the fatigue life of shaft parts. This process improves the efficiency and accuracy of the test method through the automated operation of the computer program, and ensures the scientificity and reliability of the fatigue life prediction of shaft parts.

[0132] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A fatigue life test method for shaft parts, wherein the shaft parts to be tested are fixed in a fatigue life test device, and a distributed optical fiber sensor is preset on the fatigue life test device, characterized in that: The fatigue life test method comprises: Using the distributed optical fiber sensor to collect strain difference data and temperature difference data at various positions in the shaft parts; Perform non-contact deformation measurement on the surface of the shaft parts by using digital image correlation technology to obtain surface displacement field and surface strain distribution data; Analyzing the surface displacement field and surface strain distribution data by a finite element analysis method to obtain stress field data, and establishing a stress amplitude change curve based on the stress field data and the strain difference data; performing fitting analysis on the stress amplitude change curve by using a curve fitting algorithm to obtain fitting parameters and residuals; and calculating the stress change amplitude according to the fitting parameters and the residuals; A transient temperature field model of shaft parts is established by using the surface displacement field, the surface strain distribution data and the temperature difference data; temperature distribution data of each shaft segment in the shaft parts is calculated according to the transient temperature field model, a temperature change curve is established based on the temperature distribution data, a frequency domain analysis is performed on the temperature change curve by a statistical analysis method, and a temperature change amplitude is extracted based on the analysis result; The stress change amplitude and the temperature change amplitude are integrated to obtain comprehensive amplitude data, and the comprehensive amplitude data is input into a preset fatigue life prediction model for prediction, and the fatigue life of shaft parts is output.

2. The fatigue life testing method for shaft parts according to claim 1, characterized in that: The step of collecting strain difference data and temperature difference data at various positions in the shaft parts by using the distributed optical fiber sensor comprises: Before the test begins, the distributed optical fiber sensor is used to collect the initial strain data and initial temperature data of the shaft parts under static load conditions; Starting the fatigue life testing device to test the shaft parts, and using the distributed optical fiber sensor to collect real-time strain data and real-time temperature data of each shaft segment in the shaft parts; Performing multi-level filtering and trend decomposition on the real-time strain data by fast Fourier transform to obtain a strain sequence; Performing time-frequency analysis on the strain sequence by wavelet transform to obtain local strain offset parameters and global strain balance parameters, performing correlation comparison between the local strain offset parameters and the global strain balance parameters to obtain strain correction reference data, and performing amplitude and baseline calibration on the strain sequence according to the strain correction reference data to obtain processed strain data; Performing differential analysis on the real-time temperature data using thermodynamic equations to obtain fluctuating temperature dynamic data, and performing envelope analysis on the fluctuating temperature dynamic data using Hilbert transform to obtain temperature amplitude characteristic parameters and temperature frequency domain characteristic parameters; Performing weighted correction on the temperature frequency domain characteristic parameter based on the temperature amplitude characteristic parameter to obtain processed temperature data; The processed strain data is compared with the initial strain data to obtain strain difference data; the processed temperature data is compared with the initial temperature data to obtain temperature difference data.

3. The fatigue life testing method for shaft parts according to claim 2, characterized in that: The step of performing non-contact deformation measurement on the surface of the shaft part by digital image correlation technology to obtain surface displacement field and surface strain distribution data includes: During the fatigue test, a high-speed camera is used to synchronously capture the speckle pattern on the surface of the shaft part from multiple angles to obtain multi-view image data; Aligning the multi-view image data with the processed strain data and the processed temperature data with reference to the recording time, and performing spatial matching according to the sensor installation position and the shooting angle parameters to obtain a synchronous data set corresponding to time and space synchronization; grayscale equalization and edge invalid area cropping are performed on the images in the synchronous data set by an image processing algorithm to obtain processed image data, and a digital image correlation method is used to perform multiple iterations of searches on the processed image data and a preset initial speckle pattern to obtain a characteristic point displacement, and a surface displacement field is calculated according to the characteristic point displacement, wherein the initial speckle pattern is a speckle pattern randomly sprayed on the surface of the shaft part before the test starts; The displacements of adjacent points are differentiated and solved according to the surface displacement field to obtain surface strain distribution data.

4. The fatigue life testing method for shaft parts according to claim 3 is characterized in that: The step of performing multiple iterative searches on the processed image data and the preset initial speckle pattern using a digital image correlation method to obtain characteristic point displacements, and calculating the surface displacement field according to the characteristic point displacements comprises: identifying feature points of a speckle pattern of the processed image data by using a feature extraction algorithm, tracking the feature points of the speckle pattern with the feature points of the initial speckle pattern by using a point-by-point matching algorithm, calculating the displacement between the feature points of the speckle pattern and the feature points of the initial speckle pattern to obtain feature point displacement, and calculating a surface displacement field according to the feature point displacement; The calculation formula for calculating the surface displacement field according to the displacement of the feature points is as follows: in, and Respectively expressed in Direction and The displacement component in the direction; and represents the first The coordinates of the feature points, and represents the coordinates of the corresponding feature points in the processed image data, is the number of feature points involved in the calculation.

5. The fatigue life testing method for shaft parts according to claim 1, characterized in that: The step of integrating the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, inputting the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and outputting the fatigue life of shaft parts comprises: Integrate the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, perform feature decomposition on the comprehensive amplitude data by a hierarchical clustering analysis method to obtain key characteristic parameters, perform multivariate statistical analysis on the key characteristic parameters by a correlation coefficient calculation method to obtain correlation parameters between stress and temperature, and use the correlation parameters as input data; The input data is input into a preset fatigue life prediction model, in which the input data is calculated and analyzed through numerical simulation to obtain a fatigue life prediction value, and the fatigue life prediction value is output as the fatigue life of the shaft part.

6. A fatigue life test device for shaft parts, wherein the shaft parts to be tested are fixed in the fatigue life test device, and a distributed optical fiber sensor is preset on the fatigue life test device, characterized in that: The fatigue life testing device comprises: A collection module, used for collecting strain difference data and temperature difference data at various positions in the shaft parts by using the distributed optical fiber sensor; A measuring module, used for performing non-contact deformation measurement on the surface of the shaft parts by digital image correlation technology to obtain surface displacement field and surface strain distribution data; The first analysis module is used to analyze the surface displacement field and the surface strain distribution data by a finite element analysis method to obtain stress field data, and to establish a stress amplitude change curve based on the stress field data and the strain difference data; to perform fitting analysis on the stress amplitude change curve by using a curve fitting algorithm to obtain fitting parameters and residuals; and to calculate the stress change amplitude according to the fitting parameters and the residuals; The second analysis module is used to establish a transient temperature field model of the shaft parts by using the surface displacement field, the surface strain distribution data and the temperature difference data; according to the transient temperature field model, the temperature distribution data of each shaft segment in the shaft parts is calculated, a temperature change curve is established based on the temperature distribution data, a frequency domain analysis is performed on the temperature change curve by a statistical analysis method, and a temperature change amplitude is extracted based on the analysis result; The prediction module is used to integrate the stress change amplitude and the temperature change amplitude to obtain comprehensive amplitude data, input the comprehensive amplitude data into a preset fatigue life prediction model for prediction, and output the fatigue life of shaft parts.

7. An electronic device, characterized in that: It comprises a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and the processor implements the fatigue life test method for shaft parts as described in any one of claims 1 to 5 when executing the computer program.

8. A computer-readable storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the processor is enabled to execute the fatigue life test method for shaft parts as claimed in any one of claims 1 to 5.

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