A structural fatigue life prediction method and system considering strength degradation effect
By introducing a nonlinear cumulative damage model with strength degradation effect, the problem of insufficient accuracy in fatigue life prediction is solved, achieving higher prediction accuracy and a simplified model that is suitable for engineering practice.
Patent Information
- Application Number
- CN202411591883.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-08
- Publication Date
- 2026-08-25
- Estimated Expiration
- 2044-11-08
AI Technical Summary
Existing technologies fail to effectively consider the residual strength degradation effect in fatigue failure prediction, resulting in insufficient accuracy in fatigue life prediction and failing to meet actual engineering needs.
A nonlinear cumulative damage model considering strength degradation effect is adopted. By acquiring strain-time data and converting it into a stress spectrum, the nonlinear cumulative damage model is corrected using the strength degradation coefficient, and the residual damage and fatigue life of the structure are calculated.
It improves the accuracy of fatigue life prediction, especially under multi-level load conditions, the prediction results are more accurate and consistent with the actual damage process, and the model complexity and parameter dependence are reduced.
Smart Images
Figure CN119475771B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of equipment life prediction technology, and in particular to a method and system for predicting the fatigue life of structures that takes into account the effect of strength degradation. Background Technology
[0002] With the continuous improvement of industrial technology, modern mechanical equipment is developing towards larger scale, higher speed, and higher performance. Fatigue failure, one of the most common failure modes of mechanical equipment, is not only insidious and sudden, but also accounts for 50%-90% of all mechanical structural failures.
[0003] Studies have shown that most materials exhibit a highly nonlinear relationship between cumulative damage and cyclic loading. Nonlinear cumulative damage theory considers the load order and the interaction between loads, but it cannot be directly applied to engineering practice due to its overly complex expressions and the difficulty in determining the experimental coefficients it introduces.
[0004] The number of load cycles and load level are the dominant factors determining the cumulative fatigue damage value. However, the current damage state and stress state of the structure also affect the accumulation of fatigue damage. This is because as loads are continuously applied and damage accumulates, the load-bearing capacity of the structure degrades, i.e., residual strength degrades. The interaction between residual strength degradation and cyclic loading will affect subsequent cumulative damage, especially in the middle and later stages of the structure's lifespan. As the residual strength of the structure continues to degrade, the mechanical properties continue to deteriorate, accelerating the accumulation of fatigue damage. Considering the influence of residual strength degradation when calculating cumulative fatigue damage is more consistent with the actual damage process and is of great significance for improving the accuracy of fatigue life prediction for engineering structures. Summary of the Invention
[0005] The purpose of this invention is to provide a method and system for predicting the fatigue life of structures that takes into account the strength degradation effect, which greatly improves the accuracy of fatigue life prediction.
[0006] To achieve the above objectives, the present invention provides the following solution:
[0007] A method for predicting the fatigue life of structures considering strength degradation effects, the method comprising:
[0008] Acquire strain-time data of the structure under at least one operating condition;
[0009] The strain-time data is converted into a stress spectrum;
[0010] The stress spectrum is processed using a nonlinear cumulative damage model that considers the strength degradation effect to obtain the residual damage of the structure;
[0011] The fatigue life of the structure is calculated based on the remaining damage.
[0012] Optionally, the method further includes: constructing a nonlinear cumulative damage model that considers the intensity degradation effect, specifically including:
[0013] Obtain a nonlinear cumulative damage model and a strength degradation model that take into account the effects of load loading history;
[0014] A strength degradation coefficient is introduced based on the strength degradation model;
[0015] The nonlinear cumulative damage model is updated using the load ratio and the strength degradation coefficient to obtain a nonlinear cumulative damage model that considers the strength degradation effect.
[0016] Optionally, the nonlinear cumulative damage model is:
[0017]
[0018] Where: D i N represents the damage to the structure under the i-th level load; fi This refers to the fatigue life under the corresponding stress level; n i δ is the number of cycles under the i-th stress level; i These are model parameters that depend only on N. f And given the stress level, δ i =-1.25 / lnN fi ;γ i,i+1 For load interaction factor, σ i For the i-th stress; n i+1,i This is the equivalent cycle number;
[0019] The intensity degradation model is as follows:
[0020]
[0021] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f This corresponds to the peak stress value;
[0022] Optionally, the load ratio and strength degradation coefficient can be combined using an exponential function.
[0023] Among them, the strength degradation coefficient is
[0024] Optionally, converting the strain-time data into a stress spectrum specifically includes:
[0025] Acquire the strain-time data; the strain-time data is the strain signal collected by the strain gauge;
[0026] The strain-time data are subjected to unadjusted balancing processing;
[0027] Digital filtering processing;
[0028] The filtered strain signal is converted into a stress signal;
[0029] Zero-point drift processing is performed on the stress signal;
[0030] Abnormal signal processing;
[0031] The stress signal was calculated using the rainflow counting method, and the stress spectrum of strain-time data was obtained.
[0032] A structural fatigue life prediction system considering strength degradation effects, the system comprising:
[0033] The data acquisition unit is used to acquire strain-time data of the structure under at least one working condition.
[0034] The data conversion unit is used to convert the strain-time data into a stress spectrum;
[0035] The stress spectrum processing unit is used to process the stress spectrum using a nonlinear cumulative damage model that considers the strength degradation effect to obtain the residual damage of the structure.
[0036] The fatigue life calculation unit is used to calculate the fatigue life of the structure based on the remaining damage.
[0037] Optionally, the system further includes: a model building unit, specifically comprising:
[0038] The model acquisition sub-unit is used to acquire the nonlinear cumulative damage model and strength degradation model that take into account the load loading history.
[0039] A strength degradation coefficient is introduced into a sub-unit, which is used to introduce a strength degradation coefficient according to the strength degradation model;
[0040] The model update sub-unit is used to update the nonlinear cumulative damage model using the load ratio and the strength degradation coefficient, so as to obtain a nonlinear cumulative damage model that takes into account the strength degradation effect.
[0041] Optionally, the nonlinear cumulative damage model is:
[0042]
[0043] Where: D i N represents the damage to the structure under the i-th level load; fi This refers to the fatigue life under the corresponding stress level; n i δ is the number of cycles under the i-th stress level; i These are model parameters that depend only on N.f And given the stress level, δ i =-1.25 / lnN fi ;γ i,i+1 For load interaction factor, σ i For the i-th stress; n i+1,i This is the equivalent cycle number;
[0044] The intensity degradation model is as follows:
[0045]
[0046] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f This corresponds to the peak stress value;
[0047] Optionally, the load ratio and strength degradation coefficient can be combined using an exponential function.
[0048] Among them, the strength degradation coefficient is
[0049] Optionally, the data conversion unit specifically includes:
[0050] The data acquisition subunit is used to acquire the strain-time data; the strain-time data is the strain signal collected by the strain gauge.
[0051] An unadjusted balancing processing subunit is used to perform unadjusted balancing processing on the strain-time data;
[0052] Digital filtering subunit, used for digital filtering processing;
[0053] The conversion subunit is used to convert the filtered strain signal into a stress signal;
[0054] The zero-point drift processing subunit is used to perform zero-point drift processing on the stress signal;
[0055] The exception handling subunit is used to process exception signals;
[0056] The stress signal calculation subunit is used to calculate the stress signal using the rainflow counting method to obtain the stress spectrum of strain-time data.
[0057] According to specific embodiments provided by the present invention, the present invention discloses the following technical effects:
[0058] (1) This invention modifies the nonlinear cumulative damage model by introducing stress ratio and strength degradation coefficient. The function is mainly expressed in exponential form, which can better reflect the degradation performance of the material. Moreover, the model is simple in form and does not require the introduction of additional physical parameters, so it can be well applied in practical engineering.
[0059] (2) This invention takes into account the strength degradation effect and the prediction results of fatigue life under multi-level load loading are also more accurate, which greatly improves the accuracy of structural fatigue life prediction. Attached Figure Description
[0060] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.
[0061] Figure 1 A flowchart of the structural fatigue life prediction method considering strength degradation effect provided by the present invention;
[0062] Figure 2 The prediction results of different models for the damage value of No. 35 steel;
[0063] Figure 3 The predicted damage values of 30CrMnSiA steel using different models;
[0064] Figure 4 The predicted damage values of Q235 steel using different models;
[0065] Figure 5 A schematic diagram of the structural fatigue life prediction system considering strength degradation effects provided by the present invention. Detailed Implementation
[0066] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.
[0067] The purpose of this invention is to provide a method and system for predicting the fatigue life of structures that takes into account the strength degradation effect. By considering the strength degradation effect, the accuracy of predicting the fatigue life of structures is greatly improved.
[0068] To make the above-mentioned objects, features and advantages of the present invention more apparent and understandable, the present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments.
[0069] Figure 1 This is a flowchart of a structural fatigue life prediction method considering strength degradation effects provided in an embodiment of the present invention. The prediction method specifically includes:
[0070] S1: Obtain strain-time data of the structure under at least one operating condition.
[0071] S2: Convert strain-time data into stress spectrum.
[0072] S3: The stress spectrum is processed using a nonlinear cumulative damage model that considers the strength degradation effect to obtain the residual damage of the structure.
[0073] S4: Calculate the fatigue life of the structure based on the remaining damage.
[0074] To obtain strain-time data for a structure, it is necessary to combine actual operating conditions to determine the fatigue-prone area; conduct dynamic stress testing experiments to obtain the stress-time history of the fatigue-prone area under multiple actual operating cycles.
[0075] The specific experimental implementation for acquiring strain-time data includes:
[0076] Test vehicle: One of the car bodies of a locomotive that has been in operation for a long time.
[0077] Test route: Shenmu to Shenchi South.
[0078] Loading conditions: Sandbags are placed in the vehicle to simulate heavy vehicle conditions.
[0079] Test speed: The test was conducted at a nighttime window test point, with the train stopping at the station in normal operating mode and without opening or closing the doors.
[0080] Stress test points are selected as follows: 1) Stress concentration areas that can be estimated, such as areas of abrupt changes in structural shape and weld areas; 2) Areas that are estimated to be under high stress based on strength calculation results; 3) Areas that require attention in welding and processing.
[0081] Experimental process
[0082] The stress on the vehicle chassis was measured using a 32-channel dynamic synchronous acquisition device with a sampling frequency of 500Hz.
[0083] 1. The paint on the vehicle body underframe can affect stress data acquisition, so it needs to be dissolved with paint solvent when attaching strain gauges.
[0084] 2. To make the metal surface shiny, rub the metal surface with gauze, and then use acetone solution to remove the dirt from the metal surface.
[0085] 3. Apply 502 glue to the strain gauge to ensure it adheres well to the vehicle chassis, and then remove any air bubbles from the strain gauge to ensure it is completely adhered to the vehicle chassis.
[0086] 4. Apply AB glue strain gauges and cure and moisture-proof the strain gauges.
[0087] 5. Use sandbags to simulate the load and connect the test equipment and circuitry.
[0088] 6. Measure the stress-time data of the vehicle body during normal operation.
[0089] Dynamic stress testing is characterized by its complexity, large signal capacity, and long testing cycle. The data acquisition process and scientific processing of the data directly affect the accuracy of stress spectrum compilation and the prediction of vehicle fatigue life.
[0090] The raw data collected by the equipment is downloaded to the computer. The information of each channel in the raw data is checked for normality. Based on the actual operation schedule of the vehicles, the start and end times of the valid data segments in the collected data are determined. Valid data is extracted and zeroed. The waveforms of each channel in the collected data are examined to determine if zero-point drift exists. Data with zero drift is removed. Noise interference caused by the surrounding environment or equipment operation is filtered. The raw strain signal is converted into a stress signal according to the formula between stress and strain signals. The converted stress signal is checked for glitches in both the time and frequency domains. Data with glitches is de-glitched. Finally, the stress spectrum is compiled.
[0091] The stress spectrum at various measuring points on the vehicle body is shown in Table 1 below:
[0092] Table 1 Stress Spectra at Measurement Points
[0093]
[0094]
[0095] By using the nonlinear cumulative damage model considering strength degradation effect constructed in this invention to calculate the stress spectrum at each measuring point of the vehicle body, the cumulative damage and fatigue life of the vehicle body structure can be obtained.
[0096] The construction process for the nonlinear cumulative damage model that considers the strength degradation effect is as follows:
[0097] Construct a nonlinear cumulative damage model for the vehicle body material to be predicted:
[0098]
[0099] Where: Di N represents the damage to the structure under the i-th level load; fi This refers to the fatigue life under the corresponding stress level; n i γ is the number of cycles under the i-th stress level; i,i+1 For load interaction factor, σ i For the i-th stress; n i+1,i δ is the equivalent cycle number. i These are model parameters that depend only on N. f And the given stress level:
[0100] δ i =-1.25 / lnN fi (2)
[0101] Assuming a stress of magnitude σ1 is applied to the structure and cyclically n1 times, the cumulative fatigue damage is D1. Before the two-stage loading, since the first-stage loading has already caused damage to the structure, according to the equivalence principle, the damage D1 can be represented by a stress cycle of magnitude σ2 n times. 21 The damage caused by this incident is represented by D2.
[0102]
[0103] At this point, the residual damage of the model under two levels of loading can be obtained:
[0104]
[0105] The cumulative fatigue damage of the structure under two levels of loading is:
[0106] The cumulative fatigue damage of the structure under multi-level loads is:
[0107] The residual damage under multi-level load loading is:
[0108]
[0109] Construct a strength degradation model for the vehicle body material to be predicted:
[0110]
[0111] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f This corresponds to the peak stress.
[0112] Assuming a stress of magnitude σ1 is applied to the structure and cyclically n1 times, the remaining strength is R1. Before the two-stage loading, the first-stage loading has already caused a decrease in the strength of the structure. According to the equivalence principle, the strength decrease of R1 can be represented by a stress cycle of magnitude σ2 n times.21 The intensity of the decrease is represented by R2.
[0113] R1 = R2 (7)
[0114] Then the equivalent cycle number n 21 for:
[0115]
[0116] Similarly, when a material is subjected to multiple levels of load until final failure, then...
[0117]
[0118] To reflect the impact of strength degradation on cumulative damage, a strength degradation coefficient is introduced:
[0119]
[0120] To address the issue that the model does not consider the strength degradation effect, the load ratio and the strength degradation coefficient are combined using an exponential function:
[0121]
[0122] Substituting equation (12) into equation (1) yields a new equivalent damage model:
[0123]
[0124] The residual damage of the model in this paper under two levels of loading is obtained as follows:
[0125]
[0126] The residual damage under multi-stage loading is:
[0127]
[0128] Equation (13) is a nonlinear fatigue cumulative damage model after considering residual strength degradation correction. This model is corrected by introducing stress ratio and strength degradation coefficient. The function is mainly expressed in exponential form, which can better reflect the degradation performance of the material. The model is simple in form and does not require the introduction of additional physical parameters. The prediction results of fatigue life under multi-level load loading are also relatively accurate.
[0129] To verify the accuracy of the nonlinear cumulative damage model of the present invention that considers the strength degradation effect, the present invention verifies the model using existing fatigue test data of metallic materials.
[0130] The model was validated by selecting fatigue test data of some metallic materials at levels two, four, and six through data search. To better demonstrate the advantages of the proposed model in life prediction, an error factor E was introduced to describe the prediction error of the four models, as shown in Equation (16). Specifically, the mean M and standard deviation S of E represent the "accuracy" and "reliability" of the model prediction, respectively. The smaller the mean and standard deviation and the closer they are to 0, the more accurate and reliable the prediction, and vice versa.
[0131]
[0132] In the formula, The test cycle ratio; To predict the cycle ratio.
[0133] (1) Comparative analysis of fatigue life prediction results for No. 35 steel
[0134] C35 steel possesses excellent strength, plasticity, and toughness, and is widely used in various fields such as machinery, automobiles, construction, and shipbuilding. It is a high-quality carbon structural steel. C35 steel exhibits fatigue lives of 52,000, 110,000, 400,000, and 760,000 cycles under loading stress amplitudes of 353 MPa, 334 MPa, 294 MPa, and 275 MPa, respectively. Its tensile strength is 458 MPa. Taking the two-stage loading test of C35 steel in the literature as an example, the fatigue life of the Miner, Ye, and modified nonlinear cumulative damage models were predicted. The predicted and experimental values and their errors are shown in Table 2. The mean and standard deviation of the error values of the four models were then calculated, and the results are shown in Table 3. Finally, the predicted and experimental fatigue life values are compared... Figure 2 As shown, where Figure 2 (a) is 334-294MPa and 294-334MPa. Figure 2 (b) is 353-275MPa and 275-353MPa.
[0135] Table 2. Fatigue test data and fatigue life prediction values of 35 steel specimens under two-stage loading.
[0136]
[0137] Table 3. Statistical table of prediction errors for No. 35 steel specimens under two-stage loading.
[0138]
[0139] from Figure 2It can be seen that 41.67% of the prediction results of the model in this paper are within the 20% error band and closer to the 0% error band, while the other three models only have some prediction results within the 20% error band after correcting the nonlinear cumulative damage model, and all prediction results of the Miner model are within the 50% error band. This indicates that the model of this invention has better prediction results than the other models for No. 35 steel.
[0140] (2) Comparative analysis of fatigue life prediction results for 30CrMnSiA steel
[0141] 30CrMnSiA is an ultra-high strength alloy structural steel, commonly used in the manufacture of aircraft engine mounts, engine compressor blades, etc. The fatigue lives of 30CrMnSiA steel under loading stress amplitudes of 586MPa, 482MPa, 732MPa, and 836MPa are 52,000, 760,000, 55,793, and 7,186 cycles, respectively. Taking the two-stage loading test of 30CrMnSiA in the literature as an example, the fatigue life of four models was predicted. The predicted values, experimental values, and error values are shown in Table 4. The mean and standard deviation of the error values of the four models were then calculated, and the results are shown in Table 5. Finally, the predicted and experimental fatigue life values are compared... Figure 3 As shown, where, Figure 3 (a) is 586-482MPa and 482-586MPa. Figure 3 (b) is 836-732MPa and 732-836MPa.
[0142] Table 4. Fatigue test data and fatigue life prediction values of each model under two-stage loading of 30CrMnSiA
[0143]
[0144] Table 5. Statistical Table of Prediction Errors under Two-Stage Loading of 30CrMnSiA
[0145]
[0146] From Table 4, Table 5 and Figure 3 It can be seen that the corrected nonlinear cumulative damage model and the model in this paper have better prediction results for 30CrMnSiA, with average errors of 14.09% and 9.31% respectively compared with the experimental values, while the average errors of the Miner and Ye models compared with the experimental values are around 30%.
[0147] Q235B is a commonly used welding material for welded bogie frames in vehicles. Taking the four-level loading test of Q235B in the literature as an example, the fatigue life of four models was predicted respectively. The predicted values, experimental values, and error values are shown in Table 6. The comparison between the predicted and experimental fatigue life values is as follows: Figure 4 As shown.
[0148] Table 6. Fatigue data and fatigue life predictions of Q235 under level 4 loading.
[0149]
[0150] From Table 6 and Figure 4 As can be seen, the model in this paper has a significant improvement in the prediction accuracy of Q235 compared with other models, while the Ye model has a situation where it cannot predict.
[0151] 41Cr4 is the most commonly used alloy structural steel, with high tensile strength, yield strength, and hardenability. It is often used for quenched and tempered parts operating under alternating loads, medium speeds, and medium loads, such as gears, sleeves, shafts, crankshafts, and pins. Taking the six-level loading test of 41Cr4 in the literature as an example, the fatigue life of four models was predicted. The predicted values and experimental values, as well as the error values, are shown in Table 7.
[0152] Table 7. Fatigue data and fatigue life predictions of various models for 41Cr4 under level 6 loading.
[0153]
[0154] Both the SN curve and the cumulative damage model are indispensable when calculating the fatigue life of the vehicle body. In order to clarify the SN curve of the vehicle body material, this technical solution introduces the 7608 standard, which can be used to obtain the SN curve by looking at the weld grade, structural form, etc.
[0155] Table 8 shows a comparison of damage under the nonlinear cumulative damage model considering strength degradation, the nonlinear cumulative damage model, and the linear cumulative damage model. As can be seen from Table 8, the damage at each measurement point, from smallest to largest, is represented by the model of this technical solution, the nonlinear cumulative damage model, and the Miner linear cumulative damage model. This indicates that the linear cumulative damage model is more conservative than the nonlinear cumulative damage model; however, all three models maintain a consistent trend in damage prediction.
[0156] Table 8 Damage prediction values of the three models
[0157] 1 <![CDATA[8.93×10 -9 ]]> <![CDATA[8.59×10 -9 ]]> <![CDATA[8.08×10 -9 ]]> 2 <![CDATA[2.66×10 -8 ]]> <![CDATA[1.78×10 -8 ]]> <![CDATA[3.14×10 -9 ]]> 3 <![CDATA[4.75×10 -12 ]]> <![CDATA[1.67×10 -12 ]]> <![CDATA[8.63×10 -13 ]]> 4 <![CDATA[9.46×10 -7 ]]> <![CDATA[8.45×10 -7 ]]> <![CDATA[7.22×10 -8 ]]> 5 <![CDATA[2.77×10 -6 ]]> <![CDATA[8.88×10 -7 ]]> <![CDATA[2.39×10 -7 ]]> 6 <![CDATA[7.42×10 -7 ]]> <![CDATA[1.22×10 -7 ]]> <![CDATA[2.07×10 -8 ]]> 7 <![CDATA[1.20×10 -13 ]]> <![CDATA[9.79×10 -14 ]]> <![CDATA[8.05×10 -14 ]]>
[0158] Figure 5 This is a schematic diagram of a structural fatigue life prediction system considering strength degradation effects provided by the present invention. The structural fatigue life prediction method considering strength degradation effects of the present invention can be implemented. The prediction system includes: a data acquisition unit, a data conversion unit, a stress spectrum processing unit, and a fatigue life calculation unit.
[0159] The data acquisition unit is used to acquire strain-time data of the structure under at least one working condition.
[0160] The data conversion unit is used to convert strain-time data into stress spectra.
[0161] The stress spectrum processing unit is used to process the stress spectrum using a nonlinear cumulative damage model that considers the strength degradation effect, in order to obtain the residual damage of the structure.
[0162] The fatigue life calculation unit is used to calculate the fatigue life of a structure based on residual damage.
[0163] The data conversion unit specifically includes: a data acquisition subunit, an unadjusted balance processing subunit, a digital filtering subunit, a conversion subunit, a zero-point drift processing subunit, an anomaly processing subunit, and a stress signal calculation subunit.
[0164] The data acquisition subunit is used to acquire strain-time data; strain-time data is the strain signal collected by the strain gauge.
[0165] The unbalanced processing subunit is used to perform unbalanced processing on strain-time data.
[0166] The digital filtering subunit is used for digital filtering processing.
[0167] The conversion subunit is used to convert the filtered strain signal into a stress signal.
[0168] The zero-point drift processing subunit is used to perform zero-point drift processing on the stress signal.
[0169] The exception handling subunit is used to process exception signals.
[0170] The stress signal calculation subunit is used to calculate the stress signal using the rainflow counting method to obtain the stress spectrum of strain-time data.
[0171] The prediction system also includes a model building unit, which specifically includes a model acquisition subunit, an intensity degradation coefficient introduction subunit, and a model update subunit.
[0172] The model acquisition sub-unit is used to acquire a nonlinear cumulative damage model and a strength degradation model that take into account the effects of load history.
[0173] The strength degradation coefficient introduction sub-unit is used to introduce the strength degradation coefficient according to the strength degradation model.
[0174] The model update sub-unit is used to update the nonlinear cumulative damage model using the load ratio and strength degradation coefficient, resulting in a nonlinear cumulative damage model that considers the strength degradation effect.
[0175] The nonlinear cumulative damage model is as follows:
[0176]
[0177] Where: D i N represents the damage to the structure under the i-th level load; fi This refers to the fatigue life under the corresponding stress level; n i δ is the number of cycles under the i-th stress level; i These are model parameters that depend only on N. f And given the stress level, δ i =-1.25 / lnN fi ;γ i,i+1 For load interaction factor, σ i For the i-th stress; n i+1,i This is the equivalent cycle number;
[0178] The intensity degradation model is as follows:
[0179]
[0180] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f This corresponds to the peak stress value;
[0181] The load ratio and strength degradation coefficient are combined in the form of an exponential function.
[0182] Among them, the strength degradation coefficient is
[0183] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the descriptions are relatively simple; relevant parts can be referred to the method section.
[0184] This document uses specific examples to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the method and core ideas of the present invention. Furthermore, those skilled in the art will recognize that, based on the ideas of the present invention, there will be changes in the specific implementation methods and application scope. Therefore, the content of this specification should not be construed as a limitation of the present invention.
Claims
1. A method for predicting the fatigue life of structures considering strength degradation effects, characterized in that, The method includes: Acquire strain-time data of the structure under at least one operating condition; The strain-time data is converted into a stress spectrum; Obtain a nonlinear cumulative damage model and a strength degradation model that take into account the effects of load loading history; A strength degradation coefficient is introduced based on the strength degradation model; The nonlinear cumulative damage model is updated using the load ratio and the strength degradation coefficient to obtain a nonlinear cumulative damage model that considers the strength degradation effect. The stress spectrum is processed using a nonlinear cumulative damage model that considers the strength degradation effect to obtain the residual damage of the structure; The fatigue life of the structure is calculated based on the remaining damage. Specifically, converting the strain-time data into a stress spectrum includes: Acquire the strain-time data; the strain-time data is the strain signal collected by the strain gauge; The strain-time data are subjected to unadjusted balancing processing; Digital filtering processing; The filtered strain signal is converted into a stress signal; Zero-point drift processing is performed on the stress signal; Abnormal signal processing; The stress signal was calculated using the rainflow counting method, and the stress spectrum of strain-time data was obtained. The nonlinear cumulative damage model is as follows: ； in: For the structure in the first Damage under level load; It represents the fatigue life under the corresponding stress level; For the first Number of cycles under stress level; For model parameters, only depend on And given stress level, ; For load interaction factor, ; For the first i Level stress; This is the equivalent cycle number; The load ratio and strength degradation coefficient are combined in the form of an exponential function. The strength degradation coefficient is ; The intensity degradation model is as follows: ； in: Remaining strength; This represents the initial tensile strength. For the corresponding peak stress The nonlinear cumulative damage model considering the strength degradation effect is as follows: 。 2. A structural fatigue life prediction system considering strength degradation effects, characterized in that, The system includes: The data acquisition unit is used to acquire strain-time data of the structure under at least one working condition. The data conversion unit is used to convert the strain-time data into a stress spectrum; The model acquisition unit is used to acquire a nonlinear cumulative damage model and a strength degradation model that take into account the historical effects of load loading. A strength degradation coefficient introduction unit is used to introduce a strength degradation coefficient according to the strength degradation model; The model update unit is used to update the nonlinear cumulative damage model using the load ratio and the strength degradation coefficient to obtain a nonlinear cumulative damage model that considers the strength degradation effect. The stress spectrum processing unit is used to process the stress spectrum using a nonlinear cumulative damage model that considers the strength degradation effect to obtain the residual damage of the structure. A fatigue life calculation unit is used to calculate the fatigue life of the structure based on the remaining damage. Specifically, the data conversion unit includes: The data acquisition subunit is used to acquire the strain-time data; the strain-time data is the strain signal collected by the strain gauge. An unadjusted balancing processing subunit is used to perform unadjusted balancing processing on the strain-time data; Digital filtering subunit, used for digital filtering processing; The conversion subunit is used to convert the filtered strain signal into a stress signal; The zero-point drift processing subunit is used to perform zero-point drift processing on the stress signal; The exception handling subunit is used to process exception signals; The stress signal calculation subunit is used to calculate the stress signal using the rainflow counting method to obtain the stress spectrum of strain-time data. The nonlinear cumulative damage model is as follows: ； in: For the structure in the first Damage under level load; It represents the fatigue life under the corresponding stress level; For the first Number of cycles under stress level; For model parameters, only depend on And given stress level, ; For load interaction factor, ; For the first i Level stress; This is the equivalent cycle number; The load ratio and strength degradation coefficient are combined in the form of an exponential function. The strength degradation coefficient is ; The intensity degradation model is as follows: ; in: Remaining strength; This represents the initial tensile strength. This corresponds to the peak stress value; The nonlinear cumulative damage model considering the strength degradation effect is as follows: 。