Structure fatigue life prediction method and system considering strength degradation effect

Abstract

The invention relates to a structural fatigue life prediction method considering a strength degradation effect and a corresponding system, and belongs to the technical field of equipment life prediction. The prediction method comprises the following steps: acquiring strain-time data of a structure under at least one working condition; converting the strain-time data into a stress spectrum; processing the stress spectrum by using a nonlinear cumulative damage model considering a strength degradation effect to obtain residual damage of the structure; and calculating the fatigue life of the structure according to the residual damage. According to the method, the model is corrected by introducing the stress ratio and the strength degradation coefficient, the degradation performance of the material can be well reflected, the model is simple in form, additional physical parameters do not need to be introduced, and the method can be well applied to engineering practice; and the model can be applied to the prediction of the fatigue life under the multi-stage loading, so that the prediction precision of the fatigue life of the structure is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of equipment life prediction, and in particular to a method and system for predicting structural fatigue life taking strength degradation effects into consideration. Background Art

[0002] With the continuous improvement of industrial technology, modern mechanical equipment is developing towards large-scale, high-speed and high-performance. Fatigue failure, one of the main failure modes of mechanical equipment, is not only hidden and sudden, but also accounts for 50%-90% of the total failure of mechanical structures.

[0003] Studies have shown that most materials exhibit an extremely nonlinear relationship between cumulative damage and cyclic loads. The nonlinear cumulative damage theory takes into account the interaction between the load sequence and the loads, but it cannot be directly used in engineering practice due to factors such as overly complex expressions and difficulty in determining the introduced experimental coefficients.

[0004] The number of loads and the load level are the dominant factors that determine the fatigue cumulative damage value, and the current damage state and stress state of the structure will also affect the accumulation of fatigue damage. This is because with the continuous application of loads and the continuous accumulation of damage, the bearing capacity of the structure will degrade, that is, the residual strength degradation. The interaction between residual strength degradation and cyclic loads will have an impact on the subsequent cumulative damage, especially in the middle and late stages of the structure's life. With the continuous degradation of the structure's residual strength, the mechanical properties continue to deteriorate, which will accelerate the accumulation of fatigue damage. Considering the impact of residual strength degradation when calculating fatigue cumulative damage is more in line with the actual damage process, which is of great significance to improving the accuracy of fatigue life prediction of engineering structures. Summary of the invention

[0005] The purpose of the present invention is to provide a method and system for predicting the fatigue life of a structure taking into account the strength degradation effect, thereby greatly improving the accuracy of fatigue life prediction.

[0006] To achieve the above object, the present invention provides the following solutions:

[0007] A structural fatigue life prediction method considering strength degradation effect, the method comprising:

[0008] Acquiring strain-time data of the structure under at least one load condition;

[0009] converting the strain-time data into a stress spectrum;

[0010] The stress spectrum is processed by using a nonlinear cumulative damage model considering strength degradation effect to obtain the residual damage of the structure;

[0011] The fatigue life of the structure is calculated based on the residual damage.

[0012] Optionally, the method further includes: constructing a nonlinear cumulative damage model taking into account the strength degradation effect, specifically including:

[0013] Obtain nonlinear cumulative damage model and strength degradation model considering the effect of load history;

[0014] Introducing a strength degradation coefficient according to the strength degradation model;

[0015] The nonlinear cumulative damage model is updated by using the load ratio and the strength degradation coefficient to obtain a nonlinear cumulative damage model that takes strength degradation effects into consideration.

[0016] Optionally, the nonlinear cumulative damage model is:

[0017]

[0018] Where: D i is the damage of the structure under the i-th load level; N fi is the fatigue life at the corresponding stress level; n i is the number of cycles under the i-th level stress; δ i is a model parameter that depends only on N f and a given stress level, δ i =-1.25 / lnN fi ; γ i,i+1 is the load interaction factor, σ i is the i-th level stress; n i+1,i is the equivalent cycle number;

[0019] The strength degradation model is:

[0020]

[0021] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f is the corresponding stress peak value;

[0022] Optionally, the load ratio is combined with the strength degradation factor in the form of an exponential function,

[0023] 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 a strain signal collected by a strain gauge;

[0026] performing unbalanced processing on the strain-time data;

[0027] Digital filtering processing;

[0028] converting the filtered strain signal into a stress signal;

[0029] Perform zero-drift processing on stress signals;

[0030] Abnormal signal processing;

[0031] The stress signal is calculated using the rainflow counting method to obtain the stress spectrum of the strain-time data.

[0032] A structural fatigue life prediction system considering strength degradation effect, the system comprising:

[0033] A data acquisition unit, used to acquire strain-time data of the structure under at least one working condition;

[0034] A data conversion unit, used for converting the strain-time data into a stress spectrum;

[0035] A stress spectrum processing unit, used for processing the stress spectrum using a nonlinear cumulative damage model taking into account strength degradation effects, to obtain residual damage of the structure;

[0036] The fatigue life calculation unit is used to calculate the fatigue life of the structure according to the residual damage.

[0037] Optionally, the system further includes: a model building unit, specifically including:

[0038] Model acquisition subunit, used to obtain nonlinear cumulative damage model and strength degradation model considering the effect of load history;

[0039] A strength degradation coefficient introduction subunit is used to introduce a strength degradation coefficient according to the strength degradation model;

[0040] The model updating subunit 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 takes into account the strength degradation effect.

[0041] Optionally, the nonlinear cumulative damage model is:

[0042]

[0043] Where: D i is the damage of the structure under the i-th load level; N fi is the fatigue life at the corresponding stress level; n i is the number of cycles under the i-th level stress; δ i is a model parameter that depends only on Nf and a given stress level, δ i =-1.25 / lnN fi ; γ i,i+1 is the load interaction factor, σ i is the i-th level stress; n i+1,i is the equivalent cycle number;

[0044] The strength degradation model is:

[0045]

[0046] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f is the corresponding stress peak value;

[0047] Optionally, the load ratio is combined with the strength degradation factor in the form of an exponential function,

[0048] The strength degradation coefficient is

[0049] Optionally, the data conversion unit specifically includes:

[0050] A data acquisition subunit, used to acquire the strain-time data; the strain-time data is a strain signal collected by a strain gauge;

[0051] An unbalanced processing subunit, used for performing unbalanced processing on the strain-time data;

[0052] A digital filtering subunit, used for digital filtering processing;

[0053] A conversion subunit, used for converting the filtered strain signal into a stress signal;

[0054] A zero drift processing subunit, used for performing zero drift processing on stress signals;

[0055] An exception processing subunit, used for processing the exception signal;

[0056] The stress signal calculation subunit is used to calculate the stress signal using the rain flow counting method to obtain the stress spectrum of the strain-time data.

[0057] According to the specific embodiments provided by the present invention, the present invention discloses the following technical effects:

[0058] (1) The present 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. The model form is simple and does not require the introduction of additional physical parameters. It can be well applied in practical engineering.

[0059] (2) The present invention takes into account the strength degradation effect, and the prediction results of fatigue life under multi-level load are also relatively accurate, which greatly improves the prediction accuracy of structural fatigue life. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] 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 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.

[0061] Figure 1 A flow chart of a structural fatigue life prediction method considering strength degradation effect provided by the present invention;

[0062] Figure 2 The prediction results of the damage value of 35 steel by different models;

[0063] Figure 3 The prediction results of damage value of 30CrMnSiA steel by different models;

[0064] Figure 4 The prediction results of Q235 steel damage value by different models;

[0065] Figure 5 Schematic diagram of the structural fatigue life prediction system considering strength degradation effect provided by the present invention. DETAILED DESCRIPTION

[0066] 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 only 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.

[0067] The purpose of the present invention is to provide a method and system for predicting the fatigue life of a structure taking into account the strength degradation effect, which greatly improves the accuracy of predicting the fatigue life of the structure by taking into account the strength degradation effect.

[0068] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0069] Figure 1 A flow chart of a structural fatigue life prediction method considering strength degradation effect provided by an embodiment of the present invention, the prediction method specifically includes:

[0070] S1: Obtain strain-time data of the structure under at least one working condition.

[0071] S2: Convert strain-time data into stress spectra.

[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 residual damage.

[0074] To obtain the strain-time data of the structure, it is necessary to determine the fatigue danger zone in combination with the actual operating conditions; conduct dynamic stress test experiments to obtain the stress-time history of the fatigue danger zone under multiple actual operating cycles.

[0075] The specific contents of the strain-time data acquisition experiment include:

[0076] Test vehicle: one of the 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 is carried out at the night window debugging point. The train stops at the station in normal operation without opening or closing doors.

[0080] The stress test points are selected as follows: 1) areas where stress concentration can be estimated, such as areas where the structural shape changes suddenly and weld areas; 2) areas where high stress is estimated by strength calculation results; 3) areas that require attention in terms of welding and processing.

[0081] Test process

[0082] The stress on the vehicle body chassis is collected using a 32-channel dynamic synchronous acquisition device with a sampling frequency of 500 Hz.

[0083] 1. The paint on the vehicle chassis will affect the stress data collection and needs to be dissolved with paint solvent when pasting the strain gauge.

[0084] 2. In order to make the metal surface smooth, rub the metal surface with gauze, and then use acetone solution to remove the dirt on the metal surface.

[0085] 3. Apply 502 glue on the strain gauge so that it can stick well to the vehicle body frame, and then remove the bubbles on the strain gauge so that it can stick completely to the vehicle body frame.

[0086] 4. Apply AB glue strain gauge and cure and moisture-proof the strain gauge.

[0087] 5 Use sandbags to simulate the load and connect the test facilities and circuits.

[0088] 6. Measure the stress-time data of the vehicle body during normal operation.

[0089] Dynamic stress testing has the characteristics of complex testing, large test signal capacity, and long test cycle. The data collection process and scientific data processing directly affect the accuracy of stress spectrum compilation and the results of predicting the fatigue life of the vehicle body.

[0090] Download the raw data collected by the equipment to the computer, check whether the information of each channel of the raw data collected this time is normal, and determine the start and end time of the valid data segment in the collected data in combination with the actual operation table of the operating vehicle, extract the valid data and perform zero adjustment processing; check the waveform of each channel in the collected data this time to determine whether there is a zero drift phenomenon, and remove the zero drift of the data with zero drift; filter the noise interference caused by the test environment or equipment operation; convert the original strain signal into a stress signal according to the conversion formula between stress and strain signals; check whether the converted stress signal has burrs in the time domain and frequency domain respectively, remove the burrs of the data with burrs, and finally compile the stress spectrum.

[0091] The stress spectra of each measuring point of the vehicle body are shown in Table 1 below:

[0092] Table 1 Stress spectrum of measuring points

[0093]

[0094]

[0095] By using the nonlinear cumulative damage model considering the strength degradation effect constructed by the present invention to calculate the stress spectrum of each measuring point of the vehicle body, the cumulative damage and fatigue life of the vehicle body structure can be obtained.

[0096] For the nonlinear cumulative damage model considering the strength degradation effect, the construction process is as follows:

[0097] Construct a nonlinear cumulative damage model for the vehicle body material to be predicted:

[0098]

[0099] Where: Di is the damage of the structure under the i-th load level; N fi is the fatigue life at the corresponding stress level; n i is the number of cycles under the i-th level stress; γ i,i+1 is the load interaction factor, σ i is the i-th level stress; n i+1,i is the equivalent cycle number; δ i is a model parameter that depends only on N f and a given stress level:

[0100] δ i =-1.25 / lnN fi (2)

[0101] Assume that at this time, a stress of magnitude σ1 acts on the structure and cycles n1 times, and the fatigue accumulated damage D1. Before the two-stage loading, the first-stage loading has already caused damage to the structure. According to the equivalent principle, the damage D1 can be converted into the stress of magnitude σ2 for n cycles. 21 The damage caused is represented by D2.

[0102]

[0103] At this point, the residual damage of the model under the two-level load can be obtained:

[0104]

[0105] The fatigue cumulative damage of the structure under two-level loads is:

[0106] Then the fatigue cumulative damage of the structure under multi-level loads is

[0107] The residual damage under multi-level loading is:

[0108]

[0109] Construct the strength degradation model of the vehicle body material to be predicted:

[0110]

[0111] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f is the corresponding stress peak.

[0112] Assume that at this time, a stress of magnitude σ1 acts on the structure and cycles n1 times, and the residual strength is R1. Before the two-stage loading, the strength of the structure has been reduced due to the first-stage loading. According to the equivalent principle, the strength reduced by R1 can be used to cycle n times with a stress of magnitude σ2.21 The intensity R2 is the decrease.

[0113] R1=R2 (7)

[0114] Then the equivalent cycle number n 21 for:

[0115]

[0116] Similarly, when a material is subjected to multiple loads until it is finally destroyed,

[0117]

[0118] In order to reflect the influence of strength degradation on cumulative damage, the strength degradation coefficient is introduced:

[0119]

[0120] Since the model does not consider the strength degradation effect, the load ratio and the strength degradation coefficient are combined in the form of 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 the two-level load is obtained:

[0125]

[0126] The residual damage under multi-level loading is:

[0127]

[0128] Formula (13) is a nonlinear fatigue cumulative damage model after considering the correction of residual strength degradation. The 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 form is simple 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] In order to verify the accuracy of the nonlinear cumulative damage model of the present invention that takes into account the strength degradation effect, the present invention verifies the model through existing metal material fatigue test data.

[0130] Through data search, the second, fourth, and sixth level fatigue test data of some metal materials are selected for verification. In order to better demonstrate the advantages of the proposed model in life prediction, an error factor E is introduced to describe the prediction error of the four models, as shown in formula (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, is the test cycle ratio; To predict the circulation ratio.

[0133] (1) Comparative analysis of fatigue life prediction results of 35 steel

[0134] No. 35 steel has good strength, plasticity and toughness. It is widely used in various fields such as machinery, automobiles, construction and ships. It is a high-quality carbon structural steel. When the loading stress amplitude of C35 steel is 353MPa, 334MPa, 294MPa and 275MPa, the fatigue life of C35 steel is 52000, 110000, 400000 and 760000 times respectively. Its tensile strength is 458MPa. Taking the two-stage loading test of No. 35 steel in the literature as an example, the fatigue life of Miner, Ye and modified nonlinear cumulative damage models are predicted respectively. The results and error values ​​of the predicted values ​​and experimental values ​​are shown in Table 2. The error values ​​of the four models are then calculated by the mean and standard deviation, and the results are shown in Table 3. Finally, the predicted value and experimental value of fatigue life are compared. Figure 2 As shown, Figure 2 (a) is 334-294MPa and 294-334MPa, Figure 2 (b) are 353-275MPa and 275-353MPa.

[0135] Table 2 Fatigue test data of 35 steel specimens under two-level loading and fatigue life prediction values ​​of each model

[0136]

[0137] Table 3 Prediction error statistics of 35 steel specimens under two-level 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 correct the nonlinear cumulative damage model part of the prediction results within the 20% error band, and all the prediction results of the Miner model are within the 50% error band. This means that the prediction results of the model of the present invention for 35# steel are better than those of the other models.

[0140] (2) Comparative analysis of fatigue life prediction results of 30CrMnSiA steel

[0141] 30CrMnSiA is an ultra-high strength alloy structural steel, commonly used in the manufacture of aircraft engine frames, engine compressor blades, etc. When the loading stress amplitudes of 30CrMnSiA steel are 586MPa, 482MPa, 732MPa, and 836MPa, the fatigue life of 30CrMnSiA steel is 52,000, 760,000, 55,793, and 7,186 times, respectively. Taking the two-stage loading test of 30CrMnSiA in the literature as an example, the fatigue life of the four models is predicted respectively, and the results and error values ​​of the predicted values ​​and experimental values ​​are shown in Table 4. The error values ​​of the four models are then calculated for the mean and standard deviation, and the results are shown in Table 5. Finally, the predicted value and experimental value of the fatigue life are compared. Figure 3 As shown, Figure 3 (a) is 586-482MPa and 482-586MPa, Figure 3 (b) are 836-732MPa and 732-836MPa.

[0142] Table 4 Fatigue test data of 30CrMnSiA under two-level loading and fatigue life prediction values ​​of each model

[0143]

[0144] Table 5 Prediction error statistics of 30CrMnSiA under two-level loading

[0145]

[0146] From Table 4, Table 5 and Figure 3 It can be seen that the prediction results of the corrected nonlinear cumulative damage and the proposed model for 30CrMnSiA are relatively good, with mean errors of 14.09% and 9.31% compared with the experimental values, while the mean error of the Miner and Ye models compared with the experimental values ​​is about 30%.

[0147] Q235B is a commonly used welding material for vehicle bogie welded frames. Taking the four-level loading test of Q235B in the literature as an example, the fatigue life of four models is predicted respectively. The results and error values ​​of the predicted values ​​and experimental values ​​are shown in Table 6. Figure 4 shown.

[0148] Table 6 Fatigue data of Q235 under four-level loading and fatigue life prediction values ​​of each model

[0149]

[0150] From Table 6 and Figure 4 It can be seen that the prediction accuracy of the proposed model for Q235 is greatly improved compared with other models, among which the Ye model has unpredictable situations.

[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 under alternating load, medium speed and medium load, 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 is predicted respectively. The results and error values ​​of the predicted values ​​and test values ​​are shown in Table 7.

[0152] Table 7 Fatigue data of 41Cr4 under six-level loading and fatigue life prediction values ​​of each model

[0153]

[0154] Both the SN curve and the cumulative damage model are indispensable in 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 obtain its SN curve by checking the weld grade, structural form, etc.

[0155] Table 8 shows the damage comparison table of the nonlinear cumulative damage model considering strength degradation, the nonlinear cumulative damage model and the linear cumulative damage model. It can be seen from Table 8 that the damage of each measuring point is from small to large, which is the model of this technical solution, the nonlinear cumulative damage model, and the Miner linear cumulative damage model, indicating that the linear cumulative damage model is more conservative than the nonlinear cumulative damage model; but the three models keep the same trend in damage prediction.

[0156] Table 8 Damage prediction values ​​of three models

[0157] Measuring point number Miner Model Nonlinear Cumulative Model This article model 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 The schematic diagram of the structural fatigue life prediction system considering the strength degradation effect provided by the present invention can implement the structural fatigue life prediction method considering the strength degradation effect of the present invention. 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 takes into account the strength degradation effect to obtain the residual damage of the structure.

[0162] The fatigue life calculation unit is used to calculate the fatigue life of the structure based on the 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 drift processing subunit, an abnormality processing subunit and a stress signal calculation subunit.

[0164] The data acquisition subunit is used to acquire strain-time data; the 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 drift processing subunit is used to perform zero drift processing on the stress signal.

[0169] The exception processing subunit is used to process the exception signal.

[0170] The stress signal calculation subunit is used to calculate the stress signal using the rain flow counting method to obtain the stress spectrum of the strain-time data.

[0171] The prediction system also includes: a model building unit, which specifically includes: a model acquisition subunit, a strength degradation coefficient introduction subunit and a model update subunit.

[0172] The model acquisition subunit is used to obtain the nonlinear cumulative damage model and strength degradation model considering the effect of load history.

[0173] The strength degradation coefficient introducing subunit is used to introduce the strength degradation coefficient according to the strength degradation model.

[0174] The model updating subunit 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 takes into account the strength degradation effect.

[0175] The nonlinear cumulative damage model is:

[0176]

[0177] Where: D i is the damage of the structure under the i-th load level; N fi is the fatigue life at the corresponding stress level; n i is the number of cycles under the i-th level stress; δ i is a model parameter that depends only on N f and a given stress level, δ i =-1.25 / lnN fi ; γ i,i+1 is the load interaction factor, σ i is the i-th level stress; n i+1,i is the equivalent cycle number;

[0178] The strength degradation model is:

[0179]

[0180] Where: R(n) is the residual strength; R0 is the initial tensile strength; S f is the corresponding stress peak value;

[0181] The load ratio is combined with the strength degradation coefficient in the form of an exponential function.

[0182] The strength degradation coefficient is

[0183] In this specification, each embodiment is described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the embodiments can be referred to each other. For the system disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part.

[0184] This article uses specific examples to illustrate the principles and implementation methods of the present invention. The above examples are only used to help understand the method and core ideas of the present invention. At the same time, for those skilled in the art, according to the ideas of the present invention, there will be changes in the specific implementation methods and application scope. In summary, the content of this specification should not be understood as limiting the present invention.

Claims

1. A structural fatigue life prediction method considering strength degradation effect, characterized in that: The method comprises: Acquiring strain-time data of the structure under at least one load condition; converting the strain-time data into a stress spectrum; The stress spectrum is processed by using a nonlinear cumulative damage model considering strength degradation effect to obtain the residual damage of the structure; The fatigue life of the structure is calculated based on the residual damage.

2. The structural fatigue life prediction method considering strength degradation effect according to claim 1 is characterized in that: The method further includes: constructing a nonlinear cumulative damage model taking into account the strength degradation effect, specifically including: Obtain nonlinear cumulative damage model and strength degradation model considering the effect of load history; Introducing a strength degradation coefficient according to the strength degradation model; The nonlinear cumulative damage model is updated by using the load ratio and the strength degradation coefficient to obtain a nonlinear cumulative damage model that takes strength degradation effects into consideration.

3. The structural fatigue life prediction method considering strength degradation effect according to claim 2 is characterized in that: The nonlinear cumulative damage model is: Where: D i is the damage of the structure under the i-th level load; N fi is the fatigue life at the corresponding stress level; n i is the number of cycles under the i-th level stress; δ i is a model parameter that depends only on N f and a given stress level, δ i =-1.25 / lnN fi ; γ i,i+1 is the load interaction factor, σ i is the i-th level stress; n i+1,i is the equivalent cycle number; The strength degradation model is: Where: R(n) is the residual strength; R0 is the initial tensile strength; S f is the corresponding stress peak.

4. The structural fatigue life prediction method considering strength degradation effect according to claim 3 is characterized in that: The load ratio is combined with the strength degradation coefficient in the form of an exponential function. The strength degradation coefficient is 5. The structural fatigue life prediction method considering strength degradation effect according to claim 1 is characterized in that: The converting the strain-time data into a stress spectrum specifically comprises: Acquire the strain-time data; the strain-time data is a strain signal collected by a strain gauge; performing unbalanced processing on the strain-time data; Digital filtering processing; converting the filtered strain signal into a stress signal; Perform zero-drift processing on stress signals; Abnormal signal processing; The stress signal is calculated using the rainflow counting method to obtain the stress spectrum of the strain-time data.

6. A structural fatigue life prediction system considering strength degradation effect, characterized in that: The system comprises: A data acquisition unit, used to acquire strain-time data of the structure under at least one working condition; A data conversion unit, used for converting the strain-time data into a stress spectrum; A stress spectrum processing unit, used for processing the stress spectrum using a nonlinear cumulative damage model taking into account strength degradation effects, to obtain residual damage of the structure; The fatigue life calculation unit is used to calculate the fatigue life of the structure according to the residual damage.

7. The structural fatigue life prediction system considering strength degradation effect according to claim 6, characterized in that: The system further comprises: a model building unit, specifically comprising: Model acquisition subunit, used to obtain nonlinear cumulative damage model and strength degradation model considering the effect of load history; A strength degradation coefficient introduction subunit is used to introduce a strength degradation coefficient according to the strength degradation model; The model updating subunit 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 takes into account the strength degradation effect.

8. The structural fatigue life prediction system considering strength degradation effect according to claim 7, characterized in that: The nonlinear cumulative damage model is: Where: D i is the damage of the structure under the i-th level load; N fi is the fatigue life at the corresponding stress level; n i is the number of cycles under the i-th level stress; δ i is a model parameter that depends only on N f and a given stress level, δ i =-1.25 / lnN fi ; γ i,i+1 is the load interaction factor, σ i is the i-th level stress; n i+1,i is the equivalent cycle number; The strength degradation model is: Where: R(n) is the residual strength; R0 is the initial tensile strength; S f is the corresponding stress peak.

9. The structural fatigue life prediction system considering strength degradation effect according to claim 8, characterized in that: The load ratio is combined with the strength degradation coefficient in the form of an exponential function. The strength degradation coefficient is 10. The structural fatigue life prediction system considering strength degradation effect according to claim 6, characterized in that: The data conversion unit specifically includes: A data acquisition subunit, used to acquire the strain-time data; the strain-time data is a strain signal collected by a strain gauge; An unbalanced processing subunit, used for performing unbalanced processing on the strain-time data; A digital filtering subunit, used for digital filtering processing; A conversion subunit, used for converting the filtered strain signal into a stress signal; A zero drift processing subunit, used for performing zero drift processing on stress signals; An exception processing subunit, used for processing the exception signal; The stress signal calculation subunit is used to calculate the stress signal using the rain flow counting method to obtain the stress spectrum of the strain-time data.

Images