The Application of Neural Network in Lifetime Prediction of Concrete

There are many difficulties in concrete endurance prediction, especially in accurate predicting service life of concrete engineering. It is determined by the concentration of S042-/ Mg2+ / Cl- /Ca2+ , reactionareas , the cycles of freezing and dissolving, alternatives of dry and wet state, the kind of cement, etc. . In general , because of complexity itself and cognitive limitation, endurance prediction under sulphate erosion is still illegible and uncertain, so this paper adopts neural network technology to research this problem. Through analyzing , the paper sets up a 3 - levels neural network and a 4 - levels neural network to predict the endurance undersulphate erosion. The 3 - levels neural network includes 13 inputting nodes, 7 outputting nodes and 34 hidden nodes. The 4 - levels neural network also has 13 inputting nodes and 7 outputting nodes with two hidden levels which has 1 nodes and 8 nodes separately. In the end the paper give a example with laboratorial data and discussion the result and deviation. The paper shows that deviation results from some faults of training specimens; such as few training specimens and few distinctions among training specimens. So the more specimens should be collected to reduce data redundancy and improve the reliability of network analysis conclusion.

A new analytical model for thermal stresses in multi-phase materials and lifetime prediction methods

Based on the fundamental equations of the mechanics of solid continuum, the paper employs an analytical model for determination of elastic thermal stresses in isotropic continuum represented by periodically distributed spherical particles with different distributions in an infinite matrix, imaginarily divided into identical cells with dimensions equal to inter-particle distances, containing a central spherical particle with or without a spherical envelope on the particle surface. Consequently, the multi-particle-（envelope）- matrix system, as a model system regarding the analytical modelling, is applicable to four types of multi-phase materials. As functions of the particle volume fraction v, the inter-particle distances dl, d2, d3 along three mutually per- pendicular axes, and the particle and envelope radii, R1 and R2, respectively, the thermal stresses within the cell, are originated during a cooling process as a consequence of the difference in thermal expansion coefficients of phases rep- resented by the matrix, envelope and particle. Analytical-（experimental）-computational lifetime prediction methods for multi-phase materials are proposed, which can be used in engineering with appropriate values of parameters of real multi-phase materials.

CRACK PROPAGATION BEHAVIOR AND LIFETIME PREDICTION IN ALUMINA AND ZIRCONIA

The lifetime prediction of ceramics is discussed on the basis of therelationship between stress intensity factor K_1 and crack velocity nu. The effects of waterenvironment, the cyclic loading and microstructure of material on K_1-nu characteristics are studiedby carrying out the crack growth tests by the double torsion (DT) method under the static andcyclic loading in both environments of air and water for alumina and zirconia. K_1-nucharacteristics determined by the double torsion method are used to predict time-to-failure underthe cyclic loading of alumina and zirconia ceramics. The predictions agree qualitatively with theexperimental results.

Residual lifetime prediction model of nonlinear accelerated degradation data with measurement error

For the product degradation process with random effect (RE), measurement error (ME) and nonlinearity in step-stress accelerated degradation test (SSADT), the nonlinear Wiener based degradation model with RE and ME is built. An analytical approximation to the probability density function (PDF) of the product's lifetime is derived in a closed form. The process and data of SSADT are analyzed to obtain the relation model of the observed data under each accelerated stress. The likelihood function for the population-based observed data is constructed. The population-based model parameters and its random coefficient prior values are estimated. According to the newly observed data of the target product in SSADT, an analytical approximation to the PDF of its residual lifetime (RL) is derived in accordance with its individual degradation characteristics. The parameter updating method based on Bayesian inference is applied to obtain the posterior value of random coefficient of the RL model. A numerical example by simulation is analyzed to verify the accuracy and advantage of the proposed model.

Remaining lifetime prediction for nonlinear degradation device with random effect

For the large number of nonlinear degradation devices existing in a project, the existing methods have not systematically studied the effects of random effect on the remaining lifetime(RL),the accuracy and efficiency of the parameters estimation are not high, and the current degradation state of the target device is not accurately estimated. In this paper, a nonlinear Wiener degradation model with random effect is proposed and the corresponding probability density function(PDF) of the first hitting time(FHT)is deduced. A parameter estimation method based on modified expectation maximum(EM) algorithm is proposed to obtain the estimated value of fixed coefficient and the priori value of random coefficient in the model. The posterior value of the random coefficient and the current degradation state of target device are updated synchronously by the state space model(SSM) and the Kalman filter algorithm. The PDF of RL with random effect is deduced. A simulation example is analyzed to verify that the proposed method has the obvious advantage over the existing methods in parameter estimation error and RL prediction accuracy.

Highly Improved Creep Resistance in Polypropylene Through Thermally Reduced Graphene Oxide and Its Creep Lifetime Prediction

Polypropylene(PP) exhibits suboptimal creep resistance due to the presence of methyl groups on its main chain, leading to irregular chain segment distribution, diminished inter-chain interaction, and crystallinity. This structural feature causes chain slippage in PP under stress,significantly constraining its service lifetime. In this study, thermally reduced graphene oxide(TrGO) nanosheets were incorporated into the PP matrix, yielding a nanocomposite with exceptional creep resistance performance. Results demonstrated that at a stress of 25 MPa, a 2.0 wt% TrGO content could enhance the creep failure lifetime of PP by 21.5 times compared to neat PP. Rheology, transmission electron microscopy(TEM),and scanning electron microscopy(SEM) characterization techniques were employed to analyze the mechanism of TrGO's influence on PP's creep behavior. It was observed that when TrGO content exceeded 1.0 wt%, an effective particle network structure formed within the PP matrix. This homogeneously dispersed TrGO-formed particle network structure restricted the migration and rearrangement of PP molecular chains, enabling prolonged stress resistance without structural failure. By combining the time-strain superposition method with the critical failure strain as a criterion, generalized creep compliance curves for PP and its composites were established, facilitating the prediction of material creep failure lifetimes, with a strong agreement between experimental and predicted lifetime values. This research proposes a novel strategy aimed at developing polypropylene materials and products with enhanced long-term stability and durability, thus extending service life, reducing failure risk, and broadening their potential across various application domains.

Data Fusion with Genetic Algorithm Based Lifetime Prediction for Dependable Multi-Processor System-on-Chips

With the prevalence of big-data technology,intricate,nanoscale Multi-Processor System-on-Chips(MP-SoCs)have been used in various safety-critical applications.However,with no extra countermeasures taken,this widespread use of MP-SoCs can lead to an undesirable decrease in their dependability.This study presents a promising approach using a group of Embedded Instruments(EIs)inside a processor core for health monitoring.Multiple health monitoring datasets obtained from the employed EIs are sampled and collated via the implemented experiment and thereafter used for conducting its remaining useful lifetime prognostics.This enables MP-SoCs to undertake preventive self-repair,thus realizing a zero mean downtime system and ensuring improved dependability.In addition,a principal component analysis based algorithm is designed for realizing the EI data fusion.Subsequently,a genetic algorithm based degradation optimization is employed to create a lifetime prediction model with respect to the processor.

Lifetime prediction of electrical connectors under multiple environment stresses of temperature and particulate contamination

Electrical connectors play a significant role in the electronic and communication systems. As they are often exposed in the atmosphere environment, it is extremely easy for them to cause electrical contact failure. It is essential to carry out the reliability modeling and predict the lifetime. In the present work, the accelerated lifetime testing method which is on account of the uniform design method was designed to obtain the degradation data under multiple environmental stresses of temperature and particulate contamination for electrical connectors. Based on the degradation data, the pseudo life can be acquired. Then the reliability model was established by analyzing the pseudo life. Accordingly, the reliability function and reliable lifetime function were set up, and the reliable lifetime of the connectors under the multiple environment stresses of temperature and particulate contamination could be predicted for electrical connectors,

The development of machine learning-based remaining useful life prediction for lithium-ion batteries

Lithium-ion batteries are the most widely used energy storage devices,for which the accurate prediction of the remaining useful life(RUL)is crucial to their reliable operation and accident prevention.This work thoroughly investigates the developmental trend of RUL prediction with machine learning(ML)algorithms based on the objective screening and statistics of related papers over the past decade to analyze the research core and find future improvement directions.The possibility of extending lithium-ion battery lifetime using RUL prediction results is also explored in this paper.The ten most used ML algorithms for RUL prediction are first identified in 380 relevant papers.Then the general flow of RUL prediction and an in-depth introduction to the four most used signal pre-processing techniques in RUL prediction are presented.The research core of common ML algorithms is given first time in a uniform format in chronological order.The algorithms are also compared from aspects of accuracy and characteristics comprehensively,and the novel and general improvement directions or opportunities including improvement in early prediction,local regeneration modeling,physical information fusion,generalized transfer learning,and hardware implementation are further outlooked.Finally,the methods of battery lifetime extension are summarized,and the feasibility of using RUL as an indicator for extending battery lifetime is outlooked.Battery lifetime can be extended by optimizing the charging profile serval times according to the accurate RUL prediction results online in the future.This paper aims to give inspiration to the future improvement of ML algorithms in battery RUL prediction and lifetime extension strategy.

FATIGUE DAMAGE AND LIFETIME PREDICTION OF AERONAUTIC WELDED STRUCTURES UNDER HIGH TEMPERATURE

The fatigue damage evolution equations and the relation of fatigue damage parameter with maximum cyclic stress of superalloy GH150 and its welded structures are established. The fatigue damage evolution equations in a multiaxial stress state are also given. By use of cyclic thermal elastoplastic damage constitutive relations, the fatigue damage and lifetime predictions are carried out for the welded combustion chamber of aeroengine.

Lifetime and Aging Degradation Prognostics for Lithium-ion Battery Packs Based on a Cell to Pack Method

Aging diagnosis of batteries is essential to ensure that the energy storage systems operate within a safe region.This paper proposes a novel cell to pack health and lifetime prognostics method based on the combination of transferred deep learning and Gaussian process regression.General health indicators are extracted from the partial discharge process.The sequential degradation model of the health indicator is developed based on a deep learning framework and is migrated for the battery pack degradation prediction.The future degraded capacities of both battery pack and each battery cell are probabilistically predicted to provide a comprehensive lifetime prognostic.Besides,only a few separate battery cells in the source domain and early data of battery packs in the target domain are needed for model construction.Experimental results show that the lifetime prediction errors are less than 25 cycles for the battery pack,even with only 50 cycles for model fine-tuning,which can save about 90%time for the aging experiment.Thus,it largely reduces the time and labor for battery pack investigation.The predicted capacity trends of the battery cells connected in the battery pack accurately reflect the actual degradation of each battery cell,which can reveal the weakest cell for maintenance in advance.

Numerical and Experimental Investigations of the Thermal Fatigue Lifetime of CBGA Packages

A thermal fatigue lifetime prediction model of ceramic ball grid array(CBGA)packages is proposed based on the Darveaux model.A finite element model of the CBGA packages is established,and the Anand model is used to describe the viscoplasticity of the CBGA solder.The average viscoplastic strain energy density increment △Wave of the CBGA packages is obtained using a finite element simulation,and the influence of different structural parameters on theWave is analyzed.A simplified analytical model of the △Wave is established using the simulation data.The thermal fatigue lifetime of CBGA packages is obtained from a thermal cycling test.The Darveaux lifetime predictionmodel ismodified based on the thermal fatigue lifetime obtained fromthe experiment and the corresponding △Wave.A validation test is conducted to verify the accuracy of the thermal fatigue lifetime prediction model of the CBGA packages.This proposed model can be used in engineering to evaluate the lifetime of CBGA packages.

Lifetime estimation of IGBT module using square-wave loss discretization and power cycling test

The insulated gate bipolar transistor(IGBT)module is one of the most age-affected components in the switch power supply, and its reliability prediction is conducive to timely troubleshooting and reduction in safety risks and unnecessary costs. The pulsed current pattern of the accelerator power supply is different from other converter applications;therefore, this study proposed a lifetime estimation method for IGBT modules in pulsed power supplies for accelerator magnets. The proposed methodology was based on junction temperature calculations using square-wave loss discretization and thermal modeling.Comparison results showed that the junction temperature error between the simulation and IR measurements was less than 3%. An AC power cycling test under real pulsed power supply applications was performed via offline wearout monitoring of the tested power IGBT module. After combining the IGBT4 PC curve and fitting the test results,a simple corrected lifetime model was developed to quantitatively evaluate the lifetime of the IGBT module,which can be employed for the accelerator pulsed power supply in engineering. This method can be applied to other IGBT modules and pulsed power supplies.

An analytical model for predicting battery lifetime

We used an analytical high-level battery model to estimate the battery lifetime for a given load.The experimental results show that this model to predict battery lifetime under variable loads is more appropriate than that under constant loads.

Investigation of creep behaviours of gypsum specimens with flaws under different uniaxial loads

The aim of this study is to identify the influence of the dip angle of a pre-existing macrocrack on the lifetime and ultimate deformation of rock-like material. Prediction of lifetime has been studied for three groups of specimens under axial static compressive load levels. The specimens were investigated from 65% to 85% of UCS(uniaxial compressive strength) at an interval of 10% of UCS for the groups of specimens with a single modelled open flaw with a dip angle to the loading direction of 30°(first group), at an interval of 5% of UCS increment for the groups of specimens with single(second group), and double sequential open flaws with a dip angle to the loading direction of 60°(third group). This study shows that crack propagation in specimens with a single flaw follows the same sequences. At first, wing cracks appear, and then shear crack develops from the existing wing cracks. Shear cracking is responsible for specimen failure in all three groups. A slip is expected in specimens from the third group which connects two individual modelled open flaws. The moment of the slip is noticed as a characteristic rise in the axial deformation at a constant load level. It is also observed that axial deformation versus time follows the same pattern, irrespective of local geometry. Specimens from the first group exhibit higher axial deformation under different load levels in comparison with the specimens from the second and third groups.

Fatigue Life Estimation Considering Strengthening of Low Amplitude Loads Using Improved Unequal Interval Grey Model

Under variable loading,fatigue life prediction is very important for the selection,design,and safety assessments of these components.In this study,based on the Miner rule,an improved damage accumulation rule was proposed to consider the strengthening and damaging of low amplitude loads.The complexity of fatigue phenomenon results in predicting fatigue life difficulty.Since grey models(GMs)only require a limited amount of data to estimate the behavior of unknown systems,they are used in this paper to account for the uncertainties resulting from various sources when fatigue life of component is predicted.An improved unequal interval GM(IUGM(1,1))has been developed and applied successfully to estimation of fatigue life.An example is used to illustrate how the method works.The results show that the proposed model not only overcomes the limitations of the traditional grey forecasting model of linear change series,but also increases the scope of GM in the fatigue life prediction of mechanical components,and its accuracy is higher than that of the traditional model.Moreover,the results indicate that the IUGM(1,1)is capable of predicting component fatigue life better than the traditional Miner rule,and yields a high prediction precision.

End-life Prediction of Commercial PLA Used for Food Packaging through Short Term TGA Experiments: Real Chance or Low Reliability?

A long-term （about nine months） isothermal degradation experiment of two different commercial polylactide （PLA） samples used for food packaging was carried out at a relatively low temperature （423 K）. Thermooxidative degradations of the same polymers were carried out in a thermogravimetric （TG） analyser, at higher temperatures （453 K 〈 T 〈 523 K）, under isothermal heating conditions. The obtained set of experimental TG data was used to determine the apparent activation energy （Ea） of degradation through two isothermal kinetic methods. The results from long-term experiment evidenced considerable mass loss for both PLA samples in the investigated period, but the experimental data were not in agreement with those from the short-term degradations at higher temperatures, thus suggesting a different degradation kinetics, and, then a low reliability of the lifetime predictions for polymers in service or degradation forecasts for the end of their life based on experiments at higher temperatures.

锂离子电池加速老化:连接电池老化机制分析与寿命预测的桥梁

储能行业和新能源汽车的爆发式增长要求对锂离子电池的老化行为尤其是电池的寿命行为有更加深入的了解.准确地预测电池在各种工作条件下的使用寿命有助于优化电池的实际运行条件和延长电池的使用寿命,最终达到降低电池生命周期内总成本的目的.加速老化是一种经济高效的产品寿命评价方法,可以在短时间内获得大量的电池老化信息,快速预测锂离子电池在各种工作应力下的寿命特征.然而,基于加速老化进行电池寿命预测的前提是电池老化机理的一致性.本文综述了锂离子电池内各部件的老化机理和应力加速条件下的电池衰退机制以及相应的老化模式,为评价电池老化机理的一致性提供了参考.此外,本文还介绍了一些基于加速老化的电池寿命经验预测模型并为今后锂离子电池的加速老化研究提供了一些建议.

EBSD parameter assessment and constitutive models of crept HR3C austenitic steel

Creep damage and evolution of HR3C steel at 650℃ were investigated using electron backscatter diffraction(EBSD),and EBSD-based parameter assessments were conducted.EBSD analyses show that the grain size is almost unchanged and no obvious texture formed after creep at different creep rates.The lowest proportion of low Σ coincidence site lattice grain boundaries under 150 MPa implies that the primary twin structures are preserved under the low stress level,while some twin structures evolved into general grain boundaries at the high creep level.Two main damage features of microcracks and cavities can be seen along the grain boundaries:the former emerged at higher stress levels,while the latter appeared at the lower stress level,and both were shown under medium stress.Band contrast shows that the most severe creep damage is present at 170 MPa.It implies that the creep mechanism differs distinctly under different stress levels,and the transition point is around 170 MPa.Kernel average misorientation is better to describe the local plastic deformation related to the strain distribution while grain reference orientation deviation describes the inhomogeneous strain distribution.Creep lifetime prediction models including the isothermal method,Larson-Miller parameter method and Monkman–Grant relation were evaluated by the experimental data and literature data,and they are valid for predicting creep behavior.