Lifetime Prediction Research Articles

Archeological analogs for the lifetime prediction of the canister for spent nuclear fuel in the deep geological repository: Part II. Evolution of the corrosion rate over time

AbstractThis work focused on evaluating the evolution of the corrosion product layer and associated corrosion rates on archeological artifacts from 16 sites. Sites with clay soils and maximum waterlogging (pond bottoms and wetlands) were monitored. The corrosion products were evaluated by X‐ray imaging, X‐ray diffraction, scanning electron microscope, Brunauer–Emmett–Teller, and nanoindentation. Two stages of corrosion attack with slightly different mechanisms were observed. Corrosion rates observed at sites with continuous flooding show average corrosion rates of 0.4–1 μm.a−1. These values demonstrate a minimum service life of the container outer case of 15 × 103 years, which is several times longer than the required lifetime of the outer case.

Hydrothermal Aging and Humidity Exposure of Carbon and Basalt Fibers and Life Time Prediction

Hydrothermal Aging and Humidity Exposure of Carbon and Basalt Fibers and Life Time Prediction

Fatigue lifetime predictions of notched specimens based on the critical distance and stress concentration factors

The theory of critical distances is used for evaluation of effects of notches on load-bearing capacity of notched components under static or cyclic fatigue loading. The theory can also be used to predict the fatigue life of notched components. One of the assumptions for a reliable prediction is that the critical distance is independent of the notch geometry and its stress concentration level. The study shows that the critical distance depends not only on the number of cycles to failure (under fatigue loading), but also on the notch radius. In this case, direct use of the critical distance leads to unreliable predictions. The study quantifies the relation between the model notch (from which the critical distance is determined) and the predicted notch by means of the ratio of their stress concentration factors. The predictions modified by the ratio of stress concentration factors provide results that are in satisfactory agreement with the experimental data. The predictions were made and verified on the basis of fatigue data measured on two stainless steels 1.4306 and 1.4307, and high strength structural steel S690QL. Smooth and notched specimens were tested on an ultrasonic fatigue machine in a symmetrical tension–compression mode. The results were evaluated in the regions of high cycle and very high cycle fatigue.

Uncertainty Degradation Model for Initiating Explosive Devices Based on Uncertain Differential Equations

The performance degradation of initiating explosive devices is influenced by various internal and external factors, leading to uncertainties in their reliability and lifetime predictions. This paper proposes an uncertain degradation model based on uncertain differential equations, utilizing the Liu process to characterize the volatility in degradation rates. The ignition delay time is selected as the primary performance parameter, and the uncertain distributions, expected values and confidence intervals are derived for the model. Moment estimation techniques are employed to estimate the unknown parameters within the model. A real data analysis of ignition delay times under accelerated storage conditions demonstrates the practical applicability of the proposed method.

Effect of thermo-oxidative ageing on changes of physical-mechanical parameters of aramid and UHMWPE laminates

Abstract This work is focused on monitoring of the effect of thermo-oxidative ageing of laminates on their physical-mechanical parameters, such as tensile strength, tensile modulus of elasticity, flexural strength, flexural modulus of elasticity and hardness. Changes of mentioned parameters were observed on two types of laminates based on aramid and two types based on UHMWPE (Ultra High Molecular Weight Polyethylene). The laminates were exposed to the accelerated thermo-oxidative ageing under higher temperature in the presence of atmospheric oxygen. The aim of the work was to find the physical-mechanical parameter with the greatest changes caused by thermo-oxidative ageing. This parameter was consequently chosen for the prediction of laminates lifetime. From the obtained results, it can be seen that aramid laminates show a significantly higher temperature resistance compared to UHWPE-based laminates.

Increased accuracy of service life prediction for fiber metal laminates by consideration of the manufacturing-induced residual stress state

Fiber metal laminates (FML) combine the high specific properties of fiber-reinforced polymers with the ductility and damage tolerance of metals. However, during the manufacturing process of FMLs, in-plane residual stresses arise in the single layers of the laminate. The stresses mainly originate from the different coefficients of thermal expansion of the two materials and lead to an inhomogeneous stress state in the thickness direction of the laminate. This work investigates the influence of such thermal residual stress (TRS) on the fatigue life in an FML by experimentally varying the TRS in identical layups using modified cure cycles. The experimental investigations show that the amount of TRS directly influences the fatigue performance of such laminates. Reducing the TRS, particularly the tensile residual stresses in the metal layers, slows the fatigue crack growth during cyclic loading. This leads to a significantly delayed crack initiation and slower crack propagation. Knowledge of material behavior is essential for utilizing the full potential of structural health monitoring (SHM) in component lifetime predictions. Predictive models require a set of input parameters to perform this task. Since the TRS in an FML significantly contributes to the component's fatigue life, its consideration in predictive approaches is inevitable. This finding is discussed based on a phenomenological damage accumulation model initially developed for composite materials.

Crack layer modeling of butt-fusion joints induced slow crack growth in high-density polyethylene pipes

The design and reliability of high-density polyethylene (HDPE) pipes can be dictated by the damage tolerance of their butt-fusion joints. A slow crack growth (SCG) model based on the crack layer theory for HDPE pipes internal and external circumferential and butt-fusion joints cracks is developed to investigate the discontinuous SCG behavior and lifetime tf variations. Using the developed model, the discontinuous SCG patterns, jump lengths and lifetime tf can be accurately simulated. In addition, the effects of pipes standard dimension ratio, internal pressure, and temperature on the SCG behavior and tf are investigated. Compared to pipe cracks, it was found that butt-fusion induced SCG can reduce the pipe tf > 60 %. Unlike longitudinal cracks, tf of external circumferential cracks, were found shorter than the internal ones, mandating an earlier evaluation. The new SCG model shows good accuracy with the experimental results. The same substitute geometry approach used can be followed for othercomplex designs, which aids in establishing a fundamental methodology for more realistic lifetime predictions.

Change point detection of health stage and remaining useful lifetime prediction for electro-mechanical brake unit on trains

Electro-mechanical brake units (EMBUs) have complex degradation process. There are challenges such as component fault coupling, unclear degradation characteristics, random disturbance and so on. A remaining useful life (RUL) framework for EMBUs is developed in this paper. To eliminate the issue of time lag, a data pre-processing method based on zero-phase filtering is proposed for health indicator (HI) construction. Then, the 3 health stages (HSs) characteristics of degradation process is proposed, and is modeled using the Wiener process. To overcome perturbation of large diffusion coefficient and random fluctuation, the change point (CP) detection methods based on the variance ratio hypothesis test and maximal overlap discrete wavelet transform — multi-resolution analysis (MODWT-MRA) are developed. Besides, a consecutive anomaly window detection strategy using sliding windows is employed. Finally, the MODWT-MRA-based RUL prediction methods is proposed. The proposed methods are verified by the durability test data. The proposed data pre-processing method can avoid 10% time error compared to the common difference method. The proposed MODWT-MRA-based CP detection and RUL prediction method can overcome the interference of large random fluctuations in the non-healthy state of EMBU.

Thermal Degradation Kinetics of Natural Fibers: Determination of the Kinetic Triplet and Lifetime Prediction

Thermal Degradation Kinetics of Natural Fibers: Determination of the Kinetic Triplet and Lifetime Prediction

Attention Mechanism-Based Neural Network for Prediction of Battery Cycle Life in the Presence of Missing Data

As batteries become widespread applications across various domains, the prediction of battery cycle life has attracted increasing attention. However, the intricate internal mechanisms of batteries pose challenges to achieving accurate battery lifetime prediction, and the inherent patterns within temporal data from battery experiments are often elusive. Meanwhile, the commonality of missing data in real-world battery usage further complicates accurate lifetime prediction. To address these issues, this article develops a self-attention-based neural network (NN) to precisely forecast battery cycle life, leveraging an attention mechanism that proficiently manages time-series data without the need for recurrent frameworks and adeptly handles the data-missing scenarios. Furthermore, a two-stage training approach is adopted, where certain network hyperparameters are fine-tuned in a sequential manner to enhance training efficacy. The results show that the proposed self-attention-based NN approach not only achieves superior predictive precision compared with the benchmarks including Elastic Net and CNN-LSTM but also maintains resilience against missing-data scenarios, ensuring reliable battery lifetime predictions. This work highlights the superior performance of utilizing attention mechanism for battery cycle life prognostics.

Early battery lifetime prediction based on statistical health features and box-cox transformation

Early battery lifetime prediction is important for both safety reasons and battery development. It predicts battery lifetime before it degrades significantly and has the advantage of being low-cost, time-saving, and providing timely feedback. However, due to the nonlinear degradation behavior and limited data in the early stage, early battery lifetime prediction is difficult. In this paper, statistical health features are extracted from temperature and voltage data, and a statistical transformation method is proposed to enhance the feature's importance and help increase the prediction accuracy of battery lifetime. First, candidate health features are constructed by analyzing temperature and voltage data, and Box-Cox transformation (BCT) is used to enhance their linear correlation with battery lifetime to help with feature selection. The Pearson correlation coefficients of extracted temperature and voltage features with battery lifetime reach −0.73 and − 0.90 respectively. Early battery lifetime prediction based on the first 100 cycles of data is then conducted using Gaussian process regression with a root-mean-square-error (RMSE) of 112.14 cycles. Comparison experiments show that Gaussian process regression outperforms the other four common machine learning methods in both BCT and non-BCT situations. Moreover, adding temperature feature and BCT have a positive effect. When they are not used, RMSEs would increase by 13.8 % and at most 209 %, respectively. At last, prediction based on fewer data (50 cycles) is tested and gained acceptable result of an RMSE of 149.68 cycles.

Analysis of a raise boring reamer in operation based on acceleration measurements

AbstractThe understanding of the complex dynamic phenomena occurring during the reaming operations of the raise boring process is limited. Any operational damage of the reamer is very likely to be caused by component failure due to fatigue effects from the dynamic processes. Therefore, an innovative measurement setup consisting of acceleration sensors in three axes was installed on a reamer to capture its movements and vibrations during operation. The capture of the reaming process by acceleration sensors was successful, however no operational damage on the reamer was observed during the reaming of three vertical shafts. The collected vibration data was implemented into a dynamic finite element assessment, which is intended to serve for lifetime predictions of components and predictive maintenance. A further step is investigating the potential for the implementation of the measurement data into a condition monitoring system and for optimizing the reaming operation.

Structure-dependent residual stress of thermal barrier coating on turbine blade with exposure to high temperature

This work aims to investigate the structure-dependent residual stress of thermal barrier coating systems (TBCs) on actual turbine blades with curved surfaces, and explore the relationship between stress and failure in different blade regions. A specialized non-destructive stress measurement setup was developed for this purpose. The residual stresses in the top coat (TC) and thermally grown oxide (TGO) layer at different blade regions, namely the convex surface, the concave surface, and the leading-edge surface, were measured during isothermal oxidation services. A phenomenon of residual stress reversal was discovered. The structure-dependent failures of TBCs in different blade regions were found to be strongly correlated with the residual stress in the TGO layer, rather than the TC layer. These results significantly enhance our understanding of the failure mechanism and contribute to the prediction of service lifetimes for turbine blades equipped with TBCs.

Expected lifetime prediction for time- and space-dependent structural systems based on active learning surrogate model

Predicting the expected lifetime of structural systems is fundamental for effective design and maintenance. However, existing methods based on physics overlook critical spatial considerations, particularly those related to random field inputs. In this study, we propose a new active learning surrogate-based method to predict the expected lifetime of structural systems affected by both time and space factors. The method starts by training an initial Kriging model with random samples and time-space coordinates. An extended expected feasibility function is proposed to select new training samples and their corresponding time-space coordinates, refining the Kriging model. A novel parallel learning strategy is proposed to expedite computations. By using the constructed Kriging model and the first failure time of random samples, we can predict the expected lifetime. The proposed method is adaptable to structural systems with multiple failure modes, implicit functions, and random field variations. The efficiency and accuracy of the proposed method are validated through four examples.

Multi-technique characterization and thermal degradation study of epoxy modified resins designed for multifunctional applications

AbstractTetraglycidyl methylene dianiline (TGMDA) was mixed with 1,4-Butanediol diglycidyl ether (BDE) (in a 4:1 mass ratio) and with a stoichiometric amount of the curing agent diaminodiphenyl sulfone which was solubilized at 120 °C for 20 min in the liquid mixture TGMDA + BDE. The so obtained unfilled epoxy resin matrix, denoted as ER, was blended with glycidyl polyhedral oligomeric silsesquioxane and carbon nanotubes in suitable proportions to obtain binary and ternary mixtures. Characterization of the formulated materials was performed using different experimental techniques, such as Dynamic mechanical analysis, Thermogravimetry (TG), Field emission scanning electron microscopy. Furthermore, the investigation of the flame behavior was carried out by the limiting oxygen index and mass loss calorimeter measurements. Direct current measurements and investigation by Tunneling atomic force microscopy of the conductive nanodomain map allowed the evaluation of the electrical properties of the developed nanofilled systems. The TG data related to thermal decomposition of ER and its binary and ternary mixtures were processed according to isoconversional kinetic analysis by assuming a non-Arrhenian behavior of the temperature function, and lifetime prediction was estimated at suitable relatively low temperatures and possible relation between the thermal stability and the presence of each component was discussed. This method of kinetic analysis paves the way for the possibility of evaluating in a more realistic way, on the basis of thermal stability, the potential application of structural resins with primary load functions in contact with hot areas of aeronautical aircraft engines.

Investigation of the impacts of artificial ageing, re-ageing and cryogenic cycles on multiplex second phase precipitation and life-time prediction in 7075 alloy

In this study, artificial ageing, re-ageing and multiple levels of cryogenic treatments were practiced on 7075 aluminium alloy. Microstructural changes in the material as a result of the final cryogenic cycles were characterized by SEM and TEM, and the resulting phases with successive upsurge stages of nucleation sites were observed. Throughout crystallographic and X-ray analyses; dislocation densities, strain values of the planes, texture coefficient and plane orientations were determined. Micro-hardness measurements were also carried out to mechanically support the data attained from these characterization methods. According to the results, dislocation density and hardness values particularly after −40 °C cryogenic treatment, achieved the highest value. Heating rate and activation energy-based phase life time predictions against temperature, together with nucleation and growth rates of precipitates were evaluated by thermal analyses. Life time calculations pointed out that the −40 °C was the critical point of sub-zero progression where, the longest useful life expectancy at temperatures of between 80 °C and 300 °C could be reached out meticulously.

Review of the Modelling of Corrosion Processes and Lifetime Prediction for HLW/SF Containers—Part 2: Performance Assessment Models

Review of the Modelling of Corrosion Processes and Lifetime Prediction for HLW/SF Containers—Part 2: Performance Assessment Models

Quantitative failure analysis of lithium-ion batteries based on direct current internal resistance decomposition model

Accurate failure analysis plays a pivotal role in the optimization design and lifetime prediction of 4.45 V high-voltage LiCoO2/Graphite (LCO/Gr) batteries. Multiphysics coupling model brings great opportunities to conduct battery failure analysis quantitatively, although it is quite challenging because many model parameters need to be handled properly. Herein, we systematically elaborate the differences of ion and electron transport properties before and after cycling ageing of LCO/Gr batteries by constructing direct current internal resistance (DCR) decomposition model. The key parameters acquisition method is established, and the mechanism of DCR growth is elucidated. Furthermore, the aforementioned model parameters are refined by using a hybrid power pulse characteristics (HPPC) curve optimization algorithm based on DCR decomposition results obtained from the three-electrode battery system. Through analyzing the influence of single-factor parameter ageing on battery voltage output capacity and discharge temperature rise, the main factors affecting battery failure process are identified. For instance, the account of the positive electrochemical reaction resistance related to the kpos increased from the initial 22.9% of total DCR to 37.3% after ageing. This work provides a reliable quantitative analysis basis for the global optimization design of advanced LIBs.

Lifetime Prediction of Permanent Magnet Synchronous Motor in Selective Compliance Assembly Robot Arm Considering Insulation Thermal Aging.

The direct-drive selective compliance assembly robot arm (SCARA) is widely used in high-end manufacturing fields, as it omits the mechanical transmission structures and has the advantages of high positioning accuracy and fast movement speed. However, due to the intensifying dynamic coupling problem of structures in the direct-drive SCARA, the permanent magnet synchronous motors (PMSMs) located at the joints will take on nonstationary loads, which causes excessive internal temperature and reduces the lifetime of PMSMs. To address these issues, the lifetime prediction of PMSMs is studied. The kinematic and dynamic models of the SCARA are established to calculate the torque curve required by the PMSM in specific typical motion tasks. Additionally, considering thermal stress as the main factor affecting lifetime, accelerated degradation tests are conducted on insulation material. Then, the reliability function of the PMSM is formulated based on the accelerated degradation model. Based on the parameters and working conditions of the PMSM, the temperature field distribution is obtained through simulation. The maximum temperature is used as the reference temperature to conduct reliability evaluation and lifetime prediction of the PMSM. The research results show that for a typical point-to-point task, the PMSM can run for 102,623 h while achieving the reliability requirement of 0.99.

Thermo-mechanical analysis and creep lifetime prediction of coated turbine vanes considering thermal barrier coating spallation characteristics

Quantitative analysis of the influence of thermal barrier coating spallation on the thermo-mechanical behaviors and creep lifetime of turbine vanes is crucial for their maintenance and reliability improvement. The temperature and stress distribution of vanes with preset coating spallation damage are investigated in this study, using the thermal-fluid-solid coupling method. Additionally, a further computational analysis is conducted to predict the hazardous regions and creep lifetime of the vanes. The results indicate that coating spallation leads to significant changes of the temperature and stress distribution at the spalled regions of vanes. The remaining coating on the unspalled regions continues to provide effective protection. Stress concentration primarily occurs at the upstream and downstream of the leading edge film holes, while high-stress regions are observed between adjacent rows of film holes, forming a serrated shape. The creep lifetime of the vanes decreases significantly at the region with coating spallation. When the same spallation area is considered, the coating spallation at the leading edge has a more serious influence on creep lifetime, which is more likely to cause the vanes failure. This study reveals the influence of coating spallation characteristics on the thermo-mechanical behaviors and creep lifetime of vanes, providing valuable insights for durability assessment of coated high-temperature components.