Journal articles: 'Life Prediction Model'

1

Ahmad, J., U. Santhosh, and S. Hoff. "A Metal Matrix Composite Damage and Life Prediction Model." Journal of Engineering for Gas Turbines and Power 120, no. 4 (October 1, 1998): 825–32. http://dx.doi.org/10.1115/1.2818475.

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A simple analytical model is derived for the prediction of time-dependent deformation and damage response of metal matrix composites under fiber direction loading. The model can be used in conjunction with a number of viscoplastic constitutive models to describe the matrix material behavior. Damage in the form of progressive fiber fractures is incorporated using a mechanistic approach. The criterion for fiber fractures can be based on statistical information on fiber strength. When used in conjunction with a prescribed failure condition for a composite, the model provides a means for predicting composite life under general thermomechanical load conditions. Based on comparison of results with detailed finite element analyses and with laboratory test data, it appears that the simple model provides reasonably accurate predictions.

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Cruse, T. A., S. E. Stewart, and M. Ortiz. "Thermal Barrier Coating Life Prediction Model Development." Journal of Engineering for Gas Turbines and Power 110, no. 4 (October 1, 1988): 610–16. http://dx.doi.org/10.1115/1.3240179.

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Ceramic thermal barrier coating tests show that the coating fails by ceramic spallation. Analysis of life data indicates that cyclic thermal loading and thermal exposure play synergistic roles in controlling the spallation life of the coating. A life prediction algorithm has been developed, based on a damage accumulation algorithm that includes both cyclic and time-dependent damage. The cyclic damage is related to the calculated cyclic inelastic strain range in the ceramic coating; the time-dependent damage is related to the oxidation kinetics at the bond-ceramic interface. Cyclic inelastic strain range is calculated using a modified form of the Walker viscoplastic material model. Calculation of the oxidation kinetics is based on traditional oxidation algorithms using experimentally determined parameters. A relation between oxide growth and cycle parameters was derived from test data. The life prediction model was evaluated by predicting the lives of a set of thermal cyclic tests whose heating and cooling rates were significantly greater than those used to correlate the life parameters. Correlation between the actual and predicted spallation lives is within a factor of 3. This is judged to be satisfactory, relative to fatigue life prediction scatter in metals.

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3

Pilsner, B., R. Hillery, R. McKnight, T. Cook, and M. Hartle. "Thermal barrier coating life prediction model." Surface and Coatings Technology 32, no. 1-4 (November 1987): 305–6. http://dx.doi.org/10.1016/0257-8972(87)90115-0.

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Fu, Zhuo, Xiang Li, Sha Zhang, Hanqing Xiong, Chi Liu, and Kun Li. "Establishment and Verification of Multiaxis Fatigue Life Prediction Model." Scanning 2021 (February 2, 2021): 1–6. http://dx.doi.org/10.1155/2021/8875958.

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A fatigue life prediction model with multiaxis load is proposed. The model introduces a new effective cyclic parameter, equivalent stress on the critical surface, to modify the Suntech model. The new damage parameters are not related to empirical constants, hence more applicable for practical application in engineering. The multiaxis fatigue test was carried out with high-strength aluminum alloy 7075-T651, and the multiaxis fatigue life prediction of the test piece was conducted with the finite element software. The experiment result shows that the model proposed is effective for predicting the fatigue life under multiaxis load.

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Salganik, Matthew J., Ian Lundberg, Alexander T. Kindel, Caitlin E. Ahearn, Khaled Al-Ghoneim, Abdullah Almaatouq, Drew M. Altschul, et al. "Measuring the predictability of life outcomes with a scientific mass collaboration." Proceedings of the National Academy of Sciences 117, no. 15 (March 30, 2020): 8398–403. http://dx.doi.org/10.1073/pnas.1915006117.

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How predictable are life trajectories? We investigated this question with a scientific mass collaboration using the common task method; 160 teams built predictive models for six life outcomes using data from the Fragile Families and Child Wellbeing Study, a high-quality birth cohort study. Despite using a rich dataset and applying machine-learning methods optimized for prediction, the best predictions were not very accurate and were only slightly better than those from a simple benchmark model. Within each outcome, prediction error was strongly associated with the family being predicted and weakly associated with the technique used to generate the prediction. Overall, these results suggest practical limits to the predictability of life outcomes in some settings and illustrate the value of mass collaborations in the social sciences.

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Shangguan, Wen-Bin, Guo-feng Zheng, Tai-Kai Liu, Xiao-Cheng Duan, and Subhash Rakheja. "Prediction of fatigue life of rubber mounts using stress-based damage indexes." Proceedings of the Institution of Mechanical Engineers, Part L: Journal of Materials: Design and Applications 231, no. 8 (October 6, 2015): 657–73. http://dx.doi.org/10.1177/1464420715608407.

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Prediction of fatigue lives of a rubber mount necessitate formulation of models for estimating fatigue life of the rubber materials used in the mount. Moreover, the prediction accuracy of the model is strongly dependent upon the choice of damage index that are based on different strain, energy or stress measures in the vicinity of critical locations of the rubber mount. In this study, relative performance of models employing different damage indices are evaluated for prediction of fatigue lives of rubber material and a drive-train rubber mount. A combined stress and an effective stress function are proposed as a damage index for predicting fatigue lives of rubber materials and the mounts. Different damage indices, identified from the finite element models of the rubber dumbbell cylindrical specimen are applied for formulations of fatigue life prediction models. The model parameters are identified from the measured data acquired for the rubber dumbbell cylindrical specimen under 31 different uniaxial displacement loads, using least squared error minimization technique. The identified models employing different damage indices are subsequently applied for predicting fatigue lives of rubber mounts under different magnitudes of loads applied along two different directions. The correlations of the predicted lives of the rubber mount from the models employing different damage indices with measured fatigue life data were subsequently investigated for the rubber mount subject to different load conditions. It is shown that the models identified for the rubber material could be effectively used for predicting fatigue lives of the mounts, which are made of same material. The fatigue lives predicted by the models considering either effective stress or combined stress as the damage index correlated with the measured data within a factor of two for the two, suggesting that stress-based damage indices could yield more accurate predictions of fatigue lives of typical mounts.

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7

Nicholas, T. "Fatigue Life Prediction in Titanium Matrix Composites." Journal of Engineering Materials and Technology 117, no. 4 (October 1, 1995): 440–47. http://dx.doi.org/10.1115/1.2804737.

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Methods used for life prediction of titanium matrix composites under isothermal and thermomechanical (TMF) fatigue are reviewed. Models containing a single parameter are shown to have applicability only under limited conditions. Two models, a dominant damage and a life fraction model, demonstrate predictive capabilities over a broad range of loads, frequencies, temperatures, and TMF parameters. Relationships between the underlying fatigue mechanisms and the individual terms in the models are illustrated.

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Blake, J. W., and H. S. Cheng. "A Surface Pitting Life Model for Spur Gears: Part I—Life Prediction." Journal of Tribology 113, no. 4 (October 1, 1991): 712–18. http://dx.doi.org/10.1115/1.2920683.

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Surface pitting is a major failure mode for gears. Estimation of failure probability and service life are important in gear design. Current techniques give only a pass/fail rating based on semi-empirical methods. A predictive model for estimating service lives and failure probabilities has been developed. This paper discusses the life prediction analysis, which is based on propagation of a surface breaking crack under rolling/sliding contact conditions. The effects of both surface roughness and non-metallic inclusions can be included. While predicted lives are lower than expected, trends observed through parametric variation are consistent with service behavior.

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9

Hanson, R., D. Allsopp, T. Deng, D. Smith, M. S. A. Bradley, I. M. Hutchings, and M. K. Patel. "A model to predict the life of pneumatic conveyor bends." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 216, no. 3 (August 1, 2002): 143–49. http://dx.doi.org/10.1243/095440802320225284.

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A new approach to the prediction of bend lifetime in pneumatic conveyors, subject to erosive wear is described. Mathematical modelling is exploited. Commercial Computational Fluid Dynamics (CFD) software is used for the prediction of air flow and particle tracks, and custom code for the modelling of bend erosion and lifetime prediction. The custom code uses a toroidal geometry, and employs a range of empirical data rather than trying to fit classical erosion models to a particular circumstance. The data used was obtained relatively quickly and easily from a gas-blast erosion tester. A full-scale pneumatic conveying rig was used to validate a sample of the bend lifetime predictions, and the results suggest accuracy of within ±65%, using calibration methods. Finally, the work is distilled into user-friendly interactive software that will make erosion lifetime predictions for a wide range of bends under varying conveying conditions. This could be a valuable tool for the pneumatic conveyor design or maintenance engineer.

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Lee, Jun Youn, and Soon Bok Lee. "GSW0309 Development of a creep-fatigue life prediction model for type 316L stainless steels." Abstracts of ATEM : International Conference on Advanced Technology in Experimental Mechanics : Asian Conference on Experimental Mechanics 2003.2 (2003): _GSW0309–1—_GSW0309–5. http://dx.doi.org/10.1299/jsmeatem.2003.2._gsw0309-1.

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11

Zhang, Yi, Guang Jun Jiang, Chun Lin Wang, and Zhi Wei Zhao. "The Development and Application for Oil Output Prediction Software System Based on Life Model." Applied Mechanics and Materials 40-41 (November 2010): 317–21. http://dx.doi.org/10.4028/www.scientific.net/amm.40-41.317.

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Life model” is the prediction system chosen for this system. Life model method is a mathematical model of generalized convergence which can predict oil output in macroscopic view. This paper selects several life models such as Weng’s model, Hubbert model and HCZ model; bringing them into the study on the development of the systems for predicting oil output; predicting the oil output of Country M from 2010 to 2050 by this system; and making certain the features of various kinds of mathematical model by applications. The results showed that the system which is based on life model can give an effective prediction on oil output.

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Zhang, Yichi, Tao Shu, and Rixian Ding. "Bearing Life Prediction Based on SPSS and Grey Prediction Model." IOP Conference Series: Earth and Environmental Science 634 (February 5, 2021): 012051. http://dx.doi.org/10.1088/1755-1315/634/1/012051.

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13

Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (January 8, 2021): 418. http://dx.doi.org/10.3390/s21020418.

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

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Peng, Cheng, Yufeng Chen, Qing Chen, Zhaohui Tang, Lingling Li, and Weihua Gui. "A Remaining Useful Life Prognosis of Turbofan Engine Using Temporal and Spatial Feature Fusion." Sensors 21, no. 2 (January 8, 2021): 418. http://dx.doi.org/10.3390/s21020418.

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The prognosis of the remaining useful life (RUL) of turbofan engine provides an important basis for predictive maintenance and remanufacturing, and plays a major role in reducing failure rate and maintenance costs. The main problem of traditional methods based on the single neural network of shallow machine learning is the RUL prognosis based on single feature extraction, and the prediction accuracy is generally not high, a method for predicting RUL based on the combination of one-dimensional convolutional neural networks with full convolutional layer (1-FCLCNN) and long short-term memory (LSTM) is proposed. In this method, LSTM and 1- FCLCNN are adopted to extract temporal and spatial features of FD001 andFD003 datasets generated by turbofan engine respectively. The fusion of these two kinds of features is for the input of the next convolutional neural networks (CNN) to obtain the target RUL. Compared with the currently popular RUL prediction models, the results show that the model proposed has higher prediction accuracy than other models in RUL prediction. The final evaluation index also shows the effectiveness and superiority of the model.

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15

Wright,, J. T., and T. Goswami,. "Development of a Generic Life Prediction Model." Journal of the Mechanical Behavior of Materials 13, no. 5-6 (December 2002): 397–404. http://dx.doi.org/10.1515/jmbm.2002.13.5-6.397.

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16

Meier, S. M., D. M. Nissley, K. D. Sheffler, and T. A. Cruse. "Thermal Barrier Coating Life Prediction Model Development." Journal of Engineering for Gas Turbines and Power 114, no. 2 (April 1, 1992): 258–63. http://dx.doi.org/10.1115/1.2906581.

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A thermal barrier coated (TBC) turbine component design system, including an accurate TBC life prediction model, is needed to realize the full potential of available TBC engine performance and/or durability benefits. The objective of this work, which was sponsored in part by NASA under the Hot Section Technology (HOST) Program (Contract NAS3-23944), was to generate a life prediction model for electron beam-physical vapor deposited (EB-PVD) zirconia TBC. Specific results include EB-PVD zirconia mechanical and physical properties, coating adherence strength measurements, interfacial oxide growth characteristics, quantitative cyclic thermal spallation life data, and a spallation life model.

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17

Goswami,, Tarun. "Creep-Fatigue Life Prediction - A Ductility Model." High Temperature Materials and Processes 14, no. 2 (April 1995): 101–14. http://dx.doi.org/10.1515/htmp.1995.14.2.101.

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Goswami,, Tarun. "A New Creep-Fatigue Life Prediction Model." High Temperature Materials and Processes 15, no. 1-2 (January 1996): 91–96. http://dx.doi.org/10.1515/htmp.1996.15.1-2.91.

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19

Li, Yiming, Xiangmin Meng, Zhongchao Zhang, and Guiqiu Song. "A Machining State-Based Approach to Tool Remaining Useful Life Adaptive Prediction." Sensors 20, no. 23 (December 6, 2020): 6975. http://dx.doi.org/10.3390/s20236975.

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The traditional predictive model for remaining useful life predictions cannot achieve adaptiveness, which is one of the main problems of said predictions. This paper proposes a LightGBM-based Remaining useful life (RUL) prediction method which considers the process and machining state. Firstly, a multi-information fusion strategy that can effectively reduce the model error and improve the generalization ability of the model is proposed. Secondly, a preprocessing method for improving the time precision and small-time granularity of feature extraction while avoiding dimensional explosion is proposed. Thirdly, an importance coefficient and a custom loss function related to the process and machining state are proposed. Finally, using the processing data of actual tool life cycle, through five evaluation indexes and 25 sets of contrast experiments, the superiority and effectiveness of the proposed method are verified.

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Zhang, J., D. Pirzada, C. C. Chu, and G. J. Cheng. "Fatigue Life Prediction After Laser Forming." Journal of Manufacturing Science and Engineering 127, no. 1 (February 1, 2005): 157–64. http://dx.doi.org/10.1115/1.1828059.

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Analysis of the laser forming process has been focused on geometry, yield strength, and microstructure change in the past. However fatigue life has been the primary concern for engineering components in many applications. For laser forming to become a practical rapid prototyping tool, research has to be done to predict fatigue life of sheet metal after laser forming. Microstructure as well as the distribution of residual stresses and strains changes during laser forming process. The current models cannot predict the fatigue life after laser forming accurately because of differences in assumptions. This work presents a model to predict fatigue life of sheet metal after laser forming. Results from microstructure integrated finite element modeling of laser forming are incorporated in the fatigue life model. Low carbon steel is used in this work to validate the model. It is shown that the proposed model can predict the fatigue life of sheet metal after laser forming with good accuracy. The predictions from the model are consistent with experimental results. Effects of laser forming conditions on fatigue life of sheet metal are under investigation.

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Wang, Tie Liu, Xian Ming Chen, and Shui Bin Chen. "Prediction of Cutting Tool Life Based on ACO-BP Meural Network." Advanced Materials Research 655-657 (January 2013): 1714–17. http://dx.doi.org/10.4028/www.scientific.net/amr.655-657.1714.

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For predicting the tool life combine the ant colony optimization(ACO) with the back propagation (BP) neural networks, use the the ACO to train BP neural network, build the prediction model based ACO-BP neural network. Some disadvantages are overcame in the BP algorithm, such as the low convergence speed, easily falling into local minimum point and weak global search capablity in the prediction process. Satisfies the requirement of global search capability and the robustness of the model. The experiment results show the prediction model has high precision in predicting the tool life. By the prediction model can provide a reasonable basis for planing production schedule and cutting tool requirement, calculating the cost, selecting the machining parameters,etc.

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Zhang, Jun Hong, Feng Lv, and Wen Peng Ma. "Multi-Axial Fatigue Life Model Evaluation and Life Prediction for Turbine Disk." Applied Mechanics and Materials 130-134 (October 2011): 2330–34. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2330.

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Multi-axial low cycle fatigue was the main failure mode of turbine disk. Critical plane approach was an idea method for the prediction of multi-axial fatigue life. A lot of models based on critical plane approach have been put forward, but there is not a universal prediction model. In order to find a model for turbine disk, linear heteroscedastic regression analysis of the standard low cycle fatigue data was carried out to obtained fatigue parameters. After verifying the accuracy of the finite element model, the stress and strain history of the danger point was obtained based on elastic-plastic finite element analysis. The critical plane and the damage of it was found by the method of coordinate transformation. The fatigue life of turbine disk was estimated by different models, and the results were quite different. SWT-Bannantine model was more suitable for the turbine disk.

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Rejovitzky, Elisha, and Eli Altus. "On single damage variable models for fatigue." International Journal of Damage Mechanics 22, no. 2 (April 16, 2012): 268–84. http://dx.doi.org/10.1177/1056789512443902.

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This study focuses on an analytical investigation of the common characteristics of fatigue models based on a single damage variable. The general single damage variable constitutive equation is used to extract several fundamental properties. It is shown that at constant amplitude loads, damage evolution results are sufficient for predicting fatigue life under any load history. Two-level fatigue envelopes constitute an indirect measure of the damage evolution and form an alternative basis for life prediction. In addition, high-to-low and low-to-high envelopes are anti-symmetrical with respect to each other. A new integral formula for life prediction under random loads is verified with the models of Manson and Hashin, and also developed analytically for other models including Chaboche, resulting in analytical predictions. The Palmgren – Miner rule is found to yield an upper bound for fatigue life predictions under random loads, regardless of the load distribution and the specific single damage variable model.

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Li, Menghan, Xin Liu, Zhenguo Li, and Yingbo Zhang. "Fatigue crack growth prediction model under variable amplitude loading conditions." International Journal of Damage Mechanics 30, no. 9 (March 16, 2021): 1315–26. http://dx.doi.org/10.1177/1056789521998737.

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Crack size prediction under variable amplitude loading is a very complex process, which is also important for life prediction in engineering. A crack growth model considering different stress ratio for fatigue remaining life prediction is proposed in this paper. The model utilizes stress ratio to describe the variable loading sequences, which makes the calculation greatly simplified. The rain-flow method is utilized to characterize the load sequence effects under variable amplitude loading. In addition, particle filter is utilized to estimate the model parameters describing the crack growth. Finally, case study indicates that the proposed approach is efficient in predicting crack growth and fatigue remaining life.

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Tallian, T. E. "Simplified Contact Fatigue Life Prediction Model—Part II: New Model." Journal of Tribology 114, no. 2 (April 1, 1992): 214–20. http://dx.doi.org/10.1115/1.2920876.

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Part I of this two-part paper reviews eleven published models as a basis for the construction of a simplified, readily calculated engineering model encompassing the major life variables, based on explicit assumptions and derivations. Part II of the paper presents the new model. It uses the applied alternating shear stress field to define the critical stress as a function of depth, and computes life as crack propagation time through this field. The model defines surface defects as critical defects. Material fatigue susceptibility, fatigue limit stress and the defect severity distribution are the main endurance parameters. In addition to Hertz pressure, life-modifying variables are: interface traction, surface microgeometry and EHD film. The influence of the variables in the new model on life distribution and on load/life law are compared to the reviewed models and it is shown that the new model is capable of representing the influence of all major consensus parameters on life, by relationships that fall within the bounds of previously published behavior.

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Wang, De Xian, Dong Mei Ji, and Jian Xing Ren. "Research on Creep-Fatigue Life Prediction for P92 Steel under Stress-Controlled State." Advanced Materials Research 860-863 (December 2013): 972–77. http://dx.doi.org/10.4028/www.scientific.net/amr.860-863.972.

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Taking the P92 steel as the object,Creep-Fatigue (CF) tests of P92 steel at 873K under stress-controlled were carried out with GWT2504 equipment to investigate the CF life prediction. The life prediction model based on Applied Mechanical Work Density (AMWD) was developed in this study,and introduce the effective coefficient ƞ to modify the former. To verify the prediction capability of the AMWD-based and the modified model, comparisons of the models predicted lives with the experimental data of CF tests on P92 steel at 873K were made, it is found out that the AMWD-based model predictions for CF are in agreement with the experimental lives with the factors of 0.9013 and 1.0600, which verifies the model has a good predictability, and the Modified model with the factors of 0.9558 and 1.0469.

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Kim, Jeonghun, and Ohbyung Kwon. "A Model for Rapid Selection and COVID-19 Prediction with Dynamic and Imbalanced Data." Sustainability 13, no. 6 (March 11, 2021): 3099. http://dx.doi.org/10.3390/su13063099.

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The COVID-19 pandemic is threatening our quality of life and economic sustainability. The rapid spread of COVID-19 around the world requires each country or region to establish appropriate anti-proliferation policies in a timely manner. It is important, in making COVID-19-related health policy decisions, to predict the number of confirmed COVID-19 patients as accurately and quickly as possible. Predictions are already being made using several traditional models such as the susceptible, infected, and recovered (SIR) and susceptible, exposed, infected, and resistant (SEIR) frameworks, but these predictions may not be accurate due to the simplicity of the models, so a prediction model with more diverse input features is needed. However, it is difficult to propose a universal predictive model globally because there are differences in data availability by country and region. Moreover, the training data for predicting confirmed patients is typically an imbalanced dataset consisting mostly of normal data; this imbalance negatively affects the accuracy of prediction. Hence, the purposes of this study are to extract rules for selecting appropriate prediction algorithms and data imbalance resolution methods according to the characteristics of the datasets available for each country or region, and to predict the number of COVID-19 patients based on these algorithms. To this end, a decision tree-type rule was extracted to identify 13 data characteristics and a discrimination algorithm was selected based on those characteristics. With this system, we predicted the COVID-19 situation in four regions: Africa, China, Korea, and the United States. The proposed method has higher prediction accuracy than the random selection method, the ensemble method, or the greedy method of discriminant analysis, and prediction takes very little time.

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Harris, B., HE Carroll, and AJ Davies. "Validation of a Parametric Constant-Life Model for Fatigue-Life Prediction for Carbon-Fibre Laminates." Advanced Composites Letters 12, no. 1 (January 2003): 096369350301200. http://dx.doi.org/10.1177/096369350301200101.

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This letter describes an experimental validation of a parametric model for the prediction of the fatigue lives of carbon-fibre composites. The constant-life model, which has been developed at Bath over a number of years, offers the possibility of predicting life for any CFRP laminate on the basis of a very limited amount of new data. It has now been tested against fatigue data from an independent laboratory for a laminate material and lay-up different from those on which the model was based. The level of accuracy of the predicted lives is high.

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Yan, Yu Tao, Zhi Li Sun, and Feng He Zhang. "Wear Life Prediction Model on 38CrMoAlA Alloy Steel." Advanced Materials Research 118-120 (June 2010): 681–85. http://dx.doi.org/10.4028/www.scientific.net/amr.118-120.681.

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The wear experimental project on load and sliding speed was instituted by the uniform design method, the wear properties of 38CrMoAlA alloy steel under dry friction were investigated on a vertical universal friction and wear tester, the worn surfaces of specimens were observed by scanning electron microscope (SEM). The wear life predication model of 38CrMoAlA alloy steel was established by multiple regression analysis, and the significance of influencing factors was carried out. The results showed that the distribution of experimental points chosen by uniform design method was in research range, and the experimental efficiency was evidently increased, and the experimental cycle was shortened. The wear mechanism of 38CrMoAlA alloy steel was primarily abrasive wear and adhesive wear. It is found that the wear life predication model was highly significant.

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Dong, Wen Li, Xue Gong, Jing Dong, Ling Jin, and Yu Xiang Wei. "Oxidation Kinetics of Hazelnut Shelf-Life Prediction Model." Applied Mechanics and Materials 200 (October 2012): 466–69. http://dx.doi.org/10.4028/www.scientific.net/amm.200.466.

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The changing patterns of filbert peroxide value through the determination of different storage temperature conditions,research the dynamics characteristics of oxidative rancidity of filbert. By regression analysis base on the storage time and the logarithm of peroxide value,it concluded the grease oxidation reaction first-order kinetic equation of filbert.Using Arrhenius equation and Q10 model for 5 ~ 35 °C temperature within the shelf life of any temperature prediction model.

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Valentín, R., Donald Barker, and Michael Osterman. "Model for life prediction of fatigue–creep interaction." Microelectronics Reliability 48, no. 11-12 (November 2008): 1831–36. http://dx.doi.org/10.1016/j.microrel.2008.05.005.

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Goswami, T. "Low cycle fatigue life prediction—a new model." International Journal of Fatigue 19, no. 2 (1997): 109–15. http://dx.doi.org/10.1016/s0142-1123(96)00065-5.

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NI, LIYONG, CHAO LIU, and CHUNGEN ZHOU. "A LIFE PREDICTION MODEL OF THERMAL BARRIER COATINGS." International Journal of Modern Physics B 24, no. 15n16 (June 30, 2010): 3161–66. http://dx.doi.org/10.1142/s0217979210066252.

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The durability and reliability of thermal barrier coatings(TBCs) have become a major concern of hot-section components due to lack of a reliable life prediction model. In this paper, it is found that the failure location of TBCs is at the TBC/TGO interface by a sequence of crack propagation and coalescence process. The critical crack length of failure samples is 8.8mm. The crack propagation rate is 3-10µm/cycle at the beginning and increases largely to 40µm/cycle near coating failure. A life prediction model based a simple fracture mechanics approach is proposed.

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Al-Rubaie, K. S. "A general model for stress-life fatigue prediction." Materialwissenschaft und Werkstofftechnik 39, no. 6 (June 2008): 400–406. http://dx.doi.org/10.1002/mawe.200800282.

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Fish, Jacob, Mahesh Bailakanavar, Lynn Powers, and Thomas Cook. "Multiscale fatigue life prediction model for heterogeneous materials." International Journal for Numerical Methods in Engineering 91, no. 10 (August 23, 2012): 1087–104. http://dx.doi.org/10.1002/nme.4307.

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Skorupa, M., T. Machniewicz, A. Skorupa, J. Schijve, and A. Korbel. "Fatigue life prediction model for riveted lap joints." Engineering Failure Analysis 53 (July 2015): 111–23. http://dx.doi.org/10.1016/j.engfailanal.2015.03.013.

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AHMAD, J., U. SANTHOSH, and S. KINNEY. "A Life Prediction Model for Metal Matrix Composites." Journal of Reinforced Plastics and Composites 19, no. 4 (January 1, 2000): 340–51. http://dx.doi.org/10.1106/x1tk-t12y-ptfd-qrnp.

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Ahmad, J., U. Santhosh, and S. Kinney. "A Life Prediction Model for Metal Matrix Composites." Journal of Reinforced Plastics and Composites 19, no. 4 (March 2000): 340–51. http://dx.doi.org/10.1177/073168440001900406.

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Kamal, M., M. M. Rahman, and M. S. M. Sani. "Fatigue Life Prediction Using Simplified Endurance Function Model." Advances in Mechanical Engineering 5 (January 2013): 581754. http://dx.doi.org/10.1155/2013/581754.

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He, Xiao Cong. "Life Prediction of Stainless Steels under Creep-Fatigue." Key Engineering Materials 413-414 (June 2009): 725–32. http://dx.doi.org/10.4028/www.scientific.net/kem.413-414.725.

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The aim of this study is to investigate the creep-fatigue behavior of stainless steel materials. Based on the elevated-temperature tensile, creep and rupture test data, thermal creep-fatigue modelling was conducted to predict the failure life of stainless steels. In the low cycle thermal fatigue life model, Manson’s Universal Slopes equation was used as an empirical correlation which relates fatigue endurance to tensile properties. Fatigue test data were used in conjunction with different modes to establish the relationship between temperature and other parameters. Then creep models were created for stainless steel materials. In order to correlate the results of short-time elevated temperature tests with long-term service performance at more moderate temperatures, different creep prediction models, namely Basquin model, Sherby-Dorn model and Manson-Haferd model, were studied. Comparison between the different creep prediction models were carried out for a range of stresses and temperatures. A linear damage summation method was used to establish life prediction model of stainless steel materials under creep-fatigue.

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Chu, Yuanming, Mingtang Tan, Zhengkai Yi, Zhaoyang Ding, Dazhang Yang, and Jing Xie. "Shelf-Life Prediction of Glazed Large Yellow Croaker (Pseudosciaena crocea) during Frozen Storage Based on Arrhenius Model and Long-Short-Term Memory Neural Networks Model." Fishes 6, no. 3 (September 10, 2021): 39. http://dx.doi.org/10.3390/fishes6030039.

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In this study, the changes in centrifugal loss, TVB-N, K-value, whiteness and sensory evaluation of glazed large yellow croaker were analyzed at −10, −20, −30 and −40 °C storage. The Arrhenius prediction model and long-short-term memory neural networks (LSTM-NN) prediction model were developed to predict the shelf-life of the glazed large yellow croaker. The results showed that the quality of glazed large yellow croaker gradually decreased with the extension of frozen storage time, and the decrease in quality slowed down at lower temperatures. Both the Arrhenius model and the LSTM-NN prediction model were good tools for predicting the shelf-life of glazed large yellow croaker. However, for the relative error, the prediction accuracy of LSTM-NN (with a mean value of 7.78%) was higher than that of Arrhenius model (with a mean value of 11.90%). Moreover, the LSTM-NN model had a more intelligent, convenient and fast data processing capability, so the new LSTM-NN model provided a better choice for predicting the shelf-life of glazed large yellow croaker.

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Lei, Dong, Ge Li, Bin Kai Shi, and Jian Hua Zhao. "An Improved Model for Predicting Fatigue Crack Initiation Life of GH4169." Applied Mechanics and Materials 29-32 (August 2010): 468–73. http://dx.doi.org/10.4028/www.scientific.net/amm.29-32.468.

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An improved model has been developed to predict fatigue crack initiation life using the criterion of minimizing the Gibbs free energy change considering plastic energy. The prediction process was described in this paper and used to predict the fatigue crack initiation life of notched GH4169 superalloy rolled bar at room temperature and 450°C. The results are acceptable for fatigue crack initiation life prediction in engineering experience and show that the improved model for predicting fatigue crack initiation life as an extension of the concept of minimizing the Gibbs free energy change considering plastic energy is adoptable to some superplastic materials such as GH4169.

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Ahn, Gilseung, Hyungseok Yun, Sun Hur, and Siyeong Lim. "A Time-Series Data Generation Method to Predict Remaining Useful Life." Processes 9, no. 7 (June 26, 2021): 1115. http://dx.doi.org/10.3390/pr9071115.

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Accurate predictions of remaining useful life (RUL) of equipment using machine learning (ML) or deep learning (DL) models that collect data until the equipment fails are crucial for maintenance scheduling. Because the data are unavailable until the equipment fails, collecting sufficient data to train a model without overfitting can be challenging. Here, we propose a method of generating time-series data for RUL models to resolve the problems posed by insufficient data. The proposed method converts every training time series into a sequence of alphabetical strings by symbolic aggregate approximation and identifies occurrence patterns in the converted sequences. The method then generates a new sequence and inversely transforms it to a new time series. Experiments with various RUL prediction datasets and ML/DL models verified that the proposed data-generation model can help avoid overfitting in RUL prediction model.

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Kang, Ziqiu, Cagatay Catal, and Bedir Tekinerdogan. "Remaining Useful Life (RUL) Prediction of Equipment in Production Lines Using Artificial Neural Networks." Sensors 21, no. 3 (January 30, 2021): 932. http://dx.doi.org/10.3390/s21030932.

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Predictive maintenance of production lines is important to early detect possible defects and thus identify and apply the required maintenance activities to avoid possible breakdowns. An important concern in predictive maintenance is the prediction of remaining useful life (RUL), which is an estimate of the number of remaining years that a component in a production line is estimated to be able to function in accordance with its intended purpose before warranting replacement. In this study, we propose a novel machine learning-based approach for automating the prediction of the failure of equipment in continuous production lines. The proposed model applies normalization and principle component analysis during the pre-processing stage, utilizes interpolation, uses grid search for parameter optimization, and is built with multilayer perceptron neural network (MLP) machine learning algorithm. We have evaluated the approach using a case study research to predict the RUL of engines on NASA turbo engine datasets. Experimental results demonstrate that the performance of our proposed model is effective in predicting the RUL of turbo engines and likewise substantially enhances predictive maintenance results.

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Li, Jianbin, Zhange Zhang, Zhichao Meng, Junzhou Huo, Zhaohui Xu, and Jing Chen. "Tunnel boring machine cutterhead crack propagation life prediction with time integration method." Advances in Mechanical Engineering 11, no. 6 (June 2019): 168781401985345. http://dx.doi.org/10.1177/1687814019853451.

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Fatigue damage is one of the most common failure modes of large-scale engineering equipment, especially the full-face tunnel boring machine with characteristics of a thick plate structure bearing strong impact load. It is difficult to predict the location and propagation life of crack of cutterhead under strong impact load. Unseasonal maintenance of equipment caused by inaccurate prediction of life cycle of cutterhead seriously affects the construction efficiency of the equipment and the life safety of the operators. Determining the crack location of tunnel boring machine cutterhead structure under strong impact load and predicting the crack propagation life are difficult scientific problems. To solve them, first, the location of the stress concentration of the cutterhead is determined by using finite element analysis method of statics. Second, prediction model for crack propagation life of tunnel boring machine cutterhead characteristic substructure based on time integration is built. And the test of crack growth of cutterhead characteristic substructure is performed. The feasibility and accuracy of the prediction model are verified by contrasting crack prediction models and the results of the test. Finally, the life prediction of tunnel boring machine cutterhead of water diversion project in Northwest Liaoning Province is carried out by using crack propagation model based on time integration. Results show that the maximum error of theoretical prediction and experimental results of crack propagation is 16%. So the feasibility of crack propagation model based on time integration in predicting the crack growth of cutterhead is verified. It is predicted that the tunnel boring machine cutterhead panel can work normally for 5.9 km under the condition of ultimate load. Building the crack propagation model considering the influence of plate thickness and strong impact load has important research value for improving the working efficiency of engineering equipment, prolonging service time, and improving the working safety.

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Riviere, Paul, Christopher Tokeshi, Jiayi Hou, Vinit Nalawade, Reith Sarkar, Anthony J. Paravati, Melody Schiaffino, Brent Rose, Ronghui Xu, and James D. Murphy. "Claims-Based Approach to Predict Cause-Specific Survival in Men With Prostate Cancer." JCO Clinical Cancer Informatics, no. 3 (December 2019): 1–7. http://dx.doi.org/10.1200/cci.18.00111.

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PURPOSE Treatment decisions about localized prostate cancer depend on accurate estimation of the patient’s life expectancy. Current cancer and noncancer survival models use a limited number of predefined variables, which could restrict their predictive capability. We explored a technique to create more comprehensive survival prediction models using insurance claims data from a large administrative data set. These data contain substantial information about medical diagnoses and procedures, and thus may provide a broader reflection of each patient’s health. METHODS We identified 57,011 Medicare beneficiaries with localized prostate cancer diagnosed between 2004 and 2009. We constructed separate cancer survival and noncancer survival prediction models using a training data set and assessed performance on a test data set. Potential model inputs included clinical and demographic covariates, and 8,971 distinct insurance claim codes describing comorbid diseases, procedures, surgeries, and diagnostic tests. We used a least absolute shrinkage and selection operator technique to identify predictive variables in the final survival models. Each model’s predictive capacity was compared with existing survival models with a metric of explained randomness (ρ2) ranging from 0 to 1, with 1 indicating an ideal prediction. RESULTS Our noncancer survival model included 143 covariates and had improved survival prediction (ρ2 = 0.60) compared with the Charlson comorbidity index (ρ2 = 0.26) and Elixhauser comorbidity index (ρ2 = 0.26). Our cancer-specific survival model included nine covariates, and had similar survival predictions (ρ2 = 0.71) to the Memorial Sloan Kettering prediction model (ρ2 = 0.68). CONCLUSION Survival prediction models using high-dimensional variable selection techniques applied to claims data show promise, particularly with noncancer survival prediction. After further validation, these analyses could inform clinical decisions for men with prostate cancer.

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Singh, Avinash. "Development and Validation of an S-N Based Two Phase Bending Fatigue Life Prediction Model." Journal of Mechanical Design 125, no. 3 (September 1, 2003): 540–44. http://dx.doi.org/10.1115/1.1564572.

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The stress-life S-N method along with the Palmgren-Miner cumulative damage theory is the simplest and the most commonly used fatigue life prediction technique. Its main advantage is that the material properties needed are easy to collect and life calculation is simple. However under many variable amplitude loading conditions, life predictions have been found to be unreliable. Various modifications have been proposed to the Palmgren-Miner theory, but they have not lead to more reliable life predictions. In this paper, a two-stage cumulative damage model will be developed and validated. This model divides fatigue life into two phases—a crack initiation phase and a crack propagation phase. It will be shown that the proposed method results in greatly improved life prediction capabilities. Also, the proposed method retains the simplicity of the S-N based approach in that the material data is still relatively simple to generate and the calculations are straightforward.

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Zhu, Shun-Peng, Hong-Zhong Huang, Victor Ontiveros, Li-Ping He, and Mohammad Modarres. "Probabilistic Low Cycle Fatigue Life Prediction Using an Energy-Based Damage Parameter and Accounting for Model Uncertainty." International Journal of Damage Mechanics 21, no. 8 (December 21, 2011): 1128–53. http://dx.doi.org/10.1177/1056789511429836.

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Probabilistic methods have been widely used to account for uncertainty of various sources in predicting fatigue life for components or materials. The Bayesian approach can potentially give more complete estimates by combining test data with technological knowledge available from theoretical analyses and/or previous experimental results, and provides for uncertainty quantification and the ability to update predictions based on new data, which can save time and money. The aim of the present article is to develop a probabilistic methodology for low cycle fatigue life prediction using an energy-based damage parameter with Bayes’ theorem and to demonstrate the use of an efficient probabilistic method, moreover, to quantify model uncertainty resulting from creation of different deterministic model parameters. For most high-temperature structures, more than one model was created to represent the complicated behaviors of materials at high temperature. The uncertainty involved in selecting the best model from among all the possible models should not be ignored. Accordingly, a black-box approach is used to quantify the model uncertainty for three damage parameters (the generalized damage parameter, Smith–Watson–Topper and plastic strain energy density) using measured differences between experimental data and model predictions under a Bayesian inference framework. The verification cases were based on experimental data in the literature for the Ni-base superalloy GH4133 tested at various temperatures. Based on the experimentally determined distributions of material properties and model parameters, the predicted distributions of fatigue life agree with the experimental results. The results show that the uncertainty bounds using the generalized damage parameter for life prediction are tighter than that of Smith–Watson–Topper and plastic strain energy density methods based on the same available knowledge.

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Chui, Kwok Tai, Brij B. Gupta, and Pandian Vasant. "A Genetic Algorithm Optimized RNN-LSTM Model for Remaining Useful Life Prediction of Turbofan Engine." Electronics 10, no. 3 (January 25, 2021): 285. http://dx.doi.org/10.3390/electronics10030285.

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Understanding the remaining useful life (RUL) of equipment is crucial for optimal predictive maintenance (PdM). This addresses the issues of equipment downtime and unnecessary maintenance checks in run-to-failure maintenance and preventive maintenance. Both feature extraction and prediction algorithm have played crucial roles on the performance of RUL prediction models. A benchmark dataset, namely Turbofan Engine Degradation Simulation Dataset, was selected for performance analysis and evaluation. The proposal of the combination of complete ensemble empirical mode decomposition and wavelet packet transform for feature extraction could reduce the average root-mean-square error (RMSE) by 5.14–27.15% compared with six approaches. When it comes to the prediction algorithm, the results of the RUL prediction model could be that the equipment needs to be repaired or replaced within a shorter or a longer period of time. Incorporating this characteristic could enhance the performance of the RUL prediction model. In this paper, we have proposed the RUL prediction algorithm in combination with recurrent neural network (RNN) and long short-term memory (LSTM). The former takes the advantages of short-term prediction whereas the latter manages better in long-term prediction. The weights to combine RNN and LSTM were designed by non-dominated sorting genetic algorithm II (NSGA-II). It achieved average RMSE of 17.2. It improved the RMSE by 6.07–14.72% compared with baseline models, stand-alone RNN, and stand-alone LSTM. Compared with existing works, the RMSE improvement by proposed work is 12.95–39.32%.

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Wang, Jialiang, Dasheng Wei, Yanrong Wang, and Xianghua Jiang. "A Fatigue Life Prediction Model Based on Modified Resolved Shear Stress for Nickel-Based Single Crystal Superalloys." Metals 9, no. 2 (February 2, 2019): 180. http://dx.doi.org/10.3390/met9020180.

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In this paper, the viewpoint that maximum resolved shear stress corresponding to the two slip systems in a nickel-based single crystal high-temperature fatigue experiment works together was put forward. A nickel-based single crystal fatigue life prediction model based on modified resolved shear stress amplitude was proposed. For the four groups of fatigue data, eight classical fatigue life prediction models were compared with the model proposed in this paper. Strain parameter is poor in fatigue life prediction as a damage parameter. The life prediction results of the fatigue life prediction model with stress amplitude as the damage parameter, the fatigue life prediction model with maximum resolved shear stress in 30 slip directions as the damage parameter, and the McDiarmid (McD) model, are better. The model proposed in this paper has higher life prediction accuracy.

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Journal articles on the topic 'Life Prediction Model'

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