Physics of failure-based reliability prediction of turbine blades using multi-source information fusion
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Hong-Zhong Huang | He Li | Jie Zhou | Jinhua Mi | Yan-Feng Li | Hongzhong Huang | Yanfeng Li | He Li | Jie Zhou | J. Mi
[1] Ying Peng,et al. Current status of machine prognostics in condition-based maintenance: a review , 2010 .
[2] Li Lin,et al. Remaining useful life estimation of engineered systems using vanilla LSTM neural networks , 2018, Neurocomputing.
[3] Pasquale Erto. New Practical Bayes Estimators for the 2-Parameter Weibull Distribution , 1982, IEEE Transactions on Reliability.
[4] Wei Zhang,et al. A new method for astronautically phased mission system reliability assessment based on multi-source data fusion , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).
[5] K. Srinivasan,et al. Failure analysis of an un-cooled turbine blade in an aero gas turbine engine , 2017 .
[6] Peng Wang,et al. Reliability prediction based on degradation modeling for systems with multiple degradation measures , 2004, Annual Symposium Reliability and Maintainability, 2004 - RAMS.
[7] Jiye Liang,et al. An information fusion approach by combining multigranulation rough sets and evidence theory , 2015, Inf. Sci..
[8] Weiwen Peng,et al. Probabilistic Physics of Failure-based framework for fatigue life prediction of aircraft gas turbine discs under uncertainty , 2016, Reliab. Eng. Syst. Saf..
[9] Geert Willems,et al. Improved and accurate physics-of-failure (PoF) methodology for qualification and lifetime assessment of electronic systems , 2017, Microelectron. Reliab..
[10] K. Chandrashekhara,et al. Reliability-based fatigue life investigation for a medium-scale composite hydrokinetic turbine blade , 2014 .
[11] Ming Jian Zuo,et al. A new adaptive sequential sampling method to construct surrogate models for efficient reliability analysis , 2018, Reliab. Eng. Syst. Saf..
[12] André Bigand,et al. Membership function construction for interval-valued fuzzy sets with application to Gaussian noise reduction , 2016, Fuzzy Sets Syst..
[13] Hong-Zhong Huang,et al. An Application of Fuzzy Fault Tree Analysis to Uncontained Events of an Areo-Engine Rotor , 2012 .
[14] S.J. Schreck,et al. Horizontal Axis Wind Turbine Blade Aerodynamics in Experiments and Modeling , 2007, IEEE Transactions on Energy Conversion.
[15] Hong-Zhong Huang,et al. Reliability assessment of multi-state phased mission system with non-repairable multi-state components , 2018, Applied Mathematical Modelling.
[16] Noureddine Zerhouni,et al. A New Multivariate Approach for Prognostics Based on Extreme Learning Machine and Fuzzy Clustering , 2015, IEEE Transactions on Cybernetics.
[17] Ming J. Zuo,et al. Efficient reliability analysis based on adaptive sequential sampling design and cross-validation , 2018, Applied Mathematical Modelling.
[18] Yunze He,et al. Unsupervised Sparse Pattern Diagnostic of Defects With Inductive Thermography Imaging System , 2016, IEEE Transactions on Industrial Informatics.
[19] Martin Newby,et al. Bayesian reliability analysis with imprecise prior probabilities , 1992 .
[20] Ronald R. Yager,et al. A framework for multi-source data fusion , 2004, Inf. Sci..
[21] W. Y. Liu,et al. The structure healthy condition monitoring and fault diagnosis methods in wind turbines: A review , 2015 .
[22] Douglas Probert,et al. Implications of engine's deterioration upon an aero-engine HP turbine blade's thermal fatigue life , 2000 .
[23] Riti Singh,et al. Aero-engine turbine blade life assessment using the Neu/Sehitoglu damage model , 2014 .
[24] G. Krishnan,et al. Microstructural-based physics of failure models to predict fatigue reliability , 2006, RAMS '06. Annual Reliability and Maintainability Symposium, 2006..
[25] Theoklis Nikolaidis,et al. Effect of Fouling, Thermal Barrier Coating Degradation and Film Cooling Holes Blockage on Gas Turbine Engine Creep Life , 2015 .
[26] David J Smith,et al. Reliability, Maintainability and Risk: Practical Methods for Engineers , 1993 .
[27] Weiwen Peng,et al. Reliability analysis of complex multi-state system with common cause failure based on evidential networks , 2018, Reliab. Eng. Syst. Saf..
[28] Wang Yan-rong. Structure Reliability Evaluation of a Gas Turbine Disk , 2005 .
[29] Yongming Liu,et al. Physics-of-failure-based reliability and life prediction for critical components , 2017 .
[30] Fakhri Karray,et al. Multisensor data fusion: A review of the state-of-the-art , 2013, Inf. Fusion.
[31] Dale A. Lambert,et al. Consensus: A comprehensive solution to the grand challenges of information fusion , 2015, 2015 18th International Conference on Information Fusion (Fusion).
[32] Bruce R. Ellingwood,et al. Reliability-Based Service-Life Assessment of Aging Concrete Structures , 1993 .
[33] Mohammad yaghoub Abdollahzadeh Jamalabadi. Thermal radiation effects on creep behavior of the turbine blade , 2016 .
[34] Hong-Zhong Huang,et al. A discrete stress-strength interference model based on universal generating function , 2008, Reliab. Eng. Syst. Saf..
[35] Bongtae Han,et al. Physics-of-Failure, Condition Monitoring, and Prognostics of Insulated Gate Bipolar Transistor Modules: A Review , 2015, IEEE Transactions on Power Electronics.
[36] Igor Loboda,et al. Improvement of turbine blade lifetime assessment by more accurate estimation of the thermal boundary conditions , 2017 .
[37] Jie Liu,et al. Developing machine learning-based models to estimate time to failure for PHM , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).
[38] Udo Nackenhorst,et al. Fatigue life estimation of aero engine mount structure using Monte Carlo simulation , 2016 .
[39] Li Wenya,et al. Reliability Analysis of Airborne Navigation for Unmanned Aerial Vehicle Based on Multi Source Information Fusion , 2016 .
[40] Muhammad Naeem. Implications of Day Temperature for a High-Pressure-Turbine Blade's Low-Cycle-Fatigue Life Consumption , 2008 .
[41] John Dalsgaard Sørensen,et al. Physics of failure as a basis for solder elements reliability assessment in wind turbines , 2012, Reliab. Eng. Syst. Saf..
[42] Hui Ma,et al. Multi-source information fusion for power transformer condition assessment , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).
[43] Fengpo Du,et al. The Application of Information Fusion in Reliability Evaluation of Complex Electromechanical System , 2015 .
[44] Ronald P. S. Mahler,et al. Statistical Multisource-Multitarget Information Fusion , 2007 .
[45] Noureddine Zerhouni,et al. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction , 2016, J. Intell. Manuf..
[46] B. Rama,et al. FATIGUE ANALYSIS AND DESIGN OF DIFFERENT COMPRESSOR ROTOR BLADE OF AN ORPHEUS ENGINE , 2013 .
[47] Qiang Miao,et al. Improved information fusion approach based on D-S evidence theory , 2008 .
[48] Weiwen Peng,et al. Reliability of complex systems under dynamic conditions: A Bayesian multivariate degradation perspective , 2016, Reliab. Eng. Syst. Saf..
[49] Mohammad Pourgol-Mohammad,et al. Effect of strength dispersion on fatigue life prediction of composites under two-stage loading , 2015 .
[50] Gian Antonio Susto,et al. Machine Learning for Predictive Maintenance: A Multiple Classifier Approach , 2015, IEEE Transactions on Industrial Informatics.
[51] Dan Simon,et al. Multirate multisensor data fusion for linear systems using Kalman filters and a neural network , 2014 .
[52] Zoubin Ghahramani,et al. Probabilistic machine learning and artificial intelligence , 2015, Nature.
[53] Guang-Bin Huang,et al. Trends in extreme learning machines: A review , 2015, Neural Networks.
[54] Noureddine Zerhouni,et al. Enabling Health Monitoring Approach Based on Vibration Data for Accurate Prognostics , 2015, IEEE Transactions on Industrial Electronics.
[55] Mohd Fikri Mohd Masrom,et al. Impact of Operating and Health Conditions on a Helicopter Turbo-Shaft Hot Section Component Using Creep Factor , 2012 .
[56] Jürgen Schmidhuber,et al. Deep learning in neural networks: An overview , 2014, Neural Networks.
[57] Wang Jian. Research on low cycle-multiaxial fatigue-creep life prediction at high temperature for turbine blade , 2009 .
[58] Francisco Herrera,et al. A Historical Account of Types of Fuzzy Sets and Their Relationships , 2016, IEEE Transactions on Fuzzy Systems.
[59] Mohammad Modarres,et al. Probabilistic Physics of Failure Approach to Reliability: Modeling, Accelerated Testing, Prognosis and Reliability Assessment , 2017 .
[60] Meng Joo Er,et al. Data driven modeling based on dynamic parsimonious fuzzy neural network , 2013, Neurocomputing.
[61] Hong-Zhong Huang,et al. Lifetime prediction for turbine discs based on a modified Walker strain model , 2015 .
[62] Muhammad Naeem,et al. Implications of day temperature variation for an aero-engine's HP turbine-blade's creep life-consumption , 2009 .
[63] Mahardhika Pratama,et al. Metacognitive learning approach for online tool condition monitoring , 2017, Journal of Intelligent Manufacturing.
[64] Jorge A. Balazs,et al. Opinion Mining and Information Fusion: A survey , 2016, Inf. Fusion.
[65] Hong-Zhong Huang,et al. Fatigue Life Prediction of Fan Blade Using Nominal Stress Method and Cumulative Fatigue Damage Theory , 2020 .
[66] Xiang Li,et al. Machine health condition prediction via online dynamic fuzzy neural networks , 2014, Eng. Appl. Artif. Intell..
[67] Qian Fan,et al. Multi-source information fusion based fault diagnosis of ground-source heat pump using Bayesian network , 2014 .
[68] So Young Sohn,et al. Technology credit scoring model with fuzzy logistic regression , 2016, Appl. Soft Comput..
[69] Sina Sharif Mansouri,et al. Remaining Useful Battery Life Prediction for UAVs based on Machine Learning , 2017 .
[70] Yunze He,et al. Multidimensional Tensor-Based Inductive Thermography With Multiple Physical Fields for Offshore Wind Turbine Gear Inspection , 2016, IEEE Transactions on Industrial Electronics.