Physics of failure-based reliability prediction of turbine blades using multi-source information fusion

Abstract Fatigue and fracture of turbine blades are fatal to aero engines. Reliability prediction of aero engines is indispensable to guarantee their safety. For turbine blades of aero engines, most recent research works only focus on the number of cycles and excavate information from a single source. To remove these limitations, a Physics of failure-based reliability prediction method using multi-source information fusion has been developed in this paper to predict the reliability of turbine blades of aero engines. In the proposed method, the fuzzy theory is employed to represent uncertainties involved in prediction. Case studies of reliability prediction under fuzzy stress with and without fuzzy strength are conducted by using a dynamic stress-strength interference model which takes types of cycles of aero engines into consideration. Results indicate that the proposed method is better in line with engineering practice and more flexible in decision making and it can predict the reliability of aero engine turbine blades to be an interval by utilizing the proposed linear fusion algorithm. In addition, the predicted interval contains results that are predicted by other commonly used information fusion methods Hence, the proposed method conduces to remove confusion made by selection of multiple methods.

[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.