An Adaptive Model-Based Framework for Prognostics of Gas Path Faults in Aircraft Gas Turbine Engines
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Abstract
This paper presents an adaptive framework for prognostics in civil aero gas turbine engines, which incorporates both performance and degradation models, to predict the remaining useful life of the engine components that fail predominantly by gradual deterioration over time. Sparse information about the engine configuration is used to adapt a performance model, which serves as a baseline for implementing optimum sensor selection, operating data correction, fault isolation, noise reduction and component health diagnostics using nonlinear Gas Path Analysis (GPA). Degradation models, which describe the progression of faults until failure, are then applied to the diagnosed component health indices from previous run-to-failure cases. These models constitute a training library from which fitness evaluation to the current test case is done. The final remaining useful life (RUL) prediction is obtained as a weighted sum of individually evaluated RULs for each training case. This approach is validated using dataset generated by the Commercial Modular Aero-Propulsion System Simulation (CMAPSS) software, which comprises both training and testing instances of run-to-failure sensor data for a turbofan engine, some of which are obtained at different operating conditions and for multiple fault modes. The results demonstrate the capability of improved prognostics of faults in aircraft engine turbomachinery using models of system behavior, with continuous health monitoring data.
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Gas turbine, Performance, Condition Health Monitoring, Model-Based Prognostics, Remaining Useful Life, CMAPSS, Gas Path Analysis
Baraldi, P. et al., 2013. Model-based and data-driven prognostics under different available information. Probabilistic Engineering Mechanics, 32, pp.66–79. Available at: http://dx.doi.org/10.1016/j.probengmech.2013.01.003.
Biagetti, T. & Sciubba, E., 2004. Automatic diagnostics and prognostics of energy conversion processes via knowledge-based systems. Energy, 29(12–15 SPEC. ISS.), pp.2553–2572.
Chen, M., Quan Hu, L. & Tang, H., 2015. An Approach for Optimal Measurements Selection on Gas Turbine Engine Fault Diagnosis. Journal of Engineering for Gas Turbines and Power, 137(7), p.71203. Available at: http://gasturbinespower.asmedigitalcollection.asme.org/article.aspx?doi=10.1115/1.4029171.
Coble, J. & Hines, J.W., 2014. Incorporating prior belief in the general path model: A comparison of information sources. Nuclear Engineering and Technology, 46(6), pp.773–782. Available at: http://dx.doi.org/10.5516/NET.04.2014.722.
Coble, J.B. & Hines, J.W., 2008. Prognostic algorithm categorization with PHM challenge application. 2008 International Conference on Prognostics and Health Management, PHM 2008.
Cubillo, A., Perinpanayagam, S. & Esperon-miguez, M., 2016. A review of physics-based models in prognostics : Application to gears and bearings of rotating machinery. , 8(8), pp.1–21.
Decastro, J.A., Litt, J.S. & Frederick, D.K., 2008. A Modular Aero-Propulsion System Simulation of a Large Commercial Aircraft Engine. , (September).
DePold, H. & Gass, F.D., 1999. The Application of Expert Systems and Neural Networks to Gas Turbine Prognostics and Diagnostics. Journal of Engineering for Gas Turbines and Power, 121, p.607.
Frederick, D.K., Decastro, J.A. & Litt, J.S., 2007. User ’ s Guide for the Commercial Modular Aero-Propulsion System Simulation ( C-MAPSS ) October 2007. , (October).
Heimes, F.O., 2008. Recurrent Neural Networks for Remaining Useful Life Estimation. Prognostics and Health Management, 2008. PHM 2008. International Conference on, pp.1–6. Available at: http://ieeexplore.ieee.org.etechconricyt.idm.oclc.org/stamp/stamp.jsp?tp=&arnumber=4711422.
Jasmani, M.S., Li, Y.-G. & Ariffin, Z., 2010. Measurement Selection for Multi-Component Gas Path Diagnostics using Analytical Approach and Measurement Subset Concept. In Proceedings of ASME Turbo Expo 2010: Power for Land, Sea and AIr. Glasgow, UK.
Li, Y.G. & Nilkitsaranont, P., 2009. Gas turbine performance prognostic for condition-based maintenance. Applied Energy, 86(10), pp.2152–2161. Available at: http://dx.doi.org/10.1016/j.apenergy.2009.02.011.
Mosallam, A., Medjaher, K. & Zerhouni, N., 2016. Data-driven prognostic method based on Bayesian approaches for direct remaining useful life prediction. Journal of Intelligent Manufacturing, pp.1037–1048. Available at: http://dx.doi.org/10.1007/s10845-014-0933-4.
Peel, L. & Gold, I., 2008. Data Driven Prognostics using a Kalman Filter Ensemble of Neural Network Models. , pp.1–6.
Ramasso, E., 2014. Investigating computational geometry for failure prognostics. , pp.1–18.
Ramasso, E. & Saxena, A., 2014. Performance Benchmarking and Analysis of Prognostic Methods for CMAPSS Datasets. , pp.1–15.
SAE International, 2013. Prognostics for Gas Turbine Engines - AIR5871, Available at: https://doi.org/10.4271/AIR5871.
Saha, B. & Goebel, K., 2011. Model Adaptation for Prognostics in a Particle Filtering Framework. , pp.1–10.
Saxena, A. et al., 2008. Damage Propagation Modeling for Aircraft Engine Prognostics. Proceedings of IEEE International Conference on Prognostics and Health Management, pp.1–9.
Saxena, A. & Goebel, K., 2008a. C-MPASS data set. NASA Ames Prognostics Data Repository.
Saxena, A. & Goebel, K., 2008b. Turbofan Engine Degradation Simulation Data Set, Moffett Field, CA.
Sikorska, J.Z., Hodkiewicz, M. & Ma, L., 2011. Prognostic modelling options for remaining useful life estimation by industry. Mechanical Systems and Signal Processing, 25(5), pp.1803–1836.
Wang, T. et al., 2008. A Similarity-Based Prognostics Approach for Engineered Systems. In International Conference on Prognostics and Health Management.
Xu, J., Wang, Y. & Xu, L., 2014. PHM-oriented integrated fusion prognostics for aircraft engines based on sensor data. IEEE Sensors Journal, 14(4).
Yu, J., 2017. Aircraft engine health prognostics based on logistic regression with penalization regularization and state-space-based degradation framework. Aerospace Science and Technology, 68, pp.345–361. Available at: http://dx.doi.org/10.1016/j.ast.2017.05.030.