Distributed Kernel Extreme Learning Machines for Aircraft Engine Failure Diagnostics

Kernel extreme learning machine (KELM) has been widely studied in the field of aircraft engine fault diagnostics due to its easy implementation. However, because its computational complexity is proportional to the training sample size, its application in time-sensitive scenarios is limited. Therefore, in the case of largescale samples, the original KELM is difficult to meet the real-time requirements of aircraft engine onboard condition. To address this shortcoming, a novel distributed kernel extreme learning machines (DKELMs) algorithm is proposed in this paper. The distributed subnetwork is adopted to reduce the computational complexity, and then the likelihood probability and Dempster-Shafer (DS) evidence theory is used to design the fusion scheme to ensure the accuracy after fusion is not reduced. Afterwards, the verification on the benchmark datasets shows that the algorithm can greatly reduce the computational complexity and improve the real-time performance of the original KELM algorithm without sacrificing the accuracy of the model. Finally, the performance estimation and fault pattern recognition experiments of an aircraft engine show that, compared with the original KELM algorithm and support vector machine (SVM) algorithm, the proposed algorithm has the best performance considering both real-time capability and model accuracy.

[1]  Ying Chen,et al.  Performance-Based Gas Turbine Health Monitoring, Diagnostics, and Prognostics: A Survey , 2018, IEEE Transactions on Reliability.

[2]  Igor Loboda,et al.  A Benchmarking Analysis of a Data-Driven Gas Turbine Diagnostic Approach , 2018 .

[3]  M. Buscema.  A BRIEF OVERVIEW AND INTRODUCTION TO ARTIFICIAL NEURAL NETWORKS , 2002, Substance use & misuse.

[4]  Syh-Shiuh Yeh,et al.  Cutting Insert and Parameter Optimization for Turning Based on Artificial Neural Networks and a Genetic Algorithm , 2019, Applied Sciences.

[5]  Arun Kumar,et al.  Liquid Metal Corrosion Fatigue (LMCF) Failure of Aircraft Engine Turbine Blades , 2018, Journal of Failure Analysis and Prevention.

[6]  Theoklis Nikolaidis,et al.  Meta-heuristic global optimization algorithms for aircraft engines modelling and controller design; A review, research challenges, and exploring the future , 2019, Progress in Aerospace Sciences.

[7]  Feng Lu,et al.  Sensor Fault Diagnosis for Aero Engine Based on Online Sequential Extreme Learning Machine with Memory Principle , 2017 .

[8]  Y. Zhao,et al.  Fast leave-one-out evaluation for dynamic gene selection , 2006 .

[9]  Shazaib Ahsan,et al.  Prognosis of gas turbine remaining useful life using particle filter approach , 2019 .

[10]  K. Goebel,et al.  Rapid detection of faults for safety critical aircraft operation , 2004, 2004 IEEE Aerospace Conference Proceedings (IEEE Cat. No.04TH8720).

[11]  Rolf Isermann,et al.  Model-based fault-detection and diagnosis - status and applications , 2004, Annu. Rev. Control..

[12]  Lei Zhao,et al.  Adaptive modeling of aircraft engine performance degradation model based on the equilibrium manifold and expansion form , 2014 .

[13]  Marcin Wolkiewicz,et al.  Application of Self-Organizing Neural Networks to Electrical Fault Classification in Induction Motors , 2019, Applied Sciences.

[14]  Feliks Stachowicz,et al.  Fatigue analysis of compressor blade with simulated foreign object damage , 2015 .

[15]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[16]  Shisheng Zhong,et al.  A model learning strategy adapted to health assessment of multi-component systems , 2017, 2017 Prognostics and System Health Management Conference (PHM-Harbin).

[17]  Xinyang Deng,et al.  A Novel Network Security Risk Assessment Approach by Combining Subjective and Objective Weights under Uncertainty , 2018 .

[18]  Zhi Tao,et al.  Design on Structural Test and Modeling of the Mounting Structure of a GTF Aircraft Engine , 2014 .

[19]  Francisco Javier de Cos Juez,et al.  A hybrid ARIMA-SVM model for the study of the remaining useful life of aircraft engines , 2019, J. Comput. Appl. Math..

[20]  Huaqing Wang,et al.  A Rolling Bearing Fault Diagnosis Method Based on Variational Mode Decomposition and an Improved Kernel Extreme Learning Machine , 2017 .

[21]  Carlo Sansone,et al.  Electrocardiogram pattern recognition and analysis based on artificial neural networks and support vector machines: a review. , 2013, Journal of healthcare engineering.

[22]  Shuo Li,et al.  A Data-driven Approach for Remaining Useful Life Prediction of Aircraft Engines , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[23]  Sanyam Shukla,et al.  Analysis of statistical features for fault detection in ball bearing , 2015, 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC).

[24]  Joseba Zubia,et al.  Architecture for Measuring Blade Tip Clearance and Time of Arrival with Multiple Sensors in Airplane Engines , 2018 .

[25]  Feng Lu,et al.  Recursive reduced kernel based extreme learning machine for aero-engine fault pattern recognition , 2016, Neurocomputing.

[26]  Donald L. Simon,et al.  Enhanced Self Tuning On-Board Real-Time Model (eSTORM) for Aircraft Engine Performance Health Tracking , 2008 .

[27]  Jacek M. Zurada,et al.  Review and performance comparison of SVM- and ELM-based classifiers , 2014, Neurocomputing.

[28]  Pericles Pilidis,et al.  Multi-Objective Climb Path Optimization for Aircraft/Engine Integration Using Particle Swarm Optimization , 2017 .

[29]  Mohammadreza Tahan,et al.  Performance-based health monitoring, diagnostics and prognostics for condition-based maintenance of gas turbines: A review , 2017 .

[30]  Jonathan S. Litt,et al.  Online Model Parameter Estimation of Jet Engine Degradation for Autonomous Propulsion Control , 2003 .

[31]  Chee Kheong Siew,et al.  Extreme learning machine: Theory and applications , 2006, Neurocomputing.

[32]  Ruxandra Botez,et al.  Generic New Modeling Technique for Turbofan Engine Thrust , 2013 .

[33]  Chi-Man Vong,et al.  Fast detection of impact location using kernel extreme learning machine , 2014, Neural Computing and Applications.

[34]  Jonathan S. Litt,et al.  A Modular Aero-Propulsion System Simulation of a Large Commercial Aircraft Engine , 2008 .

[35]  Feng Lu,et al.  Time Series Prediction Based on Adaptive Weight Online Sequential Extreme Learning Machine , 2017 .

[36]  Hongming Zhou,et al.  Extreme Learning Machine for Regression and Multiclass Classification , 2012, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[37]  James Allen Fill,et al.  The Moore-Penrose Generalized Inverse for Sums of Matrices , 1999, SIAM J. Matrix Anal. Appl..

[38]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[39]  Changduk Kong,et al.  Review on Advanced Health Monitoring Methods for Aero Gas Turbines using Model Based Methods and Artificial Intelligent Methods , 2014 .