Group reduced kernel extreme learning machine for fault diagnosis of aircraft engine

Abstract The original kernel extreme learning machine (KELM) employs all training samples to construct hidden layer, thus avoiding the performance fluctuations caused by the ELM randomly assigning weights. However, excessive nodes will inevitably lead to structural redundancy, which hinders its application in systems with high real-time performance requirements but limited onboard storage and computing capacity. Considering the well interpretability of sparse learning, this study introduces the group sparse structure for KELM to resolve its limitation of structural redundancy. Specifically, the proposed novel method introduces a special norm to reformulate the dual optimization problem of KELM to realize group sparse structure in output weights. As a result, nodes with large weights can be selected as the significant nodes, while nodes with small weights will be regarded as the redundant nodes and neglected directly. In addition, we have also devised an alternating iterative optimization algorithm and deduced the complete proof of convergence to solve the non-smoothness optimization problem in proposed method. Then, the validity and feasibility of the proposed method are verified by extensive experiments on benchmark datasets. More importantly, tests of fault diagnosis for an aircraft engine show that the proposed approach can maintain the competitive recognition performance with much faster testing speed.

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

[2]  Allen Y. Yang,et al.  Robust Face Recognition via Sparse Representation , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Chris H. Q. Ding,et al.  R1-PCA: rotational invariant L1-norm principal component analysis for robust subspace factorization , 2006, ICML.

[4]  Feng Lu,et al.  An improved particle filtering algorithm for aircraft engine gas-path fault diagnosis , 2016 .

[5]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[6]  Pierre Dardenne,et al.  Validation and verification of regression in small data sets , 1998 .

[7]  Aytaç Altan,et al.  Real-Time Control based on NARX Neural Network of Hexarotor UAV with Load Transporting System for Path Tracking , 2018, 2018 6th International Conference on Control Engineering & Information Technology (CEIT).

[8]  Johan A. K. Suykens,et al.  Least Squares Support Vector Machine Classifiers , 1999, Neural Processing Letters.

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

[10]  Vladimir Cherkassky,et al.  The Nature Of Statistical Learning Theory , 1997, IEEE Trans. Neural Networks.

[11]  Yang Hu,et al.  Fault detection based on signal reconstruction with Auto-Associative Extreme Learning Machines , 2017, Eng. Appl. Artif. Intell..

[12]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[13]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[14]  Alexandros Iosifidis,et al.  On the kernel Extreme Learning Machine classifier , 2015, Pattern Recognit. Lett..

[15]  Jianbo Yu,et al.  Aircraft engine health prognostics based on logistic regression with penalization regularization and state-space-based degradation framework , 2017 .

[16]  Zhe Yang,et al.  Soft extreme learning machine for fault detection of aircraft engine , 2019, Aerospace Science and Technology.

[17]  Yew-Soon Ong,et al.  A Fast Reduced Kernel Extreme Learning Machine , 2016, Neural Networks.

[18]  Allan J. Volponi,et al.  Gas Turbine Engine Health Management: Past, Present, and Future Trends , 2014 .

[19]  Enrico Zio,et al.  Artificial intelligence for fault diagnosis of rotating machinery: A review , 2018, Mechanical Systems and Signal Processing.

[20]  Peter J. Fleming,et al.  Gas turbine engine prognostics using Bayesian hierarchical models: A variational approach , 2016 .

[21]  Yong-Ping Zhao,et al.  Parsimonious kernel extreme learning machine in primal via Cholesky factorization , 2016, Neural Networks.

[22]  Yaguo Lei,et al.  Applications of machine learning to machine fault diagnosis: A review and roadmap , 2020 .

[23]  A. Ray,et al.  Anomaly detection for health management of aircraft gas turbine engines , 2005, Proceedings of the 2005, American Control Conference, 2005..

[24]  Katta G. Murty,et al.  Nonlinear Programming Theory and Algorithms , 2007, Technometrics.

[25]  Qiang Liu,et al.  Random Fourier extreme learning machine with ℓ2, 1-norm regularization , 2016, Neurocomputing.

[26]  Zhe Yang,et al.  Extended least squares support vector machine with applications to fault diagnosis of aircraft engine. , 2020, ISA transactions.

[27]  S. Borguet,et al.  Comparison of adaptive filters for gas turbine performance monitoring , 2010, J. Comput. Appl. Math..

[28]  Christopher M. Bishop,et al.  Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[29]  Zhi-Qiang Li,et al.  Least squares support vector machine for class imbalance learning and their applications to fault detection of aircraft engine , 2019, Aerospace Science and Technology.

[30]  Yong-Ping Zhao,et al.  A size-transferring radial basis function network for aero-engine thrust estimation , 2020, Eng. Appl. Artif. Intell..

[31]  Kewei Cheng,et al.  Feature Selection , 2016, ACM Comput. Surv..

[32]  Johan A. K. Suykens,et al.  Bayesian Framework for Least-Squares Support Vector Machine Classifiers, Gaussian Processes, and Kernel Fisher Discriminant Analysis , 2002, Neural Computation.

[33]  Zhiqiang Ge,et al.  Dynamic Bayesian network for robust latent variable modeling and fault classification , 2020, Eng. Appl. Artif. Intell..

[34]  Belarmino Pulido Junquera,et al.  State space neural networks and model-decomposition methods for fault diagnosis of complex industrial systems , 2019, Eng. Appl. Artif. Intell..

[35]  Francesco Ferracuti,et al.  Statistical Spectral Analysis for Fault Diagnosis of Rotating Machines , 2018, IEEE Transactions on Industrial Electronics.

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

[37]  Wei Jiang,et al.  Fast sparse approximation of extreme learning machine , 2014, Neurocomputing.

[38]  Feiping Nie,et al.  Efficient and Robust Feature Selection via Joint ℓ2, 1-Norms Minimization , 2010, NIPS.

[39]  Dan Simon,et al.  Constrained Kalman filtering via density function truncation for turbofan engine health estimation , 2010, Int. J. Syst. Sci..

[40]  Aytaç Altan,et al.  Recognition Model for Solar Radiation Time Series based on Random Forest with Feature Selection Approach , 2019, 2019 11th International Conference on Electrical and Electronics Engineering (ELECO).

[41]  Corinna Cortes,et al.  Support-Vector Networks , 1995, Machine Learning.

[42]  N. Meinshausen,et al.  High-dimensional graphs and variable selection with the Lasso , 2006, math/0608017.

[43]  Aytac Altan,et al.  Model predictive control of three-axis gimbal system mounted on UAV for real-time target tracking under external disturbances , 2020 .

[44]  Nitish Srivastava,et al.  Improving neural networks by preventing co-adaptation of feature detectors , 2012, ArXiv.

[45]  Guang-Bin Huang,et al.  An Insight into Extreme Learning Machines: Random Neurons, Random Features and Kernels , 2014, Cognitive Computation.

[46]  Vishal Sethi,et al.  Optimization of Aero Gas Turbine Maintenance Using Advanced Simulation and Diagnostic Methods , 2014 .