Fault diagnosis of rotating machinery with ensemble kernel extreme learning machine based on fused multi-domain features.

Accurate and reliable fault diagnosis for rotating machinery, especially under variable working conditions remains a great challenge. Existing deep learning methods which extract features from single domain are insufficient to ensure reliable diagnosis results. In this study, a new deep learning based fault diagnosis method, which extracts features from both time and frequency domains is proposed. Two sets of deep features from multiple domains are fused into intrinsic low-dimensional features by local and global principle component analysis. And a new ensemble kernel extreme learning machine is proposed for fault pattern classification based on the fused features. Extensive experiments on gearbox, rotor and engine rolling bearing show that the proposed method has better diagnosis performance than state-of-the-art methods and is more adaptable to the fluctuation of working conditions.

[1]  Haidong Shao,et al.  Rolling bearing fault diagnosis using adaptive deep belief network with dual-tree complex wavelet packet. , 2017, ISA transactions.

[2]  Sanjay H Upadhyay,et al.  A review on signal processing techniques utilized in the fault diagnosis of rolling element bearings , 2016 .

[3]  Shan Sung Liew,et al.  Bounded activation functions for enhanced training stability of deep neural networks on visual pattern recognition problems , 2016, Neurocomputing.

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

[5]  Xiaoming Xue,et al.  A hybrid fault diagnosis approach based on mixed-domain state features for rotating machinery. , 2017, ISA transactions.

[6]  Tommy W. S. Chow,et al.  Motor Bearing Fault Diagnosis Using Trace Ratio Linear Discriminant Analysis , 2014, IEEE Transactions on Industrial Electronics.

[7]  Xiaofeng Zhang,et al.  Aero Engine Component Fault Diagnosis Using Multi-Hidden-Layer Extreme Learning Machine with Optimized Structure , 2016 .

[8]  Xiaoli Zhang,et al.  Intelligent fault diagnosis of roller bearings with multivariable ensemble-based incremental support vector machine , 2015, Knowl. Based Syst..

[9]  Gaoliang Peng,et al.  A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load , 2018, Mechanical Systems and Signal Processing.

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

[11]  Diego Cabrera,et al.  Multimodal deep support vector classification with homologous features and its application to gearbox fault diagnosis , 2015, Neurocomputing.

[12]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[13]  Yaguo Lei,et al.  Fault detection of planetary gearboxes using new diagnostic parameters , 2012 .

[14]  Xin Zhou,et al.  Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data , 2016 .

[15]  Nan Liu,et al.  Voting based extreme learning machine , 2012, Inf. Sci..

[16]  Cong Wang,et al.  Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings , 2016 .

[17]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[18]  Haibo He,et al.  Stacked Multilevel-Denoising Autoencoders: A New Representation Learning Approach for Wind Turbine Gearbox Fault Diagnosis , 2017, IEEE Transactions on Instrumentation and Measurement.

[19]  Jian Sun,et al.  Delving Deep into Rectifiers: Surpassing Human-Level Performance on ImageNet Classification , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).

[20]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[21]  Brigitte Chebel-Morello,et al.  Application of empirical mode decomposition and artificial neural network for automatic bearing fault diagnosis based on vibration signals , 2015 .

[22]  Chi-Man Vong,et al.  Sparse Bayesian extreme learning committee machine for engine simultaneous fault diagnosis , 2016, Neurocomputing.

[23]  David Zhang,et al.  SVM and ELM: Who Wins? Object Recognition with Deep Convolutional Features from ImageNet , 2015, ArXiv.

[24]  Farhat Fnaiech,et al.  Application of higher order spectral features and support vector machines for bearing faults classification. , 2015, ISA transactions.

[25]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[26]  Yicong Zhou,et al.  Extreme Learning Machine With Composite Kernels for Hyperspectral Image Classification , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[27]  Huaqing Wang,et al.  Automatic diagnosis method for structural fault of rotating machinery based on distinctive frequency components and support vector machines under varied operating conditions , 2013, Neurocomputing.

[28]  Jinde Cao,et al.  Robust fixed-time synchronization for uncertain complex-valued neural networks with discontinuous activation functions , 2017, Neural Networks.

[29]  Xiaofei He,et al.  Locality Preserving Projections , 2003, NIPS.

[30]  P. Konar,et al.  Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs) , 2011, Appl. Soft Comput..

[31]  Haidong Shao,et al.  A novel deep autoencoder feature learning method for rotating machinery fault diagnosis , 2017 .

[32]  Yoshua Bengio,et al.  Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.

[33]  Chen Lu,et al.  Fault diagnosis of rotary machinery components using a stacked denoising autoencoder-based health state identification , 2017, Signal Process..

[34]  Diego Cabrera,et al.  Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal , 2015, Sensors.

[35]  Haidong Shao,et al.  An enhancement deep feature fusion method for rotating machinery fault diagnosis , 2017, Knowl. Based Syst..

[36]  Diego Cabrera,et al.  Automatic feature extraction of time-series applied to fault severity assessment of helical gearbox in stationary and non-stationary speed operation , 2017, Appl. Soft Comput..

[37]  Chen Lu,et al.  Fault diagnosis for rotary machinery with selective ensemble neural networks , 2017, Mechanical Systems and Signal Processing.

[38]  H. Zha,et al.  Principal manifolds and nonlinear dimensionality reduction via tangent space alignment , 2004, SIAM J. Sci. Comput..

[39]  Qingguo Chen,et al.  Method of assessing the state of a rolling bearing based on the relative compensation distance of multiple-domain features and locally linear embedding , 2017 .

[40]  Liang Chen,et al.  Hierarchical adaptive deep convolution neural network and its application to bearing fault diagnosis , 2016 .

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

[42]  Nibaldo Rodríguez,et al.  Stationary Wavelet Singular Entropy and Kernel Extreme Learning for Bearing Multi-Fault Diagnosis , 2017, Entropy.

[43]  Liang Gao,et al.  A new subset based deep feature learning method for intelligent fault diagnosis of bearing , 2018, Expert Syst. Appl..

[44]  Yun Zhang,et al.  Analysis of Feature Extracting Ability for Cutting State Monitoring Using Deep Belief Networks , 2015 .

[45]  Yaguo Lei,et al.  A multidimensional hybrid intelligent method for gear fault diagnosis , 2010, Expert Syst. Appl..

[46]  Steven Verstockt,et al.  Convolutional Neural Network Based Fault Detection for Rotating Machinery , 2016 .

[47]  Qingbo He,et al.  A fusion feature and its improvement based on locality preserving projections for rolling element bearing fault classification , 2015 .

[48]  Yunchao Wei,et al.  Deep Learning with S-Shaped Rectified Linear Activation Units , 2015, AAAI.

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

[50]  Minxia Luo,et al.  Ensemble extreme learning machine and sparse representation classification , 2016, J. Frankl. Inst..

[51]  Dianhui Wang,et al.  Extreme learning machines: a survey , 2011, Int. J. Mach. Learn. Cybern..

[52]  Lei Wang,et al.  Multiple kernel extreme learning machine , 2015, Neurocomputing.

[53]  Wei Jiang,et al.  Fault diagnosis of rolling bearings with recurrent neural network-based autoencoders. , 2018, ISA transactions.

[54]  Qingbo He,et al.  Time–frequency manifold for nonlinear feature extraction in machinery fault diagnosis , 2013 .

[55]  Haidong Shao,et al.  A novel method for intelligent fault diagnosis of rolling bearings using ensemble deep auto-encoders , 2018 .

[56]  Jianbo Yu,et al.  Local and global principal component analysis for process monitoring , 2012 .

[57]  Qin Hu,et al.  Fault Diagnosis Based on Weighted Extreme Learning Machine With Wavelet Packet Decomposition and KPCA , 2018, IEEE Sensors Journal.

[58]  Liyanaarachchi Lekamalage Chamara Kasun,et al.  Generic Object Recognition with Local Receptive Fields Based Extreme Learning Machine , 2015, INNS Conference on Big Data.

[59]  Mustafa Demetgul,et al.  Fault diagnosis of rolling bearings using a genetic algorithm optimized neural network , 2014 .

[60]  Guanghua Xu,et al.  Detection of weak transient signals based on wavelet packet transform and manifold learning for rolling element bearing fault diagnosis , 2015 .