Integrative intrinsic time-scale decomposition and hierarchical temporal memory approach to gearbox diagnosis under variable operating conditions

Gearbox diagnosis under stationary operating conditions has been extensively investigated; however, variable operating conditions such as load and speed changes play important roles in affecting the accuracy of gearbox diagnosis. This article presents an integrative approach of intrinsic time-scale decomposition and hierarchical temporal memory for gearbox diagnosis under variable operating conditions. A total of two modules are emphasized including a feature extraction method and an integrative feature fusion and classification model. Intrinsic time-scale decomposition method is investigated to extract the gearbox features which are insensitive to variable operating conditions, and its performance overcomes the commonly used empirical mode decomposition in terms of decomposition result and computational efficiency. Hierarchical temporal memory integrates feature fusion and pattern classification in one model to autonomously diagnose gearbox defect. Performance comparison among the presented method, back-propagation neural network, support vector machine, and fuzzy c-means clustering using experimental data demonstrate the effectiveness of the presented method.

[1]  Antonios Gasteratos,et al.  On the optimization of Hierarchical Temporal Memory , 2012, Pattern Recognit. Lett..

[2]  Lin Hua,et al.  Study on stress intensity factors for crack on involute spur gear tooth , 2015 .

[3]  I. R. Praveen Krishna,et al.  Local fault detection in helical gears via vibration and acoustic signals using EMD based statistical parameter analysis , 2014 .

[4]  Wei Wang,et al.  Influence of cracks on dynamic characteristics and stress intensity factor of gears , 2013 .

[5]  Ming J. Zuo,et al.  Vibration signal modeling of a planetary gear set for tooth crack detection , 2015 .

[6]  Simon J. Godsill,et al.  Statistical gear health analysis which is robust to fluctuating loads and operating speeds , 2012 .

[7]  David,et al.  Pitting detection in worm gearboxes with vibration analysis , 2014 .

[8]  Rui Ma,et al.  Research on the dynamic mechanism of the gear system with local crack and spalling failure , 2012 .

[9]  Ming Yang,et al.  A wavelet approach to fault diagnosis of a gearbox under varying load conditions , 2010 .

[10]  Qingyu Yang,et al.  EMD and Wavelet Transform Based Fault Diagnosis for Wind Turbine Gear Box , 2013 .

[11]  Nguyen Phong Dien,et al.  Fault diagnosis in gears operating under non-stationary rotational speed using polar wavelet amplitude maps , 2004 .

[12]  Yu Yang,et al.  The envelope order spectrum based on generalized demodulation time–frequency analysis and its application to gear fault diagnosis , 2010 .

[13]  Stefano Cagnoni,et al.  Classifying Human Body Acceleration Patterns Using a Hierarchical Temporal Memory , 2009, AI*IA.

[14]  M. Zuo,et al.  Gearbox fault detection using Hilbert and wavelet packet transform , 2006 .

[15]  C. Y. Yang,et al.  Diagnostics of gear deterioration using EEMD approach and PCA process , 2015 .

[16]  Franz Pernkopf,et al.  Stochastic margin-based structure learning of Bayesian network classifiers , 2013, Pattern Recognit..

[17]  Jie Chen,et al.  Application of the intrinsic time-scale decomposition method to fault diagnosis of wind turbine bearing , 2012 .

[18]  Gaigai Cai,et al.  Sparsity-enabled signal decomposition using tunable Q-factor wavelet transform for fault feature extraction of gearbox , 2013 .

[19]  Hao Tian,et al.  A new feature extraction and selection scheme for hybrid fault diagnosis of gearbox , 2011, Expert Syst. Appl..

[20]  David He,et al.  On the Use of a Single Piezoelectric Strain Sensor for Wind Turbine Planetary Gearbox Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[21]  K. I. Ramachandran,et al.  Fault diagnosis of spur bevel gear box using discrete wavelet features and Decision Tree classification , 2009, Expert Syst. Appl..

[22]  Ghalib R. Ibrahim,et al.  Adaptive filtering based system for extracting gearbox condition feature from the measured vibrations , 2013 .

[23]  I. Osorio,et al.  Intrinsic time-scale decomposition: time–frequency–energy analysis and real-time filtering of non-stationary signals , 2007, Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences.

[24]  Jesus Mirapeix,et al.  Feasibility study of Hierarchical Temporal Memories applied to welding diagnostics , 2013 .

[25]  Li Zhang,et al.  Hybrid decision tree and naïve Bayes classifiers for multi-class classification tasks , 2014, Expert Syst. Appl..

[26]  Leonid M. Gelman,et al.  An automated methodology for performing time synchronous averaging of a gearbox signal without speed sensor , 2007 .

[27]  Robert X. Gao,et al.  Multi-scale enveloping order spectrogram for rotating machine health diagnosis , 2014 .

[28]  Han Zhang,et al.  Sparse Feature Identification Based on Union of Redundant Dictionary for Wind Turbine Gearbox Fault Diagnosis , 2015, IEEE Transactions on Industrial Electronics.

[29]  Xiao Wang,et al.  Human-Centered Saliency Detection , 2016, IEEE Transactions on Neural Networks and Learning Systems.

[30]  Lei Xie,et al.  Online detection of time-variant oscillations based on improved ITD , 2014 .

[31]  Hui Ma,et al.  Fault features analysis of cracked gear considering the effects of the extended tooth contact , 2015 .

[32]  Francisco B. Rodríguez,et al.  Extending the bioinspired hierarchical temporal memory paradigm for sign language recognition , 2012, Neurocomputing.

[33]  Jin Chen,et al.  Decision tree and PCA-based fault diagnosis of rotating machinery , 2007 .

[34]  Zheng Qian,et al.  Planetary gearbox fault feature enhancement based on combined adaptive filter method , 2015 .

[35]  Trevor S. Wiens,et al.  Three way k-fold cross-validation of resource selection functions , 2008 .

[36]  Francisco B. Rodríguez,et al.  Low cost remote gaze gesture recognition in real time , 2012, Appl. Soft Comput..

[37]  Tomasz Kapuscinski.  Using Hierarchical Temporal Memory for Vision-Based Hand Shape Recognition under Large Variations in Hand's Rotation , 2010, ICAISC.

[38]  Ahmad Ghasemloonia,et al.  Application and comparison of an ANN-based feature selection method and the genetic algorithm in gearbox fault diagnosis , 2011, Expert Syst. Appl..