Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

The texture feature tensor established from a subband time–frequency image (TFI) was extracted and used to identify the fault states of a rolling bearing. The TFI of adaptive optimal-kernel distribution was optimally partitioned into TFI blocks based on the minimum frequency band entropy. The texture features were extracted from the co-occurrence matrix of each TFI block. Based on the order of the segmented frequency bands, the texture feature tensor was constructed using the multidimensional feature vectors from all the blocks; this preserved the inherent characteristic of the TFI structure and avoided the information loss caused by vectorizing multidimensional features. The linear support higher order tensor machine based on the feature tensor was applied to identify the fault states of the rolling bearing.

[1]  Lei Wang,et al.  Rolling Bearing Fault Diagnosis Based on Wavelet Packet Decomposition and Multi-Scale Permutation Entropy , 2015, Entropy.

[2]  Bing Li,et al.  Classification of time-frequency representations based on two-direction 2DLDA for gear fault diagnosis , 2011, Appl. Soft Comput..

[3]  Guanghua Xu,et al.  Feature extraction and recognition for rolling element bearing fault utilizing short-time Fourier transform and non-negative matrix factorization , 2014, Chinese Journal of Mechanical Engineering.

[4]  Hongkun Li,et al.  An investigation into machine pattern recognition based on time-frequency image feature extraction using a support vector machine , 2010 .

[5]  Jérôme Antoni,et al.  The infogram: Entropic evidence of the signature of repetitive transients , 2016 .

[6]  Lalu Mansinha,et al.  Localization of the complex spectrum: the S transform , 1996, IEEE Trans. Signal Process..

[7]  Mohammad Modarres,et al.  Deep Learning Enabled Fault Diagnosis Using Time-Frequency Image Analysis of Rolling Element Bearings , 2017 .

[8]  A. Krylov,et al.  Two-dimensional hermite S-method for high-resolution inverse synthetic aperture radar imaging applications , 2010 .

[9]  Xiaowei Yang,et al.  A Linear Support Higher-Order Tensor Machine for Classification , 2013, IEEE Transactions on Image Processing.

[10]  Yongsheng Yang,et al.  Nonnegative matrix factorization and artificial immune based classification for fault diagnosis of diesel valve train , 2013, 2013 IEEE Symposium on Computational Intelligence and Data Mining (CIDM).

[11]  T. Thayaparan,et al.  Signal Decomposition by Using the S-Method With Application to the Analysis of HF Radar Signals in Sea-Clutter , 2006, IEEE Transactions on Signal Processing.

[12]  Dou Wei,et al.  Application of Image Recognition Based on Artificial Immune in Rotating Machinery Fault Diagnosis , 2007, 2007 1st International Conference on Bioinformatics and Biomedical Engineering.

[13]  Qingbo He,et al.  Time–frequency manifold correlation matching for periodic fault identification in rotating machines , 2013 .

[14]  Myeongsu Kang,et al.  Reliable Fault Diagnosis of Multiple Induction Motor Defects Using a 2-D Representation of Shannon Wavelets , 2014, IEEE Transactions on Magnetics.

[15]  Dengfeng Zhang,et al.  Multiscale singular value manifold for rotating machinery fault diagnosis , 2017 .

[16]  Cong Wang,et al.  Intelligent fault diagnosis of rolling element bearings using sparse wavelet energy based on overcomplete DWT and basis pursuit , 2015, Journal of Intelligent Manufacturing.

[17]  B.J. Oommen,et al.  On optimizing syntactic pattern recognition using tries and AI-based heuristic-search strategies , 2005, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Jin Jiang,et al.  Analysis and design of modified window shapes for S-transform to improve time–frequency localization , 2015 .

[19]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[20]  Bing Li,et al.  Classification of time-frequency representations using improved morphological pattern spectrum for engine fault diagnosis , 2013 .

[21]  Lihong Li,et al.  An in-depth study of tool wear monitoring technique based on image segmentation and texture analysis , 2016 .

[22]  Bing Li,et al.  Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization , 2011 .

[23]  Qingbo He,et al.  Time-frequency manifold histogram matching for transient signal detection , 2015, 2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings.

[24]  Dan Liu,et al.  An image dimensionality reduction method for rolling bearing fault diagnosis based on singular value decomposition , 2016 .

[25]  Hui Li,et al.  Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor , 2017 .

[26]  Wei-Gang Wang,et al.  Classification of time–frequency images based on locality-constrained linear coding optimization model for rotating machinery fault diagnosis , 2015 .

[27]  Gene H. Golub,et al.  Rank-One Approximation to High Order Tensors , 2001, SIAM J. Matrix Anal. Appl..

[28]  Diego Cabrera,et al.  Extracting repetitive transients for rotating machinery diagnosis using multiscale clustered grey infogram , 2016 .

[29]  Jong-Myon Kim,et al.  Texture analysis based feature extraction using Gabor filter and SVD for reliable fault diagnosis of an induction motor , 2018, Int. J. Inf. Technol. Manag..

[30]  Mark Holden,et al.  A Review of Geometric Transformations for Nonrigid Body Registration , 2008, IEEE Transactions on Medical Imaging.