Fault Identification of Rotor System Based on Classifying Time-Frequency Image Feature Tensor

In the field of rotor fault pattern recognition, most of classical pattern recognition methods generally operate in feature vector spaces where different feature values are stacked into one-dimensional (1D) vector and then processed by the classifiers. In this paper, time-frequency image of rotor vibration signal is represented as a texture feature tensor for the pattern recognition of rotor fault states with the linear support higher-tensor machine (SHTM). Firstly, the adaptive optimal-kernel time-frequency spectrogram visualizes the unique characteristics of rotor fault vibration signal; thus the rotor fault identification is converted into the corresponding time-frequency image (TFI) pattern recognition. Secondly, in order to highlight and preserve the TFI local features, the TFI is divided into some TFI subzones for extracting the hierarchical texture features. Afterwards, to avoid the information loss and distortion caused by stacking multidimensional features into vector, the multidimensional features from the subzones are transformed into a feature tensor which preserves the inherent structure characteristic of TFI. Finally, the feature tensor is input into the SHTM for rotor fault pattern recognition and the corresponding recognition performance is evaluated. The experimental results showed that the method of classifying time-frequency texture feature tensor can achieve higher recognition rate and better robustness compared to the conventional vector-based classifiers, especially in the case of small sample size.

[1]  Andrea Baraldi,et al.  An investigation of the textural characteristics associated with gray level cooccurrence matrix statistical parameters , 1995, IEEE Transactions on Geoscience and Remote Sensing.

[2]  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).

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

[4]  Amiya R Mohanty,et al.  Model based fault diagnosis of a rotor–bearing system for misalignment and unbalance under steady-state condition , 2009 .

[5]  Xiaowei Yang,et al.  A GA-based feature selection and parameter optimization for linear support higher-order tensor machine , 2014, Neurocomputing.

[6]  Shinichiro Omachi,et al.  Fast Template Matching With Polynomials , 2007, IEEE Transactions on Image Processing.

[7]  Hideyuki Tamura,et al.  Textural Features Corresponding to Visual Perception , 1978, IEEE Transactions on Systems, Man, and Cybernetics.

[8]  Nirupam Sarkar,et al.  An Efficient Differential Box-Counting Approach to Compute Fractal Dimension of Image , 1994, IEEE Trans. Syst. Man Cybern. Syst..

[9]  Xiaofeng Liu,et al.  Smoothing localized directional cyclic autocorrelation and application in oil-film instability analysis , 2016 .

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

[11]  Xiaofeng Liu,et al.  Identification of resonance states of rotor-bearing system using RQA and optimal binary tree SVM , 2015, Neurocomputing.

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

[13]  Zhenyuan Zhong,et al.  Fault diagnosis for diesel valve trains based on time–frequency images , 2008 .

[14]  Til Aach,et al.  Texture Classification by Modeling Joint Distributions of Local Patterns With Gaussian Mixtures , 2010, IEEE Transactions on Image Processing.

[15]  Ming-Kuei Hu,et al.  Visual pattern recognition by moment invariants , 1962, IRE Trans. Inf. Theory.

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

[17]  Qinghua Wang,et al.  Fault Diagnosis of time-frequency images based on non-negative factorization and neural network ensemble , 2008, 2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence).