Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization

Li, Bing; Zhang, Peilin; Li, Dongsheng; Mi, Shuang-shan; Ren, Guoquan; Tian, Hui

doi:10.1016/j.jsv.2010.11.019

Cited by 85 publications

(41 citation statements)

References 38 publications

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“…(20) will give the solution of f 0 and thus the shaping function. For example, if the window width of 400 is needed for a signal at 800 Hz, using Eq.…”

Section: Window Shaping For S-transformmentioning

confidence: 99%

“…For example, if the window width of 400 is needed for a signal at 800 Hz, using Eq. (20), one can obtain f 0 ¼462. Subsequently, the shaping function can be chosen asW ðα; f Þ¼expðÀ2π 2 α 2 =462 2 Þ.…”

Section: Window Shaping For S-transformmentioning

confidence: 99%

“…ST is useful for analyzing transient signals of a short duration. Applications of ST can be found in several engineering and biomedical fields [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Analysis and design of modified window shapes for S-transform to improve time–frequency localization

Jiang

2015

Mechanical Systems and Signal Processing

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“…(20) will give the solution of f 0 and thus the shaping function. For example, if the window width of 400 is needed for a signal at 800 Hz, using Eq.…”

Section: Window Shaping For S-transformmentioning

confidence: 99%

Section: Window Shaping For S-transformmentioning

confidence: 99%

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Analysis and design of modified window shapes for S-transform to improve time–frequency localization

Jiang

2015

Mechanical Systems and Signal Processing

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“…Li et al combined the transformation method, NMF method, mutual information method, and multiobjective evolutionary algorithm [9]. Li et al also, respectively, combined generalized transform method to NMF method and 2DNMF method [10,11]. Some other scholars focused on the EMD (empirical mode decomposition) method and its expansion methods.…”

Section: Introducementioning

confidence: 99%

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

Wang

et al. 2017

Journal of Sensors

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This paper mainly focuses on the representable problem of Φ-OTDR distributed vibration signals. The research included a signal extraction part and a signal representation part. Firstly, in order to extract the better Φ-OTDR signal, the time-domain data should be fully preserved. The 2D-TESP method is used to extract data in this paper. There are 29 characters in the traditional TESP method. The characters' number is reduced from 29 to 13 and the characters' dimension is expanded from 1 to 2 in the 2D-TESP method. Secondly, in order to represent Φ-OTDR signal better, the characteristics of Φ-OTDR data and damped vibration signals are combined in the paper. The EMD method and the NMF method are combined to form the new method in the paper. Some parameters in the proposed method are optimized and adjusted by GA method. After Φ-OTDR data is represented by the proposed method, there is excellent performance both on the length dimension and on the time dimension. Lastly, some experiments are carried out according to the physical truth in this paper. The experiments are carried out in the semianechoic room. The methods of the paper have better performance. The methods are proved to be effective through these experiments.

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“…In the field of fault state pattern recognition, the multidimensional features in machine learning are generally transformed to a vector representation and then processed by the classical learning algorithms operating with vectors. Although there are many ways to vectorize multidimensional data, it has been observed that such operation usually leads to significant loss of important information, since some values which were in local vicinity become differently arranged if data are arbitrarily linearized into a vector [10]. And it is necessary to reduce the dimension of time-frequency features to appropriate size with some dimensionality reduction techniques such as factor analysis, principal component analysis, independent component analysis, and singular value decomposition [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

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

Liu²,

Lin³

2017

International Journal of Rotating Machinery

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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, timefrequency 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 timefrequency 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.

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Feature extraction for rolling element bearing fault diagnosis utilizing generalized S transform and two-dimensional non-negative matrix factorization

Cited by 85 publications

References 38 publications

Analysis and design of modified window shapes for S-transform to improve time–frequency localization

Analysis and design of modified window shapes for S-transform to improve time–frequency localization

The Research on Information Representation of Φ-OTDR Distributed Vibration Signals

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

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