Rolling element bearing fault recognition approach based on fuzzy clustering bispectrum estimation
TLDR
The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures.Abstract:
A rolling element bearing fault recognition approach is proposed in this paper. This method combines the basic Higher- order spectrum (HOS) theory and fuzzy clustering method in data mining area. In the first step, all the bispectrum estimation results of the training samples and test samples are turned into binary feature images. Secondly, the binary feature images of the training samples are used to construct object templates including kernel images and domain images. Every fault category has one object templates. At last, by calculating the distances between test samples' binary feature images and the different object templates, the object classification and pattern recognition can be effectively accomplished. Bearing is the most important and much easier to be damaged component in rotating machinery. Furthermore, there exist large amounts of noise jamming and nonlinear coupling components in bearing vibration signals. The Higher Order Cumulants (HOC), which can quantitatively describe the nonlinear characteristic signals with close relationship between the mechanical faults, is introduced in this paper to de-noise the raw bearing vibration signals and obtain the bispectrum estimation pictures. In the experimental part, the rolling bearing fault diagnosis experiment results proved that the classification was completely correct.read more
Citations
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A Systematic Review of Fuzzy Formalisms for Bearing Fault Diagnosis
Chuan Li,José Valente de Oliveira,Mariela Cerrada,Diego Cabrera,René-Vinicio Sánchez,Grover Zurita +5 more
TL;DR: The main contribution is an updated, unbiased, and (to a higher extend) repeatable search, review, and analysis of the available approaches resorting to fuzzy formalisms in this trendy topic.
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Fault Diagnosis for Rotating Machinery: A Method based on Image Processing
TL;DR: Results show that the proposed method based on image-processing achieves a high accuracy, thus providing a highly effective means to fault diagnosis for rotating machinery.
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Diagnosis of compound faults of rolling bearings through adaptive maximum correlated kurtosis deconvolution
TL;DR: In this paper, an adaptive maximum correlated kurtosis deconvolution (AMCKD) method was proposed for detecting compound faults in rolling bearings compared with traditional methods, such as direct envelop spectrum, Discrete wavelet transform (DWT), and empirical mode decomposition, which extracts each fault signal related to the single failed part from the compound fault signals and effectively separates the coupled fault features.
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Observer-biased bearing condition monitoring
Chuan Li,José Valente de Oliveira,Mariela Cerrada,Fannia Pacheco,Diego Cabrera,Vinicio Sanchez,Grover Zurita +6 more
TL;DR: A novel method allowing for interactive clustering in bearing fault diagnosis is proposed and experimental results under realistic conditions show that the adopted algorithm outperforms the corresponding unbiased algorithm (fuzzy c-means) which is being widely used in this type of problems.
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Fault identification of rotor-bearing system based on ensemble empirical mode decomposition and self-zero space projection analysis
TL;DR: In this paper, a new approach based on ensemble empirical mode decomposition (EEMD) and self-zero space projection analysis is proposed to identify faults appearing in a rotor bearing system using simple algebraic calculations and projection analyses.
References
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