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

Li, Hui; Liu, Xiaofeng; Lin, Bo

doi:10.1155/2017/6542348

Cited by 2 publications

(3 citation statements)

References 17 publications

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“…In [23],we subjectively partitioned the whole frequency band into uniformly distributed subbands to extract frequency subband textures for the fault diagnosis of a rotor system. For the rotor system, the texture features of TFI mainly characterize the harmonics distribution, and the corresponding frequency band partition is just to reduce the computation cost of SHTM classification.…”

Section: Optimal Segmentation Based On Entrogrammentioning

confidence: 99%

“…For the rotor system, the texture features of TFI mainly characterize the harmonics distribution, and the corresponding frequency band partition is just to reduce the computation cost of SHTM classification. However, for the rolling bearing, the arbitrary partition in [23] might sacrifice the texture integrity of fault impulses in the TFI, and even introduce some false texture information. It is necessary to find a reasonable method of frequency band partition to keep the texture integrity of fault impulses.…”

Section: Optimal Segmentation Based On Entrogrammentioning

confidence: 99%

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Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Lin

Liu

et al. 2019

IEEE Access

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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. INDEX TERMS Texture feature tensor, frequency band entropy, linear support higher-order tensor machine, bearing fault intelligent diagnosis.

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Section: Optimal Segmentation Based On Entrogrammentioning

confidence: 99%

Section: Optimal Segmentation Based On Entrogrammentioning

confidence: 99%

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Lin

Liu

et al. 2019

IEEE Access

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“…Zhu et al [5] proposed a rotor fault diagnosis approach that transformed multiple rotor vibration signals into symmetrized dot pattern (SDP) image, and identified the SDP graphical feature characteristic of different states using a CNN. Hui et al [6] expressed the time-frequency image of the rotor vibration signal as a texture feature tensor for the pattern recognition of rotor fault states with the linear support higher-tensor machine (SHTM). The experimental results showed that the method of classifying time-frequency texture feature tensor can achieve ideal diagnosis precision.…”

Section: Introductionmentioning

confidence: 99%

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

Zhou

Wang

et al. 2023

Applied Sciences

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Rotor fault diagnosis has attracted much attention due to its difficulties such as non-stationarity of fault signals, difficulty in fault feature extraction and low diagnostic accuracy of small samples. In order to extract fault feature information of rotors more effectively and to improve fault diagnosis precision, this paper proposed a fault diagnosis method based on variational mode decomposition (VMD) symmetrical polar image and fuzzy neural network. Firstly, the original rotor vibration signal is decomposed by using the VMD method and the relevant parameter selection algorithm of the VMD method is also proposed. Secondly, the intrinsic mode functions (IMF), which are sensitive to the signal characteristics, are selected for signal reconstruction based on a comprehensive evaluation factor method. As well, the reconstructed signal is transformed into a two-dimensional snowflake image through using the symmetrical polar coordinate method. Finally, the image features are extracted by the gray level co-occurrence matrix to form the state feature vector, which is input into the fuzzy neural network to realize the rotor fault diagnosis. Through the analysis of measured signals, the experimental results show that the proposed method can reach a higher recognition rate of 98% and the k-cross-validation experiment is used to demonstrate the robustness of the fuzzy neural network, and the average recognition accuracy of this experiment is 99.2%. Compared with some similar methods, the proposed method still has the highest fault recognition precision 98.4%, and the smallest standard deviation 0.5477.

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

Cited by 2 publications

References 17 publications

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Bearing Fault Diagnosis Based on Subband Time-Frequency Texture Tensor

Rotor Fault Diagnosis Method Based on VMD Symmetrical Polar Image and Fuzzy Neural Network

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