Abstract
Vibration signal reflects the operation status of the equipment. It is widely used in the field of mechanical fault diagnosis. However, the weak fault impact signal will be masked by the vibration noise, which makes it difficult to extract the fault features of the raw vibration signal. Aiming at this feature extraction problem, a hybrid method based on minimum entropy deconvolution (MED) and transient-extracting transform (TET) is proposed. First, the original signal is pre-processed by the MED method, which effectively reduces the interference of noise on the signal and enhances the impact component. Then, TET is used to extract the transient features of the pre-processed signal. Finally, the extracted transient information is used for fault diagnosis of rolling bearing. The validation of the method is carried out on simulated signals and Case Western Reserve University (CWRU) bearing data. Also, the proposed method is compared with other feature extraction methods. Those results show that the method can effectively extract the impact components in the vibration signal under strong background noise, which the effectiveness of the method is verified.
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References
Wang: Cyclic correlation density decomposition based on a sparse and low-rank model for weak fault feature extraction of rolling bearings. Measurement 198, 111393 (2022)
Zhao. A study of the time-frequency aggregation criterion for the short-time Fourier transform. Vib. Test Diagnos. 37(05), 948–956 (2017)
Zhu. Fault diagnosis of planetary gearboxes based on improved empirical wavelet transform. J. Instrument. 37(10), 2193–2201 (2016)
Huang. The empirical mode decomposition and the hilbert spectrum for nonlinear and non-stationary time series analysis. Proc. Math. Phys. Eng. Sci. 454(1971), 903–995 (1998)
Shi: Generalized stepwise demodulation transform and synchro squeezing for time–frequency analysis and bearing fault diagnosis. J. Sound Vib. 368, 202–222 (2016)
Yu. A concentrated time–frequency analysis tool for bearing fault diagnosis. IEEE Trans. Instrument. Measur. 69(2), 371–381 (2020)
Zhang. Bearing fault diagnosis based on morphological filtering and Laplace wavelet. China Mech. Eng. 27(09), 1198–1203 (2016)
He. Identification of multiple faults in rotating machinery based on minimum entropy deconvolution combined with spectral kurtosis. Mech. Syst. Signal Process. 81, 235–249 (2016)
Gong. Application of mathematical morphology method of minimum entropy inverse fold product in rolling bearing fault feature extraction. China Mech. Eng. 27(18), 2467–2471 (2016)
Leng. Application of minimum entropy deconvolution in early fault diagnosis of rolling bearings. Mech. Transm. 39(08), 189–192 (2015)
Wiggins. Minimum entropy deconvolution. Geoexploration 16(1–2), 21–35 (1978)
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© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Shan, N., Jiang, C., Mao, X. (2024). Application of MED-TET to Feature Extraction of Vibration Signals. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 554. Springer, Cham. https://doi.org/10.1007/978-3-031-53404-1_3
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DOI: https://doi.org/10.1007/978-3-031-53404-1_3
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