Frequency Domain Signal-to-Noise Ratio (FDSNR): A useful indicator for evaluating fault information of rotating machinery
It is more robust to estimate the fault information contained in vibration signals of rotating machinery in frequency domain. The demodulation spectrum of vibration signals of faulty rotating machinery presents a harmonic related spectrum structure, such as envelope spectrum. Aiming at evaluating useful mechanical fault information, frequency domain signal-to-noise ratio (FDSNR) index is proposed to estimate useful information from frequency domain by means of harmonic related spectral structure. FDSNR has clear physical meaning and stronger robustness to interference components. Good and stable results have been achieved in rolling bearing fault diagnosis, as shown in the following two journal articles:
[1] Chen B, Song D, Zhang W, Cheng Y. A novel spectral coherence-based envelope spectrum for railway axle-box bearing damage identification. Structural Health Monitoring. 2023;22(2):879-896. doi:10.1177/14759217221095067
[2] Chen B, Cheng Y, Zhang W, Gu F, Mei G. Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: A comparative study. ISA Transactions. 2022;127:395-414. doi:10.1016/j.isatra.2021.08.025
Cite As
Chen Bingyan (2025). Frequency Domain Signal-to-Noise Ratio (FDSNR) (https://se.mathworks.com/matlabcentral/fileexchange/129774-frequency-domain-signal-to-noise-ratio-fdsnr), MATLAB Central File Exchange.
Retrieved .
Chen, Bingyan, et al. “Optimal Frequency Band Selection Using Blind and Targeted Features for Spectral Coherence-Based Bearing Diagnostics: A Comparative Study.” ISA Transactions, vol. 127, Elsevier BV, Aug. 2022, pp. 395–414, doi:10.1016/j.isatra.2021.08.025.
Chen, Bingyan, et al. “Optimal Frequency Band Selection Using Blind and Targeted Features for Spectral Coherence-Based Bearing Diagnostics: A Comparative Study.” ISA Transactions, vol. 127, Elsevier BV, Aug. 2022, pp. 395–414, doi:10.1016/j.isatra.2021.08.025.
APA
Chen, B., Cheng, Y., Zhang, W., Gu, F., & Mei, G. (2022). Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: A comparative study. ISA Transactions, 127, 395–414. Elsevier BV. Retrieved from https://doi.org/10.1016%2Fj.isatra.2021.08.025
BibTeX
@article{Chen_2022,
doi = {10.1016/j.isatra.2021.08.025},
url = {https://doi.org/10.1016%2Fj.isatra.2021.08.025},
year = 2022,
month = {aug},
publisher = {Elsevier {BV}},
volume = {127},
pages = {395--414},
author = {Bingyan Chen and Yao Cheng and Weihua Zhang and Fengshou Gu and Guiming Mei},
title = {Optimal frequency band selection using blind and targeted features for spectral coherence-based bearing diagnostics: A comparative study},
journal = {{ISA} Transactions}
}
Chen, Bingyan, et al. “A Novel Spectral Coherence-Based Envelope Spectrum for Railway Axle-Box Bearing Damage Identification.” Structural Health Monitoring, vol. 22, no. 2, SAGE Publications, May 2022, pp. 879–96, doi:10.1177/14759217221095067.
Chen, Bingyan, et al. “A Novel Spectral Coherence-Based Envelope Spectrum for Railway Axle-Box Bearing Damage Identification.” Structural Health Monitoring, vol. 22, no. 2, SAGE Publications, May 2022, pp. 879–96, doi:10.1177/14759217221095067.
APA
Chen, B., Song, D., Zhang, W., & Cheng, Y. (2022). A novel spectral coherence-based envelope spectrum for railway axle-box bearing damage identification. Structural Health Monitoring, 22(2), 879–896. SAGE Publications. Retrieved from https://doi.org/10.1177%2F14759217221095067
BibTeX
@article{Chen_2022,
doi = {10.1177/14759217221095067},
url = {https://doi.org/10.1177%2F14759217221095067},
year = 2022,
month = {may},
publisher = {{SAGE} Publications},
volume = {22},
number = {2},
pages = {879--896},
author = {Bingyan Chen and Dongli Song and Weihua Zhang and Yao Cheng},
title = {A novel spectral coherence-based envelope spectrum for railway axle-box bearing damage identification},
journal = {Structural Health Monitoring}
}
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