Extraction of bearing fault features by sliding time-frequency synchronous averaging by maximum amplitudes at potential fault frequencies in envelope spectrum.
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| Title: | Extraction of bearing fault features by sliding time-frequency synchronous averaging by maximum amplitudes at potential fault frequencies in envelope spectrum. |
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| Authors: | Liu, Tao1,2 (AUTHOR) liutao@nwpu.edu.cn, Wang, Shufeng1 (AUTHOR), Hu, Lin3 (AUTHOR), Dong, Xing4 (AUTHOR), Noman, Khandaker1 (AUTHOR), Li, Yongbo5 (AUTHOR) yongbo@nwpu.edu.cn |
| Source: | Nondestructive Testing & Evaluation. Sep2026, Vol. 41 Issue 9, p5640-5666. 27p. |
| Subjects: | Time-frequency analysis, Feature extraction, Frequency spectra, Signal processing, Fault diagnosis |
| Abstract: | In early fault diagnosis of bearings, weak fault features tend to be overshadowed by high-energy noise. To address this challenge, this paper proposes a novel algorithm for extracting fault features, termed Sliding Time-Frequency Synchronous Averaging Enhancement based on Maximum Amplitudes at Potential Fault Frequencies in the Envelope Spectrum (STFSA-MAPFFES). In this algorithm, the time-frequency coefficients are innovatively used instead of the time-domain signal for the envelope spectrum calculation. The proposed APFFES parameters utilise the physical properties of the fault to accurately locate the resonance bands in the time-frequency domain. Only the selected time-frequency coefficients are subjected to a sliding time-frequency synchronous averaging process, which achieves efficient early bearing fault feature extraction. Fault features are extracted through two primary outputs: (1) an envelope spectrum that encapsulates the majority of the fault-related information and (2) the time-frequency coefficients, which are further enhanced using the unbiased autocorrelation function and STFSA. A series of digital-analog signals is used to evaluate the performance of the algorithm proposed. Additionally, two publicly available datasets are processed, and the effectiveness of the STFSA-MAPFFES algorithm is compared with six other methods. The results demonstrate that the proposed method outperforms the comparison methods in terms of extracting fault features. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | In early fault diagnosis of bearings, weak fault features tend to be overshadowed by high-energy noise. To address this challenge, this paper proposes a novel algorithm for extracting fault features, termed Sliding Time-Frequency Synchronous Averaging Enhancement based on Maximum Amplitudes at Potential Fault Frequencies in the Envelope Spectrum (STFSA-MAPFFES). In this algorithm, the time-frequency coefficients are innovatively used instead of the time-domain signal for the envelope spectrum calculation. The proposed APFFES parameters utilise the physical properties of the fault to accurately locate the resonance bands in the time-frequency domain. Only the selected time-frequency coefficients are subjected to a sliding time-frequency synchronous averaging process, which achieves efficient early bearing fault feature extraction. Fault features are extracted through two primary outputs: (1) an envelope spectrum that encapsulates the majority of the fault-related information and (2) the time-frequency coefficients, which are further enhanced using the unbiased autocorrelation function and STFSA. A series of digital-analog signals is used to evaluate the performance of the algorithm proposed. Additionally, two publicly available datasets are processed, and the effectiveness of the STFSA-MAPFFES algorithm is compared with six other methods. The results demonstrate that the proposed method outperforms the comparison methods in terms of extracting fault features. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 10589759 |
| DOI: | 10.1080/10589759.2025.2555537 |
