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CSME 2025/10
Volume 46 No.5
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561-568
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Investigation of Planetary Gearbox Vibration Signal Characteristics and Failure Diagnosis Using Time-Spectral Kurtosis (TSK) and Ensemble Empirical Mode Decomposition (EEMD)
T- Bensana a, M-FNIDES a, L- BOURAGBI b and M-MIHOUB c
aHigher Normal School of Technological Education of Skikda, Azzaba Skikda, Algeria
bUniversity of 20th August 1955-Skikda, Department of Process Engineering, Skikda, Algeria.
cTissemsilt University, Tissemsilt, Algeria
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Abstract:
Gears are an indispensable element of industri-al equipment and power transmission in a wide range of manufacturing machinery. When faults occur on one or more gear teeth, the performance of the gear train deteriorates and the efficiency decreases. Vibration signal processing and analysis is one of the best preferred means to diagnose the planetary gear-box fault, but Vibration signal of gears is often submerged in a large amount of noise, leading to the decrease of fault diagnosis accuracy. In order to determine the faulty feature frequency of weak gear signals, a hybrid fault diagnosis method of planetary gear based on Second-generation wavelet (SGW), ensemble empirical mode decomposition (EEMD) and Time-Spectral Kurtosis (TSK) is proposed in this paper. Firstly, SGW denoising is employed to filter the initial signal then extract the signal of interest from the background noise. Next, decompose the filtered signal into IMFs and reduce modal aliasing using the EEMD approach. After that, the correlation coefficient and TSK are used to identify the faulty IMFs, which are subsequently used to reconstruct the last signal. Finally, the envelope spectrum of the reconstructed signal is used to detect the characteris-tic frequency. The results can ensure that characteris-tics of gear failures are included in the selected IMFs and confirm the effectiveness and superiority of the proposed adaptive method in gear fault diagnosis.
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Keywords: planetary gearbox, vibratory diagnostics, Second-generation wavelet (SGW), ensemble empirical mode decomposition (EEMD), time-spectral kurtosis (TSK).
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©
2025
CSME , ISSN 0257-9731
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