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Demodulation is very important for gear fault detection.
This paper proposed a new gear fault identification method based on HHT and SOM neural network.
The experimental results show remarkable improvements and enhance gear fault features.
Especially when early gear fault occurs, the weak fault feature information is submerged in interfering signal.
Meanwhile, explicit equations for calculating the characteristic frequency of local and distributed gear fault are deduced.
Appearances of certain sideband peaks in the frequency spectrum may indicate the occurrence of gear fault.
Similar(10)
The gear fault-related features in the vibration signals are extracted by using the ensemble empirical mode decomposition method (EEMD) and the marginal Hilbert spectrum analysis.
Wu, et al [50], combined DWT with ANFIS to identify gear faults.
The results show that both localized and distributed gear faults, both the sun and planet gear faults, can be diagnosed successfully.
Thus, the proposed method is effective for the diagnosis of planetary gear faults.
Finally, the vibration features of gear faults can be clearly exposed by the envelope order spectra.
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