Characterization of gear faults in variable rotating speed using Hilbert-Huang Transform and instantaneous dimensionless frequency normalization

The objective of this research is to investigate the feasibility of utilizing the instantaneous dimensionless frequency (DLF) normalization and Hilbert-Huang Transform (HHT) to characterize the different gear faults in case of variable rotating speed. The normalized DLF of the vibration signals are calculated based on the rotating speed of shaft and the instantaneous frequencies of Intrinsic Mode Functions (IMFs) which are decomposed by Empirical Mode Decomposition (EMD) process. The faulty gear features on DLF-energy distribution of vibration signal can be extracted without the presence of shaft rotating speed, so that the proposed approach can be applied for characterizing the malfunctions of gearbox system under variable shaft rotating speed. A test rig of gear transmission system is performed to illustrate the gear faults, including worn tooth, broken tooth and gear unbalance. Different methods to determine the instantaneous frequency are employed to verify the consistence of characterization results. The DLF-energy distributions of vibration signals are investigated in different faulty gear conditions. The analysis results demonstrate the capability and effectiveness of the proposed approach for characterizing the gear malfunctions at the DLFs corresponding to the meshing frequency as well as the shaft rotating frequency. The support vector machine (SVM) is then employed to classify the vibration patterns of gear transmission system at different malfunctions. Using the energy distribution at the characteristic DLFs as the features, the different fault types of gear can be identified by SVM with high accuracy.

► Characterize different gear faults under variable rotating speed. ► Instantaneous DLF normalization to remove factor of rotating speed. ► Use NHT, GZC and DQ methods to show consistency of results. ► Time-DLF-energy distributions present vibration energy concentration. ► Marginal DLF spectra demonstrate major feature of gear faults.