Improved Hilbert-Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings

Vibration signals from rotating machineries are usually of multi-component and modulated signals. Hilbert-Huang transform (HHT), hereby referring to the combination of empirical mode decomposition (EMD) and normalized Hilbert transform (NHT), is an effective method to extract useful information from the multi-component and modulated signals. However, sifting stopping criterion (SSC) that is crucial to the HHT performance has not been well explored for this sift-driven method in the past decades. This paper proposes the soft SSC, which can ease the mode-mixing problem in signal decomposition through the EMD and improve demodulation performance in signal demodulation. The soft SSC can adapt to input signals and determine the optimal iteration number of a sifting process by tracking this sifting process. Extensive simulations show that the soft SSC can enhance the performance of the HHT in signal decomposition, signal demodulation, and the estimation of the instantaneous amplitude and frequency over the existing state-of-the-art SSCs. Finally, the improved HHT with the soft SSC is demonstrated on the fault diagnosis of wheelset bearings. (C) 2021 ISA. Published by Elsevier Ltd. All rights reserved.

Automated summary

This paper proposes a soft SSC method to improve the performance of HHT in signal decomposition and signal demodulation. By tracking the sifting process, the soft SSC can adaptively determine the optimal iteration number and alleviate the mode-mixing problem in signal decomposition.