Improved Hilbert–Huang transform with soft sifting stopping criterion and its application to fault diagnosis of wheelset bearings. (June 2022)

• Record Type: Journal Article
• Authors: Liu, Zhiliang; Peng, Dandan; Zuo, Ming J.; Xia, Jianshuo; Qin, Yong
• Is Part Of: ISA transactions. Volume 125(2022)
• Page Start: 426
• Page End: 444
• Publication Date: 2022-06
• Subjects: Hilbert–Huang transform -- Empirical mode decomposition -- Normalized Hilbert transform -- Sifting stopping criterion -- Wheelset bearing -- Fault diagnosis; Engineering instruments -- Periodicals; Engineering instruments; Periodicals; Electronic journals; 629.805
• DOI: 10.1016/j.isatra.2021.07.011
• Languages: English
• ISSNs: 0019-0578

Abstract

Abstract: 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. Highlights: The SSC is reviewed and is recognized as a significant factor of HHT performance. The soft SSC is proposed to realize adaptivity of a sifting process and is embedded into HHT (including EMD and NHT). Application of the improved HHT is explored in wheelset bearing fault diagnosis.