Signal optimization based generalized demodulation transform for rolling bearing nonstationary fault characteristic extraction

Dezuo Zhao, Tianyang Wang, Robert Gao, Fulei Chu

Research output: Contribution to journalArticle

4 Citations (Scopus)


In this paper, a novel signal optimization based generalized demodulation transform (SOGDT) is proposed for rolling bearing nonstationary fault characteristic extraction. This method mainly involves five steps: (a) the resonance frequency band excited by bearing fault is obtained using the spectral kurtosis (SK) based band-pass filtering algorithm; (b) the instantaneous fault characteristic frequencies (IFCFs) are extracted via the peak search algorithm from the envelope time-frequency spectrum (TFS) of the filtered signal, and
based on the optimal criteria, an optimal signal and an optimal IFCF function are calculated; (c) the rotational frequency (RF) related phase function and fault index are calculated based on the optimal IFCF function and the fault characteristic coefficient (FCC); (d) the SOGDT-based spectrum is obtained using the generalized demodulated transform (GDT) and the fast Fourier transform (FFT); and (e) bearing fault type can be determined by contrasting peak in the spectrum with the fault index. The effectiveness of the proposed method is testified using both simulated and measured faulty bearing signal under nonstationary conditions. As its main contribution, this paper develops a SOGDT to match the rolling bearing RF. As results, the SOGDT based method can effectively detect bearing nonstationary fault characteristic without the speed measurement device and it also has more outstanding matching accuracy and anti-noise performance than the traditional GDT.
Original languageEnglish
Article number106297
JournalMechanical Systems and Signal Processing
Early online date21 Aug 2019
Publication statusPublished - 1 Dec 2019
Externally publishedYes


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