Enhanced symplectic geometry mode decomposition and its application to rotating machinery fault diagnosis under variable speed conditions

Tacholess order tracking (TLOT) has been one of the most powerful and applicable rotating machinery fault diagnosis methods. However, it is still a challenge to accurately estimate the instantaneous phase of the reference shaft under variable speed conditions. Although current available signal decomposition methods can deal with non-stationary vibration signal, they are probably interfered by some tough problems, such as prior knowledge requirement or heavy computational burden. In this paper, the aforementioned problems are addressed and an adaptive harmonic components extraction framework is proposed. Firstly, the vibration signal is low-pass filtered and down-sampled for computation speed acceleration. Harmonic components are subsequently extracted by enhanced symplectic geometry mode decomposition (ESGMD), which is based on Cramer-von Mises criterion. Secondly, surrogate data test is applied for pseudo components identification, thereby interferences induced by background noise can be filtered out adaptively. Finally, Hilbert transform is applied to obtain the instantaneous phase of the decomposed fundamental harmonic component, and thereby TLOT is conducted to realize fault diagnosis. The effectiveness of the proposed method has been validated by both simulated and experimental tests. The results exhibit that the proposed method outperforms conventional approaches in harmonic components extraction for rotating machinery fault diagnosis under variable speed conditions.

Automated summary

In this paper, an adaptive harmonic components extraction method is proposed to accurately estimate the instantaneous phase of the reference shaft under variable speed conditions, and achieve fault diagnosis for rotating machinery.