Operational modal parameters are the important indicators for condition monitoring and early fault diagnosis of journal bearings.In operational modal parameters identification for journal bearings using stochastic subspace identification,spurious modes due to background noise and order overestimation can seriously affect the identification of real modes.In order to reduce the interference of spurious modes,denoising of vibration signals is carried out using complementary ensemble empirical mode decomposition and wavelet transform.And signals are then segmented separately for modal parameters identification.A clear stabilization diagram is obtained by comparing the same order poles.Finally,hierarchical clustering analysis is conducted to extract modal parameters automatically.The effectiveness of the proposed method is verified by numerical simulation and experimental tests.
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