Abstract:Rolling bearing’s fault feature signals are non-stationary, transient, which are often submerged in the background signals associated with rotor speed and other noise components, how to separate the fault feature signals from the rolling bearing’s blind sources is an important issue. A method combined the phase space reconstruction technique and the stationary subspace analysis (SSA) was proposed. First, the fault vibration signal’s dimension was increased by the phase space technique. Second, the stationary and the non-stationary source components were distinguished by SSA from the multi-dimensional signals. Then, the selected non-stationary component which had the maximum kurtosis value was de-noised by the minimum entropy deconvolution (MED). Finally, the de-noised non-stationary component was analyzed by the envelope spectrum to extract the fault characteristic frequency. The analysis result of simulation and experiment signals indicated that the proposed method could extract the fault frequency better than the envelope demodulation method based on empirical mode decomposition (EMD).
刘尚坤,唐贵基,庞彬 . 基于相空间重构与平稳子空间分析的滚动轴承故障诊断[J]. 振动与冲击, 2015, 34(22): 187-191.
LIU Shang-kun, TANG Gui-ji, PANG Bin. Fault Diagnosis for Rolling Bearing Based on Phase Space reconstruction and Stationary Subspace Analysis. JOURNAL OF VIBRATION AND SHOCK, 2015, 34(22): 187-191.
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