In this paper, a hybrid method based on the combination of Empirical Mode Decomposition (EMD) and an optimized wavelet multi-resolution analysis (WMRA) is proposed. The pairing of these two time-frequency techniques is well adapted to analyze transient signals generated by rolling bearing defects. First, an optimal intrinsic mode function (IMF), having the most important kurtosis and covering the significant natural frequency, is selected using the classical EMD analysis. An envelope signal of the selected IMF's energy is calculated from Hilbert transform. This envelope is then analyzed by an optimized WMRA especially adapted to shock signals. A reconstructed signal is obtained and an envelope spectrum is performed to highlight the fault characteristic frequency. The results show that the proposed method can effectively get better time and frequency domain visualization of the fault occurrence compared to the application of WMRA or EMD alone.
This paper deals with the experimental study of the tool life transition and the wear monitoring during the turning operation of AISI D3 steel workpiece using coated carbide tool inserts (TiCN/Al 2 O 3 /TiN). A hybrid method, based on the combination of wavelet multi-resolution analysis (WMRA) and Empirical Mode Decomposition (EMD), is proposed to analyze vibratory signals acquired during the machining process. Using the mean power and the energy as main scalar indicators, the proposed method has been optimized and evaluated in several configurations including the cutting speed, the feed rate, and the depth of cut. The results show that the proposed hybrid method (WMRA/EMD) gives better evaluation of the tool state and the wear monitoring compared to the application of WMRA or EMD alone.
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