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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.

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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.

Publication informationMeccanica, 47 (7) Abstract -In machine defect detection, namely those of gears, the major problem is isolating the defect signature from the measured signal, especially where there is significant background noise or multiple machine components. This article presents a method of gear defect detection based on the combination of Wavelet Multi-resolution Analysis and the Hilbert transform. The pairing of these techniques allows simultaneous filtering and denoising, along with the possibility of detecting transitory phenomena, as well as a demodulation. This paper presents a numerical simulation of the requisite mathematical model followed by its experimental application of acceleration signals measured on defective gears on a laboratory test rig. Signals were collected under various gear operating conditions, including defect size, rotational speed, and frequency bandwidth. The proposed method compares favourably to commonly used analysis tools, with the advantage of enabling defect frequency isolation, thereby allowing detection of even small or combined defects.