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A rubbing fault is a complex non-linear and non-stationary fault that frequently occurs in rotating machinery such as turbines. One of the most frequently applied signal processing techniques for the analysis of rub-impact faults in rotating machines is ensemble empirical mode decomposition (EEMD). Despite the advantages of using EEMD in analyzing non-linear and non-stationary signals, it is crucial to determine which of the extracted intrinsic mode functions (IMFs) carry the most valuable and significant information about the mechanical faults under investigation. In this paper, an improvement in the IMF selection technique is introduced, which is based on the recent ratio of degree-of-presence (DPR) to the Kullback-Leibler divergence (DPR/KLdiv). The number of selected IMFs in the DPR/KLdiv-based technique is subjective with a constant threshold, whereas we apply an adaptive thresholding technique to select the most meaningful IMFs that are relevant to a rubbing fault. The experimental results demonstrate that the proposed enhanced IMF selection algorithm allows for better signal denoising properties than the original technique while preserving significant features evidencing the presence of rubbing faults in rotating machinery.

An Improved Algorithm for Selecting IMF Components in Ensemble Empirical Mode Decomposition for Domain of Rub-Impact Fault Diagnosis