Spectral Correlation Measure for Selecting Intrinsic Mode Functions

Abstract

Time series analysis implies extracting relevant features from realworld applications to improve pattern recognition tasks. In that sense, representation methods based on time series decomposition and similarity measures are combined to select representative features with physical interpretability. In this work, we introduce two similarity measures based on the cross-power spectral density to select representative intrinsic mode functions (IMF) that characterize the time series. The IMFs are obtained by Ensemble Empirical Mode Decomposition because it deals with non-stationary dynamics present into time series. The proposed similarity measures are an extension of the correlation coefficient and are validate using vibration signals acquired in a test rig under three different machine states (undamaged, unbalance and misalignment). Results show that the proposed measures improve the interpretability in terms of association between an IMF and a fault state, preserving a high classification rate.

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Machine Fault Detection Based on Filter Bank Similarity Features Using Acoustic and Vibration Analysis

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