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Modified Integration Method of Empirical Mode Decomposition and Multifractal Detrended Fluctuation Analysis for Fault Diagnosis of Bearing

Multifractal Detrend Fluctuation Analysis (MFDFA) can reveal multifractality hidden in nonlinear non-stationary vibration signals, thus providing an opportunity to develop a promising feature for bearing fault diagnosis. Nevertheless, the traditional multifractal analysis methods are not only dependent on the prior knowledge of the presetting of the fitting polynomial order and scales, but also vulnerable to the interference of background noise, which hinders the application of these traditional methods in the practical industry. To address these issues, a reliable metric based on the scaling exponent in DFA method is combined with golden ratio criterion to select desirable IMF components of Empirical Mode Decomposition (EMD). Subsequently, the improved scale-dependent fluctuation is defined by each IMF of interest. As a consequence, a Modified integration method of EMD and MFDFA (MEMD-MFDFA) is proposed to unveil dynamical behaviors of bearing vibration signals under different health conditions. A run-to-failure experiment of rolling bearing is used to verify the effectiveness of proposed method in discriminating different health states of running bearing. The result shows that it can be developed a promising method for fault diagnosis.