A new fault detection strategy using the enhancement ensemble empirical mode decomposition

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The vibration signals of mechanical components are non-linear and non-stationary and the feature frequencies of faulty bearings will be difficult extracted. This paper presents a new approach that combines the ensemble empirical mode decomposition (EEMD), the random decrement technique (RDT), and envelope spectrum for the fast detection of faults in bearings. The proposed approach uses the optimized and fast EEMD algorithm to extract intrinsic mode functions (IMFs) from vibration signals able to tack the feature frequency of bearings. If the Impulse response signal of the first IMF is unclear, it is further extracted by the RDT, and the feature frequencies are determined by analyzing the signals using envelope spectrum. The advantages of this method are its computational efficiency, and the strong non-stationary vibration signal decomposition and impulse signal extraction abilities. Numerical simulations and experimental data collected from faulty bearings are used to validate the proposed approach. The results show that the use of the EEMD, the RDT, and the envelope spectrum is a suitable and fast on-line strategy to detect faults of mechanical components.