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The use of ensemble empirical mode decomposition to improve bispectral analysis for fault detection in rotating machinery

Abstract Empirical mode decomposition (EMD) has been widely applied to analyse signals for the detection of faults in rotating machinery. However, sometimes, it cannot reveal signal characteristics accurately because of the mode mixing problem. Ensemble empirical mode decomposition (EEMD) was developed recently to alleviate the mode mixing problem of EMD. With EEMD, components that are physically meaningful can be extracted from the signals. Bispectrum, a third-order statistic, helps identify phase coupling effects, which are useful for detecting faults in rotating machinery. Utilizing the advantages of EEMD and bispectrum, this article proposes a joint method for detecting such faults. First, original vibration signals collected from rotating machinery are decomposed by EEMD and a set of intrinsic mode functions (IMFs) is produced. Then, the IMFs are reconstructed into new signals using the weighted reconstruction algorithm developed in this article. Finally, the reconstructed signals are analysed via bispectrum to detect faults. The simulation experiments and the physical experiments of two gears with a chipped tooth and a cracked tooth, respectively, demonstrate that the proposed method can detect faults more clearly than can directly performing bispectrum on the original vibration signals.