Abstract
Multi-fault diagnosis for bearings is a challenge task. It is difficult to identify all the features from measured vibration signals when there is more than one bearing fault, especially, when some fault feature at the early stage is relatively weak and easily immersed in noise and other signals. The ensemble empirical mode decomposition (EEMD) method inherits the advantage of the popular empirical mode decomposition (EMD) method and can adaptively decompose a multi-component signal into a number of different bands of signal components called intrinsic mode functions (IMFs). In this chapter, the strategies of parameter optimization and signal component combination are combined with the normal EEMD to enhance its performance on signal processing. A vibration signal collected from a multi-fault bearing was used to verify the effectiveness of the enhanced EEMD method. The results demonstrate that the proposed method can accurately extract the feature signal; meanwhile, it makes the physical meaning of each IMF clear.
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Acknowledgment
The work that is described in this chapter is fully supported by the Fundamental Research Funds for the Central Universities (Project No. ZYGX2012J105 and Project No. ZYGX2013J094). The authors wish to thank Dr. Peter W. Tse in City University of Hong Kong for the allowance of using experimental bearing data. We appreciate three anonymous reviewers for their valuable comments and suggestions for improving this chapter.
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Guo, W., Wang, K.S., Wang, D., Tse, P.W. (2015). Feature Signal Extraction Based on Ensemble Empirical Mode Decomposition for Multi-fault Bearings. In: Tse, P., Mathew, J., Wong, K., Lam, R., Ko, C. (eds) Engineering Asset Management - Systems, Professional Practices and Certification. Lecture Notes in Mechanical Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-09507-3_113
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DOI: https://doi.org/10.1007/978-3-319-09507-3_113
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