Fault diagnosis of rolling bearing based on lifting morphological wavelet and ensemble empirical mode decomposition

Abstract

Aiming at the fault diagnosis of rolling bearing in the case of complicated background, lifting morphological wavelet is used to denoise, and a method for extracting fault features is represented by combining lifting morphological wavelet with ensemble empirical mode decomposition (EEMD). The original signal is denoised firstly by max-lifting morphological… (More)

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@article{Wang2011FaultDO, title={Fault diagnosis of rolling bearing based on lifting morphological wavelet and ensemble empirical mode decomposition}, author={Shiwang Wang and Jian Zhou}, journal={2011 International Conference on Consumer Electronics, Communications and Networks (CECNet)}, year={2011}, pages={2229-2232} }