Abstract
Four noise-assisted empirical mode decomposition (EMD) algorithms, i.e., ensemble EMD (EEMD), complementary ensemble EMD (CEEMD), complete ensemble EMD with adaptive noise (CEEMDAN), and improved complete ensemble EMD with adaptive noise (ICEEMDAN), are noticeable improvements to EMD, aimed at alleviating mode mixing. However, the sampling frequency ratio (SFR), i.e., the ratio between the sampling frequency and the maximum signal frequency, may significantly impact their mode mixing alleviation performance. Aimed at this issue, we investigated and compared the influence of the SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms. The results show that for a given signal, (1) SFR has an aperiodic influence on the mode mixing alleviation performance of four noise-assisted EMD algorithms, (2) a careful selection of SFRs can significantly improve the mode mixing alleviation performance and avoid decomposition instability, and (3) ICEEMDAN has the best mode mixing alleviation performance at the optimal SFR among the four noise-assisted EMD algorithms. The applications include, for instance, tool wear monitoring in machining as well as fault diagnosis and prognosis of complex systems that rely on signal decomposition to extract the components corresponding to specific behaviors.
Highlights
Wear monitoring and fault diagnosis of complex systems rely on decomposing signals to extract the components that correspond to specific behaviors, such as abnormal phenomena or failures
To design a tool wear monitoring or fault diagnosis method based on a noise-assisted empirical mode decomposition (EMD) algorithm and choose an optimal sampling frequency ratio (SFR), one needs to analyze and compare the influence of SFR on the mode mixing alleviation performance of these four noise-assisted EMD algorithms
The results showed that ICEEMDAN has the best decomposition performance
Summary
Wear monitoring and fault diagnosis of complex systems rely on decomposing signals to extract the components that correspond to specific behaviors, such as abnormal phenomena or failures. In [16,17,18,19], EEMD, CEEMD, CEEMDAN, and ICEEMDAN were used to decompose the machining signal and alleviate the mode mixing caused by intermittent wave components for monitoring the tool wear state. These works do not consider the influence of SFR on the mode mixing alleviation performance of the four noise-assisted EMD algorithms. One can see that SFR may affect the mode mixing alleviation performance of the four noise-assisted EMD algorithms, especially for ICEEMDAN, due to the appearance of the decomposition instability phenomenon.
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