%0 Journal Article %A YANG Hang %A ZHU Yongli %T Ensemble Empirical Mode Decomposition of Partial Discharge Signal Based on Storm %D 2020 %R 10.3778/j.issn.1002-8331.1901-0253 %J Computer Engineering and Applications %P 261-267 %V 56 %N 10 %X

The stable operation of power equipment is related to people’s life and property safety. The fault diagnosis of power equipment can be realized by installing sensors to collect time series waveform signals and then analyzing and processing the signals. Ensemble Empirical Mode Decomposition(EEMD) algorithm has its unique advantages in the field of non-linear and non-stationary signal processing. However, because of its complexity, as an operation-intensive algorithm, its operation speed can not meet the actual needs in the case of serial execution. Therefore, two EEMD decomposition methods based on Storm are proposed for parallel processing of EMD process and segmented parallel processing of signals. Experiments show that both parallel schemes are faster than traditional serial execution schemes, and the piecewise parallel method has more advantages in long signal processing because of its higher parallelism. The two parallel EEMD algorithms provide a reliable solution for the fast processing of time series signals.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1901-0253