The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST...The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.展开更多
The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the...The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.展开更多
To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ense...To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)and the Improved Maximum Correlated Kurtosis Deconvolution(IMCKD).Utilizing the kurtosis criterion,the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD,which significantly reduces noise in the measured signal.This approach maximizes the energy amplitude at the fault characteristic frequency,facilitating fault feature identification.Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components.Compared with traditional methods,it significantly improves the signal-to-noise ratio and more accurately identifies fault features,meeting the requirements for discriminating rolling bearing faults.The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high-speed wire department of a Long Products Mill.It successfully extracted weak characteristic information of early bearing faults,achieving the expected diagnostic results.This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications,demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.展开更多
基金supported in part by the Major Research Plan of the National Natural Science Foundation of China[grant number91530204]the State Key Program of the National Natural Science Foundation of China[grant number 41430426]
文摘The sea surface temperature （SST） has substantial impacts on the climate; however, due to its highly nonlinear nature, evidently non-periodic and strongly stochastic properties, it is rather difficult to predict SST. Here, the authors combine the complementary ensemble empirical mode decomposition （CEEMD） and support vector machine （SVM） methods to predict SST. Extensive tests from several different aspects are presented to validate the effectiveness of the CEEMD-SVM method. The results suggest that the new method works well in forecasting Northeast Pacific SST at a 12-month lead time, with an average absolute error of approximately 0.3℃ and a correlation coefficient of 0.85. Moreover, no spring predictability barrier is observed in our experiments.
基金supported by China Southern Power Grid Science and Technology Innovation Research Project(000000KK52220052).
文摘The rapid growth of the Chinese economy has fueled the expansion of power grids.Power transformers are key equipment in power grid projects,and their price changes have a significant impact on cost control.However,the prices of power transformer materials manifest as nonsmooth and nonlinear sequences.Hence,estimating the acquisition costs of power grid projects is difficult,hindering the normal operation of power engineering construction.To more accurately predict the price of power transformer materials,this study proposes a method based on complementary ensemble empirical mode decomposition(CEEMD)and gated recurrent unit(GRU)network.First,the CEEMD decomposed the price series into multiple intrinsic mode functions(IMFs).Multiple IMFs were clustered to obtain several aggregated sequences based on the sample entropy of each IMF.Then,an empirical wavelet transform(EWT)was applied to the aggregation sequence with a large sample entropy,and the multiple subsequences obtained from the decomposition were predicted by the GRU model.The GRU model was used to directly predict the aggregation sequences with a small sample entropy.In this study,we used authentic historical pricing data for power transformer materials to validate the proposed approach.The empirical findings demonstrated the efficacy of our method across both datasets,with mean absolute percentage errors(MAPEs)of less than 1%and 3%.This approach holds a significant reference value for future research in the field of power transformer material price prediction.
基金Inner Mongolia Autonomous Region Science and Technology Major Special Project,Grant/Award Number:2021ZD0019-4。
文摘To address the difficulty in extracting early fault feature signals of rolling bearings,this paper proposes a novel weak fault diagnosis method for rolling bearings.This method combines the Improved Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise(ICEEMDAN)and the Improved Maximum Correlated Kurtosis Deconvolution(IMCKD).Utilizing the kurtosis criterion,the intrinsic mode functions obtained through ICEEMDAN are reconstructed and denoised using IMCKD,which significantly reduces noise in the measured signal.This approach maximizes the energy amplitude at the fault characteristic frequency,facilitating fault feature identification.Experimental studies on two test benches demonstrate that this method effectively reduces noise interference and highlights the fault frequency components.Compared with traditional methods,it significantly improves the signal-to-noise ratio and more accurately identifies fault features,meeting the requirements for discriminating rolling bearing faults.The method proposed in this study was applied to the measured vibration signals of the gearbox bearings in the new high-speed wire department of a Long Products Mill.It successfully extracted weak characteristic information of early bearing faults,achieving the expected diagnostic results.This further validates the effectiveness of the ICEEMDAN–IMCKD method in practical engineering applications,demonstrating significant engineering value for detecting and extracting weak impact characteristics in rolling bearings.
