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In response to the difficulty in extracting early fault feature signals of rolling bearings, a joint fault diagnosis method based on Improved Complete Ensemble Empirical Mode Decomposition (ICEEMD) and Independent Component Analysis(ICA) was proposed. This method utilized the kurtosis criterion to reconstruct the Intrinsic Mode Function (IMF) obtained from ICEEMD and combined it with Fast Independent Component Analysis (FastICA) for noise reduction and unmixing, significantly reducing the noise in the measured signals. The maximum energy amplitude was obtained at the fault feature frequency, making it easy to identify fault features. Through experimental research and analysis, it is shown that this method can significantly reduce noise interference and highlight fault frequency components. Compared with the method combining ICEEMD and envelope spectrum, the signal-to-noise ratio is inereased by 29.54%, which can more accurately identify fault features and meet the discrimination requirements for rolling bearing faults, thus providing a new approach for bearing fault feature extraction.

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Accurate seasonal peak load (SPL) prediction of underground gas storage (UGS) is of great significance for enterprises to formulate scheduling plans. In this work, a novel two-stage model is proposed to predict it. First, improved complete ensemble empirical mode decomposition (ICEEMDAN) is used to decompose the seasonal peak load into several intrinsic mode function. Second, the highest frequency components are smoothed by using Gaussian smoothing (GS), and second highest frequency component is decomposed into several components by applying wavelet transform (WT). Third, long short-term memory (LSTM) optimized by improved sparrow search algorithm (ISSA) is utilized to predict them, and the prediction results of these components are recombined to obtain the final prediction results. Finally, the historical data of Wen 23 in the Zhongyuan gas storage group is taken as a case, and three key issues are discussed. The results: (1) Utilizing ICEEMDAN, GS, WT, and ISSA can effectively improve the prediction performance of the LSTM; (2) The prediction performance of one-step is better than that of multi-step; (3) The comprehensive error and stability evaluation index is reasonable. The following conclusion is reached: the established prediction model can be used as a reference for the development of the UGS scheduling platform. • An innovative two-stage model is developed to forecast the SPL of UGS. • The proposed model is compared concerning prediction accuracy and stability. • Comparison of the developed model and four original component models is given. • The different step predictions and rationality of error indicators are discussed. [ABSTRACT FROM AUTHOR]