Search Results (703)

As global energy demand continues to rise, accurate load forecasting has become increasingly crucial for power system operations. This study proposes a novel Complete Ensemble Empirical Mode Decomposition with Adaptive Noise-Fast Fourier Transform-inverted Transformer-Long Short-Term Memory (CEEMDAN-FFT-iTransformer-LSTM) methodological framework to address the challenges of component complexity and transient fluctuations in power load sequences. The framework initiates with CEEMDAN-based signal decomposition, which dissects the original load sequence into multiple intrinsic mode functions (IMFs) characterized by different temporal scales and frequencies, enabling differentiated processing of heterogeneous signal components. A subsequent application of Fast Fourier Transform (FFT) extracts discriminative frequency-domain features, thereby enriching the feature space with spectral information. The architecture employs an iTransformer module with multi-head self-attention mechanisms to capture high-frequency patterns in the most volatile IMFs, while a gated recurrent unit (LSTM) specializes in modeling low-frequency components with longer temporal dependencies. Experimental results demonstrate the proposed framework achieves superior performance with an average 80% improvement in R-squared ($R^2$), 40.1% lower Mean Absolute Error (MAE), and 54.1% reduced Mean Squared Error (RMSE) compared to other models. This advancement provides a robust computational tool for power grid operators, enabling optimal resource dispatch through enhanced prediction accuracy to reduce operational costs. The demonstrated capability to resolve multi-scale temporal dynamics suggests potential extensions to other forecasting tasks in energy systems involving complex temporal patterns. Full article

Failures in critical industrial components (bearings, compressors, and conveyor belts) often lead to unplanned downtime, high costs, and safety concerns. Traditional diagnostic approaches underperform in noisy or changing environments due to heavy reliance on manual feature engineering and rule-based systems. In response, advanced machine learning, deep learning, and sophisticated signal processing techniques have emerged as transformative solutions for fault detection and predictive maintenance. To address the complexity of these advancements and their practical implications, this review combines analyses from large language models with expert validation to categorize key methodologies—spanning classical machine learning models, deep neural networks, and hybrid physics–data approaches. It also explores essential signal processing tools (e.g., Fast Fourier Transform (FFT), wavelets, and Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)) and methods for estimating Remaining Useful Life (RUL) while highlighting major challenges such as the scarcity of labeled data, the need for model explainability, and adaptation to evolving operational conditions. By synthesizing these insights, this article offers a path forward for the adoption of new technologies (deep learning, IoT/Industry 4.0, etc.) in complex industrial contexts, anticipating the collaborative and sustainable paradigms of Industry 5.0, where human–machine collaboration and sustainability play central roles. Full article

The forecasting of high-water-cut oil well production faces challenges of strong nonlinearity and nonstationarity due to reservoir heterogeneity and multiscale dynamic characteristics. This study proposes a hybrid CEEMDAN-SR-BiLSTM framework based on a “decomposition-feature enhancement-integration” architecture. The framework employs Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) to suppress mode mixing, reconstructs high-, medium-, and low-frequency subsequences using Hilbert-Huang Transform (HHT) combined with tercile thresholding, and finally achieves multiscale feature fusion prediction through a Bayesian-optimized bidirectional long short-term memory network (BiLSTM). Interpretability analysis based on SHapley Additive exPlanations (SHAP) values reveals the contribution degrees of parameters such as water injection volume and flowing pressure to different frequency components, establishing a mapping between production data features and physical mechanisms of oil well production. This mapping, integrated with physical mechanisms including wellbore transient flow, injection-production response lag, and reservoir pressure evolution, enables mechanistic interpretation of production phenomena and quantitative decoupling and prediction of multiscale dynamics. Experimental results show that the framework achieves a root-mean-square error (RMSE) of 3.75 in forecasting a high-water-cut well (water cut = 87.6%) in the Qaidam Basin, reducing errors by 26.0% and 50.0% compared to CEEMDAN-BiLSTM and BiLSTM models, respectively, with a coefficient of determination (R²) reaching 0.954. Full article

In order to solve the problem that the vibration signal of transformer is affected by noise and electromagnetic interference, resulting in low accuracy of fault diagnosis mode recognition, a CEEMDAN-MRAL fault diagnosis method based on Fiber Bragg Grating (FBG) was proposed to quickly and accurately evaluate the vibration fault state of transformer.The FBG sends the wavelength change in the optical signal center caused by the vibration of the transformer to the demodulation system, which obtains the vibration signal and effectively avoids the noise influence caused by strong electromagnetic interference inside the transformer. The vibration signal is decomposed into several intrinsic mode functions (IMFs) by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the wavelet threshold denoising algorithm improves the signal-to-noise ratio (SNR) to 1.6 times. The Markov transition field (MTF) is used to construct a training and test set. The unique MRAL-Net is proposed to extract the spatial features of the signal and analyze the time series dependence of the features to improve the richness of the signal feature scale. This proposed method effectively removes the noise interference. The average accuracy of fault diagnosis of the transformer winding core reaches 97.9375%, and the time taken on the large-scale complex training set is only 1705 s, which has higher diagnostic accuracy and shorter training time than other models. Full article

Accurate prediction of Global Navigation Satellite System-derived precipitable water vapor (GNSS-PWV), which is a crucial indicator for climate change monitoring, holds significant scientific value for climate disaster prevention and mitigation. In the study of GNSS-PWV prediction, the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) algorithm within a decomposition–integration framework effectively addresses the non-stationarity and complexity of PWV sequences, enhancing prediction accuracy. However, residual noise and pseudo-modes from decomposition can distort signals, reducing the predictor system’s reliability. Additionally, independent modeling of all decomposed components decreases computational efficiency. To address these challenges, this paper proposes a hybrid model combining the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), autoregressive integrated moving average (ARIMA), and long short-term memory (LSTM) networks. Enhanced by local mean optimization and adaptive noise regulation, the ICEEMDAN algorithm effectively suppresses pseudo-modes and minimizes residual noise, enabling its decomposed intrinsic mode functions (IMFs) to more accurately capture the multi-scale features of GNSS-PWV. Sample entropy (SE) is used to quantify the complexity of IMFs, and components with similar entropy values are reconstructed into the following three sub-sequences: high-frequency, low-frequency, and trend. This process significantly reduces modeling complexity and improves computational efficiency. We propose different modeling strategies tailored to the dynamics of various subsequences. For the nonlinear and non-stationary high-frequency components, the LSTM network is used to effectively capture their complex patterns. The LSTM’s gating mechanism and memory cell design proficiently address the long-term dependency issue. For the stationary and weakly nonlinear low-frequency and trend components, linear patterns are extracted using ARIMA. Differencing eliminates trends and moving average operations capture random fluctuations, effectively addressing periodicity and trends in the time series. Finally, the prediction results of the three components are linearly combined to obtain the final prediction value. To validate the model performance, experiments were conducted using measured GNSS-PWV data from several stations in Hong Kong. The results demonstrate that the proposed model reduces the root mean square error by 56.81%, 37.91%, and 13.58% at the 1 h scale compared to the LSTM, EMD-LSTM, and ICEEMDAN-SE-LSTM benchmark models, respectively. Furthermore, it exhibits strong robustness in cross-month forecasts (accounting for seasonal influences) and multi-step predictions over the 1–6 h period. By improving the accuracy and efficiency of PWV predictions, this model provides reliable technical support for the real-time monitoring and early warning of extreme weather events in Hong Kong while offering a universal methodological reference for multi-scale modeling of geophysical parameters. Full article

This study introduces a novel forecast combination method for monthly Japanese tourism demand, analyzed at both aggregated and disaggregated levels, including tourist, business, and other travel purposes. The sample period spans from January 1996 to December 2018. Initially, the time series data were decomposed into high and low frequencies using the Ensemble Empirical Mode Decomposition (EEMD) technique. Following this, Autoregressive Integrated Moving Average (ARIMA), Neural Network (NN), and Support Vector Machine (SVM) forecasting models were applied to each decomposed component individually. The forecasts from these models were then combined to produce the final predictions. Our findings indicate that the two-stage forecast combination method significantly enhances forecasting accuracy in most cases. Consequently, the combined forecasts utilizing EEMD outperform those generated by individual models. Full article

Prolonged and high-intensity human–robot interaction can cause muscle fatigue. This fatigue leads to changes in both the time domain and frequency domain of the surface electromyography (sEMG) signals, which are closely related to human body movements. Consequently, these changes affect the accuracy and stability of using sEMG signals to recognize human body movements. Although numerous studies have confirmed that the median frequency of sEMG signals decreases as the degree of muscle fatigue increases—and this has been used for classifying fatigue and non-fatigue states— there is still a lack of quantitative characterization of the degree of muscle fatigue. Therefore, this paper proposes a method for quantitatively characterizing the degree of muscle fatigue during periodic exercise, based on the high-frequency components obtained through ensemble empirical mode decomposition (EEMD). Firstly, the sEMG signals of the estimated individuals are subjected to EEMD to obtain the high-frequency components, and the short-time Fourier transform is used to calculate the median frequency (MF) of these high-frequency components. Secondly, the obtained median frequencies are linearly fitted, and based on this, a standardized median frequency distribution range (SMFDR) of sEMG signals under muscle fatigue is established. Finally, a muscle fatigue estimator is proposed to achieve the quantification of the degree of muscle fatigue based on the SMFDR. Experimental validation across five subjects demonstrated that this method effectively quantifies cyclical muscle fatigue, with results revealing the methodology exhibits superiority in identifying multiple fatigue states during cyclical movements under consistent loading conditions. Full article

Determining the sea surface height using Global Navigation Satellite System (GNSS) buoys is an important method for satellite altimetry calibration. The buoys observe the absolute height of the sea surface using GNSS positioning technology, which is then used to correct the systematic deviation of the altimeter of the orbiting satellite. Due to the challenging observational conditions, such as significant multipath errors in GNSS code observation and complex variations in buoy position and attitude, gross errors in GNSS buoy positioning reduce the accuracy and stability of the calculated sea surface heights. To accurately detect and remove these gross errors from GNSS coordinate time series, the complementary ensemble empirical mode decomposition (CEEMD) method and the interquartile range (IQR) method were adopted to enhance the accuracy and stability of GNSS sea surface altimetry. Firstly, the raw GNSS sequential coordinate series are decomposed into main terms, such as trend contents and periodic contents, and high-frequency noise terms using the CEEMD method. Subsequently, the high-frequency noise terms of the GNSS coordinate series are regarded as the residual sequences, which are used to detect gross errors using the IQR method. This approach, which integrates the CEEMD and IQR methods, was named CEEMD-IQR and enhances the ability of the traditional IQR method to detect subtle gross errors in GNSS coordinate time series. The results indicated that the CEEMD-IQR method effectively detected gross errors in offshore GNSS coordinate time series using GNSS buoys, presenting a significant enhancement in the gross error detection rate of at least 35.3% and providing a “clean” time series for sea level measurements. The resulting GNSS sea surface altimetry accuracy was found to be better than 1.51 cm. Full article

The accurate and stable prediction of crude oil prices holds significant value, providing insightful guidance for investors and decision-makers. The intricate interplay of factors influencing oil prices and the pronounced fluctuations present significant obstacles within the realm of oil price forecasting. This study introduces a novel hybrid model framework, distinct from the conventional methods, that integrates influencing factors for oil price prediction. First, using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) extract mode components from crude oil prices. Second, using the Adaptive Copula-based Feature Selection (ACBFS), rooted in Copula theory, facilitates the integration of the influencing factors; ACBFS enhances both accuracy and stability in feature selection, thereby amplifying predictive performance and interpretability. Third, low-frequency modes are predicted through an Attention Mechanism-based Long and Short-Term Memory Neural Network (AM-LSTM), optimized using Bayesian Optimization and Hyperband (BOHB). Conversely, high-frequency modes are forecasted using Extreme Gradient Boosting Models (XGboost). Finally, the error correction mechanism further enhances the predictive accuracy. The experimental results show that the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of the proposed hybrid prediction framework are the lowest compared to the benchmark model, at 0.7333 and 1.1069, respectively, which proves that the designed prediction structure has better efficiency and higher accuracy and stability. Full article

Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A demand response program can adjust the power load demand from the demand side to promote the balance of supply and demand. Load forecasting can facilitate the implementation of this program. However, as electricity consumption patterns become more diverse, the resulting load data grows increasingly irregular, making precise forecasting more difficult. Therefore, this paper developed a specialized forecasting scheme. First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, the nonlinear power load data were decomposed into multiple subsequences using ICEEMDAN. Finally, each subsequence was independently predicted using the iTransformer model, and the overall forecast was derived by integrating these individual predictions. Data from Singapore was selected for validation. The results showed that the BWO–ICEEMDAN–iTransformer model outperformed the other comparison models, with an R² of 0.9873, RMSE of 48.0014, and MAE of 66.2221. Full article

Lightning Very-High-Frequency (VHF) radiation source mapping technology represents a pivotal advancement in the study of lightning discharge processes and their underlying physical mechanisms. This paper introduces a novel methodology for reconstructing lightning discharge channels by employing the Multiple Signal Classification (MUSIC) algorithm to estimate the Direction of Arrival (DOA) of lightning VHF radiation sources, specifically tailored for both non-uniform and uniform L-shaped arrays (2D-MUSIC). The proposed approach integrates the Random Sample Consensus (RANSAC) algorithm with 2D-MUSIC, thereby enhancing the precision and robustness of the reconstruction process. Initially, the array data are subjected to denoising via the Ensemble Empirical Mode Decomposition (EEMD) algorithm. Following this, the covariance matrix of the processed array data is decomposed to isolate the signal subspace, which corresponds to the signal components, and the noise subspace, which is orthogonal to the signal components. By exploiting the orthogonality between these subspaces, the method achieves an accurate estimation of the signal incidence direction, thereby facilitating the precise reconstruction of the lightning channel. To validate the feasibility of this method, comprehensive numerical simulations were conducted, revealing remarkable accuracy with elevation and azimuth angle errors both maintained below 1 degree. Furthermore, VHF non-uniform and uniform L-shaped lightning observation systems were established and deployed to analyze real lightning events occurring in 2021 and 2023. The empirical results demonstrate that the proposed method effectively reconstructs lightning channel structures across diverse L-shaped array configurations. This innovative approach significantly augments the capabilities of various broadband VHF arrays in radiation source imaging and makes a substantial contribution to the study of lightning development processes. The findings of this study underscore the potential of the proposed methodology to advance our understanding of lightning dynamics and enhance the accuracy of lightning channel reconstruction. Full article

The integration of energy storage systems is an effective solution to grid fluctuations caused by renewable energy sources such as wind power and solar power. This paper proposes a hybrid energy storage system (HESS) capacity optimization method combining flywheel and battery energy storage. Firstly, improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the original wind–solar power signal into a grid-connected signal and a leveling command signal. Low-pass filtering is then applied to separate the leveling command signal by frequency and assign it to the flywheel and battery of the HESS, respectively. Secondly, with the goal of minimizing the full lifecycle cost, a capacity optimization model for a flywheel–battery HESS aimed at minimizing wind–solar power fluctuation is established based on the particle swarm optimization (PSO) algorithm. Finally, a simulation analysis is conducted on a microgrid consisting of a 10 MW wind power generation system, a 10 MW solar power generation system, and a flywheel-battery HESS. The results show that the use of hybrid energy storage has a significant power smoothing effect, with a maximum power fluctuation rate of 3.2% in 1-min intervals and a maximum power fluctuation of less than 8% in 10-min intervals in most cases. Under the same stabilizing effect, the HESS reduces costs by 45.1% compared to single-battery energy storage. Full article

With the rapid development of new energy vehicle technologies, higher demands have been placed on fault diagnosis for automotive transmission gearboxes. To address the poor adaptability of traditional methods under complex operating conditions, this paper proposes a sensor data-driven fault diagnosis method based on improved ensemble empirical mode decomposition (EEMD) combined with convolutional neural networks (CNNs) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The method incorporates a dynamic noise adjustment mechanism, allowing the noise amplitude to adapt to the characteristics of the signal. This improves the stability and accuracy of signal decomposition, effectively reducing the instability and error accumulation associated with fixed-amplitude white noise in traditional EEMD. By combining the CNN and BiLSTM modules, the approach achieves efficient feature extraction and dynamic modeling. First, vibration signals of the transmission gearbox under different operating states are collected via sensors, and an improved EEMD method is employed to decompose the signals, removing background noise and nonstationary components to extract diagnostically significant intrinsic mode functions (IMFs). Then, the CNN is utilized to extract features from the IMFs, deeply mining their spatiotemporal characteristics, while the BiLSTM captures the temporal sequence dependencies of the signals, enhancing the comprehensive modeling of nonlinear and dynamic fault features. The combination of these two networks enables efficient adaptation to complex conditions, achieving accurate classification and identification of multiple gearbox fault modes. Results indicate that the proposed approach is highly accurate and robust for identifying gearbox fault modes, significantly exceeding the performance of conventional methods and isolated network models. This provides an efficient and intelligent solution for fault diagnosis of automotive transmission gearboxes. Full article

Ultra-high molecular weight polyethylene (UHMWPE) fiber-reinforced concrete (UHMWPE-FRC) is a hard–soft multiphase hybrid composite with exceptional toughness and impact resistance compared to conventional concrete. However, its cutting characteristics and processing performance have not been sufficiently investigated, potentially causing accelerated saw blade wear, higher energy consumption, and poor cutting quality, thus increasing project costs and duration. In order to intelligently evaluate the performance of diamond saw blades when cutting UHMWPE-FRC, a feature extraction method, based on feature classification and an improved Hilbert–Huang transform (HHT), is proposed, which consider Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN) and wavelet threshold de-noising. By conducting the cutting experiments, the cutting force was analyzed by the improved HHT, in terms of noise reduction and time-frequency. Five types of characteristics were preliminarily screened, including depth of cut (a_p), cutting speed (V_c), feed rate (V_f), concrete strength, and the type of concrete. A feature correlation analysis method for UHMWPE-FRC cutting, based on feature classification, is proposed. The five features were classified into continuous variable features and unordered categorical variable features; correlation analyses were carried out by Spearman correlation coefficient testing and Kruskal–Wallis and Dunn’s testing, respectively. It was found that the a_p and concrete strength exhibited a strong positive correlation with cutting force, making them the primary influencing factors. Meanwhile, the influence of aggregates on cutting force can be identified in the low-frequency range, while the influence of fibers can be identified in the high-frequency range. The feature classification-based correlation analysis effectively distinguishes the influence of V_c on cutting force. Full article

The ground-based real aperture radar (GB-RAR), with its non-contact, high-precision, continuous monitoring capabilities, is widely used in bridge safety. To reduce noise interference in GB-RAR monitoring, a denoising method based on complementary ensemble empirical mode decomposition (CEEMD), wavelet threshold denoising (WTD), and principal component analysis (PCA) was applied to the safety monitoring of the East Lake High-tech Bridge in Wuhan. The method involved CECEEMD of GB-RAR data, WTD for high-frequency noise Intrinsic Mode Function (IMF) components, and PCA for low-frequency IMF power spectrum matrices to remove coloured noise. PCA shows a symmetric balance between noise removal and signal retention. The experimental results show that the proposed denoising method ensures the integrity of the reconstructed signal by symmetrically processing the IMF of high and low frequencies and improves the signal-to-noise ratio (SNR) of the three piers to 8.30, 19.87 and 15.06, respectively, and the Root Mean Square Errors (RMSE) are 0.10 mm, 0.06 mm and 0.09 mm, respectively. Noise removal reduced uncertainty by 42.3%, 35.8%, and 33.1%, demonstrating the method’s effectiveness in enhancing deformation monitoring precision. Full article