The acoustic pressure signal generated by blades is one of the key indicators for condition monitoring and fault diagnosis in the field of turbines. Generally, the working conditions of the turbine are harsh, resulting in a large amount of interferen...
As rock fractures caused by micro-seismic events has potential safety hazards to underground workers, it is often necessary to accurately locate the micro-seismic source for hidden danger investigation. Micro-seismic data are generated in complex und...
A novel low-pass filtering self-adaptive (LPFA) denoising method combining complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and a wavelet threshold (WT) strategy is proposed to solve the problem of the aero-engine gas-path...
Grouting power serves as a critical parameter reflecting real-time energy input during grouting operations, and its accurate prediction is essential for intelligent control and engineering safety. Existing prediction methods often struggle to handle...
Rail corrugation intensifies wheel–rail vibrations, often leading to damage in vehicle–track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble...
Due to the non-linear and non-stationary characteristics of ship radiated noise (SR-N) signal, the traditional linear and frequency-domain denoising methods cannot be used for such signals. In this paper, an SR-N signal denoising method based on modi...
To address the issue of weak geoelectric current field signals that are severely affected by noise and cannot be directly used for geological structure analysis in injected geoelectric current field detection technology, this study proposes a complet...
Given that the power load data are stochastic and it is difficult to obtain accurate forecasting results by a single algorithm. In this study, a combined forecasting method for short-term power load was proposed based on the Complete Ensemble Empiric...
In this article, a method is proposed to effectively extract weak fault features and accurately diagnose faults in ball screws, even in the presence of strong background noise. This method combines singular value decomposition (SVD), complete ensembl...
An inertial guidance system based on a fiber optic gyroscope (FOG) is an effective way to guide long-distance curved pipe jacking. However, environmental disturbances such as vibration, electromagnetism, and temperature will cause the FOG signal to g...
From the viewpoint of BDS bridge displacement monitoring, which is easily affected by background noise and the calculation of a fixed threshold value in the wavelet filtering algorithm, which is often related to the data length. In this paper, a data...
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 Gr...
The operational complexities of the elastic tooth drum pepper harvester (ETDPH), characterized by variable drum loads that are challenging to recognize due to varying pepper densities, significantly impact pepper loss rates and mechanical damage. Thi...
The analysis of the vibration-transmission path is one of the keys to the vibration control and safety monitoring of a hydropower house, and the vibration source of the hydropower house is complex, making it more difficult to analyze the vibration-tr...
When utilizing underwater gliders to observe submerged targets, ensuring the quality and reliability of the acquired target characteristic signals is paramount. However, the signal acquisition process is significantly compromised by noise generated f...
Accurate forecasting of significant wave height (WVHT) is essential for marine disaster prevention, offshore operations, and coastal management. However, WVHT time series typically exhibit strong nonlinearity and non-stationarity, which pose signific...
Effective diagnosis of vibration fault is of practical significance to ensure the safe and stable operation of power transformers. Aiming at the traditional problems of transformer vibration fault diagnosis, a novel feature extraction method based on...
Short-term load is influenced by multiple external factors and shows strong nonlinearity and volatility, which increases the forecasting difficulty. However, most of existing short-term load forecasting methods rely solely on the original load data o...
The issue of power quality disturbances in modern power systems has become increasingly complex and severe, with multiple disturbances occurring simultaneously, leading to a decrease in the recognition accuracy of traditional algorithms. This paper p...
In many practical communication environments, the presence of uncertain and hard-to-estimate noise poses significant challenges to cognitive radio spectrum sensing systems, especially when the noise distribution deviates from the Gaussian distributio...
The noise of near-infrared spectra and spectral information redundancy can affect the accuracy of calibration and prediction models in near-infrared analytical technology. To address this problem, the improved Complete Ensemble Empirical Mode Decompo...
Frequency-modulated continuous-wave (FMCW) LiDAR systems frequently experience noise interference during multi-target measurements in real-world applications, resulting in target overlapping and diminished detection accuracy. Conventional denoising a...
A comprehensive fault diagnosis method of rolling bearing about noise interference, fault feature extraction, and identification was proposed. Based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), detrended fluctuatio...
Landslides are a typical geological disaster, and are a great challenge to land use management. However, the traditional landslide displacement model has the defect of ignoring random displacement. In order to solve this situation, this paper propose...
Carbon trading has been deemed as the most effective mechanism to mitigate carbon emissions. However, during carbon trading market operation, competition among market participants will inevitably occur; hence, the precise forecasting of the carbon tr...
Despite the increased attention that has been given to the unmanned aerial vehicle (UAV)-based magnetic survey systems in the past decade, the processing of UAV magnetic data is still a tough task. In this paper, we propose a novel noise reduction me...
Among renewable energy sources, wind energy is regarded as one of the fastest-growing segments, which plays a key role in enhancing environmental quality. Wind turbines are generally located in remote and harsh environments. Bearings are a crucial co...
Electrophysiological signals are one of the key ways that fish convey information and govern movement. Changes in physiological electrical signals may indirectly reflect changes in fish sensory thresholds and locomotor behavior. The acquisition of ph...
Owing to the problems that imperfect decomposition process of empirical mode decomposition (EMD) denoising algorithm and poor self-adaptability, it will be extremely difficult to reduce the noise of signal. In this paper, a noise reduction method of...
Plant surface electrical signals are key representations for non-destructive monitoring of changes in cell membrane potential, enabling real-time reflection of physiological responses and regulatory processes under external stimuli. However, the low-...
Urban water quality is facing strongly adverse degradation in rapidly developing areas. However, there exists a huge challenge to estimating the inner features and predicting the variation of long-term water quality due to the lack of related monitor...
A dam deformation prediction method based on interpretable component decomposition and integration is proposed to address the problems of weak interpretability, difficult identification of key factors, and insufficient accuracy in the prediction mode...
In order to improve wind power prediction accuracy and increase the utilization of wind power, this study proposes a novel complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN)–variational modal decomposition (VMD)–...
Extracting useful features from ship-radiated noise can improve the performance of passive sonar. The entropy feature is an important supplement to existing technologies for ship classification. However, the existing entropy feature extraction method...
Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) effectively separates the fault vibration signals of rolling bearings and improves the diagnosis of rolling bearing faults. However, CEEMDAN has high memory requirements and...
This work introduces a novel approach to extract beam bridge mode shapes using the residual response between consecutive contact points of vehicles passing through a bridge. A comprehensive investigation is conducted on several critical parameters, i...
Frequent and severe icing on transmission lines poses a serious threat to the stability and safe operation of the power system. Meteorological data, inherently stochastic and uncertain, requires effective preprocessing and feature extraction to ensur...
The accuracy of hydro-turbine fault diagnosis directly impacts the safety and operational efficiency of hydroelectric power generation systems. This paper addresses the challenge of low diagnostic accuracy in traditional methods under complex environ...
In order to remove noise and preserve the important features of a signal, a hybrid de-noising algorithm based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), Permutation Entropy (PE), and Time-Frequency Peak Filtering...
An accurate groundwater level (GWL) forecast at multi timescales is vital for agricultural management and water resource scheduling in arid irrigated areas such as the Hexi Corridor, China. However, the forecast of GWL in these areas remains a challe...
Because of the random volatility of traffic data, short-term traffic flow forecasting has always been a problem that needs to be further researched. We developed a short-term traffic flow forecasting approach by applying a secondary decomposition str...
A novel electrocardiogram (ECG) signal de-noising and baseline wander correction method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and wavelet threshold is proposed. Although CEEMDAN is based on empirical mo...
To eliminate the influence of white noise in partial discharge (PD) detection, we propose a novel method based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and approximate entropy (ApEn). By introducing adaptive noi...
A rolling bearing vibration signal fault feature enhancement method based on adaptive complete ensemble empirical mode decomposition with adaptive noise algorithm (CEEMDAN) and maximum correlated kurtosis deconvolution (MCKD) is proposed to address t...
The cutting sound signal of a coal mining shearer is an important signal source for identifying the coal–rock cutting mode and load state. However, the coal–rock cutting sound signal directly collected from the industrial field always con...
Wheat dominates the Australian grain production market and accounts for 10–15% of the world’s 100 million tonnes annual global wheat trade. Accurate wheat yield prediction is critical to satisfying local consumption and increasing exports...
Accurate short-term load forecasting (STLF) plays an essential role in sustainable energy development. Specifically, energy companies can efficiently plan and manage their generation capacity, lessening resource wastage and promoting the overall effi...
During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a sig...
The prediction of landslide displacement is a challenging and essential task. It is thus very important to choose a suitable displacement prediction model. This paper develops a novel Attention Mechanism with Long Short Time Memory Neural Network (AM...
In recent years, wave energy has gained attention for its sustainability and cleanliness. As one of the most important parameters of wave energy, significant wave height (SWH) is difficult to accurately predict due to complex ocean conditions and the...
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