Decomposition Technique Research Articles

A Modified EMD Technique for Broken Rotor Bar Fault Detection in Induction Machines.

Induction machines (IMs) are commonly used in various industrial sectors. It is essential to recognize IM defects at their earliest stage so as to prevent machine performance degradation and improve production quality and safety. This work will focus on IM broken rotor bar (BRB) fault detection, as BRB fault could generate extra heating, vibration, acoustic noise, or even sparks in IMs. In this paper, a modified empirical mode decomposition (EMD) technique, or MEMD, is proposed for BRB fault detection using motor current signature analysis. A smart sensor-based data acquisition (DAQ) system is developed by our research team and is used to collect current signals wirelessly. The MEMD takes several processing steps. Firstly, correlation-based EMD analysis is undertaken to select the most representative intrinsic mode function (IMF). Secondly, an adaptive window function is suggested for spectral operation and analysis to detect the BRB fault. Thirdly, a new reference function is proposed to generate the fault index for fault severity diagnosis analytically. The effectiveness of the proposed MEMD technique is verified experimentally.

Effects of serration angle on noise reduction mechanism of trailing edge serration

This study investigates the effects of serration angle on the three-dimensional flow field and noise reduction mechanism of a NACA6512-63 airfoil using large eddy simulation (LES) and spectral proper orthogonal decomposition (SPOD) techniques. The serration angle is defined as the ratio of serration wavelength (λ) to serration amplitude (h). The mean flow field analysis on suction side of serration reveals the outward flow towards the serration edges and the downwash flow between the valleys that generate the counter-rotating vortex pair. This flow alteration changes the serration effective angle of the serration, consequently affecting the noise reduction performance. Additionally, by comparing the general solution of Lyu’s semi-analytical equation with the SPOD mode results, the noise reduction mechanism is examined, and the frequency range in which noise reduction occurs is analyzed. The linearly distributed multipole-like pressure patterns, similar to those in the semi-analytical solution, are observed in the low-frequency range for all three cases, with the λ/h = 0.3 case exhibiting the most effective noise reduction. The findings suggest that the local deviation of the turbulence axis, caused by the strong outward flow near the serration edges, can hinder the formation of the optimal pressure field distribution required for effective noise reduction.

Make optical transmission quality prediction more effective: A parallel computing view

In optical networks, ensuring high quality of transmission (QoT) is essential to prevent degradation of optical signals, especially when the signal strength falls below a specified threshold. While machine learning (ML) is widely used for QoT prediction, predicting QoT accurately for large-scale optical links presents challenges. Traditional serial methods often result in high latency and decreased processing efficiency of optical channels. To solve this problem, this paper proposes a Dask-based P-FEDformer approach. Initially, a FEDformer-based predictor is constructed, and then QoT prediction for multiple channels is realized under the Dask parallel architecture. To enhance model prediction accuracy, wavelet decomposition technique is employed. Simulation results demonstrate the method’s effectiveness in handling large amount of data with a 60% improvement in time efficiency compared to serial execution, while maintaining accurate QoT prediction.

Response surface method-based efficient approach for the load and resistance factors calibration of breakwater foundation considering soil spatial variability

This study aims to propose an efficient approach to calibrate the load and resistance factors (LRFs) for the limit state design of breakwater foundations. The spatial variability of soil was considered by employing the Cholesky decomposition technique. To overcome the low computational efficiency of the Monte Carlo simulation (MCS), a response surface method (RSM) was utilized. Additionally, an efficient algorithm, namely the adaptive boundary-based particle filtering algorithm (ABB-PFA), was proposed with the use of RSM to automatically and quickly search optimal nominal values of random variables in the LRF calibration process. Noticeably, the optimal number of particles and values of the modification factor used in the ABB-PFA were observed to enhance the efficiency of the calibration process. The results showed that the computational efficiency of the calibration process is significantly increased by utilizing the response surface method and the proposed ABB-PFA. The efficiency increases the most if the number of particles is one and the value of the modification factor is 0.5 or 0.6. The influences of the random field element size on the efficiency of the calibration process were also investigated. By using the appropriate size, the efficiency is significantly increased while still obtaining a high accuracy of the LRFs calibration process.

Performance of data-driven models based on seasonal-trend decomposition for streamflow forecasting in different climate regions of Türkiye

This study examines the ability of different methods such as machine learning, ensemble models, and metaheuristic algorithms to predict streamflow. For this purpose, five different methods were used: Artificial Neural Networks (ANN), Support Vector Machines (SVM), Adaptive Boosting, Particle Swarm Optimization (PSO), and BSPSO hybridized with Band Similarity (BS), a relatively new method. Additionally, the impact of seasonality and trend components obtained through Seasonal-Trend decomposition using LOESS (locally weighted regression and scatterplot smoothing) (STL) data decomposition technique on prediction success was investigated. Models were developed in three basins with three different climate characteristics: continental, temperate, and arid. The results showed higher prediction success in input structures including seasonality and trend components. While higher prediction successes were achieved at Karasu in the continental climate class and Körkün in the temperate climate class, model performances were lower at Küçük Muhsine in the arid climate class. While the most successful modeling for Küçük Muhsine (NSE = 0.696) and Karasu stations (NSE = 0.811) was obtained with the BSPSO method, the SVM method produced the best results for Körkün station (NSE = 0.818). Moreover, BSPSO models outperformed the prediction successes obtained by using PSO alone for each scenario at all three stations. The percentages of the BSPSO method improving the prediction success according to the NSE metric ranged from 3.53% to 17.40% at Küçük Muhsine, 0.49%–3.72% at Karasu, and 1.24%–7.24% at Körkün. The competitive results achieved by the BSPSO approach compared to ANN and SVM in flow prediction constitute the innovative aspect of this study.

Analytic analysis of free vibration problem of the plate with a rectangular cutout using symplectic superposition method combined with domain decomposition technique

Plates with rectangular cutouts is widely seen in the field of engineering structures. Therefore, it is crucial to examine analytical solutions for free vibration (FV) of these structures. Despite the existence of approximate/numerical methods, analytical solutions are lacking in the literature. In this study, we employ the symplectic superposition method to effectively analyze the FV problems encountered in plates with rectangular cutouts while integrating the domain decomposition technique. To address the issue of irregular geometry, the rectangular cutout plate is divided into multiple sub-plates. By dividing the problem into multiple sub-problems and solving them separately using variable separation and symplectic eigen expansion, we obtain analytical solutions. Finally, we combine the sub-problem solutions to resolve the initial issue. This solution method can be considered a logical, analytical, and systematic approach as it starts with the fundamental governing equation and is derived without assuming forms of solutions. The study presents a comprehensive set of numerical results that include mode shapes (MSs) and natural frequencies (NFs). The results are rigorously validated using the finite element method (FEM) and relevant literature. The symplectic superposition method demonstrates excellent convergence and precise accuracy, making it suitable for analytically modeling more complex mechanical problems of plates.

A new code for low-resolution spectral identification of white dwarf binary candidates

Close white dwarf binaries (CWDBs) are considered to be progenitors of several exotic astronomical phenomena (e.g., type Ia supernovae, cataclysmic variables). These violent events are broadly used in studies of general relativity and cosmology. However, obtaining precise stellar parameter measurements for both components of CWDBs is a challenging task given their low luminosities, swift time variation, and complex orbits. High-resolution spectra (R$> 20 000$) are preferred but expensive, resulting in a sample size that is insufficient for robust population study. Recently, studies have shown that the more accessible low-resolution (R$ 2000$) spectra (LRS) may also provide enough information for spectral decomposition. To release the full potential of the less expensive low-resolution spectroscopic surveys, and thus greatly expand the CWDB sample size, it is necessary to develop a robust pipeline for spectra decomposition and analysis. We aim to develop a spectroscopic fitting program for white dwarf binary systems based on photometry, LRS, and stellar evolutionary models. The outputs include stellar parameters of both companions in the binary including effective temperature, surface gravity, mass, radius, and metallicity in the case of MS stars. We used an artificial neural network (ANN) to build spectrum generators for DA/DB white dwarfs and main-sequence stars. Characteristic spectral lines were used to decompose the spectrum of each component. The best-fit stellar parameters were obtained by finding the least $ solution to these feature lines and the continuum simultaneously. Compared to previous studies, our code is innovative in the following aspects: (1) implementing a sophisticated binary decomposition technique in LRS for the first time; (2) using flux-calibrated spectra instead of photometry plus spectral lines, in which the latter requires multi-epoch observations; (3) applying an ANN in binary decomposition, which significantly improves the efficiency and accuracy of generated spectra. We demonstrate the reliability of our code with two well-studied CWDBs, WD 1534+503 and PG 1224+309. We also estimate the stellar parameters of 14 newly identified CWDB candidates, most of which are fitted with double component models for the first time. Our estimates agree with previous results for the common stars and follow the statistical distribution in the literature. We provide a robust program for fitting binary spectra of various resolutions. Its application to a large volume of white dwarf binary candidates will offer important statistic samples to stellar evolution studies and future gravitational wave monitoring.

Short‐term multivariate airworthiness forecasting based on decomposition and deep prediction models

AbstractThis study introduces a model for predicting airworthiness in terms of meteorology information within the viewpoint of not only formal regulations but also informal rules based on acquired indicators from flight training organization experience (AIs‐FTOE). The case study is carried out in the Hasan Polatkan Airport which is used by the Department of Flight Training of Eskişehir Technical University (ESTU‐P), which is also recognized as a flight training organization. Within the study, the constraints (derived from regulations and AIs‐FTOE) and the data set used in models are explained. Also, the models are introduced based on the gated recurrent unit (GRU) and long short‐term memory (LSTM) with the use of empirical mode decomposition (EMD) and variational mode decomposition (VMD). Finally, a model‐selective mechanism (MSM) is proposed to use the models in common. The findings show that the models presented in the study produce successful results that can be used in flight training organization's (FTO) planning studies. The MSM uses GRU and LSTM together with decomposition techniques to provide more advanced prediction capabilities. When the literature is examined, it is observed that although meteorological conditions are of vital importance in the efficiency of FTOs, there are not enough studies on airworthiness based on meteorology. So, a model that will assist in scheduling plans is presented for FTOs. Airworthiness analysis of forecasting can provide a comprehensive reference to support planning efficiency in FTOs. To the authors' knowledge, this study will be the first in the literature on airworthiness that presents the MSM using a hybrid deep learning algorithm and decomposition of time series models in concurrent.

Multiscale-integrated deep learning approaches for short-term load forecasting

Accurate short-term load forecasting (STLF) is crucial for the power system. Traditional methods generally used signal decomposition techniques for feature extraction. However, these methods are limited in extrapolation performance, and the parameter of decomposition modes needs to be preset. To end this, this paper develops a novel STLF algorithm based on multi-scale perspective decomposition. The proposed algorithm adopts the multi-scale deep neural network (MscaleDNN) to decompose load series into low- and high-frequency components. Considering outliers of load series, this paper introduces the adaptive rescaled lncosh (ARlncosh) loss to fit the distribution of load data and improve the robustness. Furthermore, the attention mechanism (ATTN) extracts the correlations between different moments. In two power load data sets from Portugal and Australia, the proposed model generates competitive forecasting results.

Identification of exon regions in eukaryotes using fine-tuned variational mode decomposition based on kurtosis and short-time discrete Fourier transform

In genomic research, identifying the exon regions in eukaryotes is the most cumbersome task. This article introduces a new promising model-independent method based on short-time discrete Fourier transform (ST-DFT) and fine-tuned variational mode decomposition (FTVMD) for identifying exon regions. The proposed method uses the N/3 periodicity property of the eukaryotic genes to detect the exon regions using the ST-DFT. However, background noise is present in the spectrum of ST-DFT since the sliding rectangular window produces spectral leakage. To overcome this, FTVMD is proposed in this work. VMD is more resilient to noise and sampling errors than other decomposition techniques because it utilizes the generalization of the Wiener filter into several adaptive bands. The performance of VMD is affected due to the improper selection of the penalty factor (α), and the number of modes (K). Therefore, in fine-tuned VMD, the parameters of VMD (K and α) are optimized by maximum kurtosis value. The main objective of this article is to enhance the accuracy in the identification of exon regions in a DNA sequence. At last, a comparative study demonstrates that the proposed technique is superior to its counterparts.

Ensemble learning of decomposition-based machine learning models for multistep-ahead daily streamflow forecasting in northwest China

ABSTRACT Accurate daily streamflow forecasts remain challenging in arid regions. A Bayesian model averaging (BMA) ensemble learning strategy was proposed to forecast 1-, 2-, and 3-day-ahead streamflow in Dunhuang Oasis, northwest China. The efficiency of BMA was compared with four decomposition-based machine learning and deep learning models. Satisfactory forecasts were achieved with all proposed models at all lead times; however, based on Nash-Sutcliffe efficiency values of 0.976, 0.967, and 0.957, BMA achieved the greatest accuracy for 1-, 2-, and 3-day-ahead streamflow forecasts, respectively. Uncertainty analysis confirmed the reliability of BMA in yielding consistently accurate streamflow forecasts. Thus, BMA could provide an efficient alternative approach to multistep-ahead daily streamflow forecasting. The incorporation of data decomposition techniques (e.g. variational mode decomposition) and deep learning algorithms (e.g. deep belief network) into BMA may provide worthy technical references for supervised learning of streamflow systems in data-scarce regions.

Disturbance rejection analysis of singular time-delay systems based on state decomposition method

The state decomposition technique (SDT) based on memory state-feedback control (MSFC) is used in this article to provide a new disturbance-rejection (DR) method for singular time-delay systems (STSs). First, under MSFC, a singular time-delay control system with disturbance estimator is constructed. Then, a DR control law, which contains disturbance information and MSFC, is designed. Second, the STS is split into algebraic and differential subsystems based on the SDT. Next, a brand-new enhanced Lyapunov–Krasovskii function (LKF) with fewer state vectors is designed. Additionally, a new delay-dependent admissible criterion with fewer decision variables is obtained based on the LKF by combining a Jensen inequality and two zero equations. Third, the controller is designed according to the decomposition component of the controller gain. Finally, the usefulness and efficiency of the strategy are demonstrated by comparing it to a sliding mode control and a traditional control technique.

Estimation of Carleman operator from a univariate time series.

Reconstructing a nonlinear dynamical system from empirical time series is a fundamental task in data-driven analysis. One of the main challenges is the existence of hidden variables; we only have records for some variables, and those for hidden variables are unavailable. In this work, the techniques for Carleman linearization, phase-space embedding, and dynamic mode decomposition are integrated to rebuild an optimal dynamical system from time series for one specific variable. Using the Takens theorem, the embedding dimension is determined, which is adopted as the dynamical system's dimension. The Carleman linearization is then used to transform this finite nonlinear system into an infinite linear system, which is further truncated into a finite linear system using the dynamic mode decomposition technique. We illustrate the performance of this integrated technique using data generated by the well-known Lorenz model, the Duffing oscillator, and empirical records of electrocardiogram, electroencephalogram, and measles outbreaks. The results show that this solution accurately estimates the operators of the nonlinear dynamical systems. This work provides a new data-driven method to estimate the Carleman operator of nonlinear dynamical systems.

The prediction on non-Gaussian characteristics of wind pressure for the long-span roof in the mountainous area using proper orthogonal decomposition–deep learning framework

To make an accurate prediction of the non-Gaussian characteristics of wind pressure for the long-span roof, this study combines the proper orthogonal decomposition (POD) technique, convolutional neural network (CNN), and long short-term memory (LSTM) network to propose a novel POD-CNN-LSTM framework. Then, the proposed framework was well validated based on the wind tunnel testing of a long-span roof structure, and some error criteria, such as mean square root error and correlation coefficient, were adopted to evaluate the prediction accuracy of the non-Gaussian characteristics. Furthermore, two other methods, POD-CNN and POD-LSTM, were also used to conduct a comparative study. The obtained results illustrate that compared to POD-CNN and POD-LSTM, the proposed framework can achieve better performance on the pulsating wind pressure coefficient. For predictions of non-Gaussian characteristics, the output results of the proposed POD-CNN-LSTM show fewer errors, which means the predictions are close to the measured results, including skewness, kurtosis, and wind pressure probability density distributions. To summarize, the proposed POD-CNN-LSTM framework shows superiority over others, which means the proposed framework has good potential for the practical application of non-Gaussian prediction of the engineering structure.

Soil moisture prediction based on integrating the CEEMDAN decomposition technique with LSTM in the proximity of Prosopis Juliflora

Human-made adaptations in plant diversity, particularly the introduction of alien invasive plant species, can result in problems such as depletion of groundwater, extraction of natural water resources, and modifications in the local biodiversity. In addition to regulating ecosystems and accurate farming, precise forecasting of soil moisture in the vicinity of these exotic plant species can greatly assist in various other agricultural applications. In this study, we present an improved approach for predicting soil moisture levels at different depths in the vicinity of Prosopis Juliflora (IAPS) by integrating long short-term memory (LSTM) and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). CEEMDAN is employed as a preprocessing and denoising technique to decompose the initial soil moisture data into several intrinsic mode functions (IMF). After that, LSTM is used to predict all the IMF (Intrinsic Mode Function) parts from the CEEMDAN process. The individual prediction outcomes of each IMF are combined to obtain the final prediction results. Soil moisture measurements were collected continuously in the Coimbatore area of Tamil Nadu at multi-layer soil depths of 10 cm, 30 cm, 60 cm, and 100 cm. The provided data was utilized for training models and achieving optimal accuracy in forecasting soil moisture levels one month prior. The proposed CEEMDAN-LSTM model achieved the highest R2, ranging from 0.985 to 0.995 at various depths when compared to RNN, LSTM, EMD-LSTM, EEMD-LSTM models, and existing literature models. The results show that the proposed method works well at predicting soil moisture at different depths, which can help improve the planting pattern and schedule of native species to increase the area’s green cover.

Novel optimized coupled rainfall model simulation based on stepwise decomposition technique.

Precipitation forecasting plays a pivotal role in guiding the effective management of regional water resources and providing crucial warnings for regional droughts and floods. Finding a monthly precipitation simulation model with robust fitting performance is a significant research endeavor in practical precipitation prediction. This paper introduces two modified African vulture optimization algorithms (MAVOA1 and MAVOA2). It provides hyperparameter optimization techniques for the least squares support vector machine (LSSVM), long short-term memory neural network (LSTM), and random forest (RF) models. These techniques are used to construct a monthly precipitation simulation model based on algorithmic optimization coupled with variational mode decomposition for full decomposition. The test results at five typical stations in the North China Plain reveal the following: (1) the LSSVM model demonstrates significantly better performance than the LSTM and RF models. (2) the MAVOA2-LSSVM model has the best-integrated effect: the average test fitting error is RMSE = 17.50 mm/month, MRE = 117.25%, NSE = 0.90, which shows its superiority in practical application and can significantly improve the accuracy of precipitation prediction; MAVOA2 is more suitable for machine learning models with more hyperparameters of its own, which provides a reference for hyperparameter optimization algorithms in the other fields.

Probabilistic Deep Learning Approach for Fatigue Crack Width Estimation and Prognosis in Lap Joint Using Acoustic Waves

Abstract Accurate fatigue crack width estimation is crucial for aircraft safety, however, limited research exists on (i) the direct relationship between fatigue crack width and Lamb wave signatures and (ii) probabilistic artificial intelligence approach for automated analysis using acoustic emission waveforms. This paper presents a probabilistic deep learning approach for fatigue crack width estimation, employing an automated wavelet feature extractor and probabilistic Bayesian neural network. A dataset constituting the fatigue experiment on aluminum lap joint specimens is considered, in which Lamb wave signals were recorded at several time instants for each specimen. Signals acquired from the piezo actuator–receiver sensor pairs are related to the optically measured surface crack length. The sensitive features are automatically extracted from the signals using decomposition techniques called maximal overlap discrete wavelet transform (MODWT). The extracted features are then mapped through the deep learning model, which incorporates Bayesian inference to account for both aleatoric as well as epistemic uncertainty, that provides outcomes in the form of providing probabilistic estimates of crack width with uncertainty quantification. Thus, employing an automated wavelet feature extractor (MODWT) on a dataset of fatigue experiments, the framework learns the relationship between Lamb wave signals and crack width. Validation on unseen in situ data demonstrates the efficacy of the approach for practical implementation, paving the way for more reliable fatigue life prognosis.

Optimisation of the Circular Economy Based on the Resource Circulation Equation

The lack of effective evaluation methods and implementation guidelines has led to frequent obstacles in the process of circular economy in enterprises. The efficiency equation for resource circulation can effectively evaluate the efficiency of an enterprise’s circular economy resource circulation from three perspectives: input, circulation, and output. Additionally, it delves into each link to identify weak points, offering guidance for optimising the enterprise’s circular economy. Utilising a value flow analysis within the context of a circular economy, this paper evaluates circular economy efficiency using a resource circulation efficiency equation. It conducts factor analysis across three dimensions: resource input, resource circulation, and waste output. This analysis aims to evaluate the corresponding resource productivity, added value output rate, and environmental efficiency. Factor decomposition techniques were then employed to identify the underlying factors contributing to poor circular economy outcomes. Furthermore, based on the relationships among three resource circulation indicators, this paper forecasts the potential advantages of integrating circular economy improvement measures and proposes practical optimisation approaches. The enhanced resource circulation efficiency resulting from the proposed optimisation approaches was validated through a case study with an aluminium company.

Sausage, kink, and fluting magnetohydrodynamic wave modes identified in solar magnetic pores by Solar Orbiter/PHI

Solar pores are intense concentrations of magnetic flux that emerge through the solar photosphere. When compared to sunspots, they are much smaller in diameter and can therefore be affected and buffeted by neighbouring granular activity to generate significant magnetohydrodynamic (MHD) wave energy flux within their confines. However, observations of solar pores from ground-based telescope facilities may struggle to capture subtle motions that are synonymous with higher-order MHD wave signatures because of the seeing effects that are produced in the Earth’s atmosphere. Hence, we exploited timely seeing-free and high-quality observations of four small magnetic pores from the High Resolution Telescope (HRT) of the Polarimetric and Helioseismic Imager (PHI) on board the Solar Orbiter spacecraft during its first close perihelion passage in March 2022 (at a distance of 0.5 au from the Sun). Through acquisition of data under stable observing conditions, we were able to measure the area fluctuations and horizontal displacements of the solar pores. Cross correlations between perturbations in intensity, area, line-of-sight velocity, and magnetic fields, coupled with the first-time application of novel proper orthogonal decomposition techniques on the boundary oscillations, provided a comprehensive diagnosis of the embedded MHD waves as sausage and kink modes. Additionally, the previously elusive m = 2 fluting mode is identified in the most magnetically isolated of the four pores. An important consideration lies in how the identified wave modes contribute to the transfer of energy into the upper solar atmosphere. Approximately 56%, 72%, 52%, and 34% of the total wave energy of the four pores we examined is associated with the identified sausage modes and about 23%, 17%, 39%, and 49% with their kink modes, while the first pore also receives a contribution of about 11% linked to the fluting mode. This study reports the first-time identification of concurrent sausage, kink, and fluting MHD wave modes in solar magnetic pores.

Mittag-Leffler function based security control for fractional-order complex network system subject to deception attacks via Observer-based AETS and its applications

The goal of this paper is to investigate the security control for uncertain fractional-order delayed complex network systems under deception attacks using the Mittag-Leffler function and observer-based adaptive event-triggered scheme (AETS) with the fractional commensurate order in q ∈ (0, 1). The adaptive event-triggering scheme is used during the data transmission process from the sensors to the observer, where the triggering threshold can be dynamically modified to reduce resource waste. We make a novel model for the estimation error system that takes into account both the effects of the adaptive event-triggered scheme and the effects of deception attacks. A sufficient condition is obtained to guarantee the stochastic mean-square stability of the augmented error system using the Mittag-Leffler (M-L) functions and the Lyapunov functional method and by using the singular value decomposition (SVD) and linear matrix inequality (LMI) techniques, the co-design problem of desired observer and controller gains is found, and it is shown that the solution ensures the stability of a closed-loop uncertain fractional-order complex networked system. At the end of this study, two numerical examples and diesel engine system model are given to show that the above findings are correct.