Search Results (320)

Effective fault feature extraction and classification methods serve as the foundation for achieving the efficient fault diagnosis of planetary gearboxes. Considering the vibration signals of planetary gearboxes that contain both symmetrical and asymmetrical components, this paper proposes a novel feature extraction method integrating an adaptive multi-bandpass filter (AMBPF) and refined composite multi-scale fuzzy entropy (RCMFE). And a dream optimization algorithm (DOA)–least squares support vector machine (LSSVM) is also proposed for fault classification. Firstly, the AMBPF is proposed, which can effectively and adaptively separate the meshing frequencies, harmonic frequencies, and their sideband frequency information of the planetary gearbox, and is combined with RCMFE for fault feature extraction. Secondly, the DOA is employed to optimize the parameters of the LSSVM, aiming to enhance its classification efficiency. Finally, the fault diagnosis of the planetary gearbox is achieved by the AMBPF, RCMFE, and DOA-LSSVM. The experimental results demonstrate that the proposed method achieves significantly higher diagnostic efficiency and exhibits superior noise immunity in planetary gearbox fault diagnosis. Full article

As a core component of machining tools and vehicles, the load-bearing and transmission performance of rolling bearings is directly related to product processing quality and driving safety, highlighting the critical importance of fault detection. To address the nonlinearity, non-stationary modulation, and low signal-to-noise ratio (SNR) observed in bearing vibration signals, we propose a fault feature extraction method based on spectral kurtosis and Hilbert envelope demodulation. First, spectral kurtosis is employed to determine the center frequency and bandwidth of the signal adaptively, and a bandpass filter is constructed to enhance the characteristic frequency components. Subsequently, the envelope spectrum is extracted through the Hilbert transform, allowing for the precise identification of fault characteristic frequencies. In the fault diagnosis stage, a multidimensional feature vector is formed by combining the kurtosis index with the amplitude ratios of inner/outer race characteristic frequencies, and fault pattern classification is accomplished using a Least-Squares Support Vector Machine (LS-SVM). To evaluate the effectiveness of the proposed method, experiments were conducted on the bearing datasets from Case Western Reserve University (CWRU) and the Machine Failure Prevention Technology (MFPT) Society. The experimental results demonstrate that the proposed method surpasses other comparative approaches, achieving identification accuracies of 95% and 100% for the CWRU and MFPT datasets, respectively. Full article

The displacement of a concrete gravity dam is a direct manifestation of its deformation. It provides an intuitive reflection of the dam’s overall operational behavior and serves as a key indicator of the dam’s safe operating condition. In this paper, we propose a factor set that considers the hysteresis effects of temperature on displacement and ranks the importance of the features to select the optimal factor sets at different measurement points by the ReliefF method. Then, we realize the simultaneous prediction of the displacements at multiple measurement points by the multi-input multi-output least-squares support vector machine with particle swarm optimization (MIMO-PSO-LSSVM). The case study demonstrates that this method effectively enhances the accuracy and efficiency of gravity dam displacement prediction, thereby providing a novel reference for dam safety monitoring and health service diagnosis. Full article

One of the most critical aspects of turning, and machining in general, is the surface roughness of the finished product, which directly influences the performance, functionality, and longevity of machined components. The accurate prediction of surface roughness is vital for enhancing component quality and machining efficiency. This study presents a machine learning-driven framework for modeling mean roughness depth (Rz) during the dry machining of super duplex stainless steel (SDSS 2507). SDSS 2507 is known for its exceptional mechanical strength and corrosion resistance, but it poses significant challenges in machinability. To address this, this study employs flank-face textured cutting tools to enhance machining performance. Experiments were designed using the L₂₇ orthogonal array with three continuous factors, cutting speed, feed rate, and depth of cut, and one categorical factor, tool texture type (dimple, groove, and wave), along with surface roughness as an output parameter. Gaussian Data Augmentation (GDA) was employed to enrich data variability and strengthen model generalization, resulting in the improved predictive performance of the machine learning models. MATLAB R2021a was employed for preprocessing, the normalization of datasets, and model development. Two models, Least-Squares Support Vector Machine (LSSVM) and Multi-Gene Genetic Programming (MGGP), were trained and evaluated on various statistical metrics. The results showed that both LSSVM and MGGP models learned well from the training data and accurately predicted Rz on the testing data, demonstrating their reliability and strong performance. Of the two models, LSSVM demonstrated superior performance, achieving a training accuracy of 98.14%, a coefficient of determination (R²) of 0.9959, and a root mean squared error (RMSE) of 0.1528. It also maintained strong generalization on the testing data, with 94.36% accuracy and 0.9391 R² and 0.6730 RMSE values. The high predictive accuracy of the LSSVM model highlights its potential for identifying optimal machining parameters and integrating into intelligent process control systems to enhance surface quality and efficiency in the complex machining of materials like SDSS. Full article

The energy management strategy (EMS) is a critical technology for pure electric vehicles equipped with hybrid energy storage systems. This study addresses the challenges of limited adaptability to driving cycles and significant battery capacity degradation in lithium battery–supercapacitor hybrid energy storage systems by proposing an adaptive EMS based on Dynamic Programming-Optimized Control Rules (DP-OCR). Dynamic programming is employed to optimize the rule-based control strategy, while the grey wolf optimizer (GWO) is utilized to enhance the least squares support vector machine (LSSVM) driving cycle recognition model. The optimized driving cycle recognition model is integrated with the improved rule-based control strategy, facilitating adaptive adjustment of control parameters based on driving cycle identification results. This integration enables optimal power distribution between lithium batteries and supercapacitors, thereby improving the EMS’s adaptability to varying driving conditions and extending battery lifespan. Simulation results under complex driving cycles indicate that, compared to conventional deterministic rule-based EMS and single-battery vehicles, the proposed DP-OCR-based adaptive EMS reduces overall energy consumption by 8.29% and 17.48%, respectively. Full article

Vegetation productivity, as an essential global carbon sink, directly influences the variety and stability of ecosystems. Precise vegetation productivity monitoring and forecasting are crucial for the global carbon cycle. Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. We propose a multimodal regression prediction model utilizing the TCLA framework—comprising the Transient Trigonometric Harris Hawks Optimizer (TTHHO), Convolutional Neural Networks (CNN), Least Squares Support Vector Machine (LSSVM), and Adaptive Bandwidth Kernel Density Estimation (ABKDE)—with the Hetao Irrigation District, a vast irrigation basin in China, serving as the study area. This model employs TTHHO to effectively navigate the search space and adaptively optimize network node positions, integrates CNN-LSSVM for feature extraction and regression analysis, and incorporates ABKDE for probability density function estimation and outlier detection, resulting in accurate interval probability prediction and enhanced model resilience to interference. Experimental data indicate that the TCLA model improves prediction accuracy by 10.57–26.47% compared to conventional models (Long Short-Term Memory (LSTM), Transformer). In the presence of 5–15% outliers, the fusion of multimodal data results in a substantial drop in RMSE (p < 0.05), with a reduction of 45.18–69.66%, yielding values between 0.079 and 0.137, thereby demonstrating the model’s high robustness and resistance to interference in predicting the next three years. This work introduces a scientific approach for precisely forecasting alterations in regional vegetation productivity using the proposed multimodal TCLA model, significantly enhancing global vegetation resource management and ecological conservation techniques. Full article

Oilseed rape yield critically reflects varietal superiority. Rapid field-scale estimation enables efficient high-throughput breeding. This study evaluates unmanned aerial vehicle (UAV) hyperspectral imagery’s potential for yield prediction at the pod stage by utilizing wavelength selection and vegetation indices. Meanwhile, optimized feature selection algorithms identified effective wavelengths (EWs) and vegetation indices (VIs) for yield estimation. The optimal yield estimation models based on EWs and VIs were established, respectively, by using multiple linear regression (MLR), partial least squares regression (PLSR), extreme learning machine (ELM), and a least squares support vector machine (LS-SVM). The main results were as follows: (i) The yield prediction of oilseed rape using EWs showed better prediction and robustness compared to the full-spectral model. In particular, the competitive adaptive reweighted sampling–extreme learning machine (CARS-ELM) model (R_pre = 0.8122, RMSE_P = 170.4 kg/hm²) achieved the best prediction performance. (ii) The ELM model (R_pre = 0.7674 and RMSE_P = 187.6 kg/hm²), using 14 combined VIs, showed excellent performance. These results indicate that the remote sensing image data obtained from the UAV hyperspectral remote sensing system can be used to enable the high-throughput acquisition of oilseed rape yield information in the field. This study provides technical guidance for the crop yield estimation and high-throughput detection of breeding information. Full article

Efficient extraction and classification of fault features remain critical challenges in planetary gearbox fault diagnosis. A fault diagnosis framework is proposed that integrates hierarchical refined composite multiscale fuzzy entropy (HRCMFE) for feature extraction and a gray wolf optimization (GWO)-optimized least squares support vector machine (LSSVM) for classification. Firstly, the HRCMFE is developed for feature extraction, which combines the segmentation advantage of hierarchical entropy (HE) and the computational stability advantage of refined composite multiscale fuzzy entropy (RCMFE). Secondly, the hyperparameters of LSSVM are optimized by GWO using a proposed fitness function. Finally, fault diagnosis of the planetary gearbox is achieved by the optimized LSSVM using the HRCMFE-extracted features. Simulation and experimental study results indicate that the proposed method demonstrates superior effectiveness in both feature discriminability and diagnosis accuracy. Full article

The integration of renewable energy sources, specifically photovoltaic generation, into the grid at a large scale has significantly heightened the volatility and unpredictability of the power system. Consequently, this presents formidable challenges to ensuring the reliable operation of the grid. This study introduces a novel stacked model for photovoltaic power prediction, integrating multiple conventional data processing methods as base learners, including Group Method of Data Handling (GMDH), Least Squares Support Vector Machine (LSSVM), Radial Basis Function Neural Network (RBFNN), and Emotional Neural Network (ENN). A Backpropagation Neural Network (BPNN) serves as the meta-learner, utilizing the outputs of the base learners as input features to enhance overall prediction accuracy by mitigating individual model errors. To assess the model’s effectiveness, five evaluation metrics are employed: Bayesian Information Criterion (BIC), Percent Mean Average Relative Error (PMARE), Legates and McCabe Index (LM), Mean Absolute Deviation (MAD), and Root Mean Square Error (RMSE), ensuring long-term stability in photovoltaic power output forecasting. Additionally, the model’s effectiveness and accuracy are validated using operational data from photovoltaic power plants in a particular province of China. The results indicate that the stacked model, after training, testing, and validation on multiple performance metrics, surpasses baseline single models in performance. Full article

Traditional tool wear identification methods are usually based on the framework of “feature extraction + machine learning”, but these methods often have problems of low efficiency and low recognition accuracy. To address these problems, this paper proposes a tool wear state identification model based on particle swarm optimization (PSO) and least squares support vector machine (LS-SVM), namely the PSO-LS-SVM model. By integrating data collected by multiple sensors, key feature information reflecting the tool wear state is extracted; dimensionality reduction techniques such as principal component analysis (PCA) are used to optimize feature vectors to improve the distinguishability of features. The model parameters are optimized by the two-dimensional coordinates (c and g) of the particle swarm algorithm to adapt to the given training sample set. During the training process, the fitness of each particle is calculated and compared with its historical optimal fitness to update the optimal fitness of the particle. This process is iterated until the global optimal solution is found, thereby achieving accurate identification of the tool wear state. Experimental results show that the PSO-LS-SVM model shows high accuracy and good performance in tool wear state identification, which verifies the effectiveness of the algorithm in improving tool efficiency and extending tool life. The study is the first to combine PSO and LS-SVM for tool wear prediction in multi-sensor data fusion. This advanced recognition technology can significantly reduce the waste of resources caused by premature tool replacement, while improving the stability of the machining process and the consistency of the product. Full article

Accurate State of Health (SOH) estimation of lithium-ion batteries (LIBs) is critical for ensuring the safety of electric vehicles and improving the reliability of battery management systems (BMS). However, the use of individual health features (HFs) and the selection of hyperparameters can increase the data processing burden on the BMS and reduce the accuracy of data-driven models. To address the above issue, this paper proposes a novel SOH estimation method for lithium-ion batteries based on the PSO–GWO–LSSVM prediction model with multi-dimensional health feature extraction. To comprehensively capture the battery aging mechanisms, four categories of health features—time, energy, similarity, and second-order features—are extracted from the LIBs charging segments. The correlation between HFs and SOH is comprehensively evaluated through Pearson and Spearman correlation analyses, followed by Gaussian filtering and outlier detection to enhance feature quality. With strong generalization and robustness, least squares support vector machine (LSSVM) is widely applied to nonlinear computations and function approximation. To improve LSSVM model accuracy and efficiency, this paper develops a novel prediction model that uses particle swarm optimization (PSO) combined with grey wolf optimization (GWO) algorithms to optimize the LSSVM model. The generalization performance of the proposed method is validated through comparative experiments using a battery dataset provided by the Center for Advanced Life Cycle Engineering (CALCE) Research Center at the University of Maryland. Experimental results show that the coefficient of determination (R²) consistently exceeds 0.985, with the average absolute error in SOH prediction for four batteries remaining around 0.5%. The comparative experiments demonstrate that the proposed method has a certain degree of accuracy, robustness, and generalization capability. Full article

For potato external defect detection, ordinary spectral technology has limitations in detail detection and processing accuracy, while the machine vision method has the limitation of a long feedback time. To realize accurate and rapid external defect detection for red-skin potatoes, a non-destructive detection method using hyperspectral imaging and a machine learning model was explored in this study. Firstly, Savitzky–Golay (SG), standard normal variate transformation (SNV), multiplicative scatter correction (MSC), the normalization algorithm, and different preprocessing algorithms combined with SG were used to preprocess the hyperspectral data. Then, principal component regression (PCR), support vector machine (SVM), partial least squares regression (PLSR), and least squares support vector machine (LSSVM) algorithms were used to establish quantitative models to find the most suitable preprocessing algorithm. The successive projections algorithm (SPA) was used to obtain various characteristic wavelengths. Finally, the qualitative models were established to detect the external defects of potatoes using the machine learning algorithms of backpropagation neural network (BPNN), k-nearest neighbors (KNN), classification and regression tree (CART), and linear discriminant analysis (LDA). The experimental results showed that the SG–SNV fusion hyperspectral data preprocessing algorithm and the KNN machine learning model were the most suitable for the detection of external defects in red-skin potatoes. Moreover, multiple external defects can be detected without multiple models. For healthy potatoes, black/green-skin potatoes, and scab/mechanical-damage/broken-skin potatoes, the detection accuracy was 93%,93%, and 83%, which basically meets the production requirements. However, enhancing the prediction accuracy of the scab/mechanical-damage/broken-skin potatoes is still a challenge. The results also demonstrated the feasibility of using hyperspectral imaging technology and machine learning technology to detect potato external defects and provided new insights for potato external defect detection. Full article

Tailings ponds can recycle water resources through the water recirculation system by clarifying and purifying the wastewater discharged from the mining production process. Due to factors such as flooding and heavy rainfall, once a tailings dams burst, the spread of heavy metals in the tailings causes underground and surface water pollution, endangering the lives and properties of people downstream. To effectively assess the potential impact of tailings dams bursting, many problems such as the difficulty of taking values in predicting the volume of silt penetration through empirical formulae, model testing, and numerical simulation need to be solved. In this study, 65 engineering cases were collected to develop a sample dataset containing dam height and storage capacity. The Support Vector Machine (SVM) algorithm was used to develop a nonlinear regression model for tailings discharge volume after tailings dam failure. In addition, the model penalty parameter C and kernel function g were optimized using the powerful global search capability of the Slime Mold Algorithm (SMA) to develop an SMA–SVM prediction model for tailings discharge volume. The results indicate that the volume of tailings discharged increases nonlinearly with increasing dam height and tailings storage capacity. The SMA-SVM model showed higher prediction accuracy compared to the predictions made by the Random Forest (RF), Radial Basis Function (RBF), and Least Squares SVM (LS-SVM) algorithms. The average absolute error in tailings discharge volume compared to actual values was 30,000 m³, with an average relative error of less than 25%. This is very close to practical engineering scenarios. The ability of the SMA-SVM optimization algorithm to produce predictions with minimal error relative to actual values was further confirmed by the combination of numerical simulations. In addition, the numerical simulations revealed the flow characteristics and inundation area of the discharged sediment during tailings dam failure, and the research results can provide reference for water resource protection and downstream safety prevention and control of tailings ponds. Full article

Wind turbine planetary gearboxes have complex structures and operating environments, which makes it difficult to extract fault features effectively. In addition, it is difficult to achieve efficient fault diagnosis. To improve the efficiency of feature extraction and fault diagnosis, a fault diagnosis method based on improved multivariate variational mode decomposition (IMVMD) and ensemble refined composite multivariate multiscale dispersion entropy (ERCmvMDE) with multi-channel vibration data is proposed. Firstly, the IMVMD is proposed to obtain the optimal parameters of the MVMD, which would make the MVMD more effective. Secondly, the ERCmvMDE is proposed to extract rich and effective feature information. Finally, the fault diagnosis of the planetary gearbox is achieved using the least squares support vector machine (LSSVM) with features consisting of ERCmvMDE. Simulations and experimental studies indicate that the proposed method performs feature extraction well and obtains higher fault diagnosis accuracy. Full article

In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. In this study, fluorescence spectrum acquisition and hyperspectral imaging processing of tomato leaf samples with and without pesticides were conducted, and spectral data from regions of interest of hyperspectral images were extracted. The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. According to the results, R² of the model of hyperspectral data after SG-SNV preprocessing and PLSR modeling reached 0.9974, RMSEC reached 0.0221, and RMSEP reached 0.0565. R² of the model of fluorescence spectral data after SG-MSC preprocessing and SVM modeling reached 0.9986, RMSEC reached 0.2496, and RMSEP reached 0.4193. Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. Therefore, the fluorescence spectral and hyperspectral imaging techniques applied to tomato leaf pesticide detection enjoy a promising application prospect. Full article