Search Results (114)

To address the problems of noise sensitivity, insufficient modeling of long-term time-series dependence, and high cost of labeled data in the fault diagnosis of pumping station units, an intelligent diagnosis method integrating the improved Sparrow Search Algorithm (tSSA) and Informer model is proposed in this study. Firstly, an adaptive t-distribution strategy is introduced into the Sparrow Search Algorithm to dynamically adjust the degree of freedom parameters of the mutation operator, balance global search and local development capabilities, avoid the algorithm converging to the origin, and enhance optimization accuracy, with time complexity consistent with the original SSA. Secondly, by combining the sparse self-attention and self-attention distillation mechanisms of Informer, the model’s ability to extract key features of long sequences is optimized, and its hyperparameters are adaptively optimized via tSSA. Experiments were conducted based on 12 types of fault vibration data acquired from pumping station units. Outliers were removed using the interquartile range (IQR) method, and dimensionality reduction was achieved through kernel principal component analysis (KPCA). The results indicate that the average diagnostic accuracy of tSSA-Informer under noise-free conditions reaches 98.73%, which is significantly higher than that of models such as SSA-Informer and GA-Informer; under noise interference of SNR = −1 dB, it still maintains an accuracy of 87.47%, outperforming comparative methods like 1D-DCTN; when the labeled sample size is reduced to 10%, its accuracy is 61.32%, which is more than 40% higher than that of traditional models. These results verify the robustness and practicality of the proposed method in strong-noise and small-sample scenarios. This study provides an efficient solution for the intelligent fault diagnosis of complex industrial equipment. Full article

In complex process systems, accurate real-time anomaly detection is essential to ensure operational safety and reliability. This study proposes a novel detection method that combines information granulation with kernel principal component analysis (KPCA). Here, information granulation is introduced as a general framework, with the principle of justifiable granularity (PJG) adopted as the specific implementation. Time series data are first granulated using PJG to extract compact features that preserve local dynamics. The KPCA model, equipped with a radial basis function kernel, is then applied to capture nonlinear correlations and construct monitoring statistics including $T^2$ and SPE. Thresholds are derived from training data and used for online anomaly detection. The method is evaluated on the Tennessee Eastman process and Continuous Stirred Tank Reactor datasets, covering various types of faults. Experimental results demonstrate that the proposed method achieves a near-zero false alarm rate below 1% and maintains a missed detection rate under 6%, highlighting its effectiveness and robustness across different fault scenarios and industrial datasets. Full article

The reliability of hydraulic valves is critical for the safety and efficiency of industrial systems. While vibration and pressure sensors are widely deployed for condition monitoring, leveraging the heterogeneous data from these multi-sensor systems for accurate remaining useful life (RUL) prediction remains challenging due to noise, outliers, and inconsistent sampling rates. This study proposes a sensor data-driven framework that integrates multi-step signal preprocessing, time–frequency feature fusion, and a machine learning model to address these challenges. Specifically, raw data from vibration and pressure sensors are first harmonized through a multi-step preprocessing pipeline including Hampel filtering for impulse noise, Robust Scaler for outlier mitigation, Butterworth low-pass filtering for effective frequency band retention, and resampling to a unified rate. Subsequently, vibro-acoustic features are extracted from the preprocessed sensor signals, including Fast Fourier Transform (FFT)-based frequency domain features and Wavelet Packet Decomposition (WPD)-based time–frequency features, to comprehensively characterize the valve’s degradation. A health indicator (HI) is constructed by fusing the most sensitive features. Finally, a Kernel Principal Component Analysis (KPCA)-optimized Random Forest model is developed for HI prediction, which strongly correlates with RUL. Validated on the UCI hydraulic condition monitoring dataset through 20-run Monte-Carlo cross-validation, our method achieves a root mean square error (RMSE) of 0.0319 ± 0.0090, a mean absolute error (MAE) of 0.0109 ± 0.0014, and a coefficient of determination (R²) of 0.9828 ± 0.0097, demonstrating consistent performance across different data partitions. These results confirm the framework’s effectiveness in translating multi-sensor data into actionable insights for predictive maintenance, offering a viable solution for industrial health management systems. Full article

Amid intensifying global competition, industrial product quality has become a critical determinant of competitive advantage. However, persistent quality-related faults in production environments threaten product integrity. To address this challenge, a Fusion Maximal Information Coefficient-based Quality-Related Kernel Component Analysis (FMIC-QRKCA) methodology is proposed in this paper by capitalizing on information fusion principles and statistical metric theory. Based on information fusion principles, a Fusion Maximal Information Coefficient (FMIC) strategy is first studied to quantify correlations between process variables and multivariate quality indicators. Subsequently, by integrating the proposed FMIC method with Kernel Principal Component Analysis (KPCA), a Quality-Related Kernel Component Analysis (QRKCA) method is proposed. In the proposed QRKCA strategy, the complete latent variable space is first obtained; on this basis, FMIC is further applied to quantify the correlation between each latent variable and quality variables, thereby completing the screening of quality-related latent variables. Additionally, the $T^2$ and squared prediction error monitoring statistics are used as the key indices to determine the occurrence of faults. This integration overcomes the limitation of conventional KPCA, which does not explicitly consider quality indicators during the principal component extraction, thereby enabling precise isolation of quality-related fault features. Validation through the numerical case and the industrial process case demonstrates that FMIC-QRKCA significantly outperforms established methods in detection accuracy for quality-related faults. Full article

Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction based on Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) models optimized by the Improved Sparrow Search Algorithm (ISSA). Nine conventional hydrochemical indicators are selected, including Ca²⁺, Mg²⁺, Na⁺+K⁺, HCO₃⁻, Cl⁻, SO₄²⁻, total hardness, alkalinity, and pH. KPCA can realize the dimensionality reduction to eliminate the redundancy of information between discriminant indices, simplify the model structure, and enhance the calculation speed of the predicted model. The penalty factor C and kernel parameter g of the SVM model are optimized by the Sparrow Search Algorithm (SSA). In addition, comparative analysis with the SVM, SSA-SVM, and ISSA-SVM models demonstrates that the KPCA and ISSA significantly enhance the classification performance of the SVM model. The KPCA-ISSA-SVM model outperforms three contrastive models in terms of accuracy, precision, recall, Kappa coefficient, Matthews Correlation Coefficient, and geometric mean values of 90.75%, 0.90, 0.88, 0.89, 0.87, and 0.89, respectively. These outcomes underscore the superior performance of the KPCA-ISSA-SVM hybrid model and its potential for effectively identifying mine water sources. This research can serve to identify the mine water sources. Full article

Accurately decoding human locomotion intention from physiological signals remains a significant hurdle for the seamless control of advanced rehabilitation devices like exoskeletons and intelligent prosthetics. Conventional recognition methods often falter, exhibiting limited accuracy and struggling to capture the complex, nonlinear dynamics inherent in biological data streams. Addressing these critical limitations, this study introduces a novel framework for lower-limb motion intent recognition, integrating Kernel Principal Component Analysis (KPCA) with a Support Vector Machine (SVM) optimized via an Improved Sparrow Search Algorithm (ISSA). Our approach commences by constructing a comprehensive high-dimensional feature space from synchronized surface electromyography (sEMG) and inertial measurement unit (IMU) data—a potent combination reflecting both muscle activation and limb kinematics. Critically, KPCA is employed for nonlinear dimensionality reduction; leveraging the power of kernel functions, it transcends the linear constraints of traditional PCA to extract low-dimensional principal components that retain significantly more discriminative information. Furthermore, the Sparrow Search Algorithm (SSA) undergoes three strategic enhancements: chaotic opposition-based learning for superior population diversity, adaptive dynamic weighting to adeptly balance exploration and exploitation, and hybrid mutation strategies to effectively mitigate premature convergence. This enhanced ISSA meticulously optimizes the SVM hyperparameters, ensuring robust classification performance. Experimental validation, conducted on a challenging 13-class lower-limb motion dataset, compellingly demonstrates the superiority of the proposed KPCA-ISSA-SVM architecture. It achieves a remarkable recognition accuracy of 95.35% offline and 93.3% online, substantially outperforming conventional PCA-SVM (91.85%) and standalone SVM (89.76%) benchmarks. This work provides a robust and significantly more accurate solution for intention perception in human–machine systems, paving the way for more intuitive and effective rehabilitation technologies by adeptly handling the nonlinear coupling characteristics of sEMG-IMU data and complex motion patterns. Full article

As a critical precision transmission element in numerical control (NC) machines, the linear rolling guide (LRG) suffers from surface wear degradation, which significantly impairs machining accuracy and operational reliability. Despite its importance, effective identification methods for LRG degradation remain limited. In this study, a hybrid approach combining multi-scale fuzzy entropy (MFE) with a gray wolf-optimized random forest (GWO-RF) algorithm was proposed to identify the surface wear state of the LRG. Preload degradation and vibration signals were collected at three surface wear stages throughout the LGR’s service life. The vibration signals were decomposed and reconstructed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), followed by multi-scale fuzzy entropy analysis of the reconstructed signals. After dimensionality reduction via kernel principal component analysis (KPCA), the processed features were fed into the GWO-RF model for classification. Experimental results demonstrated a recognition accuracy of 97.9%. Full article

It is difficult for a single feature in wind power data to fully reflect the multifactor coupling relationship with wind power, while the forecast model hyperparameters rely on empirical settings, which affects the prediction accuracy. In order to effectively predict the continuous power in the future time period, an ultra-short-term prediction model of wind power based on fused features and an improved convolutional neural network (CNN) is proposed. Firstly, the historical power data are decomposed using dynamic modal decomposition (DMD) to extract their modal features. Then, considering the influence of meteorological factors on power prediction, the historical meteorological data in the sample data are extracted using kernel principal component analysis (KPCA). Finally, the decomposed power modal and the extracted meteorological components are reconstructed into multivariate time-series features; the snow ablation optimisation algorithm (SAO) is used to optimise the convolutional neural network (CNN) for wind power prediction. The results show that the root-mean-square error of the prediction result is 31.9% lower than that of the undecomposed one after using DMD decomposition; for the prediction of the power of two different wind farms, the root-mean-square error of the improved CNN model is reduced by 39.8% and 30.6%, respectively, compared with that of the original model, which shows that the proposed model has a better prediction performance. Full article

Understanding how crystal structure influences electronic properties is crucial for discovering new functional materials. In this study, we utilized Kernel Principal Component Analysis (KPCA) to classify and analyze the Density of States (DOS) of transition metal sulfide (TMS) compounds, particularly copper-based sulfides. By mapping high-dimensional DOS data into a lower-dimensional space, we identify clusters corresponding to different crystal systems and detect outliers with significant deviations from their expected groups. These outliers exhibit unusual electronic configurations, suggesting potential applications in semiconductors, thermoelectric devices, and optoelectronic devices. Projected Density of States (PDOS) analysis further reveals how orbital hybridization governs the electronic structure of these materials, highlighting key differences between structurally similar compounds. Additionally, we analyze phase stability through inter-cluster distance measurements, identifying potential phase transformations between closely related structures. The implications for this work in terms of modifying chemistries and generalized DOS predictions are discussed. Full article

The thermal data of the hypersonic wind tunnel field accurately reflect the aerodynamic performance and key parameters of the aircraft model. However, the prediction of the temperature in hypersonic wind tunnels has problems such as a large delay, nonlinearity and multivariable coupling. In order to reduce the influence brought by temperature changes and improve the accuracy of temperature prediction in the field control of hypersonic wind tunnels, this paper first combines kernel principal component analysis (KPCA) with phase space reconstruction to preprocess the temperature data set of wind tunnel tests, and the processed data set is used as the input of the temperature-prediction model. Secondly, support vector regression is applied to the construction of the temperature prediction model for the hypersonic wind-tunnel temperature field. Meanwhile, aiming at the problem of difficult parameter-combination selection in support vector regression machines, an Improved African Vulture Optimization Algorithm (IAVOA) based on adaptive chaotic mapping and local search enhancement is proposed to conduct combination optimization of parameters in support vector regression. The improved African Vulture Optimization Algorithm (AVOA) proposed in this paper was compared and analyzed with the traditional AVOA, PSO (Particle Swarm Optimization Algorithm) and GWO (Grey Wolf Optimizer) algorithms through 10 basic test functions, and the superiority of the improved AVOA algorithm proposed in this paper in optimizing the parameters of the support vector regression machine was verified in the actual temperature data in wind-tunnel field control. Full article

Carbon monoxide (CO) is a toxic pollutant emitted by municipal solid waste incineration (MSWI), which has a strong correlation with dioxins. In terms of the sustainable development of an ecological environment, CO emission concentration is strictly controlled by the environmental departments of various countries in the world. The construction of its prediction model is conducive to pollution reduction control. The MSWI process is affected by multi-factors such as MSW component fluctuation, equipment wear and maintenance, and seasonal change, and has complex nonlinear and time-varying characteristics, which makes it difficult for the CO prediction model based on offline historical data to adapt to the above changes. In addition, the continuous emission monitoring system (CEMS) used for conventional pollutant detection has unavoidable misalignment and failure problems. In this article, a novel prediction model of CO emission from the MSWI process based on semi-supervised concept drift (CD) detection in kernel feature space is proposed. Firstly, the CO emission deep prediction model and the kernel feature space detection model are constructed based on offline batched historical data, and the historical data set for the real-time construction of the pseudo-labeling model is obtained. Secondly, the drift detection for the CO emission prediction model is carried out based on real-time data by using unsupervised kernel principal component analysis (KPCA) in terms of feature space. If CD occurs, the pseudo-label model is constructed, the pseudo-truth value is obtained, and the drift sample is confirmed and selected based on the Page–Hinkley (PH) test. If no CD occurs, the CO emission concentration is predicted based on the historical prediction model. Then, the updated data set of the CO emission prediction model and kernel feature space detection is obtained by combining historical samples and drift samples. Finally, the offline history model is updated with a new data set when the preset conditions are met. Based on the real data set of an MSWI power plant in Beijing, the validity of the proposed method is verified. Full article

Scientific and rational monitoring of eco-environmental effects induced by mining activities is a prerequisite for optimizing mining planning and contributes to the advancement of ecological civilization. Remote sensing and multi-source data provide advanced methods for long-term dynamic evaluation of mining-induced eco-environmental effects. This study systematically constructs eco-environmental effect indicators tailored to mining characteristics and establishes quantitative extraction methods based on Landsat data and spectral indices. The Mine Eco-environmental Effect Index (MEEI) was developed using kernel principal component analysis (KPCA). The Heidaigou Open-pit Coal Mine in Jungar Banner was selected as the study area to validate the MEEI’s performance and analyze ecological dynamics across five key temporal phases. Results indicate the following: (1) the KPCA-based MEEI effectively integrates multi-indicator features, offering an objective representation of comprehensive eco-environmental impacts; (2) from 1990 to 2020, the ecological trajectory of the coal mine followed a pattern of “sharp deterioration → gradual slowdown → relative stabilization”, with post-mining restoration and management measures significantly mitigating negative impacts and improving regional ecological quality. This study provides a methodological framework for dynamic evaluation of mining-related eco-environmental effects, supporting sustainable mining practices and ecological governance. Full article

As the proportion of renewable energy continues to increase, the large-scale grid integration of photovoltaic (PV) generation presents new technical challenges for reactive power balance in power systems. This paper proposes a coordinated reactive power and voltage optimization method based on Filtered Multiband Decomposition (FMD). First, to address the stochastic fluctuations of PV power, an improved FMD-based prediction model is developed. The model employs an adaptive finite impulse response (FIR) filter to decompose signals and captures periodicity and uncertainty through kurtosis-based feature extraction. By utilizing adaptive function windows for multiband signal decomposition, combined with kernel principal component analysis (KPCA) for dimensionality reduction and a long short-term memory (LSTM) network for prediction, the model significantly enhances forecasting accuracy. Second, to tackle the challenges of integrating high-penetration distributed PV while maintaining reactive power balance, a multi-head attention-based velocity update strategy is introduced within a multi-objective particle swarm optimization (MOPSO) framework. This strategy quantifies the spatial distance and fitness differences of historical best solutions, constructing a dynamic weight allocation mechanism to adaptively adjust particle search direction and step size. Finally, the effectiveness of the proposed method is validated through an improved IEEE 33-bus test case. Full article

This study addresses the problem of early detection of leaf miner infestations in chickpea crops, a significant agricultural challenge. It is motivated by the potential of hyperspectral imaging, once properly combined with machine learning, to enhance the accuracy of pest detection. Originality consists of the application of these techniques to chickpea plants in controlled laboratory conditions using a natural infestation protocol, something not previously explored. The two major methodologies adopted in the approach are as follows: (1) spectral feature-based classification using hyperspectral data within the 400–1000 nm range, wherein a random forest classifier is trained to classify a plant as healthy or infested with eggs or larvae. Dimensionality reduction methods such as principal component analysis (PCA) and kernel principal component analysis (KPCA) were tried, and the best classification accuracies (over 80%) were achieved. (2) VI-based classification, leveraging indices associated with plant health, such as NDVI, EVI, and GNDVI. A support vector machine and random forest classifiers effectively classified healthy and infested plants based on these indices, with over 81% classification accuracies. The main objective was to design an integrated early pest detection framework using advanced imaging and machine learning techniques. Results show that both approaches have resulted in high classification accuracy, highlighting the potential of this approach in precision agriculture for timely pest management interventions. Full article

This paper proposes a hybrid forecasting method (VMD–KPCA–xLSTM–Informer) based on variational-mode decomposition (VMD), kernel principal component analysis (KPCA), extended long short-term memory network (xLSTM), and the Informer model. First, the method decomposes the original power load data and environmental parameter data using VMD to capture their multi-scale characteristics. Next, KPCA extracts nonlinear features and reduces the dimensionality of the decomposed modals to eliminate redundant information while retaining key features. The xLSTM network then models temporal dependencies to enhance the model’s memory capability and prediction accuracy. Finally, the Informer model processes long-sequence data to improve prediction efficiency. Experimental results demonstrate that the VMD–KPCA–xLSTM–Informer model achieves an average absolute percentage error (MAPE) as low as 2.432% and a coefficient of determination ($R^2$) of 0.9532 on dataset I, while, on dataset II, it attains a MAPE of 4.940% and an $R^2$ of 0.8897. These results confirm that the method significantly improves the accuracy and stability of short-term power load forecasting, providing robust support for power system optimization. Full article