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Action reliability analysis under random-interval hybrid uncertainty of chain conveyor based on adaptive intelligent extremum surrogate model

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QUALITY AND RELIABILITY ENGINEERING INTERNATIONAL 2024年第4期40卷 1719-1742页

作者： Wen, Hao Hou, Baolin Lin, Yubin Jin, Xin Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Jiangsu Peoples R China

The action responses of controlled mechanisms are often multiparametric, nonlinear, and uncertain. Complex dynamics and limited uncertain information pose difficulties for action reliability analysis. This paper develops an adaptive intelligent extremum surrogate model (AIESM) method for the action reliability under random-interval hybrid uncertainty of a chain conveyor. First, a dynamic model of the chain conveyor is established, which considers the impact and frictional effects within the mechanical system, the regulating effects of the control system, and the external disturbances of the system. After that, a hybrid kernel extreme learning machine optimized by the sparrow search algorithm is employed as an intelligent surrogate model to construct the initial surrogate model from the hybrid uncertain variables to the limit state function (LSF) response and the extremum surrogate model (ESM) from the random variables to the LSF extremum response. An adaptive infilling strategy combining active learning and opposition-based learning is applied to improve the accuracy and efficiency of the ESM and reduce the estimation error of action reliability. Finally, the action reliability interval bounds are obtained by Monte Carlo simulation based on the ESM. Two numerical examples are presented to illustrate the validity of the AIESM method. The action reliability interval of the chain conveyor provided by the proposed method is [0.9706, 0.9923].

关键词： action reliability adaptive infilling strategy extremum surrogate model kernel extreme learning machine sparrow search algorithm

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A novel hybrid model for air quality prediction via dimension reduction and error correction techniques

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ENVIRONMENTAL MONITORING AND ASSESSMENT 2024年第1期197卷 1-22页

作者： Huang, Rui Hu, Rui Chen, Huayou Anhui Univ Sch Math Sci Hefei 230601 Anhui Peoples R China Tongling Univ Sch Math & Comp Tongling 244000 Anhui Peoples R China Anhui Univ Sch Big Data & Stat Hefei 230601 Anhui Peoples R China

The monitoring of air pollution through the air quality index (AQI) is a fundamental tool in ensuring public health protection. Accurate prediction of air quality is necessary for the timely implementation of measures to control and manage air pollution, thereby mitigating its detrimental impact on human health. A novel hybrid prediction model is proposed, which is EMD-KMC-EC-SSA-VMD-LSTM. Raw AQI index data are decomposed into intrinsic mode functions (IMFs) by empirical mode decomposition (EMD) method. Subsequently, sample entropy (SE) is utilized to assess the intricacy of IMFs, and K-means clustering (KMC) is used to reconstruct them into joint intrinsic mode functions (Co-IMFs). Then, the variational mode decomposition (VMD) is used to transform the complex Co-IMF0 into simpler IMFs. Long short-term memory (LSTM), optimized either by the sparrow search algorithm (SSA), is applied to forecast all IMFs, generating the first prediction sequence. To further refine the forecasting, an error correction (EC) technique is adopted. The error sequence is obtained by subtracting the forecasting sequence from the raw sequence, which is then decomposed by EMD-SSA-VMD. Subsequently, SSA-LSTM is engaged to forecast the decomposed error sequence, generating the error forecasting sequence. Finally, the forecast outcomes are combined with the error predictions to generate the final AQI prediction sequence. The proposed approach undergoes validation across four urban centers and undergoes comparison against a set of eight prediction models. Experimental findings underscore the heightened precision of this hybrid forecasting model in predicting AQI metrics.

关键词： Decomposition-reconstruction-prediction Dimension reduction sparrow search algorithm Error correction AQI prediction

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MGGSED-SSA: An improved sparse deconvolution method for rolling element bearing diagnosis

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APPLIED ACOUSTICS 2024年 220卷

作者： Shi, Peiming Gao, Shiming Tan, Hang Xu, Xuefang Li, Ruixiong Yanshan Univ Sch Elect Engn Qinhuangdao 066004 Hebei Peoples R China Chengdu Normal Univ Coll Phys & Engn Chengdu 611130 Peoples R China SouthWest Petr Univ Key Lab Oil & Gas Equipment Minist Educ Chengdu 611130 Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710048 Peoples R China

Sparse deconvolution methods can not only adaptively design the filter to counteract the negative effect of the transmission path but also exhibit superior performance compared to traditional deconvolution methods based on kurtosis. Yet, determining the optimal filter length remains challenging, and there is still further study to be done on how to make sparse deconvolution methods work better under heavy noise and harmonic interference. To address these issues, this paper proposes a novel sparse deconvolution technique named the maximum generalized Gini squared envelope optimized with the sparrow search algorithm (MGGSED-SSA). First, a new sparse index is constructed based on the generalized Gini index, which is less sensitive to filter length and more robust to interference. Second, the MGGSED-SSA is derived by considering the new sparse index as a deconvolution objective function. Third, the sparrow search algorithm is applied to optimize filter coefficients in the generalized spherical coordinate transformation. Simulation cases and experimental cases concerning bearing with inner and outer race faults are used to verify the effectiveness of the proposed method. It can be seen that the proposed method is less sensitive to filter length and holds better diagnosis performance even when bearings suffer from heavy noise and harmonic interference, compared with traditional methods.

关键词： Bearing fault diagnosis Deconvolution methods Gini index sparrow search algorithm

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A Lidar wind speed measurement system based on SSA-1DCNN algorithm

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JOURNAL OF MODERN OPTICS 2025年第1-3期72卷 50-60页

作者： Ying, Na Yin, Jiazheng Hu, Miao Li, Hongyu Dai, Shaogang Hangzhou Dianzi Univ Sch Commun Engn Hangzhou 310018 Peoples R China

When traditional algorithms are used for calculating wind speed using, noise or distortions in the Lidar echo signals can compromise the accuracy and reliability, and developing denoising algorithms could increase time cost. To overcome these shortcomings, this paper designed an experimental platform and introduced the SSA-1DCNN algorithm. In the SSA-1DCNN algorithm, 1D-CNN is used for feature extraction, while SSA is employed to determine optimal parameters for 1D-CNN. Specifically, the experimental platform employs both an anemometer and a Lidar to sample wind at varying speeds. The anemometer's sampled values are used to label the Lidar's sampled values. Then, a dataset is established. Subsequently, the aforementioned dataset is used to train the wind speed measurement model. Using this model, we developed a networked Lidar-based wind speed measurement system, which is capable of transmitting wind speed data to remote locations. Compared to baseline algorithms, our algorithm demonstrates a distinct advantage.

关键词： Coherent Doppler Lidar wind speed measurement one-dimensional Convolutional Neural Network sparrow search algorithm

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Investigation of Trajectory Tracking Control in Hip Joints of Lower-Limb Exoskeletons Using SSA-Fuzzy PID Optimization

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SENSORS 2025年第5期25卷 1335-1335页

作者： Li, Wei Wei, Xiaojie Sun, Dawen Zong, Siyu Yue, Zhengwei Changchun Univ Coll Mech & Vehicle Engn Changchun 130022 Peoples R China Minist Educ Key Lab Intelligent Rehabil & Barrier Free Educ Di Changchun 130022 Peoples R China Changchun Univ Coll Comp Sci & Technol Changchun 130022 Peoples R China Shandong Jite Ind Technol Co Ltd Rizhao 276800 Peoples R China

The application of lower-limb exoskeleton robots in rehabilitation is becoming more prevalent, where the precision of control and the speed of response are essential for effective movement tracking. This study tackles the challenge of optimizing both control accuracy and response speed in trajectory tracking for lower-limb exoskeleton hip robots. We introduce an optimization strategy that integrates the sparrow search algorithm (SSA) with fuzzy Proportional-Integral-Derivative (PID) control. This approach addresses the inefficiencies and time-consuming process of manual parameter tuning, thereby improving trajectory tracking performance. First, recognizing the complexity of hip joint motion, which involves multiple degrees of freedom and intricate dynamics, we employed the Lagrangian method. This method is particularly effective for handling nonlinear systems and simplifying the modeling process, allowing for the development of a dynamic model for the hip joint. The SSA is subsequently utilized for the online self-tuning optimization of both the proportional and quantization factors within the fuzzy PID controller. Simulation experiments confirm the efficacy of this strategy in tracking hip joint trajectories during flat walking and standing hip flexion rehabilitation exercises. Experimental results from diverse test populations demonstrate that SSA-fuzzy PID control improves response times by 27.8% (for flat walking) and 30% (for standing hip flexion) when compared to traditional PID control, and by 6% and 2%, respectively, relative to fuzzy PID control. Regarding tracking accuracy, the SSA-fuzzy PID approach increases accuracy by 81.4% (for flat walking) and 80% (for standing hip flexion) when compared to PID control, and by 57.5% and 56.8% relative to fuzzy PID control. The proposed strategy significantly improves both control accuracy and response speed, offering substantial theoretical support for rehabilitation training in individuals with lower-limb impairmen

关键词： lower-limb exoskeleton sparrow search algorithm fuzzy PID control trajectory tracking

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Capacity prediction method of lithium-ion battery in production process based on eXtreme Gradient Boosting

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IONICS 2025年第2期31卷 1759-1777页

作者： Liu, Zhengyu Xu, Rui Wang, Hao Hefei Univ Technol Sch Mech Engn Tunxilu Campus He Fei 230009 Anhui Peoples R China Hefei Univ Technol Intelligent Mfg Inst HFUT Garden Ave Hefei 230041 Anhui Peoples R China

Measuring capacity through the lithium-ion battery (LIB) formation and grading process takes tens of hours and accounts for about one-third of the cost at the production stage. To improve this problem, the paper proposes an eXtreme Gradient Boosting (XGBoost) approach to predict the capacity of LIB. Multiple electrochemical features are extracted from the cell voltage curves obtained during the formation and 20% grading processes, and these features are ranked using the grey relational analysis (GRA) method. The charging polarization voltage, fixed-voltage rise time, and static-stage voltage difference, which show a high degree of correlation, form the optimal feature set. The sparrow search algorithm (SSA) improves accuracy and efficiency by optimizing the hyperparameters of XGBoost model. The experimental results indicate that the root mean square error (RMSE) and mean absolute percentage error (MAPE) of this method are respectively 0.1543 Ah and 0.2456%, which are lower than those of other data-driven methods and predict low-capacity cells with equal accuracy. Economically, our method significantly reduces the energy consumption by approximately 278 Wh (56.5%) and shortens the time required for the grading stage by about 7 h 30 min (80.36%) for each cell, the generalization of the model is verified by other types of battery data.

关键词： Lithium-ion battery Battery capacity prediction XGBoost sparrow search algorithm

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Research on PV Hosting Capacity of Distribution Networks Based on Data-Driven and Nonlinear Sensitivity Functions

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2025年第1期16卷 483-495页

作者： Su, Le Pan, Xueping Sun, Xiaorong Guo, Jinpeng Anvari-Moghaddam, Amjad Hohai Univ Sch Elect & Power Engn Nanjing 211100 Peoples R China Aalborg Univ AAU Energy DK-9220 Aalborg Denmark

Voltage calculations are critical for assessing photovoltaic hosting capacity;however, acquiring precise parameters and the topology of the medium voltage distribution networks poses a significant challenge, thereby rendering traditional power flow computational methods ineffective. To address this issue, this paper introduces a hybrid method that utilizes a data-driven approach in conjunction with nonlinear functions to determine node voltages. Firstly, a deep neural network model for distribution network's power flow and voltage-power sensitivity analysis is established using historical data. This model captures the data-driven error, which reduces time consumption and increases accuracy. Secondly, a fourth-order Taylor expansion of power to voltage is derived based on the power flow mathematical equation to extrapolate voltage. This is necessary because when photovoltaic generators are connected to the nodes, the load data often exceeds the historical data range, rendering neural networks inapplicable. Finally, the sparrow search algorithm is employed to determine the hosting capacity. The proposed methods are validated using IEEE 33 and IEEE 69 case systems, demonstrating that the data-driven approach, combined with nonlinear functions, can ensure the accuracy in obtaining node voltage and the hosting capacity.

关键词： Voltage Load flow Sensitivity Distribution networks Artificial neural networks Accuracy Neural networks Photovoltaic hosting capacity power flow analysis deep neural network nonlinear power-voltage sensitivity sparrow search algorithm

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Open-circuit fault diagnosis of DC charging pile rectifier based on sparse data and CNN-ISSA-BiLSTM

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ENERGY REPORTS 2025年 13卷 3024-3034页

作者： Feng, Li Zhu, Yuhuan Xu, Shuiqing Yang, Rui Chongqing Jiaotong Univ Coll Traff & Transportat Chongqing 400074 Peoples R China HFUT Sch Elect Engn & Automat Hefei 230009 Anhui Peoples R China Chongqing Yudian Qual Inspect Co Ltd Chongqing 401120 Peoples R China

A Bidirectional Long Short-Term Memory (BiLSTM) fault diagnosis model based on sparse data and Convolutional Neural Network optimized with Improved sparrow search algorithm (ISSA) is proposed to address the problem of high redundancy of open circuit fault data and low fault diagnosis accuracy in rectifiers of the electric vehicle DC charging pile. Firstly, a Sparse Autoencoder is used to perform sparse dimensionality reduction on the redundant features of the rectifier, and the CNN is used to extract local features. Then, the Bat algorithm (BA) is used to update the position of the SSA discoverer to improve the convergence performance of SSA in the early stage, the Gaussian difference mutation algorithm is used to mutate the optimal population position of SSA to improve the diversity of SSA in the later stage, and ISSA is used to optimize the BiLSTM hyperparameters. Finally, ISSA-BiLSTM is used to identify temporal features and evaluate the diagnostic results of the proposed method and traditional deep learning methods under strong noise. The experimental results show that the fault classification accuracy of this method is as high as 99.70%.

关键词： DC charging pile rectifier Fault diagnosis Bidirectional long short-term memory network sparrow search algorithm

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A DF-SSA analytical framework for revealing variations in multidimensional EEG features of epileptic seizures

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BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 100卷

作者： Chen, Guibin Li, Gang Xu, Wanxiu Wu, Hanfan Ye, Suhong Zhou, Bin Zhejiang Normal Univ Coll Comp Sci & Technol Jinhua 321004 Peoples R China Zhejiang Normal Univ Coll Math Med Jinhua 321004 Peoples R China Zhejiang Normal Univ Coll Engn Jinhua 321004 Peoples R China Jinhua Municipal Cent Hosp Jinhua 321016 Peoples R China Second Hosp Jinhua Jinhua 321016 Peoples R China

Epilepsy, recognized as the most prevalent chronic neurological disorder globally, markedly impacts affected individuals' lives. Epileptic states are effectively detected by electroencephalography (EEG), yet the potential of machine learning to epilepsy's neural mechanisms remains underutilized. In this study, an analytical framework employing the Deep Forest (DF) classifier and sparrow search algorithm (SSA) was introduced to extract an optimal subset of multidimensional EEG features from the CHB-MIT and JHMCHH datasets, aimed at revealing significant variations in epilepsy. Three pivotal epileptic states-interictal, preictal, and ictal-were identified, with Relative Power (RP), Sample Entropy (SE), and Mutual Information (MI) computed for each. The importance ranking and selection of features were facilitated by the DF-SSA framework, leading to the identification of an optimal subset that achieved notable classification accuracies of 98.38 +/- 0.42 % and 99.09 +/- 0.91 %, which represent increases of 0.56 % and 2.04 % over the baseline, respectively. Additionally, significant changes within the beta and gamma bands across the three states were revealed by analyzing variations in cerebral cortex activity, with SE showcasing consistent patterns and a marked elevation from the interictal to preictal, and finally to ictal periods. Surprisingly, SE more readily distinguished the three epileptic states than RP, due to its sensitivity to signal complexity changes. Additionally, a reorganization of functional connectivity across all brain regions was uncovered to be triggered by seizures. Through this innovative analytical framework's employment, three key epileptic seizure states were identified, revealing significant variations in brain electrical features and offering insights into epilepsy's complexity.

关键词： Epilepsy Electroencephalography Feature selection Deep forest sparrow search algorithm

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Enhancing Servo Performance of a Two-Degree-of-Freedom Rotary Table Using Intelligent Control Optimized by SSA-GA

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INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS 2025年第1期2025卷

作者： Gao, Xinan Guan, Xiaorong Li, Huibin Wang, Zheng Kang, Jinyu Yang, Yanlong Nanjing Univ Sci & Technol Sch Mech Engn Nanjing 210094 Peoples R China 208 Res Inst China Ordnance Ind Dept 5 Beijing 100081 Peoples R China

The two-degree-of-freedom rotation stage serves as a crucial component in ground unmanned platforms, and its servo performance is pivotal to the platform's overall functionality. To enhance the servo performance of the two-degree-of-freedom rotation stage, we proposed a novel adaptive control approach: SSA-GA optimization of RBFNN integration into PID control. This method leverages the SSA-GA algorithm to optimize the parameters within the RBFNN, which is then seamlessly integrated into the PID control framework. This integration enables precise control of the two-degree-of-freedom rotation stage, overcoming the limitations of traditional PID controllers. By simulating various real-world situations such as step, noise, sinusoidal, transient excitation, impulse and modelling errors, it is demonstrated that the proposed control method achieves significant improvement in terms of control accuracy, fast response and robustness. It offers a more effective and reliable method for controlling the two-degree-of-freedom rotation stage, addressing the challenges posed by various interfering factors. Meanwhile, through comprehensive comparisons with various optimization algorithms, we have proven that SSA-GA has the shortest optimization time. Consequently, the proposed method exhibits excellent application potential and broad prospects for future applications.

关键词： genetic algorithm model optimization PID control RBF neural network sparrow search algorithm

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