2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)最新文献_第10页

A Parameter Optimized Variational Mode Decomposition Method for Harmonic and Inter-harmonic Detection 谐波及间谐波检测的参数优化变分模态分解方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166482

X. Xi, Pengqi Sun, C. Xing, Shengnan Li, Xincui Tian

{"title":"A Parameter Optimized Variational Mode Decomposition Method for Harmonic and Inter-harmonic Detection","authors":"X. Xi, Pengqi Sun, C. Xing, Shengnan Li, Xincui Tian","doi":"10.1109/DDCLS58216.2023.10166482","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166482","url":null,"abstract":"An variational mode decomposition (VMD) has been applied in the field of harmonic detection, but the error will be large if the decomposition parameters are set artificially. To improve the accuracy of VMD in inter-harmonics detection, we need to determine the number of wolves, maximum number of iterations, convergence factor and other parameters, and then select component sample entropy function as the fitness function of the Grey Wolf algorithm. The variational mode decomposition can be utilized to extract the harmonic signal and choose a minimum envelope entropy weight as the best component. The Fourier transform is used to obtain the amplitude and frequency information of interharmonic signals. The simulation results show that the proposed method can effectively optimize the parameters and reduce the VMD decomposition error. Compared with empirical mode decomposition (EMD), complementary ensemble empirical mode decomposition (CEEMD) and empirical wavelet transform (EWT), the VMD with optimized parameters can significantly improve the accuracy of interharmonic detection and improve the accurate trace of accident source.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129227813","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rolling Bearing Fault Diagnosis Based on Time-Frequency Transform-Assisted CNN: A Comparison Study 基于时频变换辅助CNN的滚动轴承故障诊断比较研究

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166631

Baoye Song, Yiyan Liu, Peng Lu, Xingzhen Bai

引用次数: 0

Ship Adaptive RBF Neural Network Course Keeping Control Considering System Uncertainty 考虑系统不确定性的船舶自适应RBF神经网络航向保持控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166581

Sihang Zhang, Qiang Zhang, Wen-Li Su, Haoyang Li, Xudong Gai

引用次数: 0

Time-frequency Hypergraph Neural Network for Rotating Machinery Fault Diagnosis with Limited Data 基于时频超图神经网络的有限数据旋转机械故障诊断

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167156

Haobin Ke, Zhi-wen Chen, Jiamin Xu, Xinyu Fan, Chao Yang, Tao Peng

{"title":"Time-frequency Hypergraph Neural Network for Rotating Machinery Fault Diagnosis with Limited Data","authors":"Haobin Ke, Zhi-wen Chen, Jiamin Xu, Xinyu Fan, Chao Yang, Tao Peng","doi":"10.1109/DDCLS58216.2023.10167156","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10167156","url":null,"abstract":"Due to the scarcity of fault samples and the weakness of processing higher-order interactive information, the most existing intelligence methods fail to achieve the optimal effect in fault diagnosis. To address these problems, a time-frequency hypergraph neural network-based fault diagnosis method is proposed. In the proposed network, the limited data is initially segmented using the sliding window mechanism to obtain a set of time-domain signal instances. Additionally, the Fast Fourier Transform (FFT) is applied to each signal instance to extract corresponding frequency-domain signals, so as to capture more fault-sensitive features. Subsequently, a two-layer convolutional neural network is used to extract fault-attention features from both the time and frequency domain signals. Also, in order to reduce computational complexity, the time-frequency domain features are adaptively stacked based on a self-attention mechanism. Furthermore, a feature similarity graph is constructed for the time-frequency domain features using a k-nearest neighbor algorithm. This graph is then input into the hypergraph neural network (HGNN) to obtain the final diagnosis results. One comparative experiment shows that the proposed method not only mitigates the performance degradation caused by limited samples and noisy environments, but also effectively leverages the higher-order interaction information among nodes in the hypergraph.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129418186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Extended State Observer based Iterative Learning Control for Systems with Nonrepetitive Disturbances 非重复扰动系统的扩展状态观测器迭代学习控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167248

Shiyan Li, Xuefang Li

引用次数: 0

A Soft Sensor Method based on Unsupervised Multi-layer Domain Adaptation for Batch Processes 基于无监督多层域自适应的批处理软测量方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166816

Qin Xiong, Huaiping Jin, Bin Wang, Haipeng Liu, Wangyang Yu

{"title":"A Soft Sensor Method based on Unsupervised Multi-layer Domain Adaptation for Batch Processes","authors":"Qin Xiong, Huaiping Jin, Bin Wang, Haipeng Liu, Wangyang Yu","doi":"10.1109/DDCLS58216.2023.10166816","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10166816","url":null,"abstract":"In batch processes, soft sensors frequently face the problem of irregular distributions between current and past data owing to variations in operating circumstances, as well as and poor model performing resulting from the absence with labels in the current data. This paper proposes a soft sensor method that is founded on dynamic multi-layer domain adaptation (DMDA). The method being proposed first training a convolutional neural network model with a substantial quantity of labeled data in the source domain, and subsequently use the obtained parameters as the beginning stage for the target model. Then, by utilizing multi-kernel maximum mean discrepancy (MK-MMD) and conditional embedding operator discrepancy (CEOD), the multi-layer convolutional neural network can effectively measure the difference in the overall (marginal) and specific (conditional) distributions between the source and target domains. Furthermore, the incorporation of an adaptive factor facilitates the dynamic adjustment of distribution weight, enabling precise fine-tuning of the target model. Finally, a regression model is established using the distribution-adapted historical data to achieve unsupervised soft sensor modeling. The substrate concentration in different fermentation tanks of the chlortetracycline fermentation process can be predicted through the utilization of the proposed approach. The experimental findings indicate that this method can accomplish tank-to-tank knowledge transfer, and significantly outperform traditional transfer learning-based soft sensor methods.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130241150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improved BP neural network based active disturbance rejection control for magnetic sensitivity calibration system 基于改进BP神经网络的磁灵敏度标定系统自抗扰控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167186

Minlin Wang, Xueming Dong, X. Ren

引用次数: 0

A Cyber-Security Framework for ST-MPC of State and Input Constrained CPS Under False Data Injection Attacks 假数据注入攻击下状态ST-MPC和输入约束CPS的网络安全框架

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10165990

Ning He, Yuxiang Li, Kai Ma

{"title":"A Cyber-Security Framework for ST-MPC of State and Input Constrained CPS Under False Data Injection Attacks","authors":"Ning He, Yuxiang Li, Kai Ma","doi":"10.1109/DDCLS58216.2023.10165990","DOIUrl":"https://doi.org/10.1109/DDCLS58216.2023.10165990","url":null,"abstract":"Self-triggered model predictive control (ST-MPC) is widely applied in various aspects currently, however, the ST-MPC mechanisms that have seldom been developed consider the possible malicious false data injection (FDI) attacks in the cyber-physical system (CPS). Therefore, in this paper, a novel resilient ST-MPC strategy based on input reconstruction (IR) against FDI attacks is proposed for a nonlinear input-affine discrete-time system with state and input constraints, which combines both cyber security and resource consumption. More specifically, when faced with FDI attacks in controller-to-actuator (C-A) channels at the triggering instants, on the actuator side, two key control data are selected to reconstruct input control signals for application into the system, otherwise, the optimal input control signals will be applied into the controlled system. Furthermore, a resilient ST-MPC algorithm with a dual-mode control strategy is proposed, and its closed-loop stability is also analyzed, in which the state constraint is elaborated. Finally, a simulation and its resultant comparisons illustrate the effectiveness of the proposed method.","PeriodicalId":415532,"journal":{"name":"2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS)","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128759224","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Granger causality analysis method based on GRBF network 基于GRBF网络的Granger因果分析方法

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10166901

Huang Chen, Jianguo Wang, Pangbin Ding, X. Ye, Yuan Yao, He-Lin Chen

引用次数: 0

Robust Fuzzy Adaptive Funnel Control of Flexible Exoskeleton Joints Based on Singular Perturbation Method 基于奇异摄动法的柔性外骨骼关节鲁棒模糊自适应漏斗控制

2023 IEEE 12th Data Driven Control and Learning Systems Conference (DDCLS) Pub Date : 2023-05-12 DOI: 10.1109/DDCLS58216.2023.10167076

Chengwu Jin, Yong Yang, Xia Liu, Xiaoyu Shi

引用次数: 0