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Stochastic process-based degradation modeling and RUL prediction: from Brownian motion to fractional Brownian motion

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Science China(Information Sciences) 2021年第7期64卷 5-24页

作者： Hanwen ZHANG Maoyin CHEN Jun SHANG Chunjie YANG Youxian SUN School of Automation and Electrical Engineering University of Science and Technology Beijing State Key Laboratory of Industrial Control Technology College of Control Science and EngineeringZhejiang University Department of Automation Tsinghua University School of Automation and Electrical Engineering Linyi University Department of Electrical and Computer Engineering University of Alberta

Brownian motion(BM) has been widely used for degradation modeling and remaining useful life(RUL) prediction, but it is essentially Markovian. This implies that the future state in a BM-based degradation process relies only on its current state, independent of the past states. However, some practical industrial devices such as Li-ion batteries, ball bearings, turbofans, and blast furnace walls show degradations with long-range dependence(LRD), where the future degradation states depend on both the current and past degradation states. This type of degradation naturally brings two interesting problems, that is, how to model the degradations and how to predict their RULs. Recently, in contrast to the work that uses only BM, fractional Brownian motion(FBM) is introduced to model practical degradations. The most important feature of the FBM-based degradation models is the ability to characterize the non-Markovian degradations with LRD. Although FBM is an extension of BM, it is neither a Markovian process nor a semimartingale. Therefore, how to obtain the first passage time of an FBM-based degradation process has become a challenging task. In this paper, a review of the transition of RUL prediction from BM to FBM is provided. The peculiarities of FBM when addressing the LRD inherent in some practical degradations are discussed. We first review the BM-based degradation models of the past few decades and then give details regarding the evolution of FBM-based research. Interestingly, the existing BM-based models scarcely consider the effect of LRD on the prediction of RULs. Two practical cases illustrate that the newly developed FBMbased models are more generalized and suitable for predicting RULs than the BM-based models, especially for degradations with LRD. Along with the direction of FBM-based RUL prediction, we also introduce some important and interesting problems that require further study.

关键词： remaining useful life degradation model Brownian motion fractional Brownian motion long-range dependence

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Intelligent Predetermination of Generator Tripping Scheme: Knowledge Fusion-based Deep Reinforcement Learning Framework

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CSEE Journal of Power and Energy Systems 2024年第1期10卷 66-75页

作者： Lingkang Zeng Wei Yao Ze Hu Hang Shuai Zhouping Li Jinyu Wen Shijie Cheng State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronics EngineeringHuazhong University of Science and TechnologyWuhan 430074China Dispatching and Control Center Central China Branch of State Grid Corporation of ChinaWuhan 430077China. Department of Electrical Engineering and Computer Science University of TennesseeKnoxvilleTN 37996USA

Generator tripping scheme(GTS)is the most commonly used scheme to prevent power systems from losing safety and ***,GTS is composed of offline predetermination and real-time scenario ***,it is extremely time-consuming and labor-intensive for manual predetermination for a large-scale modern power *** improve efficiency of predetermination,this paper proposes a framework of knowledge fusion-based deep reinforcement learning(KF-DRL)for intelligent predetermination of ***,the Markov Decision Process(MDP)for GTS problem is formulated based on transient instability ***,linear action space is developed to reduce dimensionality of action space for multiple controllable ***,KF-DRL leverages domain knowledge about GTS to mask invalid actions during the decision-making *** can enhance the efficiency and learning ***,the graph convolutional network(GCN)is introduced to the policy network for enhanced learning *** simulation results obtained on New England power system demonstrate superiority of the proposed KF-DRL framework for GTS over the purely data-driven DRL method.

关键词： Deep reinforcement learning generator tripping scheme graph convolutional network invalid action masking knowledgefusion

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Distributed estimation-based output consensus control of heterogeneous leader-follower systems with antagonistic interactions

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Science China(Information Sciences) 2023年第3期66卷 305-306页

作者： Yanzhi WU Qingpeng LIANG Yiyi ZHAO Jiangping HU Linying XIANG School of Electrical Engineering Southwest Jiaotong University School of Information Science and Technology Southwest Jiaotong University School of Business Administration Southwestern University of Finance and Economics School of Automation Engineering University of Electronic Science and Technology of China School of Control Engineering Northeastern University at Qinhuangdao

Dear editor, This study addresses an output bipartite consensus problem for leader-follower heterogeneous multi-agent systems,and thus further generalizes the results in [1, 2]. The main contributions include three aspects.(i) Novel distributed estimation schemes are introduced to estimate the unavailable information of the leader.(ii) Owing to the unknown system matrix of the leader,

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Temporal Dynamics in instrumental Learning: Integrating Entropy Measures and Computational Models 31

Temporal Dynamics in instrumental Learning: Integrating Entr...

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31st National and 9th International Iranian Conference on Biomedical engineering, ICBME 2024

作者： Hassanzadeh, Zakieh Bahrami, Fariba University College of Engineering University of Tehran Human Motor Control and Computational Neuroscience Lab School of Electrical and Computer Engineering Tehran Iran University College of Engineering University of Tehran School of of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798331529710

This research explores the dynamics of decision-making within an instrumental learning framework, combining analyses of response times, entropy measures, and computational modeling. We conducted a study using an RLWM task, involving 27 participants and varying set sizes to examine the relationship between task complexity and cognitive functions. Our methodology incorporates Shannon entropy, Cumulative Residual Entropy (CRE), and Instantaneous CRE to assess uncertainty and mental effort across different task conditions. We evaluate two computational models - RLWM and its enhanced version, ~ p i H - to interpret both choice patterns and reaction times. Our findings indicate that set size significantly impacts response times and entropy measures, with increased complexity leading to higher entropy and slower responses. The temporal analysis of iCRE suggests a dynamic shift in cognitive strategies throughout the task. Model comparisons highlight distinct capabilities of RLWM and piH in capturing various aspects of cognitive processes. This comprehensive approach provides novel viewpoints into how the brain balances complex learning mechanisms with simpler rules for efficient decision-making under limited mental resources, contributing to our understanding of adaptive behavior in complex environments. © 2024 IEEE.

关键词： Adversarial machine learning

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A Novel Belief Rule-Based Fault Diagnosis Method with Interpretability

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computer Modeling in engineering & Sciences 2023年第8期136卷 1165-1185页

作者： Zhijie Zhou Zhichao Ming Jie Wang Shuaiwen Tang You Cao Xiaoxia Han Gang Xiang Rocket Force University of Engineering Xi’an710025China School of Automation Science and Electrical Engineering Beihang UniversityBeijing100191China Beijing Aerospace Automatic Control Institute Beijing100070China

Fault diagnosis plays an irreplaceable role in the normal operation of equipment.A fault diagnosis model is often required to be interpretable for increasing the trust between humans and the *** to the understandable knowledge expression and transparent reasoning process,the belief rule base(BRB)has extensive applications as an interpretable expert system in fault *** is an effective means to weaken the subjectivity of experts in BRB,where the interpretability of BRB may be ***,to obtain a credible result,the weakening factors of interpretability in the BRB-based fault diagnosis model are firstly analyzed,which are manifested in deviation from the initial judgement of experts and over-optimization of *** these two factors,three indexes are proposed,namely the consistency index of rules,consistency index of the rule base and over-optimization index,tomeasure the interpretability of the *** both the accuracy and interpretability of amodel,an improved coordinate ascent(I-CA)algorithmis proposed to fine-tune the parameters of the fault diagnosis model based on *** I-CA,the algorithm combined with the advance and retreat method and the golden section method is employed to be one-dimensional search ***,the random optimization sequence and adaptive step size are proposed to improve the accuracy of the ***,a case study of fault diagnosis in aerospace relays based on BRB is carried out to verify the effectiveness of the proposed method.

关键词： Fault diagnosis belief rule base interpretability weakening factors improved coordinate ascent

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loT-enabled Rapid Global Maximum Power Point Tracking for Multiple Photovoltaic Inverters 15

loT-enabled Rapid Global Maximum Power Point Tracking for Mu...

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15th IEEE International Symposium on Power Electronics for Distributed Generation Systems, PEDG 2024

作者： Zhou, Kangjia Gao, Feng Qin, Caiyun Meng, Xiangjian School of Electrical Engineering Shandong University Jinan China School of Control Science and Engineering Shandong University Jinan China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

ISBN: (纸本)9798350361001

Maximum power point tracking (MPPT) is a crucial technology for enhancing photovoltaic (PV) array power generation efficiency. Under scenarios with partial shading, the output power characteristic curve of a PV array often displays multiple peaks, necessitating the use of global peak-seeking techniques. Particle Swarm Optimization (PSO) and its advanced variants have been widely adopted to address global optimization challenges. However, the current landscape primarily includes PSO based MPPT methods designed for single inverters, which tend to exhibit slow optimization speeds. This paper presents an Internet of Things (IoT)-based multi- inverters approach for global MPPT. In this approach, all inverters within the network are treated as a single particle. This transformation shifts the conventional serial PSO operation into a parallel one, enabling data exchange among inverters to jointly track the maximum power point. Simulation results demonstrate that the proposed methodology significantly accelerates the optimization process, effectively mitigating the limitations of traditional approaches. © 2024 IEEE.

关键词： Particle swarm optimization (PSO)

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A WSN Node Fault Diagnosis Model Based on BRB with Self-Adaptive Quality Factor

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computers, Materials & Continua 2023年第4期75卷 1157-1177页

作者： Guo-Wen Sun Gang Xiang Wei He Kai Tang Zi-Yi Wang Hai-Long Zhu School of Computer Science and Information Engineering Harbin Normal UniversitySongbei DistrictHarbin150025China Beihang University School of Automation Science and Electrical Engineering Beijing100191China Beijing Aerospace Automatic Control Institute Beijing100074China Rocket Force University of Engineering Xi’anShaanxi Province710025China

Wireless sensor networks (WSNs) operate in complex and harshenvironments;thus, node faults are inevitable. Therefore, fault diagnosis ofthe WSNs node is essential. Affected by the harsh working environment ofWSNs and wireless data transmission, the data collected by WSNs containnoisy data, leading to unreliable data among the data features extracted duringfault diagnosis. To reduce the influence of unreliable data features on faultdiagnosis accuracy, this paper proposes a belief rule base (BRB) with a selfadaptivequality factor (BRB-SAQF) fault diagnosis model. First, the datafeatures required for WSN node fault diagnosis are extracted. Second, thequality factors of input attributes are introduced and calculated. Third, themodel inference process with an attribute quality factor is designed. Fourth,the projection covariance matrix adaptation evolution strategy (P-CMA-ES)algorithm is used to optimize the model’s initial parameters. Finally, the effectivenessof the proposed model is verified by comparing the commonly usedfault diagnosis methods for WSN nodes with the BRB method consideringstatic attribute reliability (BRB-Sr). The experimental results show that BRBSAQFcan reduce the influence of unreliable data features. The self-adaptivequality factor calculation method is more reasonable and accurate than thestatic attribute reliability method.

关键词： Self-adaptive quality factor belief rule base wireless sensor networks fault diagnosis

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Structure-free containment control for uncertain underactuated multiple Euler-Lagrange systems

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Science China(Information Sciences) 2023年第11期66卷 150-168页

作者： Housheng SU Yali WU Liren ZHANG Xia CHEN Key Laboratory of Image Processing and Intelligent Control of Education Ministry of China School of Artificial Intelligence and Automation Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Engineering and Technology School of Electrical and Electronic Engineering Huazhong University of Science and Technology

This article studies the problem of structure-free distributed containment control for uncertain underactuated multiple Euler-Lagrange systems(MELSs) considering disturbances by using a layered approach. First, the second layer is the virtual layer constructed artificially for hierarchical control. We move all virtual nodes to the convex hull formed by leaders in the first layer by implementing containment control algorithms on all virtual nodes. Then, the third layer is the following layer, and we propose an adaptive robust tracking controller to ensure that each follower in the third layer tracks the corresponding virtual node in the second layer. So far, the underactuated MELSs can achieve containment control. Furthermore,through the theoretical derivation, sufficient conditions are obtained to achieve the objective of structure-free containment control. Finally, the effectiveness of the proposed stratified structure-free containment control method is verified by a simulation example.

关键词： Euler-Lagrange systems underactuated system containment control adaptive-robust control

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IGBT Temperature Field Monitoring Based on Reduced-order Model

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CES Transactions on electrical Machines and Systems 2023年第2期7卷 129-136页

作者： Ziyu Zhou Yi Su Xu Zhang Chengde Tong Ping Zheng Mingjun Zhu School of Electrical Engineering&Automation Harbin Institute of TechnologyHarbin 150001China IEEE Flight control department Xi'an Flight Automatic Control Research InstituteXi'an 710076China

With the rapid development of the world economy,IGBT has been widely used in motor drive and electric energy *** order to timely detect the fatigue damage of IGBT,it is necessary to monitor the junction temperature of *** order to realize the fast calculation of IGBT junction temperature,a finite element method of IGBT temperature field reduction is proposed in this ***,the finite element calculation process of IGBT temperature field is introduced and the linear equations of finite element calculation of temperature field are *** field data of different working conditions are obtained by finite element simulation to form the sample *** the covariance matrix of the sample space is constructed,whose proper orthogonal decomposition and modal extraction are carried *** basis vector space is selected to complete the low dimensional expression of temperature vector inside and outside the sample ***,the reduced-order model of temperature field finite element is obtained and *** results of the reduced order model are compared with those of the finite element method,and the performance of the reduced-order model is evaluated from two aspects of accuracy and rapidity.

关键词： IGBT Junction temperature Proper orthogonal decomposition Reduced-order model

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