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Spatiotemporal Transform Network-Based Anomaly Detection and Localization of distributed parameter systems

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2025年第4期21卷 2770-2778页

作者： Wei, Peng Zhu, Wenchao Yang, Yang Fei, Zicheng Xie, Changjun Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China Wuhan Univ Technol Sch Automot Engn Wuhan 430070 Peoples R China Soochow Univ Sch Future Sci & Engn Suzhou 215006 Peoples R China

Due to complex spatiotemporal couplings, it is difficult to detect and locate spatiotemporal abnormal sources for distributed parameter systems (DPSs) with unknown governing equations. In this research, a spatiotemporal transform network-based anomaly detection and localization framework is proposed for unknown DPSs. Considering the orthogonality, the spatial basis functions (SBFs) are optimized by the nonlinear space-time separation network to achieve the minimal reconstruction error. The Gaussian process regression is used to identify the temporal dynamics, based on which the temporal statistic is constructed. A comprehensive statistic is designed by considering the temporal dynamics and spatial dissimilarity for reliable detection. With the spatial construction, the weighted absolute error of SBFs is constructed for anomaly localization. The anomaly detectability is proven by theoretical analysis. Experiments on a lithium-ion battery demonstrate the effectiveness and superiority of the proposed method in detecting and localizing battery internal short circuits.

关键词： Location awareness Spatiotemporal phenomena Anomaly detection Mathematical models Indexes Accuracy Transforms Fault diagnosis Probability density function distributed parameter systems anomaly localization distributed parameter system (DPS) interpretable neural network Li-ion battery (LIB)

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Multiscale Fusion for Abnormality Detection and Localization of distributed parameter systems

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2025年

作者： Wei, Peng Li, Han-Xiong Wuhan Univ Technol Sch Automat Wuhan 430070 Peoples R China City Univ Hong Kong Dept Syst Engn Hong Kong 999077 Peoples R China

Early internal abnormalities in the distributed parameter systems (DPSs) may develop into uncontrollable thermal failures, causing serious safety incidents. However, traditional first-principle methods heavily depend on governing equations, and existing data-based single-scale methods have insufficient performance under dynamically changing conditions. Based on these considerations, the multiscale information fusion is proposed for internal abnormality detection and localization of DPSs under different scenarios. We introduce the dissimilarity statistic to identify abnormalities for lumped variables, whereas the spatial and temporal statistics are presented for abnormality detection for distributed variables. Through appropriate parameter optimization, these statistic functions are integrated into the comprehensive multiscale detection index, which outperforms traditional single-scale detection methods. The proposed multiscale statistic has good physical interpretability from the system disorder degree. Experiments on the internal short circuit (ISC) of a battery system have demonstrated that our proposed method can swiftly identify ISC abnormalities within 20 s and accurately pinpoint problematic battery cells under different testing currents and fault types.

关键词： Batteries Battery charge measurement Mathematical models Location awareness Voltage measurement Temperature distribution Couplings Testing State of charge distributed parameter systems Abnormality detection abnormality localization battery system distributed parameter system (DPS) information fusion

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Time/Space Separation-Based Physics-Informed Machine Learning for Spatiotemporal Modeling of distributed parameter systems

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IEEE TRANSACTIONS ON systems MAN CYBERNETICS-systems 2025年第1期55卷 137-148页

作者： Wang, Bing-Chuan Dai, Cong-Ling Wang, Yong Li, Han-Xiong Cent South Univ Sch Automat Changsha 410083 Peoples R China City Univ Hong Kong Dept Syst Engn Hong Kong Peoples R China

This article introduces a novel time/space separation-based physics-informed machine learning (T/S-PIML) modeling method by making full use of the complementary strengths of the physics-informed neural network (PINN) and the time/space separation methodology. T/S-PIML is the first attempt to seamlessly integrate structural (including spatial and temporal) physical information with data for effective spatiotemporal modeling of distributed parameter systems (DPSs). With the help of the spectral method, spatial basis functions are first extracted to capture spatial physical information. Subsequently, a reduced-order system is derived to characterize the corresponding temporal physical information. Upon the structural physical information, PINN is developed for temporal modeling. Following the time/space synthesis, a small amount of sensing data is utilized to calibrate system errors. Experiments on a benchmark DPS and the thermal process of a lithium-ion battery demonstrate the effectiveness of T/S-PIML.

关键词： distributed parameter systems spatiotemporal modeling time/space separation physics-informed machine learning

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Iterative learning control for one-dimensional fourth order distributed parameter systems

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Science China(Information Sciences) 2017年第1期60卷 177-189页

作者： Qin FU Panpan GU Jianrong WU School of Mathematics and Physics Suzhou University of Science and Technology

This paper addresses the problem of iterative learning control algorithm for high order distributed parameter systems in the presence of initial errors. And the considered distributed parameter systems are composed of the one-dimensional fourth order parabolic equations or the one-dimensional fourth order wave equations. According to the characteristics of the systems, iterative learning control laws are proposed for such fourth order distributed parameter systems based on the P-type learning scheme. When the learning scheme is applied to the systems, the output tracking errors on 2L space are bounded, and furthermore, the tracking errors on 2L space can tend to zero along the iteration axis in the absence of initial errors. Simulation examples illustrate the effectiveness of the proposed method.

关键词： P-type learning scheme iterative learning control distributed parameter systems fourth order parabolic equation fourth order wave equation

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ON THE PERSISTENCE OF EXCITATION IN THE ADAPTIVE ESTIMATION OF distributed-parameter systems

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 1994年第5期39卷 1117-1123页

作者： DEMETRIOU, MA ROSEN, IG N CAROLINA STATE UNIV DEPT MATHCTR RES SCI COMPUTATRALEIGHNC 27695

Persistence of excitation is a sufficient condition for parameter convergence in adaptive identification schemes for dynamical systems. For abstract parabolic and hyperbolic distributed parameter systems, this condition requires that a family of bounded linear functionals be norm bounded away from zero. The level of persistence of excitation of the plant and its implications are considered for a simple parabolic and hyperbolic system. Its effect on the qualitative and quantitative behavior of the estimators is investigated.

关键词： Adaptive estimation distributed parameter systems Convergence Partial differential equations Tuning Sufficient conditions parameter estimation Adaptive control Nonlinear equations Damping

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Improved control for distributed parameter systems with time-dependent spatial domains utilizing mobile sensor-actuator networks

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Chinese Physics B 2017年第9期26卷 7-16页

作者：张建中崔宝同庄波 Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education)jiangnan University Wuxi 214122China

A guidance policy for controller performance enhancement utilizing mobile sensor-actuator networks （MSANs） is proposed for a class of distributed parameter systems （DPSs）, which are governed by diffusion partial differential equations （PDEs） with time-dependent spatial domains. Several sufficient conditions for controller performance enhancement are presented. First, the infinite dimensional operator theory is used to derive an abstract evolution equation of the systems under some rational assumptions on the operators, and a static output feedback controller is designed to control the spatial process. Then, based on Lyapunov stability arguments, guidance policies for collocated and non-collocated MSANs are provided to enhance the performance of the proposed controller, which show that the time-dependent characteristic of the spatial domains can significantly affect the design of the mobile scheme. Finally, a simulation example illustrates the effectiveness of the proposed policy.

关键词： distributed parameter systems time-dependent spatial domains mobile actuator-sensor networks,Lyapunov stability

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CONFIGURING A SENSOR NETWORK FOR FAULT DETECTION IN distributed parameter systems

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INTERNATIONAL JOURNAL OF APPLIED MATHEMATICS AND COMPUTER SCIENCE 2008年第4期18卷 513-524页

作者： Patan, Maciej Ucinski, Dariusz Univ Zielona Gora Inst Control & Computat Engn PL-65246 Zielona Gora Poland

The problem of fault detection in distributed parameter systems (DPSs) is formulated as that of maximizing the power of a parametric hypothesis test which checks whether or not system parameters have nominal values. A computational scheme is provided for the design of a network of observation locations in a spatial domain that are supposed to be used while detecting changes in the underlying parameters of a distributed parameter system. The setting considered relates to a situation where from among a finite set of potential sensor locations only a subset can be selected because of the cost constraints. As a suitable performance measure, the D-s-optimality criterion defined on the Fisher information matrix for the estimated parameters is applied. Then, the solution of a resulting combinatorial problem is determined based on the branch-and-bound method. As its essential part, a relaxed problem is discussed in which the sensor locations are given a priori and the aim is to determine the associated weights, which quantify the contributions of individual gauged sites. The concavity and differentiability properties of the criterion are established and a gradient projection algorithm is proposed to perform the search for the optimal solution. The delineated approach is illustrated by a numerical example on a sensor network design for a two-dimensional convective diffusion process.

关键词： branch-and-bound constrained experimental design distributed parameter systems fault detection parameter estimation sensor location

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Worst-case and distributional robustness analysis of finite-time control trajectories for nonlinear distributed parameter systems

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IEEE TRANSACTIONS ON CONTROL systems TECHNOLOGY 2003年第5期11卷 694-704页

作者： Nagy, ZK Braatz, RD Univ Babes Bolyai R-3400 Cluj Napoca Romania Univ Illinois Urbana IL 61801 USA

A novel approach is proposed that quantifies the influence of parameter. and control implementation uncertainties upon the states and outputs of finite-time control trajectories for nonlinear lumped and distributed parameter systems. The worst-case values of the states and outputs due to model parameter uncertainties are computed as a function of time along the control trajectories. The algorithm can also compute the part of the optimal control trajectory for which implementation inaccuracies are of increased importance. An analytical expression is derived that provides an estimate of the distribution of the states and outputs as a function of time, based on simulation results. The approaches require a, relatively low computational burden to perform the analysis, compared to Monte Carlo approaches for robustness analysis. The technique is applied to the crystallization of an inorganic chemical with uncertainties in the nucleation and growth parameters and in the implementation of the control trajectory.

关键词： crystallization distributed parameter systems optimal control probabilistic analysis robustness analysis worst-case analysis

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Robustness with respect to delays for exponential stability of distributed parameter systems

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SIAM JOURNAL ON CONTROL AND OPTIMIZATION 1998年第1期37卷 230-244页

作者： Rebarber, R Townley, S Univ Nebraska Dept Math & Stat Lincoln NE 68588 USA Univ Exeter Dept Math Exeter EX4 4QE Devon England Univ Exeter Ctr Syst & Control Engn Exeter EX4 4QE Devon England

In this paper we address the question of whether the open-loop exponential growth rate of a linear system can be improved by a feedback in such a way that this improvement is robust with respect to small delays in the feedback loop. When the input operator is admissible, and the class of possible feedbacks consists of compact operators, we find that if a feedback can improve the exponential growth rate, then it can do so robustly. Furthermore, we find that if the control space is finite dimensional and a bounded feedback cannot be found to improve exponential stability, then a large class of unbounded feedbacks cannot improve the exponential growth rate robustly, even if such feedbacks can improve the exponential growth rate in the absence of delays.

关键词： robustness delays feedback control distributed parameter systems

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Spatial basis functions based fault localisation for linear parabolic distributed parameter systems

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IET CONTROL THEORY AND APPLICATIONS 2020年第20期14卷 3580-3588页

作者： Feng, Yun Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Natl Engn Lab Robot Visual Percept & Control Tech Changsha 410082 Hunan Peoples R China

Fault localisation for distributed parameter systems is as important as fault detection but is seldom discussed in the literature. The main reason is that an infinite number of sensors in the space are needed to construct a distributed residual signal, which is nearly impossible in practice. In this study, a fault detection and localisation filter which only uses a finite number of sensors is initiated based on an approximated ordinary differential equation model. Considering the limitations on computation resources for higher-order models in practice, a novel set of spatial basis functions is applied to the reduced-order fault detection and localisation filter design. Under certain conditions, the novel spatial basis functions obtain smaller state truncation error while the order is lower compared to the mostly used eigenfunctions of the spatial operator.

关键词： eigenvalues and eigenfunctions fault diagnosis reduced order systems distributed parameter systems differential equations linear parabolic distributed parameter systems fault localisation distributed residual signal approximated ordinary differential equation model higher-order models spatial basis functions reduced-order fault detection and localisation filter design spatial operator

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