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2nd DEVS Integrative M and S Symposium, DEVS 2006, Part of the 2006 Spring Simulation Multiconference, SpringSim 2006

作者： Pawletta, Thorsten Deatcu, Christina Pawletta, Sven Hagendorf, Olaf Colquhoun, Gary RG Computational Engineering and Automation Wismar University Germany School of Engineering LiverpooI John Moores University United Kingdom

This paper describes our current research in the area of Discrete Event System Simulation (DEVS) and its implementation for programmable Scientific and technical Computing Environments (SCEs) with a focus on variable- structure and hybrid systems. Engineers, unlike scientists, are usually familiar with the use of SCEs such as Matlab rather than high level programming language simulation libraries. DEVS-based modeling and simulation until now has not been available for SCEs. This research has led to the development of a fully compatible toolbox for the Matlab environment with the potential to interact with other toolboxes. The paper reviews the advantages of DEVS/SCEs integration and concludes by describing the potential benefits in applications with other toolboxes.

关键词： Discrete event simulation

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An approach for simulation based structure optimisation of discrete event systems

An approach for simulation based structure optimisation of d...

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2008 Spring Simulation Multiconference, SpringSim'08

作者： Hagendorf, Olaf Pawletta, Thorsten Liverpool John Moores University School of Engineering United Kingdom RG Computational Engineering and Automation Wismar University Germany

ISBN: (纸本)1565553195

Modelling and simulation with integrated parameter optimisation is a well established technique. This paper introduces an extended, simulation based optimisation method. Behind automatic parameter value variation the method is able to re-configure model structure during optimisation. This is achieved through a combination of optimisation, simulation and model management methods. Using this approach simulation is used to establish the performance of a current model structure and parameter value set and the optimiser searches for an optimal solution with repeated model structure and model parameter value changes assisted by the model management methods.

关键词： Discrete event simulation

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An approach for simulation based systems

An approach for simulation based systems

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Joint Symposium of DEVS Integrative M and S, DEVS 2008 and High Performance Computing and Simulation Symposium, HPCS 2008, Part of the SCS Spring Simulation Multiconference, SpringSim 2008

作者： Hagendorf, Olaf Pawletta, Thorsten School of Engineering Liverpool John Moores University United Kingdom RG Computational Engineering and Automation Wismar University Germany

关键词： Discrete event simulation

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Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation

arXiv

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arXiv 2020年

作者： Zhang, Jingxin Chen, Maoyin Chen, Hao Hong, Xia Zhou, Donghua Department of Automation TNList Tsinghua University Beijing China Department of Computer Science School of Mathematical Physical and Computational Sciences University of Reading RG6 6AY United Kingdom College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao China

By integrating two powerful methods of density reduction and intrinsic dimensionality estimation, a new data-driven method, referred to as OLPP-MLE (orthogonal locality preserving projection-maximum likelihood estimation), is introduced for process monitoring. OLPP is utilized for dimensionality reduction, which provides better locality preserving power than locality preserving projection. Then, the MLE is adopted to estimate intrinsic dimensionality of OLPP. Within the proposed OLPP-MLE, two new static measures for fault detection TOLP2P and SPEOLP P are defined. In order to reduce algorithm complexity and ignore data distribution, kernel density estimation is employed to compute thresholds for fault diagnosis. The effectiveness of the proposed method is demonstrated by three case studies. © 2020, CC BY.

关键词： Process monitoring

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Continual learning-based probabilistic slow feature analysis for monitoring multimode nonstationary processes

arXiv

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arXiv 2022年

作者： Zhang, Jingxin Zhou, Donghua Chen, Maoyin Hong, Xia Department of Automation Tsinghua University Beijing100084 China College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao266000 China Department of Computer Science School of Mathematical Physical and Computational Sciences University of Reading RG6 6AY United Kingdom

This paper proposes a novel continual learning-based probabilistic slow feature analysis (PSFA) algorithm for monitoring multimode nonstationary processes, where multimode slow features are extracted and elastic weight consolidation (EWC) is adopted for sequential modes. EWC was originally introduced in the setting of machine learning of sequential multi-tasks with the aim of avoiding catastrophic forgetting issue, which equally poses as a major challenge in multimode nonstationary process monitoring. When a new mode arrives, a small set of data should be collected so that this mode can be identified by PSFA and limited prior knowledge. Then, a regularization term is introduced to prevent new data from significantly interfering with the learned knowledge, where the parameter importance measures are estimated. The proposed method is denoted as PSFA–EWC, which is updated continually and capable of achieving excellent performance. PSFA–EWC furnishes backward and forward transfer ability by a single model. The significant features of previous modes are retained while consolidating new information, which may contribute to learning new relevant modes. The effectiveness of the proposed method is demonstrated via a continuous stirred tank heater and a practical coal pulverizing system. Note to Practitioners—Since industrial systems operate in varying modes and data are nonstationary within each mode, multimode nonstationary process monitoring is increasingly important. Traditional multimode monitoring methods generally need complete data from all possible modes and may need to be retrained from scratch when a new mode arrives, which require expensive computational resources and storage space. Besides, it is difficult to distinguish real faults from normal variations in multimode nonstationary processes. This paper proposes a novel continual learning-based probabilistic slow feature analysis, where elastic weight consolidation is employed to consolidate the previously learne

关键词： Process monitoring

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