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作者： Giarola Enrio University of Trento

学位级别：博士

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distributed monitoring for Energy Consumption Optimization in Smart Buildings

Distributed Monitoring for Energy Consumption Optimization i...

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IEEE Conference on Antenna Measurements and Applications (CAMA)

作者： Viani, F. Giarola, E. Robol, F. Oliveri, G. Massa, A. Univ Trento ELEDIA Res Ctr DISI I-38123 Trento Italy

ISBN: (纸本)9781479936786

Efficient strategies for energy saving and solutions to increase user awareness on energy resources management are important aspects from both social and economic viewpoints. The traditional power grid needs new monitoring and control tools for the optimal management of producers and consumers through adaptive energy distribution schemes. The problem of energy consumption reduction can be addressed at different scales, ranging from the management of single appliances up to building, district, city level. In this paper, a wireless system architecture for the distributed monitoring and intelligent supervision of energy consumptions is proposed. The energy information related to a multi-user scenario are acquired by distributed wireless sensors and processed by a decision-making support tool aimed at the adaptive optimization of user energy-habits and successive energy cost reduction.

关键词： building management systems cost reduction decision making energy consumption optimisation adaptive optimization decision-making support tool distributed monitoring distributed wireless sensors energy consumption optimization energy consumptions energy resources management intelligent supervision multiuser scenario smart buildings successive energy cost reduction wireless system architecture Decision support systems Energy consumption Home appliances monitoring Optimization Sensors Wireless sensor networks energy consumption intelligent buildings distributed monitoring adaptive optimization Building management systems COST REDUCTION decision support systems Wireless sensor networks Appliances Sensors decision support tool

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Performance-driven optimal design of distributed monitoring for large-scale nonlinear processes

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CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS 2016年 155卷 151-159页

作者： Jiang, Qingchao Li, Juan Yan, Xuefeng E China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China Qingdao Agr Univ Coll Mech & Elect Engn Qingdao 266109 Peoples R China

Centralized monitoring generally involves all measured variables in one model. However, the existence of variables without beneficial information may cause redundancy in the monitoring and degrade monitoring performance. This paper proposes a performance-driven distributed monitoring scheme that incorporates kernel principal analysis (KPCA) and Bayesian diagnosis system for large-scale nonlinear processes. First, a stochastic optimization method is utilized to select a subset of variables that provide the best possible performance for each fault and to decompose the process into several sub-blocks. Second, a KPCA model is established in each block to deal with nonlinearity and generate fault signature evidence. Finally, a Bayesian fault diagnosis system is established to identify the fault status of the entire process. Considering the significant calculation amount in Bayesian diagnosis, optimal evidence source selection is performed to reduce the redundancy. Case studies on the Tennessee Eastman benchmark process and a continuous stirred tank reactor process demonstrate the efficiency of the proposed scheme. (C) 2016 Elsevier B.V. All rights reserved.

关键词： distributed monitoring Large-scale nonlinear process Bayesian fault diagnosis Kernel principal component analysis

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On heuristics for optimal configuration of hierarchical distributed monitoring systems

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JOURNAL OF SYSTEMS AND SOFTWARE 1998年第3期43卷 197-206页

作者： Cao, JN Zhang, K de Vel, O Hong Kong Polytech Univ Dept Comp Hung Hom Kowloon Peoples R China Macquarie Univ Dept Comp N Ryde NSW 2109 Australia James Cook Univ N Queensland Dept Comp Sci Townsville Qld 4811 Australia

This paper considers the problem of optimal configuration of the monitoring units in a hierarchical distributed monitoring system-A hierarchical distributed monitoring system consists of a hierarchy of monitoring units which are grouped and distributed onto the physical network. The architecture lends itself to parallel processing, reducing the complexity of distributed monitoring caused by factors such as collection and processing of the large quantities of monitoring data. Furthermore, the topology-specific partitioning of the monitoring units allow complex, topology-specific events to be monitored and evaluated in a natural and efficient way. The optimal configuration problem is concerned with finding an optimal hierarchical partition of the monitoring units such that the total processing cost is the minimum. It is a NP-complete problem. In this paper, we study the heuristics for obtaining near-optimal grouping of monitoring units. Simulation of heuristic algorithms for mesh and hypercube networks are presented. The results suggest further system topology specific heuristics. Although the paper is targeted at distributed monitoring, we believe that the results can also be applied to other hierarchical control problems in distributed computing. (C) 1998 Elsevier Science Inc. All rights reserved.

关键词： parallel and distributed computing hierarchical distributed systems distributed monitoring optimization

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From Local to Global Consistency in distributed monitoring of Petri Net Models

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IEEE TRANSACTIONS ON AUTOMATIC CONTROL 2023年第1期68卷 494-501页

作者： Jiroveanu, George Boel, Rene K. K. Romanian Natl Power Grid Co CNTEE Bucharest 030786 Romania Univ Ghent ELIA Syst Res Grp B-9042 Ghent Belgium

In this technical article we consider a distributed analysis of a plant given by local petri net models (components) that interact via shared transitions. For each component, there is a local agent that performs local calculations and exchanges information with its neighbours for monitoring the plant. We relax the standard requirement that the interaction graph between components is a tree and, for the general case, we investigate under which conditions the local consistency of the local estimates imply their global consistency. Moreover, we show that if the information exchanged between agents incorporates additional information related to the execution time intervals of the shared transitions, the globally consistent estimates become equal to the results derived in a CR.

关键词： distributed monitoring global consistency (GC) local consistency (LC) petri nets (PN)

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Total Variable Decomposition Based on Sparse Cointegration Analysis for distributed monitoring of Nonstationary Industrial Processes

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IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY 2020年第4期28卷 1542-1549页

作者： Zhao, Chunhui Sun, He Tian, Feng Zhejiang Univ Coll Control Sci & Engn State Key Lab Ind Control Technol Hangzhou 310027 Peoples R China

For nonstationary processes, it is difficult to detect the abnormality which may be hidden by the normal nonstationary variations. The key issue is how to fully explore the underlying nonstationary variable relationships. In this paper, a sparse cointegration analysis (SCA)-based total variable decomposition and distributed modeling algorithm is proposed for nonstationary processes. First, different cointegration relationships among nonstationary variables are fully decomposed by variable selection with replacement and without replacement based on the SCA. In each block, the nonstationary variables are closely linearly correlated and can be well explored by a local cointegration vector. Different blocks may have some variables in common or have no overlapping variables. Second, the lower level distributed monitoring strategy is proposed to reflect the local nonstationary behaviors of the variables in each block. The upper level monitoring model is constructed to consider the relationships among different nonstationary blocks which have not been addressed by the lower level monitoring models. The feasibility and performance of the proposed method are illustrated with a real industrial process.

关键词： Principal component analysis Market research Decentralized control Complexity theory Fault detection distributed monitoring nonstationary processes sparse cointegration analysis (SCA) total variable decomposition

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Bayesian Fault Diagnosis With Asynchronous Measurements and Its Application in Networked distributed monitoring

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IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS 2016年第10期63卷 6316-6324页

作者： Jiang, Qingchao Huang, Biao Ding, Steven X. Yan, Xuefeng Univ Alberta Dept Chem & Mat Engn Edmonton AB T6G 2V4 Canada Univ Duisburg Essen Inst Automat Control & Complex Syst AKS D-47057 Duisburg Germany East China Univ Sci & Technol Key Lab Adv Control & Optimizat Chem Proc Minist Educ Shanghai 200237 Peoples R China

Conventional Bayesian fault diagnosis assumes that all measurements are available synchronously;however, this condition does not always hold in practical industry because a process can be characterized by multiple sampling or transmitting rates. This paper introduces a Bayesian fault diagnosis system incorporating both historical and online information to address the asynchronous measurement problem. First, the Expectation Maximization approach is utilized to deal with the historical asynchronous measurements;second, the online incomplete measurements are handled through a Bayesian marginalization method within a moving horizon. Then, a Bayesian diagnosis system revealing both the underlying fault status of the whole plant and the unavailable statuses of the corresponding local units is established, which is more robust for practical application. The proposed scheme is tested on a numerical example, the distributed monitoring problem of Tennessee Eastman benchmark process, and the distributed monitoring problem of an industrial tail gas treatment plant. monitoring results demonstrate the feasibility and efficiency of the proposed approach.

关键词： Asynchronous measurements Bayesian fault diagnosis distributed monitoring multirate process process monitoring

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Data-Driven Design of distributed monitoring and Optimization System for Manufacturing Systems

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2024年第7期20卷 9455-9464页

作者： Wang, Hao Luo, Hao Ren, Lei Huo, Mingyi Jiang, Yuchen Kaynak, Okyay Harbin Inst Technol Control & Simulat Ctr Harbin 150001 Peoples R China Beihang Univ Sch Automat Sci & Elect Engn Beijing 100191 Peoples R China Bogazici Univ TR-34342 Istanbul Turkiye Ningbo Univ Sci & Technol Ningbo Peoples R China

The intelligent manufacturing system is a complex, large-scale, interconnected system composed of many intelligent agents, and there may be physical or information space couplings between the agents. A distributed monitoring system and optimization control method are proposed to ensure the system completes its tasks safely and efficiently. The distributed monitoring system based on the average consensus algorithm is equivalent to the centralized design method, in which the submonitoring system only requires local and neighbor subsystem information. The advantage of this design is that it uses local and interactive information to achieve global diagnosis. In addition, sending data from all subsystems to a central computing node is challenging to implement in large-scale manufacturing systems. Based on the centralized plug-and-play (PnP) optimization control method, an average consensus algorithm distributed manufacturing system PnP optimization control method is proposed. Its advantage is that it uses local information and interactive information to achieve global control optimization. On this basis, an integrated architecture for distributed fault detection and optimization control is developed. The simulation results verify the feasibility and effectiveness of proposed method.

关键词： Data-driven distributed monitoring distributed optimization manufacturing system

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A local-global transformer for distributed monitoring of multi-unit nonlinear processes

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JOURNAL OF PROCESS CONTROL 2023年第1期122卷 13-26页

作者： Yi, Yongshuai Zhao, Haitao Hu, Zhengwei Peng, Jingchao East China Univ Sci & Technol Sch Informat Sci & Engn Automat Dept Shanghai 200237 Peoples R China

Nonlinear modeling of modern industrial processes with multi-unit, large-scale characteristics is very challenging. Centralized modeling involving all process variables at a time may neglect local behaviors. And most local-global modeling methods tend to ignore the correlation between units. To preserve the intra-unit information and inter-unit correlation, this paper proposes a local-global transformer (LGT) for distributed process monitoring. First, the local representation of each unit is extracted based on feedforward neural networks (FNN). Considering that the units have a fixed order in the process, the designed orthogonal positional encoding (OPE) is added to the local representation to obtain the token of each unit, which also enhances the local behaviors. Then the attention mechanism in the transformer can adaptively adjust the attention to different units and learn the inter-unit correlation from the tokens to extract global features. Finally, the distributed monitoring framework and the variable contribution rate are combined to achieve fault detection and location. The proposed LGT demonstrates the feasibility through a numerical simulation. Extensive experimental results on Tennessee Eastman (TE) process and three-phase flow (TPF) process show the superiority of LGT. The source code of LGT can be found in https://***/YiQian-137/Local--global-transformer.(c) 2022 Elsevier Ltd. All rights reserved.

关键词： Fault detection Fault location distributed monitoring Attention mechanism Transformer encoder

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Double-Layer distributed monitoring Based on Sequential Correlation Information for Large-Scale Industrial Processes in Dynamic and Static States

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2021年第9期17卷 6419-6428页

作者： Huang, Jian Yang, Xu Peng, Kaixiang Univ Sci & Technol Beijing Key Lab Knowledge Automat Ind Proc Minist Educ Sch Automat & Elect Engn Beijing 100083 Peoples R China

Due to the complex static, dynamic, and large-scale characteristics for modern industrial processes, in this article, we propose a double-layer distributed monitoring approach based on multiblock slow feature analysis and multiblock independent component analysis. To this end, the processed dataset is divided into the static and dynamic blocks on the basis of the sequential information of each variable in the first layer. Considering the correlations between the variables in the large-scale processes, the sequential correlation matrices in two blocks are calculated, which serves as the second-layer block division rule. Then, the static and dynamic blocks are further divided into several static and dynamic subblocks in which the variables in each subblock are strongly correlated and in the same state. The slow feature analysis and independent component analysis monitoring models are, respectively, generated for the dynamic and static subblocks. Finally, the monitoring results in each subblock are integrated by Bayesian inference to get the final statistics. The average fault detection rate of the proposed method for the Tennessee Eastman process is 0.842, while those of the other traditional methods are lower than 0.75, which shows the advantages of the proposed method.

关键词： monitoring Correlation Feature extraction Integrated circuits Covariance matrices Independent component analysis Principal component analysis distributed monitoring dynamic and static characteristics independent component analysis (ICA) sequential mutual information (SMI) slow feature analysis (SFA)

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