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An adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window KPCA and information geometric causal inference

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JOURNAL OF INTELLIGENT MANUFACTURING 2021年第7期32卷 2007-2021页

作者： Sun, Yanning Qin, Wei Zhuang, Zilong Xu, Hongwei Shanghai Jiao Tong Univ Sch Mech Engn Shanghai Peoples R China

In recent years, fault detection and diagnosis for industrial processes have been rapidly developed to minimize costs and maximize efficiency by taking advantages of cheap sensors and microprocessors, data analysis and artificial intelligence methods. However, due to the nonlinear and dynamic characteristics of industrial process data, the accuracy and efficiency of fault detection and diagnosis methods have always been an urgent problem in industry and academia. Therefore, this study proposes an adaptive fault detection and root-cause analysis scheme for complex industrial processes using moving window kernel principle component analysis (KPCA) and information geometric causal inference (IGCI). The proposed scheme has three main contributions. Firstly, a research scheme combining moving window KPCA with adaptive threshold is presented to handle the nonlinear and dynamic characteristics of complex industrial processes. Then, the multiobjective evolutionary algorithm is employed to select the optimal hyperparameters for fault detection, which not only avoids the blindness of hyperparameters selection, but also maximize model accuracy. Finally, the IGCI-based fault root-cause analysis method can help field operators to take corrective measures in time to resume the normal process. The proposed scheme is tested by the Tennessee Eastman platform. Its results show that this scheme has a good performance in reducing the faulty false alarms and missed detection rates and locating fault root-cause.

关键词： Fault detection Root-cause analysis Moving window KPCA multiobjective evolutionary algorithm Causal inference

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Computational design and analysis of modular cells for large libraries of exchangeable product synthesis modules

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METABOLIC ENGINEERING 2021年 67卷 453-463页

作者： Garcia, Sergio Trinh, Cong T. Univ Tennessee Dept Chem & Biomol Engn 1512 Middle DrDougherty 432 Knoxville TN 37996 USA Oak Ridge Natl Lab Ctr Bioenergy Innovat Oak Ridge TN USA

Microbial metabolism can be harnessed to produce a large library of useful chemicals from renewable resources such as plant biomass. However, it is laborious and expensive to create microbial biocatalysts to produce each new product. To tackle this challenge, we have recently developed modular cell (ModCell) design principles that enable rapid generation of production strains by assembling a modular (chassis) cell with exchangeable pro-duction modules to achieve overproduction of target molecules. Previous computational ModCell design methods are limited to analyze small libraries of around 20 products. In this study, we developed a new computational method, named ModCell-HPC, that can design modular cells for large libraries with hundreds of products with a highly-parallel and multi-objective evolutionary algorithm and enable us to elucidate modular design properties. We demonstrated ModCell-HPC to design Escherichia coli modular cells towards a library of 161 endogenous production modules. From these simulations, we identified E. coli modular cells with few genetic manipulations that can produce dozens of molecules in a growth-coupled manner with different types of fermentable sugars. These designs revealed key genetic manipulations at the chassis and module levels to accomplish versatile modular cells, involving not only in the removal of major by-products but also modification of branch points in the central metabolism. We further found that the effect of various sugar degradation on redox metabolism results in lower compatibility between a modular cell and production modules for growth on pentoses than hexoses. To better characterize the degree of compatibility, we developed a method to calculate the minimal set cover, identifying that only three modular cells are all needed to couple with all compatible production modules. By determining the unknown compatibility contribution metric, we further elucidated the design features that allow an existing modular cell

关键词： Modular cell design Modular (chassis) cell Production modules Compatibility ModCell ModCell-HPC multiobjective optimization multiobjective evolutionary algorithm Master-slave parallelization Island parallelization High performance computing

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Biologically Inspired Control System for 3-D Locomotion of a Humanoid Biped Robot

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2016年第7期46卷 898-911页

作者： Saputra, Azhar Aulia Botzheim, Janos Sulistijono, Indra Adji Kubota, Naoyuki Tokyo Metropolitan Univ Grad Sch Syst Design Tokyo 1910065 Japan Politeknik Elekt Negeri Surabaya Grad Sch Engn Technol Surabaya 60111 Indonesia

This paper proposes the control system for 3-D locomotion of a humanoid biped robot based on a biological approach. The muscular system in the human body and the neural oscillator for generating locomotion signals are adapted in this paper. We extend the neuro-locomotion system for modeling a multiple neuron system, where motoric neurons represent the muscular system and sensoric neurons represent the sensor system inside the human body. The output signals from coupled neurons representing the angle joint level are controlled by gain neurons that represent the energy burst for driving the joint in each motor. The direction and the length of step in robot locomotion can be adjusted by command neurons. In order to form the locomotion pattern, we apply multiobjective evolutionary computation to solve the multiobjective problem when optimizing synapse weights between the motoric neurons. We use recurrent neural network (RNN) for the stabilization system required for supporting locomotion. RNN generates a dynamic weight synapse value between the sensoric neuron and the motoric neuron. The effectiveness of our system is demonstrated in open dynamic engine computer simulation and in a real robot application that has 12 degrees of freedom (DoFs) in legs and four DoFs in hands.

关键词： Biped robot locomotion multiobjective evolutionary algorithm neural oscillator recurrent neural network (RNN)

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Reference-Inspired Many-Objective evolutionary algorithm Based on Decomposition

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COMPUTER JOURNAL 2018年第7期61卷 1015-1037页

作者： Fu, Xiaogang Sun, Jianyong Shanghai Dianji Univ Sch Elect Engn Shanghai 201306 Peoples R China Xi An Jiao Tong Univ Sch Math & Stat Xian 710049 Shaanxi Peoples R China

Keeping balance between convergence and diversity for many-objective optimisation problems (having four or more objectives) is a very difficult task as revealed in existing research in multiobjective evolutionary optimisation. In this paper, we propose a reference-inspired multiobjective evolutionary algorithm for many-objective optimisation. The main idea is (1) to summarise information inspired by a set of randomly generated reference points in the objective space to strengthen the selection pressure towards the Pareto front;and (2) to decompose the objective space into subregions for diversity management and recombination. We showed that the mutual relationship between a population of solution and the reference points provides not only a new dominance relation to producing fine selection pressure but also a balanced convergence-diversity information that is able to adapt search dynamics. The partition of the objective space into several subregions is able to preserve the Pareto front's diversity. Moreover, a restricted stable match strategy is proposed to choose appropriate parent solutions from solution sets constructed at the subregions for high-quality offspring generation. Controlled experiments conducted on commonly used benchmark test suites have shown the effectiveness and competitiveness of the proposed algorithm compared with several state-of-the-art many-objective evolutionary algorithms.

关键词： multiobjective evolutionary algorithm many-objective optimisation references points reference vectors

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Exergy optimization of a multi-stage solar micro-cogeneration system

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IET RENEWABLE POWER GENERATION 2023年第2期17卷 217-228页

作者： Kallio, Sonja Siroux, Monica Strasbourg Univ INSA Strasbourg ICUBE Strasbourg France

This paper proposes a dynamic model of a solar-based micro-cogeneration system called photovoltaic-thermal (PVT) collector to perform a design optimization of the multi-stage PVT system. The parametric study reveals the most important design parameters influencing the water-based flat-plate PVT system performance. The analysis also shows an existing trade-off between thermal and electrical efficiencies during the PVT operation. A novel exergy-based multi-objective design optimization method is demonstrated to find a trade-off design solution of the multi-stage PVT collector which compromises between the electrical and thermal exergy efficiencies under different weather conditions. The electrical and thermal exergy efficiencies are defined to be the objective functions of the Matlab gamultiobj-function, which is a multi-objective evolutionary algorithm using non-dominated sorting genetic algorithm-II (NSGA-II). As a result of the algorithm, the Pareto optimal sets were derived that revealed the optimal solutions taking into account the trade-off nature of the optimization problem. The decision-making method called an ideal point method was used in the decision-making process to find the final optimal solutions for different weather conditions. The results revealed that the optimal number of the PVT collectors in series depended on the weather conditions and decreased from 3 to 2 if the conditions got cooler.

关键词： genetic algorithms Optimisation techniques water-based flat-plate PVT system performance trade-off nature objective functions Solar power stations and photovoltaic power systems multiobjective evolutionary algorithm dynamic model Pareto optimisation sorting genetic algorithm-II multistage PVT collector PVT collectors photovoltaic-thermal collector evolutionary computation optimization problem microcogeneration system final optimal solutions PVT operation solar absorber-convertors important design parameters electrical efficiencies multistage PVT system cogeneration novel exergy-based multiobjective design optimization method Other direct energy conversion optimal number existing trade electrical exergy efficiencies thermal exergy efficiencies exergy Optimisation decision-making method exergy optimization decision making trade-off design solution optimisation Pareto optimal sets

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evolutionary multi-objective optimisation of the pulse burst waveform in solid-state VHF moving target detection radar

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IET RADAR SONAR AND NAVIGATION 2019年第12期13卷 2093-2101页

作者： Jevtic, Milos Zogovic, Nikola Graovac, Stevica Univ Belgrade Sch Elect Engn Bulevar Kralja Aleksandra 73 Belgrade Serbia Univ Belgrade Inst Mihailo Pupin Volgina 15 Belgrade Serbia

A solid-state, very high frequency (VHF) band, moving target detection air surveillance radar requires a complex pulse burst waveform to mitigate the visibility issues originating from the blind Doppler intervals and range eclipsing. The waveform employs multiple pulse repetition frequencies to mitigate the effects of the blind Doppler intervals and interleaves short and long pulses to mitigate range eclipsing. In the authors' previous works, they pointed out that the waveform design is a multi-objective optimisation problem and defined the mathematical model of the waveform optimisation problem. They also presented how the exact Pareto optimal (PO) set can be determined by means of exhaustive search. In this paper, they improve the mathematical model of the waveform optimisation problem by altering the way in which one of the objective functions is calculated and adding a new constraint, which eliminates meaningless solutions. Finally, they propose a solution method based on a multi-objective evolutionary algorithm. The performance evaluation test indicates that compared to the exhaustive search, the proposed method provides a solution that is insignificantly different. However, the proposed method is more scalable and requires over three orders of magnitude smaller number of comparisons to determine the PO set, which makes it more viable for the online waveform adaptation.

关键词： Doppler radar radar detection search radar object detection evolutionary computation radar tracking radar signal processing Pareto optimisation search problems complex pulse burst waveform blind Doppler intervals range eclipsing multiple pulse repetition frequencies waveform design multiobjective optimisation problem mathematical model waveform optimisation problem exact Pareto optimal set exhaustive search objective functions multiobjective evolutionary algorithm online waveform adaptation evolutionary multiobjective optimisation high frequency band performance evaluation test PO set solid-state VHF band moving target detection air surveillance radar very high frequency band

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Revenue and Energy Cost-Optimized Biobjective Task Scheduling for Green Cloud Data Centers

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IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 2021年第2期18卷 817-830页

作者： Yuan, Haitao Li, Heng Bi, Jing Zhou, MengChu New Jersey Inst Technol Dept Elect & Comp Engn Newark NJ 07102 USA Beijing Univ Technol Fac Informat Technol Sch Software Engn Beijing 100124 Peoples R China

The significant growth in the number and types of tasks of heterogeneous applications in green cloud data centers (GCDCs) dramatically increases their providers' revenue from users as well as energy consumption. It is a big challenge to maximize such revenue, while minimizing energy cost in a market where prices of electricity, availability of renewable power generation, and behind-the-meter renewable generation contract models differ among the geographical sites of the GCDCs. A multiobjective optimization method that investigates such spatial differences in the GCDCs is for the first time proposed to trade off such two objectives by cost-effectively executing all tasks while meeting their delay constraints. In each time slot, a constrained biobjective optimization problem is formulated and solved by an improved multiobjective evolutionary algorithm based on decomposition. Realistic data-based simulations prove that the proposed method achieves a larger total profit in faster convergence speed than the two state-of-the-art algorithms. Note to Practitioners-This article considers the tradeoff between profit maximization and energy cost minimization for the green cloud data center (GCDC) providers while meeting the delay constraints of all tasks. Current task-scheduling methods fail to take the advantage of spatial variations in many factors, e.g., prices of electricity and availability of renewable power generation at geographically distributed GCDC locations. As a result, they fail to execute all tasks of heterogeneous applications within their delay bounds in a high-revenue and low-energy-cost manner. In this article, a multiobjective optimization method that addresses the disadvantages of the existing methods is proposed. It is realized by a proposed intelligent optimization algorithm. Simulations demonstrate that in comparison with the two state-of-the-art scheduling algorithms, the proposed one increases the profit and reduces the convergence time. It can be rea

关键词： Task analysis Data centers Green products Servers Optimization Cloud computing Virtual machining Data centers energy optimization green clouds multiobjective evolutionary algorithm task scheduling

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Design of near-field ultra-high frequency radio-frequency identification antenna with fragmented wires for electrically-large coverage

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第11期10卷 1218-1224页

作者： Ding, Dawei Tao, Yonghui Wang, Gang Univ Sci & Technol China Dept Elect Engn & Informat Sci Hefei 230027 Peoples R China Jiangsu Univ Sch Comp Sci & Telecommun Engn Zhenjiang 212013 Peoples R China Univ Sci & Technol China Dept Modern Mech Hefei 230027 Peoples R China Chinese Acad Sci Key Lab Electromagnet Space Informat Hefei 230027 Peoples R China

Design of ultra-high frequency radio-frequency identification (UHF RFID) reader antenna covering electrically-large near-field area is still challenging for near-field UHF RFID applications. In such design, magnetic field distribution on a large coverage area is required to be as uniform as possible. Fragment-type wire antennas are quite suitable for such demand because distribution of fragmented wires in a designated electrically-large area can be optimised to counterpoise the magnetic fields generated by non-uniform current distribution. By optimisation searching with multiobjective evolutionary algorithm based on decomposition combined with enhanced genetic operators, fragment-type RFID reader antenna is designed to provide uniform magnetic field distribution within dynamic range of 4 dB in an electrically-large near-field coverage of 320 x 320 mm at 915 MHz. The designed antenna is fabricated and tested. Both simulation and measurement results show good impedance matching, satisfactory near-field coverage, and desirable omni-directional pattern.

关键词： UHF antennas radiofrequency identification wire antennas genetic algorithms antenna radiation patterns near-field ultra-high frequency radio-frequency identification antenna UHF RFID reader antenna fragment-type wire antennas multiobjective evolutionary algorithm uniform magnetic field distribution omni-directional pattern

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A Multi-Population Multi-Objective evolutionary algorithm Based on the Contribution of Decision Variables to Objectives for Large-Scale Multi/Many-Objective Optimization

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IEEE TRANSACTIONS ON CYBERNETICS 2023年第11期53卷 6998-7007页

作者： Xu, Ying Xu, Chong Zhang, Huan Huang, Lei Liu, Yiping Nojima, Yusuke Zeng, Xiangxiang Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Osaka Prefecture Univ Grad Sch Engn Sakai Osaka 5998531 Japan

Most existing multiobjective evolutionary algorithms treat all decision variables as a whole to perform genetic operations and optimize all objectives with one population at the same time. Considering different control attributes, different decision variables have different optimization effects on each objective, so decision variables can be divided into convergence-or diversity-related variables. In this article, we propose a new metric called the optimization degree of the convergence-related decision variable to each objective to calculate the contribution objective of each decision variable. All decision variables are grouped according to their contribution objectives. Then, a multiobjective evolutionary algorithm, namely, decision variable contributing to objectives evolutionary algorithm (DVCOEA), has been proposed. In order to balance the convergence and diversity of the population, the DVCOEA algorithm combines the multipopulation multiobjective framework, where two different optimization strategies are designed to optimize the subpopulation and individuals in the external archive, respectively. Finally, DVCOEA is compared with several state-of-the-art algorithms on a number of benchmark functions. Experimental results show that DVCOEA is a competitive approach for solving large-scale multi/many-objective problems.

关键词： Optimization evolutionary computation Convergence Statistics Sociology Diversity methods Clustering algorithms Decision variables analysis large-scale many-objective optimization large-scale multiobjective optimization multiobjective evolutionary algorithm multipopulation multiobjective evolutionary algorithm

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Fast multi-objective surrogate-assisted design of multi-parameter antenna structures through rotational design space reduction

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IET MICROWAVES ANTENNAS & PROPAGATION 2016年第6期10卷 624-630页

作者： Koziel, Slawomir Bekasiewicz, Adrian Reykjavik Univ Sch Sci & Engn Menntavegur 1 IS-101 Reykjavik Iceland

A technique for fast multi-objective optimisation of antennas is introduced. The core of the proposed methodology is a reliable initial estimation of the design space subset that contains a set of Pareto optimal solutions, i.e. those representing the best possible trade-offs between the conflicting objectives (such as the antenna size and its electrical performance parameters). A fast response surface approximation (RSA) surrogate is subsequently constructed in a reduced search space using sampled coarse-discretisation electromagnetic (EM) simulation data. Owing to the authors' reduction approach, the surrogate model construction is computationally feasible even when the number of antenna parameters is high. The RSA model is optimised using a multi-objective evolutionary algorithm to yield an initial approximation of the Pareto set. The latter is further refined (to obtain its representation at the high-fidelity EM antenna model level). The approach is illustrated using two design cases. A comparison with previously published methods, as well as experimental validation, is also provided.

关键词： Pareto optimisation approximation theory evolutionary computation search problems antennas fast multiobjective surrogate assisted design multiparameter antenna structures rotational design space reduction multiobjective optimisation reliable initial estimation Pareto optimal solutions electrical performance parameters response surface approximation RSA search space sampled coarse discretisation electromagnetic EM simulation data surrogate model construction multiobjective evolutionary algorithm

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