检索结果-内蒙古大学图书馆

Towards Real-Time Machining Tool Failure Forecast Approach for Smart Manufacturing systems

IFAC-PapersOnLine 2022年第2期55卷 548-553页

作者： Nicolas Nebelung Mario D.S. de Oliveira Santos Sofia T. Helena Athon F.C.S. de Moura Leite Matheus B. Canciglieri Anderson L. Szejka Control and Automation Engineering Department Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil Junior Scientific Initiation Program at the Nossa Senhora de Medianeira School and Pontifical Catholic University of Paraná Curitiba – Paraná Brazil Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Curitiba Brazil

Industry 4.0 is characterized by a dynamic market that constantly looking for new methods to optimize and integrate manufacturing processes. In this context, Artificial Intelligence has gained prominence in problem-solving, such as failure prediction and decision making, thus improving product quality, and consequently bringing competitiveness to the company. Aiming to contribute to this scenario, this research develops a data treatment system that, from an intelligent tool and an interoperable ontological model, automates the prediction and detection of failures in machining machines lines. The system was developed for the prediction of faults in machining lines includes an artificial intelligence formed from prediction algorithms and inferences, it is possible to guarantee the correct treatment and communication of data at different stages of the process. For the experimental research, was used data collected from a machining line of a public dataset. The information is collected and classified by Artificial Intelligence that supports a decision system. The prediction of tool wear would enable the system to infer the type of problem that is causing this wear, a possible root cause, and the needed maintenance based on the ontological inference tool. By this classification of data, it is possible to achieve, through inferences, a reduction in the decision scope, bringing the possible problems caused by the incoming value. The semantic interoperability ensures correct data exchange and processing, which generates a more assertive view of production failures. The system may help companies to increase their productive process by helping them identify future failures in production if applied in a real scenario.

关键词： Industry 4.0 Smart System Artificial Intelligence Machine Learning Ontology Machine Failure Forecast

来源：评论

学校读者我要写书评

暂无评论

Simulation-Based Optimization for the Integrated Control of production and Logistics: A Performance Comparison

引用

IFAC-PapersOnLine 2020年第2期53卷 10639-10644页

作者： Matheus C. Pires Enzo M. Frazzon Djonathan Quadras Eike Broda Michael Freitag Department of Production Engineering and Systems (EPS) Federal University of Santa Catarina Florianópolis Brazil Graduate Program in Production Engineering Federal University of Santa Catarina Florianópolis Brazil Faculty of Production Engineering University of Bremen Bremen Germany BIBA - Bremer Institut für Produktion und Logistik GmbH at the University of Bremen Bremen Germany

Manufacturing systems’ efficiency depends on the proper assignment of orders to resources. Due to existing interdependencies, the integrated consideration of production, inventory and delivery processes can improve the overall manufacturing performance. However, the integration can result in high complexity and stochasticity. Thus, the three areas are rarely addressed together. Thereof, this paper proposes an integrated simulation-based optimization method to cope with uncertainty and complexity. The proposed approach was compared to a benchmark approach and the obtained results show that the first is able to handle the complexity and stochasticity of real-world manufacturing systems, surpassing the performance of the latter.

关键词： Supply Chain Planning Control Simulation-based Optimization Integrated Planning Simulation Optimization Simulated Annealing

来源：评论

学校读者我要写书评

暂无评论

A model for renewable energy symbiosis networks in eco-industrial parks

引用

IFAC-PapersOnLine 2020年第2期53卷 13137-13142页

作者： Maria Angela Butturi Miguel A. Sellitto Francesco Lolli Elia Balugani Alessandro Neri Department of Sciences and Methods for Engineering University of Modena and Reggio Emilia 42100 Reggio Emilia Italy Production and Systems Engineering Graduate Program Universidade do Vale do Rio dos Sinos 93.022-000 Brazil

Renewable energy technologies integration within industrial districts can boost carbon emissions reduction in the industry sector. The eco-industrial parks model promotes the sustainable use of energy and the application of energy synergies and energy exchanges that can include renewable sources of energy. This paper presents an optimization methodology based on a multi-stakeholder perspective to evaluate energy symbiosis including the integration of renewable energy sources within the parks. The study results in three scenarios providing to managers of single firms and parks relevant information for supporting decision making regarding the economic sustainability and the environmental impacts of the energy synergies. The results show that the optimization of the collective point of view ensures more efficient management of the energy supplied by renewables as well as by firms that can provide an energy surplus.

关键词： Renewable energy systems Decision making Models Environment Energy dependence Energy management system Industrial production systems

来源：评论

学校读者我要写书评

暂无评论

Manta Ray Foraging Optimization Approaches on Multivariable PID Controller Tuning

Manta Ray Foraging Optimization Approaches on Multivariable ...

引用

Congress on Evolutionary Computation

作者： Yan Lieven Souza Luúio Luiza Scapinello Aquino Da Silva Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Brazil

This work presents a performance comparison between the newly developed Manta Ray Foraging Optimization (MRFO) and two different variants created for tuning a decentralized fractional order proportional-integral-derivative (FOPID) controller for a multiple-input multiple-output (MIMO) application. The application consists of a ball mill pulverizing system to pulverize coal and maximize fuel efficiency. MRFO and its applications have the task of finding the optimal controller variable values to control the system's temperature and pressure. The MRFO is a metaheuristic based on the behavior of manta rays, and although it shows efficient performance it may be improved through the use of techniques which generate new variants of the algorithm. Two techniques were used in the de-velopment of new variants: Quantum mechanics and opposition-based learning. The three different versions of MRFO were used to minimize a custom fitness function which is a combination of the integral time squared error (ITSE) and the overshoot of the system response. Simulations were carried using Simulink and Matlab softwares. For analyzing the performances, statistical measures such as minimum, maximum, best, mean, median, and standard deviation of the fitness function over 50 runs were used. Additionally, the different variants were also compared for minimizing 10 benchmark functions. The results show that the use of the previously mentioned techniques improve the performance of the original MRFO in optimizing the FOPID to control the ball mill pulverizing system.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Multi-Objective Grouped Grey Wolf Optimization of PID Controllers Applied to a Water Treatment Plant Model

Multi-Objective Grouped Grey Wolf Optimization of PID Contro...

引用

Congress on Evolutionary Computation

作者： Yan Lieven Souza Lúcio Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil

A comparison of proportional-integral-derivative (PID) controller tuning for a 2x2 Multiple-Input Multiple-Output (MIMO) water treatment plant model is presented. The PID tuning procedures are based on Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Grouped Grey Wolf Optimizer (MOGGWO) are performed under two different initial conditions. The tuning procedure is structured as a multi-objective optimization problem with four objective functions, the integral time-squared error is minimized in each output response, and the integral of the control actions is minimized in each input. MOGWO is a metaheuristic based on the hunting behavior of grey wolves, following the hierarchy and leadership procedures that these animals have, while the proposed MOGGWO is a new version proposed in this study with two different groups with different functionalities. The performance was analyzed by 50 runs based on statistical metrics. MOGGWO can find more local minima since its Pareto front is more dispersed than that found by MOGWO.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Proposal for the Support of Demand Required from production Through the Alignment of production Planning and Control Strategies and Maintenance Planning and Control: An Analytical Approach

引用

Procedia Manufacturing 2019年 39卷 868-876页

作者： Alexandre Milkiewicz Sanches Lourival José de Souza Sergio E. Gouvea da Costa Edson Pinheiro de Lima Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) Curitiba 80215-901 Brazil Federal institute of Parana (IFPR) Curitiba 82530-200 Brazil Federal University of Technology – Parana (UTFPR) Pato Branco 85503-390 Brazil

Operation and maintenance are important administrative and operational functions of the production, as they can guarantee the required demand. Together, they have the responsibilities of doing right (quality advantage), fast (speed advantage), timely (reliability advantage), quick setup (flexibility advantage) and affordability. In addition, they are close to several important activities such as: safety, environment, productivity, marketing, quality, reliability and accounting systems. With this new vision, changes and opportunities are being presented to these sectors of operation and maintenance. In the new paradigm, the maintenance and operation teams are motivated to strategically guarantee the full operation of the production system and not only to preserve the equipment. And, thus, to develop an efficient strategy, providing the sustainability and the increase of the assets of the company, reaching the goals of the planning and control of the production established by the company. The main objective of this work is to analyze a proposal to meet the required production demand through the alignment of production control planning strategies and maintenance planning and control. The analysis of the research points to some significant contributions, for example, to the areas of strategy and logistics of organizations that work mainly in environments with a high level of competitiveness. Through a case study, the analysis pointed to the positive impact of alignment on parameters such as: sustainability of the competitive strategy, productivity, costs, reliability and availability of physical assets, and flexibilization of the productive organizational system.

关键词： Maintenance management production management competitive strategy production strategy competitive priorities

来源：评论

学校读者我要写书评

暂无评论

Use of PROMETHEE Method for Decision Making in Bus Fleet Maintenance Proposal of Framework

引用

Procedia Manufacturing 2019年 39卷 1913-1920页

作者： Alexandre Milkiewicz Sanches Eduardo de Freitas Rocha Loures Edson Pinheiro de Lima Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR) Curitiba 80215-901 Brazil Federal institute of Paraná (IFPR) Curitiba 82530-200 Brazil Federal University of Technology – Parana (UTFPR) Pato Branco 85503-390 Brazil

In the business environment, decision making is a differential when the team responsible for it does it right. This implies some assumptions, such as knowledge of the activity, internal and external variables that impact the company’s business, as well as the quality of the workforce. Thus, when the maintenance issue is addressed, be it in industrial or service companies, the expected results will be translated by the satisfaction indexes of its users. This work aims to propose a framework with the elements that lead to the proper decision making in maintenance for bus companies. For this, the multicriteria decision-making method chosen was PROMETHEE. The PROMETHEE method has the flexibility to treat data both qualitatively and quantitatively, offering different scenarios to managers on the best decision to make. It is known that companies seek to minimize their maintenance costs and maximize the quality of service provided.

关键词： Maintenance costs decision making multicriteria method

来源：评论

学校读者我要写书评

暂无评论

A WebLab Control Experiment using the Ball and Beam System and Multiobjective Optimization

A WebLab Control Experiment using the Ball and Beam System a...

引用

Experiment@ International Conference (exp.at)

作者： Ricardo Massao Kagami Guinther Kovalski da Costa Luciano Antônio Mendes Roberto Zanetti Freire Industrial and Systems Engineering Graduate Program – PPGEPS Pontifícia Universidade Católica do Paraná – PUCPR Curitiba Brazil Department of Control and Automation Engineering Pontifícia Universidade Católica do Paraná - PUCPR Curitiba Brazil

By associating active learning methodologies and digital media as a form to improve educational quality, the diversity of experimentations in engineering education has been motivating researches in many areas with possibilities of increasing both students' knowledge and skills. Control engineering is an area where educational laboratories are usually costly, thus the use of virtual laboratories for remote experimentation became an interesting approach. In this work, it is presented a remote experimentation procedure for the ball and beam system, which is a nonlinear and unstable open loop process. Considering the advanced control topic, multiobjective optimization was introduced in the experiment in order to tune Proportional-Integral-Derivative (PID) controllers. Through the description of the system model, the multiobjective optimization technique, and the experiment configuration, students can simulate and test their controllers remotely in a real plant.

关键词： Laboratories Mathematical model PD control Optimization Process control PI control

来源：评论

学校读者我要写书评

暂无评论

Hardware-based parallel firefly algorithm for embedded applications

Hardware-based parallel firefly algorithm for embedded appli...

引用

NASA/ESA Conference on Adaptive Hardware and systems (AHS)

作者： Daniel M. Muñoz Carlos H. Llanos Leandro dos Santos Coelho Mauricio Ayala-Rincón Electronics Engineering Graduate Program Faculty of Gama University of Brasilia Brasilia DF Brazil Department of Mechanical Engineering University of Brasilia Brasilia DF Brazil Industrial and Systems Engineering Graduate Program LAS/PPGEPS Pontifical Catholic University of Parana Curitiba Parana Brazil Departments of Computer Science and Mathematics University of Brasilia Brasilia DF Brazil

ISBN: (纸本)9781467363822

The firefly algorithm (FA) is a new population-based metaheuristic bioinspired on the behavior of the flashing characteristics of fireflies. As a population-based algorithm, the FA suffers from large execution times specifically for embedded optimization problems with computational limitations. For reducing execution times we propose a hardware parallel architecture of the FA algorithm that facilitates the implementation in Field programmable Gate Arrays (FPGAs). In addition, this work proposes the application of the opposition-based learning (OBL) approach to the FA algorithm. The respective hardware implementation (HPOFA) was mapped into a Virtex5 FPGA device and numerical experiments using four well-known benchmark problems demonstrate that the opposition-based approach allows the FA algorithm to improve its functionality, preserving the swarm diversity and avoiding the premature convergence problem. Synthesis results point out that the HPOFA architecture is effectively mapped in hardware and is suitable for embedded applications.

关键词： Hardware Computer architecture Optimization Equations Random access memory Field programmable gate arrays Search problems

来源：评论

学校读者我要写书评

暂无评论

Supply Chain Optimization Using Chaotic Differential Evolution Method

Supply Chain Optimization Using Chaotic Differential Evoluti...

引用

IEEE International Conference on systems, Man and Cybernetics

作者： Leandro dos Santos Coelho Heitor Silverio Lopes Production and Systems Engineering Graduate Program Automation and Systems Laboratory Pontifical Catholic University of Paraná Curitiba Parana Brazil UTFPR/CPGEI Federal Technological University of Parana Curitiba Parana Brazil

This paper describes the application of differential evolution approaches to the optimization of a supply chain. Although simplified, this supply chain included stocks, production, transportation and distribution, in an integrated production-inventory-distribution system. The supply chain problem model is presented as well as a short introduction to each evolutionary algorithm. Differential evolution (DE) is an emergent evolutionary algorithm that offers three major advantages: it finds the global minimum regardless of the initial parameter values, it involves fast convergence, and it uses few control parameters. Inspired by the chaos theory, this work presents a new global optimization algorithm based on different DE approaches combined with chaotic sequences (DEC), called chaotic differential evolution algorithm. The performance of three evolutionary algorithm approaches (genetic algorithm, DE and DEC) and branch and bound method were evaluated with numerical simulations. Results were also compared with other similar approach in the literature. DEC was the algorithm that led to better results, outperforming previously published solutions. The simplicity and robustness of evolutionary algorithms in general, and the efficiency of DEC, in particular, suggest their great utility for the supply chain optimization problem, as well as other logistics-related problems.

关键词： Supply chains Optimization methods Chaos Evolutionary computation production systems Transportation Convergence Genetic algorithms Numerical simulation Robustness

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：