Industrial System

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The Experts below are selected from a list of 473907 Experts worldwide ranked by ideXlab platform

Benoît Furet - One of the best experts on this subject based on the ideXlab platform.

Jean-françois Petiot - One of the best experts on this subject based on the ideXlab platform.

Benoa T Iung - One of the best experts on this subject based on the ideXlab platform.

  • formalisation of a new prognosis model for supporting proactive maintenance implementation on Industrial System
    Reliability Engineering & System Safety, 2008
    Co-Authors: Alexandre Muller, Mariechristine Suhner, Benoa T Iung
    Abstract:

    The importance of the maintenance function has increased because of its role in keeping and improving System availability and safety, as well as product quality. To support this role, the maintenance concept has undergone several major developments that have led to proactive considerations mainly based on a prognosis process, which normally allows selection of the best maintenance action to be carried out. This paper proposes the deployment and experimentation of a prognosis process within an e-maintenance architecture. The deployment follows a methodology based on the combination of both a probabilistic approach for modelling the degradation mechanism and of an event one for dynamical degradation monitoring. The feasibility and benefits of this new prognosis process is investigated with an experiment using a manufacturing TELMA (TELe-MAintenance) platform supporting the unwinding of metal bobbins.

Shanying Hu - One of the best experts on this subject based on the ideXlab platform.

  • Optimization and analysis of a bioethanol agro-Industrial System from sweet sorghum.
    Renewable Energy, 2010
    Co-Authors: Shanying Hu, Yourun Li, Dingjiang Chen, Karl M. Smith
    Abstract:

    The use of non-food crops for bioethanol production represents an important trend for renewable energy in China. In this paper, a bioethanol agro-Industrial System with distributed fermentation plants from sweet sorghum is presented. The System consists of the following processes: sweet sorghum cultivation, crude ethanol production, ethanol refining and by-product utilization. The plant capacities of crude ethanol and pure ethanol, in different fractions of useful land, are optimized. Assuming a minimum cost of investment, transport, operation and so on, the optimum capacity of the pure ethanol factory is 50,000tonnes/year. Moreover, this bioethanol System, which requires ca. 13,300ha (hectares) of non-cultivated land to supply the raw materials, can provide 26,000 jobs for rural workers. The income from the sale of the crops is approximately 71 million RMB Yuan and the ethanol production income is approximately 94 million RMB Yuan. The potential savings in CO2 emissions are ca. 423,000tonnes/year and clear economic, social and environmental benefits can be realized.

  • evaluating waste treatment recycle and reuse in Industrial System an application of the emergy approach
    Ecological Modelling, 2003
    Co-Authors: Hui Yang, Yourun Li, Jingzhu Shen, Shanying Hu
    Abstract:

    This paper deals with the application of emergy analysis of Industrial Systems in considering wastes. Making process System engineering decisions that are ecologically conscious requires emergy analysis of both Industrial and ecological processes. The traditional emergy analysis methods of a natural ecological System usually do not consider the impact of wastes. This paper considers the impact of wastes and improves existing emergy analysis methods for Industrial System. A new index of sustainability for Industrial processes is presented.

Benoît Iung - One of the best experts on this subject based on the ideXlab platform.

  • Industrial System functioning/dysfunctioning-based approach for indicator identification to support proactive maintenance
    2017
    Co-Authors: Thomas Laloix, Benoît Iung, Alexandre Voisin, Salah Deeb, Eric Romagne, Florian Lorange
    Abstract:

    Failure Mode, Effects and Criticality Analysis (FMECA) is a well-known method used in safety analysis, reliability analysis, risk assessment and maintenance objectives. Moreover, in the manufacturing domain and particularly in the context of the deployment of the factory of the future, its usage is not entirely satisfactory in relation to the indicator to be observed. Thus, the paper presents a new methodology based on a coupled approach of FMECA and Hazard Operability analysis (HAZOP) which aim is to contribute to the deployment of proactive maintenance strategies by clearly identify pertinent indicator. This approach is based on the formalization of concepts of knowledge which permit to constitute the first pillars of proactive maintenance approach. Applicability of this methodology is illustrated on a machining center sub-System.

  • Industrial System knowledge formalization to aid decision making in maintenance strategies assessment
    Engineering Applications of Artificial Intelligence, 2015
    Co-Authors: Gabriela Medina-oliva, Philippe Weber, Benoît Iung
    Abstract:

    High competitiveness and the emergence of new Information and Communication Technologies in Industrial enterprises require a higher understanding and mastering of their operation Systems to improve expected performances. In that sense, managers should take decisions about the strategies to be implemented as well as the resources to be used to achieve the target performances. Decisions result either from subjective considerations either from models allowing performances assessment. To help managers in the decision making process, it is necessary to represent Industrial Systems through models to better control them. However, this task presents two major issues. The first one deals with the development of these models which is time and money consuming for the enterprises. This issue leads the consideration of formalizing generic knowledge by means, for example, of generic patterns, as a relevant solution to support models capitalization. The second issue deals with the degree of confidence of the models regarding to the reality of the Industrial Systems in order to avoid unrealistic assumptions, decreasing complexity etc. To face these challenges, this paper presents a methodology to represent, in a generic way, the key concepts of an Industrial System and the relationships between the concepts materialized by semantic rules. More precisely, this methodology is investigated in the domain of dependability in order to assess performances, from the concepts formalization of both the production System and the maintenance one, based on the maintenance strategies applied. Thus generic patterns are cogent to support knowledge capitalization and reused for leading to Components Off The Shelf (COTS). Patterns are built on a Probabilistic Relational Model (PRM) and can be instantiated then assembled to form a global model of a specific Industrial System. The global model allows simulation step for maintenance strategies assessment helping the decision making process. The feasibility and added-value of this methodology, mainly in terms of patterns capitalization and reuse, are shown on two case studies: a pumping System and a real harvest production System. Moreover, lessons-learned issued from these applications are discussed

  • Probabilistic relational model (PRM)_based technical knowledge formalization for dependability of an Industrial System
    2009
    Co-Authors: Gabriela Medina-oliva, Éric Levrat, Philippe Weber, Benoît Iung
    Abstract:

    This paper proposes a methodology to develop a decision-making aid tool which purpose is to assess the dependability and performances of an Industrial System. This model is based on a new formalism, called the probabilistic relational model (PRM) which is adapted to deal with large and complex Systems. The objective is to evaluate System's performances in order to optimize the enterprise maintenance strategies. The methodology is formalized from functional, dysfunctional and informational studies of the technical Industrial Systems. This methodology is applied, for modeling a water heater System to estimate its reliability and its output flow attributes.

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