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Dynamic Predictive Maintenance in industry 4.0 based on real time information: Case study in automotive industries

机译:基于实时信息的工业4.0动态预测维护:汽车行业案例研究

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In order to respond to today’s dynamic needs of customers, customized mass production systems have been more and more developed that, are facing with different challenges. Maintenance planning and scheduling is one of the most important manufacturing components in such systems, due to importance of availability and high investment for this kind of system. In order to consider real machine operation state, recently, predictive maintenance method is proposed. However, in traditional methods, historical failure data is the main source for this planning. In this paper, we propose a methodology for dynamic predictive maintenance for a real case in automotive industries with considering multi-component structural and positive economic dependencies between them. In our methodology, we propose to gather data science with mathematical optimization method. Prediction of Remaining Useful Life (RUL) of machine parts has been made by Artificial Neural Network method with considering sensors data. With this RUL values and other cost values and optimization model parameters, and by solving proposed mathematical model, an optimal schedule is achieved with minimization of maintenance costs. Through a dynamic proposed procedure, when a new data is received, RUL values and model parameters are readjusted and new optimal solution for maintenance planning and scheduling can be achieved. Further, some scenarios are defined for analyzing the dynamicity of the proposed procedure and relating results, conclusion and perspectives of these researched are discussed.
机译:为了响应当今客户的动态需求,定制化的批量生产系统已经越来越成熟,并面临着不同的挑战。维护计划和调度是此类系统中最重要的制造组件之一,这归因于此类系统的可用性和高投资的重要性。为了考虑真实的机器运行状态,最近提出了一种预测性维护方法。但是,在传统方法中,历史故障数据是此计划的主要来源。在本文中,我们提出了一种针对汽车行业实际案例进行动态预测维护的方法,其中考虑了它们之间的多组件结构和积极的经济依存关系。在我们的方法论中,我们建议使用数学优化方法来收集数据科学。通过考虑传感器数据的人工神经网络方法,对机械零件的剩余使用寿命(RUL)进行了预测。利用此RUL值和其他成本值以及优化模型参数,并通过求解所提出的数学模型,可以在最小化维护成本的情况下实现最佳计划。通过动态提出的程序,当接收到新数据时,将重新调整RUL值和模型参数,并可以获得维护计划和调度的新的最佳解决方案。此外,定义了一些方案来分析所提出程序的动态性,并讨论了相关结果,讨论的结论和观点。

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