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Assessment method of the multicomponent systems future ability to achieve productive tasks from local prognoses

机译:多组件系统未来通过本地预测完成生产任务的能力的评估方法

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摘要

Conditioned-based maintenance and prognostics and health management enable to optimize maintenance by scheduling the necessary repairs and replacements of technical system components according to their present and future health states. The assessment of future health states is the prognostics and health management keystone. Many technical production systems are made of numerous components implementing their functions. A method to assess the ability of multicomponent systems to carry out future production tasks is proposed to provide decision supports for production and maintenance planning for a better compromise between their objectives. It is based on components prognoses. To handle inherent uncertainties of these prognoses, the method is based on the Dempster Shafer theory and Bayesian networks inferences. Local prognoses are categorized and transformed to be compliant to Dempster Shafer theory. Patterns of systems are identified for which inferences are defined. The patterns are then used to model systems and to assess their abilities to achieve future tasks. An identification of components that should first undergo maintenance is proposed. An example implementing a fictitious complex systems is presented to show how the provided decision supports can be used for production and maintenance planning purposes.
机译:基于条件的维护,预测和健康管理能够通过根据技术系统组件的当前和将来的健康状况安排必要的维修和更换,从而优化维护。未来健康状况的评估是预测和健康管理的重点。许多技术生产系统由执行其功能的众多组件组成。提出了一种评估多组件系统执行未来生产任务的能力的方法,以为生产和维护计划提供决策支持,以更好地折衷其目标。它基于组件的预测。为了处理这些预后的内在不确定性,该方法基于Dempster Shafer理论和贝叶斯网络推论。对本地预后进行分类和转换,以符合Dempster Shafer理论。确定定义了推理的系统模式。然后将这些模式用于对系统进行建模并评估其完成未来任务的能力。建议确定应首先进行维护的组件。给出了一个实现虚拟复杂系统的示例,以说明如何将提供的决策支持用于生产和维护计划目的。

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