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Predictive maintenance using FMECA method and NHPP models

机译：使用FMECA方法和NHPP模型进行预测性维护

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摘要

Most of predictive maintenance technologies are inaccessible to small scale and medium scale industries due to their demanding cost. This paper proposes a predictive maintenance policy using failure mode effect and criticality analysis (FMECA) and non-homogeneous Poisson process (NHPP) models which require minimal use of advanced monitoring technologies and sophisticated data acquisition systems. Most of the repairable systems show long term reliability degradation with repeated overhauls. Here, critical component of a system or machinery exhibiting sad (deteriorating) trend is used as an indicator to predict overall maintenance time of a system. Firstly, the component to be used as an indicator for predictive maintenance is chosen using FMECA method, in which the most critical component is chosen. Secondly, the failure data of the chosen component is analysed using NHPP models and based on analysis of the data, relevant NHPP model is selected and finally, the Mean Time Between Failure (MTBF) of the component is compared with the threshold mean time between failure [MTBF(Th)] of the component to decide the overall maintenance time for the system. The developed methodology is validated on an overhead crane in a steel manufacturing company.
机译：由于预测性维护技术的成本很高，因此大多数预测性维护技术是小型和中型工业无法获得的。本文提出了一种使用故障模式影响和临界分析（FMECA）以及非均质泊松过程（NHPP）模型的预测性维护策略，这些策略要求最少使用先进的监控技术和复杂的数据采集系统。大多数可修复系统由于反复大修而显示出长期可靠性下降。在这里，系统或机器的关键组件表现出令人讨厌的（恶化）趋势，被用作预测系统总体维护时间的指标。首先，使用FMECA方法选择用作预测性维护指标的组件，其中选择最关键的组件。其次，使用NHPP模型分析所选组件的故障数据，并在数据分析的基础上，选择相关的NHPP模型，最后，将组件的平均故障间隔时间（MTBF）与阈值平均故障间隔时间进行比较组件的[MTBF（Th）]决定系统的总体维护时间。所开发的方法在一家钢铁制造公司的桥式起重机上得到了验证。

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