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Predictive Maintenance Modelling for Through-Life Engineering Services

机译：通过生活工程服务预测维护建模

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摘要

Predictive maintenance needs to forecast the numbers of rejections at any overhaul point before any failure occurs in order to accurately and proactively take adequate maintenance action. In healthcare, prediction has been applied to foretell when and how to administer medication to improve the health condition of the patient. The same is true for maintenance where the application of prognostics can help make better decisions. In this paper, an overview of prognostic maintenance strategies is presented. The proposed data-driven prognostics approach employs a statistical technique of (i) the parameter estimation methods of the time-to-failure data to predict the relevant statistical model parameters and (ii) prognostics modelling incorporating the reliability Weibull Cumulative Distribution Function to predict part rejection, replacement, and reuse. The analysis of the modelling uses synthetic data validated by industry domain experts. The outcome of the prediction can further proffer solution to designers, manufacturers and operators of industrial product-service systems. The novelty in this paper is the development of the through-life performance approach. The approach ascertains when the system needs to undergo maintenance, repair and overhaul before failure occurs.
机译：预测维护需要预测在任何故障发生之前的任何大修点的拒绝次数，以便准确和积极采取足够的维护行动。在医疗保健中，预测已经应用于预测，何时以及如何施用药物以改善患者的健康状况。维护也是如此，在预后的应用可以有助于做出更好的决定。本文介绍了预后维护策略的概述。所提出的数据驱动的预测方法采用（i）的统计技术，其参数估计方法的失效时间数据以预测相关的统计模型参数和（ii）预测eibull累积分布函数的预测模型建模预测部分拒绝，替换和重用。建模分析使用行业领域专家验证的合成数据。预测的结果可以进一步提供工业产品 - 服务系统的设计者，制造商和运营商的解决方案。本文的新颖性是发展越来越的性能方法。当系统需要在发生故障前进行维护，修复和大修时，该方法就确定。

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