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An adaptive predictive maintenance model for repairable deteriorating systems using inverse Gaussian degradation process

机译：使用逆高斯降解过程可修复劣化系统的自适应预测性维护模型

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摘要

Predictive maintenance is a promising solution to keep the long-run operation of industrial systems at high reliability and low cost. In this spirit, we aim to develop an adaptive predictive maintenance model for continuously deteriorating single-unit systems subject to periodic inspection, imperfect repair and perfect replacement. The development consists of four steps: degradation modeling, maintenance effect modeling, maintenance policy elaboration, and performance evaluation. Compared with existing models, ours differs in three main aspects. Firstly, we take into account the past dependency of maintenance actions in the degradation modeling via the random effect of an inverse Gaussian process. Secondly, we use both the system remaining useful life and maintenance duration to enable dynamic maintenance decision-making. Finally, we take advantage of the semi-regenerative theory to analytically evaluate the long-run cost rate of maintenance policies whose decision variables are of different nature. We validate and illustrate the developed adaptive predictive maintenance model by various numerical experiments. Comparative studies with benchmarks under different maintenance costs and degradation characteristics confirm the flexibility and cost-effectiveness of the model.
机译：预测性维护是一种有前途的解决方案，可以以高可靠性和低成本保持工业系统的长期运行。在这种精神下，我们的目的是开发一种适应性预测维护模型，用于连续恶化单单元系统，经过周期性检查，不完全修复和完美的更换。该开发由四个步骤组成：退化建模，维护效果建模，维护政策阐述和绩效评估。与现有模型相比，我们的三个主要方面不同。首先，我们考虑到通过反向高斯过程的随机效应来考虑到维护行动在降级建模中的依赖性。其次，我们使用剩余的使用寿命和维护持续时间来实现动态维护决策。最后，我们利用了半再生理论，分析了分析的维护政策的长期成本率，其决策变量是不同的性质。我们通过各种数值实验验证并说明了开发的自适应预测维护模型。不同维护成本下基准和降解特征的比较研究证实了模型的灵活性和成本效益。

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