In chemical and manufacturing processes, unit failures due to equipment degradation can lead to process downtime and significant costs. In this context, finding an optimal maintenance strategy to ensure good unit health while avoiding excessive expensive maintenance activities is highly relevant. We propose a practical approach for the integrated optimization of production and maintenance capable of incorporating uncertain sensor data regarding equipment degradation. To this end, we integrate data-driven stochastic degradation models from condition-based maintenance into a process level mixed-integer optimization problem using robust optimization. We reduce computational expense by utilizing both analytical and data-based approximations and optimize the robust optimization parameters using Bayesian optimization. We apply our framework to five instances of the state-task-network and demonstrate that it can efficiently compromise between equipment availability and cost of maintenance.

中文翻译：

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数据驱动的降解设备工艺优化

在化学和制造过程中，由于设备退化而导致的单元故障可能导致过程停机和大量成本。在这种情况下，找到一种最佳的维护策略以确保设备良好的运行状况，同时又避免过多的昂贵维护活动非常重要。我们为生产和维护的集成优化提出了一种实用的方法，该方法能够结合有关设备降级的不确定传感器数据。为此，我们将基于状态的维护的数据驱动的随机退化模型集成到使用鲁棒优化的过程级混合整数优化问题中。我们通过利用解析近似和基于数据的近似来减少计算费用，并使用贝叶斯优化来优化鲁棒的优化参数。