Poor maintenance regimes often contribute to unplanned downtimes, quality defects and accidents; thus it is crucial to apply an effective maintenance strategy to achieve efficient and safe processes. Industry 4.0 has brought about a proliferation of digital data and with it new opportunities to advance and improve the way maintenance activities are planned. Here, we propose a novel methodology that utilises machine learning to predict both machine faults and repair time, and uses this data to underpin the scheduling of maintenance activities. This can be used to plan maintenance, and optimise the schedule with a cost objective within the constraints of labour availability and plant layout. When applied to a dataset obtained using a simulated Fischertechnik (FT) model, this methodology reduced the overall plant maintenance costs by decreasing unplanned downtimes and increasing maintenance efficiency. This work provides a promising first step towards improving the way maintenance tasks are approached in Industry 4.0.

糟糕的维护制度通常会导致计划外停机、质量缺陷和事故；因此，应用有效的维护策略来实现高效和安全的流程至关重要。工业 4.0 带来了数字数据的激增，并带来了推进和改进维护活动计划方式的新机会。在这里，我们提出了一种新颖的方法，该方法利用机器学习来预测机器故障和维修时间，并使用这些数据来支持维护活动的安排。这可用于计划维护，并在劳动力可用性和工厂布局的限制内以成本目标优化时间表。当应用于使用模拟 Fischertechnik (FT) 模型获得的数据集时，这种方法通过减少计划外停机时间和提高维护效率来降低整体工厂维护成本。这项工作为改进工业 4.0 中处理维护任务的方式迈出了有希望的第一步。