The digitalization processes in manufacturing enterprises and the integration of increasingly smart shop floor devices and software control systems caused an explosion in the data points available in Manufacturing Execution Systems. The degree in which enterprises can capture value from big data processing and extract useful insights represents a differentiating factor in developing controls that optimize production and protect resources. Machine learning and Big Data technologies have gained increased traction being adopted in some critical areas of planning and control. Cloud manufacturing allows using these technologies in real time, lowering the cost of implementing and deployment. In this context, the paper offers a machine learning approach for reality awareness and optimization in cloud.

Specifically, the paper focuses on predictive production planning (operation scheduling, resource allocation) and predictive maintenance. The main contribution of this research consists in developing a hybrid control solution that uses Big Data techniques and machine learning algorithms to process in real time information streams in large scale manufacturing systems, focusing on energy consumptions that are aggregated at various layers. The control architecture is distributed at the edge of the shop floor for data collecting and format transformation, and then centralized at the cloud computing platform for data aggregation, machine learning and intelligent decisions. The information is aggregated in logical streams and consolidated based on relevant metadata; a neural network is trained and used to determine possible anomalies or variations relative to the normal patterns of energy consumption at each layer. This novel approach allows for accurate forecasting of energy consumption patterns during production by using Long Short-term Memory neural networks and deep learning in real time to re-assign resources (for batch cost optimization) and detect anomalies (for robustness) based on predicted energy data.

制造企业中的数字化过程以及越来越智能的车间设备和软件控制系统的集成导致制造执行系统中可用数据点的爆炸式增长。企业可以从大数据处理中获取价值并提取有用的见解的程度，代表着开发控制以优化生产和保护资源的一个差异因素。机器学习和大数据技术在规划和控制的某些关键领域越来越受到关注。云制造允许实时使用这些技术，从而降低了实施和部署成本。在这种情况下，本文提供了一种机器学习方法，用于在云中实现现实意识和优化。

具体来说，本文着重于预测性生产计划（运营计划，资源分配）和预测性维护。这项研究的主要贡献在于开发一种混合控制解决方案，该解决方案使用大数据技术和机器学习算法来处理大规模制造系统中的实时信息流，重点是在各个层次上汇总的能耗。控制体系结构分布在车间边缘，用于数据收集和格式转换，然后集中在云计算平台上，以进行数据聚合，机器学习和智能决策。信息以逻辑流的形式汇总，并根据相关的元数据进行合并；神经网络被训练并用于确定相对于每一层能量消耗的正常模式的可能异常或变化。通过使用长短期记忆神经网络和实时深度学习来重新分配资源（用于批处理成本优化）并基于预测的能量检测异常（用于鲁棒性），这种新颖的方法可以准确预测生产过程中的能耗模式数据。