The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. A central challenge in manufacturing sector lies in the requirement of a general framework to ensure satisfied diagnosis and monitoring performances in different manufacturing applications. Here we propose a general data-driven, end-to-end framework for the monitoring of manufacturing systems. This framework, derived from deep learning techniques, evaluates fused sensory measurements to detect and even predict faults and wearing conditions. This work exploits the predictive power of deep learning to automatically extract hidden degradation features from noisy, time-course data. We have experimented the proposed framework on ten representative datasets drawn from a wide variety of manufacturing applications. Results reveal that the framework performs well in examined benchmark applications and can be applied in diverse contexts, indicating its potential use as a critical corner stone in smart manufacturing.

翻译：根据设想,制造部门将受到人工智能技术的严重影响,计算能力和数据量急剧增加;制造部门的一个中心挑战在于要求有一个总框架,以确保对不同制造应用的满意诊断和监测业绩;我们在此提议一个监测制造系统的一般数据驱动、端对端框架;这一框架源于深层学习技术,评估引信感应测量,以探测甚至预测故障和穿戴条件;这项工作利用深层学习的预测力,从噪音、时间周期数据中自动提取隐藏的降解特征;我们试验了拟议的框架,即从各种制造应用中提取的10个具有代表性的数据集;结果显示,该框架在经过审查的基准应用中运作良好,可以应用于多种情况,表明其在智能制造中作为关键转角石的潜在用途。