The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes [1,2]. 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 data sets 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.

中文翻译：

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制造系统的通用端到端诊断框架

预计制造业将受到基于人工智能的技术的严重影响，计算能力和数据量显着增加 [1,2]。制造业的一个核心挑战在于需要一个通用框架，以确保在不同的制造应用中满足诊断和监控性能。在这里，我们提出了一个用于监控制造系统的通用数据驱动的端到端框架。该框架源自深度学习技术，可评估融合的感官测量，以检测甚至预测故障和磨损状况。这项工作利用深度学习的预测能力从嘈杂的时间过程数据中自动提取隐藏的退化特征。我们已经在来自各种制造应用程序的十个代表性数据集上对提议的框架进行了实验。结果表明，该框架在经过检验的基准应用程序中表现良好，并且可以应用于不同的环境，表明其作为智能制造的关键基石的潜在用途。