A General End-to-end Diagnosis Framework for Manufacturing Systems
This work addresses the need for a versatile diagnostic tool in the manufacturing sector, though it appears incremental as it applies existing deep learning techniques to this domain.
The authors tackled the challenge of lacking a general framework for diagnosis and monitoring in manufacturing by proposing a data-driven, end-to-end framework using deep learning to detect and predict faults from sensory data, achieving good performance across ten diverse manufacturing datasets.
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.