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 the 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.Tbis framework,derived from deep-leaing techniques,evaluates fused sensory measurements to detect and even predict faults and wearing conditions.This work exploits the predictive power of deep leaing to automatically extract hidden degradation features from noisy,time-course data.We have experimented the proposed framework on 10 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 coerstone in smart manufacturing.