An end-to-end general framework for automatic diagnosis of manufacturing systems

The manufacturing sector is envisioned to be heavily influenced by artificial intelligence-based technologies with the extraordinary increases in computational power and data volumes. Data-driven methods use sensor data, such as vibration, pressure, temperature, and energy data to extract useful features for diagnosis and prediction. 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. In a new research article published in the Beijing-based National Science Review, Prof. Ye Yuan from the School of Artificial Intelligence and Automation and Prof. Han Ding from the State Key Laboratory of Digital Manufacturing Equipment and Technology, Huazhong University of Science and Technology, jointly proposed an end-to-end diagnostic framework that can be used in diverse manufacturing systems. This framework exploits the predictive power of convolutional neural network to automatically extract hidden degradation features from noisy time-course data.