Special Issue on “Deep Learning for Diagnosis and Prognosis in Manufacturing”
摘要截稿:
全文截稿: 2018-08-31
影响因子: 3.954
期刊难度:
CCF分类: 无
中科院JCR分区:
• 大类 : 计算机科学 - 2区
• 小类 : 计算机:跨学科应用 - 3区
Overview
With increased complexity of modern manufacturing systems, exponential growth of data has been seen in manufacturing industry. Efficient utilization of those big data would provide intelligence to infer the health conditions of manufacturing machines, for improved fault detection, diagnosis, prognosis, health management, and maintenance scheduling. Machine learning, as one of the prevailing data analytics methods, has been widely used to devise complex models and algorithms that lend themselves to derive knowledge from the data. As a branch of machine learning, deep learning attempts to model high level representations behind data and classify (predict) patterns via stacking multiple layers of information processing modules in hierarchical architectures, which has shown great potential for machine health condition inference and performance degradation prediction, especially in the big data era.
The aim of this special issue is to solicit high quality papers that report recent findings and emerging research developments of Deep Learning for Diagnosis and Prognosis in manufacturing applications. Potential authors are invited to submit original contributions and reviews to this special issue.