Deep Learning-Driven Data Curation and Model Interpretation for Smart Manufacturing  

作　　者：Jianjing Zhang Robert X.Gao 

机构地区：[1]Department of Mechanical and Aerospace Engineering,Case Wester Reserve University,Cleveland,OH 44106-7222,USA

出　　处：《Chinese Journal of Mechanical Engineering》2021年第3期52-72,共21页中国机械工程学报（英文版）

摘　　要：Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environments,smart manufacturing as envisioned under Industry 4.0 aims to improve the throughput and reliability of production beyond the state-of-the-art.While the widespread application of deep learning(DL)has opened up new opportunities to accomplish the goal,data quality and model interpretability have continued to present a roadblock for the widespread acceptance of DL for real-world applications.This has motivated research on two fronts:data curation,which aims to provide quality data as input for meaningful DL-based analysis,and model interpretation,which intends to reveal the physical reasoning underlying DL model outputs and promote trust from the users.This paper summarizes several key techniques in data curation where breakthroughs in data denoising,outlier detection,imputation,balancing,and semantic annotation have demonstrated the effectiveness in information extraction from noisy,incomplete,insufficient,and/or unannotated data.Also highlighted are model interpretation methods that address the“black-box”nature of DL towards model transparency.

关 键 词：Deep learning Data curation Model interpretation 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TH16[自动化与计算机技术—控制科学与工程]