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

• 作者: Jianjing Zhang Robert X.Gao
• 来源期刊: 中国机械工程学报
• 年，卷（期）: 2021,（3）
• 所属期刊栏目: Special Issue on AI-Enabled Monitoring Diagnosis & Prognosis
• 页码范围: 52-72
• 页数: 21页
• 语种: 英文

摘要

Characterized by self-monitoring and agile adaptation to fast changing dynamics in complex production environ-ments,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 cura-tion,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,impu-tation,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.