报告题目：Health Status Assessment of Industrial Equipment: Challenges, Methods, and Applications

报告摘要：In most real-world scenarios, industrial equipment operates under highly variable conditions, where data available from new operating environments are often limited, and data collected under different conditions typically violate the assumption of independent and identically distributed samples. Consequently, health status assessment of industrial equipment faces two key challenges: 1) How to efficiently learn key indicators from small samples at early degradation stages? 2) How to effectively handle distribution shifts across multiple operating conditions? This talk presents an AI-centric workflow that addresses these challenges in a device-agnostic way with a representative case study of lithium-ion battery health management. The workflow comprises two complementary components. First, to address the challenge of limited early-stage samples, an early information utilization strategy is introduced to transform sparse initial observations into remaining useful life prediction and health status estimation. Second, to cope with distribution discrepancies among multiple operating conditions, a domain adaptation approach is employed to learn transferable representations across individuals and conditions, thereby enhancing the dynamic adaptability of health status assessment model. Together, these elements provide a device-agnostic route to more reliable health status assessment under small samples and diverse operating conditions.

报告人简介：刘伟伯博士, 英国布鲁奈尔大学计算机系讲师，于2020年4月在英国布鲁奈尔大学计算机系获得博士学位。主要研究方向包括智能数据分析、演化计算、迁移学习、机器学习、医疗数据分析和工业大数据。曾获2019年国家优秀自费留学生奖学金。目前担任国际学术期刊Journal of Ambient Intelligence and Humanized Computing和Journal of Cognitive Computation的副编辑以及国际学术期刊Scientific Reports的编委会成员。同时也担任多项国际学术会议的程序委员会成员和多个国际期刊和会议的审稿人。

报告时间：2025年11月20日（星期四）下午 4: 00

报告地点：扬州大学瘦西湖校区数学学院208报告厅

主办单位：扬州大学数学学院

联系人：刘玉荣

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