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报告题目:MoniNet with Concurrent Analytics of Temporal and Spatial Information for Fault Detection in Industrial Processes


报告地点:苏州大学阳澄湖校区 行政楼416会议室

报告摘要:Modern industrial plants generally consist of multiple manufacturing units, and the local correlation within each unit can be used to effectively alleviate the effect of spurious correlation and meticulously reflect the operation status of the process system. Therefore, the local correlation which is named as spatial information here should also be taken into consideration when developing monitoring model. In this study, a cascaded monitoring network (MoniNet) method is proposed to develop monitoring model with concurrent analytics of temporal and spatial information. By implementing convolutional operation to each variable, the temporal information which reveals dynamic correlation of process data and spatial information which reflects local characteristic within individual operation unit can be extracted simultaneously. For each convolutional feature, a sub-model is developed and then all the sub-models are integrated to generate a final monitoring model. Based on the developed model, the operation status of the newly collected sample can be identified by comparing the calculated statistics with their corresponding control limits. Similar to convolutional neural network, the MoniNet can also expand its receptive field and capture deeper information by adding more convolutional layers. Besides, the filter selection and sub-model development in MoniNet can be replaced to generalize the proposed network to many existing monitoring strategies. The performance of the proposed method is validated using two real industrial processes. Illustration results show that the proposed method can effectively detect process anomalies by concurrent analytics of temporal and spatial information.

专家简介:余万科,中国地质大学自动化学院特任教授,主要研究方向为复杂工业过程数据解析与智能监控等。于2013年、2016年和2020年分别在东北大学、北京航空航天大学大学和浙江大学获得学士、硕士和博士学位,201812月到201906月在加拿大阿尔伯塔大学交流访问。参与国家863计划、国家自然科学基金重点项目、浙江省重点研发项目等一系列科研项目。以第一作者在IEEE汇刊发表论文九篇、IFAC会刊发表论文一篇,以第二作者(导师一作)撰写十四五规划重点图书一部,发表自动化学报优秀综述论文一篇。其中三篇论文被评为 ESI 高被引文章,Google学术引用达320余次,。为控制领域多个顶级期刊(包括IEEE TCSTCEPJPCIEEE TCIEEE TNNLSIEEE TII等)审稿,并获得2020CEP期刊Top Reviewer2019CEP期刊的Best reviewer2018Neurocomputing期刊的Outstanding reviewer