@inproceedings{c98dbb50963d4c4fa17a2135c15973cb,
title = "Health monitoring of industrial processes - Challenges and solutions",
abstract = "To ensure the health and reliability of increasingly complicated industrial processes, the study and application of health monitoring systems is necessary. Considering the difficulty of mathematical modeling and the availability of massive process data, the so-called data-driven methods gain advantage over model-based ones. Therefore, it is promising and significant to design efficient data-driven health monitoring schemes for particular industrial conditions. In this talk, three circumstances, i.e. stationary operating conditions, dynamic processes and large-scale processes involving changes, are investigated respectively. Correspondingly, the modifications of the standard multivariate statistical approaches, the advanced schemes based on the identification of key components and a novel data-driven adaptive scheme are proposed, which all provide enhanced health monitoring performance.",
keywords = "Data-driven, Dynamic, Health monitoring, Model-based, Stationary, Time-varying",
author = "Shen Yin",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 12th International Conference on Emerging Technologies, ICET 2016 ; Conference date: 18-10-2016 Through 19-10-2016",
year = "2017",
month = jan,
day = "10",
doi = "10.1109/ICET.2016.7813282",
language = "英语",
series = "ICET 2016 - 2016 International Conference on Emerging Technologies",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Ghulam Mustafa and Gul, \{Sufi Tabassum\} and Shahzad Nadeem",
booktitle = "ICET 2016 - 2016 International Conference on Emerging Technologies",
address = "美国",
}