10.5281/zenodo.1070431
https://zenodo.org/records/1070431
oai:zenodo.org:1070431
Hyun-Woo Cho
Hyun-Woo Cho
A Hybrid Scheme for on-Line Diagnostic Decision Making Using Optimal Data Representation and Filtering Technique
Zenodo
2013
Diagnostics
batch process
nonlinear representation
data filtering
multivariate statistical approach
2013-01-26
eng
10.5281/zenodo.1070430
https://zenodo.org/communities/waset
8191
Creative Commons Attribution 4.0 International
The early diagnostic decision making in industrial processes is absolutely necessary to produce high quality final products. It helps to provide early warning for a special event in a process, and finding its assignable cause can be obtained. This work presents a hybrid diagnostic schmes for batch processes. Nonlinear representation of raw process data is combined with classification tree techniques. The nonlinear kernel-based dimension reduction is executed for nonlinear classification decision boundaries for fault classes. In order to enhance diagnosis performance for batch processes, filtering of the data is performed to get rid of the irrelevant information of the process data. For the diagnosis performance of several representation, filtering, and future observation estimation methods, four diagnostic schemes are evaluated. In this work, the performance of the presented diagnosis schemes is demonstrated using batch process data.