A multi-source feature-level fusion approach for predicting strip breakage in cold rolling

Chen, Zheyuan, Liu, Ying ORCID: https://orcid.org/0000-0001-9319-5940, Valera Medina, Agustin ORCID: https://orcid.org/0000-0003-1580-7133 and Robinson, Fiona 2020. A multi-source feature-level fusion approach for predicting strip breakage in cold rolling. Presented at: 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE), Virtual, 20-24 August 2020. 2020 IEEE 16th International Conference on Automation Science and Engineering (CASE). IEEE, pp. 482-487. 10.1109/CASE48305.2020.9216854

PDF - Accepted Post-Print Version Download (482kB)

Abstract

As an undesired and instantaneous failure in the production of cold-rolled strip products, strip breakage results in yield loss, reduced work speed and further equipment damage. Typically, studies have investigated this failure in a retrospective way focused on root cause analyses, and these causes are proven to be multi-faceted. In order to model the onset of this failure in a predictive manner, an integrated multi-source feature-level approach is proposed in this work. Firstly, by harnessing heterogeneous data across the breakage-relevant processes, blocks of data from different sources are collected to improve the breadth of breakage-centric information and are pre-processed according to its granularity. Afterwards, feature extraction or selection is applied to each block of data separately according to the domain knowledge. Matrices of selected features are concatenated in either flattened or expanded manner for comparison. Finally, fused features are used as inputs for strip breakage prediction using recurrent neural networks (RNNs). An experimental study using real-world data instantaneouseffectiveness of the proposed approach.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Engineering
Subjects:	T Technology > TA Engineering (General). Civil engineering (General) T Technology > TS Manufactures
Publisher:	IEEE
ISBN:	9781728169040
Date of First Compliant Deposit:	11 June 2020
Date of Acceptance:	30 May 2020
Last Modified:	25 Jul 2024 13:11
URI:	https://orca.cardiff.ac.uk/id/eprint/132301

Citation Data

Cited 5 times in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item