RT Journal Article
T1 Evolutionary LSTM-FCN networks for pattern classification in industrial processes
A1 Ortego, Patxi
A1 Diez-Olivan, Alberto
A1 Del Ser, Javier
A1 Veiga, Fernando
A1 Penalva, Mariluz
A1 Sierra, Basilio
AB The Industry 4.0 revolution allows gathering big amounts of data that are used to train and deploy Artificial Intelligence algorithms to solve complex industrial problems, optimally and automatically. From those, Long-Short Term Memory Fully Convolutional Network (LSTM-FCN) networks are gaining a lot of attention over the last decade due to their capability of successfully modeling nonlinear feature interactions. However, they have not been yet fully applied for pattern classification tasks in time series data within the digital industry. In this paper, a novel approach based on an evolutionary algorithm for optimizing the networks hyperparameters and on the resulting deep learning model for pattern classification is proposed. In order to demonstrate the applicability of this method, a test scenario that involves a process related to blind fastener installation in the aeronautical industry is provided. The results achieved with the proposed approach are compared with shallow models and it is demonstrated that the proposed method obtains better results with an accuracy value of 95%.
SN 2210-6502
YR 2020
FD 2020-05
LK https://hdl.handle.net/11556/3283
UL https://hdl.handle.net/11556/3283
LA eng
NO Ortego , P , Diez-Olivan , A , Del Ser , J , Veiga , F , Penalva , M & Sierra , B 2020 , ' Evolutionary LSTM-FCN networks for pattern classification in industrial processes ' , Swarm and Evolutionary Computation , vol. 54 , 100650 . https://doi.org/10.1016/j.swevo.2020.100650
NO Publisher Copyright: © 2020 Elsevier B.V.
NO This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 686827. This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 686827 .
DS TECNALIA Publications
RD 1 ago 2024