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%.

工业4.0革命允许收集大量数据，这些数据用于训练和部署人工智能算法，以最佳，自动的方式解决复杂的工业问题。从中，长期记忆全卷积网络（LSTM-FCN）网络由于能够成功地对非线性特征相互作用进行建模的能力而在过去十年中引起了广泛关注。但是，它们尚未完全应用于数字行业中时间序列数据中的模式分类任务。本文提出了一种基于进化算法的优化网络超参数的新方法，并提出了一种用于模式分类的深度学习模型。为了证明此方法的适用性，提供了一种测试方案，该方案涉及与航空业中的盲紧固件安装有关的过程。将所提方法获得的结果与浅层模型进行比较，结果表明所提方法获得了更好的结果，准确度为95％。