An Adaptive Parallel Feature Learning and Hybrid Feature Fusion-Based Deep Learning Approach for Machining Condition Monitoring
提出一种深度学习方法，通过无监督并行特征提取、自适应特征重要性加权和混合特征融合，解决加工状态监测中单域空间和手工特征利用不足的问题，实验验证了其有效性。
The rapid development of information and communication technologies has facilitated machining condition monitoring toward a data-driven paradigm, of which the Industrial Internet of Things (IIoT) serves as the fundamental basis to acquire data from physical equipment with sensing technologies as well as to learn the relationship between the system condition and the collected condition monitoring data. However, most data-driven methods suffer from using a single-domain space, ignoring the importance of the learned features, and failing to incorporate the handcrafted features assisted by domain knowledge. To solve these limitations, a novel deep learning approach is proposed for machining condition monitoring in the IIoT environment, which consists of three phases, including: 1) the unsupervised parallel feature extraction; 2) adaptive feature importance weighting; and 3) hybrid feature fusion. First, separate sparse autoencoders are utilized to conduct the unsupervised parallel feature extraction, which enables to learn abstract feature representation from multiple domain spaces simultaneously. Then, an attention module is designed for the adaptive feature importance weighting, which can assign higher weights to those critical features accordingly. Moreover, a hybrid feature fusion is deployed to complement the automatic feature learning and further yield better model performance by fusing the handcrafted features assisted by domain knowledge. Finally, a real-life case study and extensive experiments have been conducted to show the effectiveness and superiority of the proposed approach.