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.

中文翻译：

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用于加工状态监测的自适应并行特征学习和基于混合特征融合的深度学习方法。

信息和通信技术的快速发展促进了加工状态监测向数据驱动的方向发展，其中工业物联网（IIoT）是利用传感技术从物理设备获取数据并学习机器学习的基础。系统状态与收集的状态监测数据之间的关系。然而，大多数数据驱动的方法都受到使用单域空间的困扰，忽略了学习特征的重要性，并且未能合并由领域知识辅助的手工特征。为了解决这些局限性，提出了一种新颖的深度学习方法，用于工业物联网环境中的加工状态监测，该方法由三个阶段组成，包括：1）无监督并行特征提取；2）自适应特征重要性加权；3）混合特征融合。首先，利用单独的稀疏自动编码器进行无监督并行特征提取，这使得能够同时从多个域空间学习抽象特征表示。然后，为自适应特征重要性加权设计了注意力模块，可以相应地为那些关键特征分配更高的权重。此外，部署混合特征融合来补充自动特征学习，并通过融合领域知识辅助的手工特征进一步产生更好的模型性能。最后，进行了现实案例研究和广泛的实验，以证明所提出方法的有效性和优越性。