An Adaptive Parallel Feature Learning and Hybrid Feature Fusion-Based Deep Learning Approach for Machining Condition Monitoring

Bufan Liu, Chun Hsien Chen, Pai Zheng (Corresponding Author), Geng Zhang (Corresponding Author)

Research output: Journal article publicationJournal articleAcademic researchpeer-review

6 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Cybernetics
Publication statusAccepted/In press - 2022


  • Adaptation models
  • Attention module
  • Condition monitoring
  • deep learning (DL)
  • Feature extraction
  • feature fusion
  • Industrial Internet of Things
  • Industrial Internet of Things (IIoT)
  • Machining
  • machining condition monitoring
  • Monitoring
  • Representation learning

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Information Systems
  • Human-Computer Interaction
  • Computer Science Applications
  • Electrical and Electronic Engineering


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