CaFANet: Causal-Factors-Aware Attention Networks for Equipment Fault Prediction in the Internet of Things

Zhenwen Gui, Shuaishuai He, Yao Lin, Xin Nan, Xiaoyan Yin, Chase Q. Wu

Research output: Contribution to journalArticlepeer-review


Existing fault prediction algorithms based on deep learning have achieved good prediction performance. These algorithms treat all features fairly and assume that the progression of the equipment faults is stationary throughout the entire lifecycle. In fact, each feature has a different contribution to the accuracy of fault prediction, and the progress of equipment faults is non-stationary. More specifically, capturing the time point at which a fault first appears is more important for improving the accuracy of fault prediction. Moreover, the progress of the different faults of equipment varies significantly. Therefore, taking feature differences and time information into consideration, we propose a Causal-Factors-Aware Attention Network, CaFANet, for equipment fault prediction in the Internet of Things. Experimental results and performance analysis confirm the superiority of the proposed algorithm over traditional machine learning methods with prediction accuracy improved by up to 15.3%.

Original languageEnglish (US)
Article number7040
Issue number16
StatePublished - Aug 2023

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


  • attention mechanism
  • causal factors
  • fault prediction
  • industrial Internet of Things


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