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

Document Type

Article

Publication Date

8-1-2023

Abstract

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%.

Identifier

85168755399 (Scopus)

Publication Title

Sensors

External Full Text Location

https://doi.org/10.3390/s23167040

ISSN

14248220

PubMed ID

37631576

Issue

16

Volume

23

Grant

2018YFB1802401

Fund Ref

National Key Research and Development Program of China

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