An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery

As the most basic component of rotating machinery, rolling bearing frequently works in harsh environments and complex working conditions, and its health status affects seriously the working efficiency. The health statuses of rolling bearing can not only reduce equipment maintenance costs but also contribute to reducing major accidents. Based on this, an adaptive diagnosis method that combines deep gated recurrent unit (DGRU) with wavelet packet decomposition (WPD) and extreme learning machine (ELM) is proposed for rolling bearing. Firstly, WPD is utilized to eliminate the noise of data. Secondly, DGRU is designed to extract the representative features of denoised data. Finally, ELM is utilized to output the diagnosis results. Massive results prove that the superiority and robustness of our approach outperform existing popular methods. Additionally, the proposed method can also achieve powerful antinoise ability.

Automated summary

This paper proposes an adaptive diagnosis method that combines DGRU, WPD, and ELM for rolling bearing, which effectively eliminates noise, extracts features, and outputs diagnosis results to monitor the health status of rolling bearings.