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, its health status affects seriously efficiency. The statuses 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) extreme learning machine (ELM) is proposed for bearing. Firstly, WPD utilized eliminate noise data. Secondly, DGRU designed extract representative features denoised Finally, ELM output results. Massive results prove superiority robustness our approach outperform existing popular methods. Additionally, achieve powerful antinoise ability.