Gated Transient Fluctuation Dual Attention Unit Network for Long-Term Remaining Useful Life Prediction of Rotating Machinery Using IIoT

The Industrial Internet of Things (IIoT) has greatly facilitated the development of prognostics health management (PHM) for rotating machinery. In the era of IIoT, the long-term prediction of remaining useful life (RUL) can provide a reliable basis for maintenance decisions of rotating machinery. However, most existing RUL prediction methods neglect to learn the transient fluctuation information in the health indicator (HI), which leads to low long-term prediction accuracy. Therefore, a new gated transient fluctuation dual attention unit (GTFDAU) network is designed to achieve sufficient learning of transient fluctuation information, which can improve long-term prediction accuracy. The GTFDAU network mainly comprises three novel gate structures: 1) transient fluctuation gate; 2) historical attention gate; and 3) current attention gate. The transient fluctuation gate is used to capture transient fluctuation information, which can deeply mine the historical information. The historical attention gate is used to adaptively focus on transient fluctuation information and the hidden state of the last moment, which can effectively reshape the historical information. The current attention gate is used to adaptively focus on historical information and current input information, which can comprehensively update the current hidden state. The proposed method has superior long-term prediction capability compared to existing state-of-the-art methods without compromising short-term prediction performance through experimental verification of RUL for rolling bearings and fatigue gears.