Health status assessment and remaining useful life prediction of aero-engine based on BiGRU and MMoE

Prognostics and health management (PHM) is a critical work to ensure the reliable operation of industrial equipment, in which health status (HS) assessment and remaining useful life (RUL) prediction are two key tasks. However, traditional PHM frameworks perform the two tasks separately, which ignore the internal relationship between the two tasks and reduce the efficiency of PHM. To solve the above issues, a dual-task network structure is proposed in this paper based on bidirectional gated recurrent unit (BiGRU) and multigate mixture-of-experts (MMoE), which simultaneously evaluates the HS and predict the RUL of industrial equipment. To be specific, BiGRU is used to bidirectionally extract shared information from sensor signals for HS and RUL, and MMoE structure is employed to adaptively differentiate between HS assessment and RUL prediction tasks and realizes a weighted decision making. Furthermore, a loss function based on homoscedastic uncertainty is adopted to learn optimal tradeoff weight between HS assessment loss and RUL prediction loss based on probabilistic modeling, which avoids a time-consuming manual weight tuning process. Experiments on C-MAPSS of aero-engines degradation dataset verify that the proposed method performs better than current popular models, and robustness of the proposed method is satisfactory.

Automated summary

Prognostics and health management (PHM) is crucial for ensuring the reliable operation of industrial equipment. This paper proposes a dual-task network structure that simultaneously evaluates the health status and predicts the remaining useful life of the equipment. The use of bidirectional gated recurrent unit and multigate mixture-of-experts enhances prediction accuracy and efficiency through information extraction and weighted decision making.