Aeroengine Life Prediction and Status Evaluation Based on Sequential Multitask Learning and Health Indicators
Anping Wan, Hua Zhang, Ting Chen, Khalil AL-Bukhaiti, Wenhui Wang, Jinglin Wang, Tianmin Shan, Luoke Hu
Abstract
In this article, we introduce a novel method that uses enhanced sequential multitask learning to predict the remaining useful life (RUL) of an aeroengine accurately and efficiently while evaluating its status. The method innovatively employs extreme gradient boosting to extract critical performance parameters and construct a robust health indicator representing performance degradation. To capture the time-series features of the health indicator, the study modifies the traditional multigate mixture-of-experts (MMoE) model and integrates it with the gated recurrent unit (GRU) network, creating a hybrid MMoE-GRU model. In addition, we propose a dynamic weight balancing method to optimize the tradeoff in the joint loss function for multitask learning. Extensive experiments on the new commercial modular aero-propulsion system simulation (N-CMAPSS) dataset demonstrate that the proposed method significantly outperforms the existing models, achieving lower error rates and higher accuracy in RUL prediction and health status evaluation. The technique has a root-mean-square error (RMSE) of 7.1%, 6.9%, 1.3%, 0.6%, 1.7%, and 1.5% lower than the long short-term memory (LSTM), GRU, sequence-based bottom information gated recurrent unit (SB-GRU), deep gated recurrent unit (DGRU), multi-scale and multi-task convolutional neural network (M<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup>STCNN), and MMoE models. The average accuracy of the proposed method is 96.732%, which is 10.629%, 1.587%, and 2.499% higher than those of the LSTM, DGRU, and MMoE, respectively. The superiority of the proposed method is validated on the N-CMAPSS dataset.