A stacked ensemble method based on TCN and convolutional bi-directional GRU with multiple time windows for remaining useful life estimation

With the widespread popularity of sensors, time-series data of engine degradation processes have been widely applied for remaining useful life (RUL) prediction. As a result, the large-dimensional, large-scale, and multi-state data characteristics of degraded data make accurate prediction very challenging. To overcome this issue, this paper proposes a stacked integration method based on temporal convolutional network (TCN) and convolutional bi-directional gate recurrent unit (CNN-Bi-GRU) with multiple time windows for RUL prediction, which has smaller ensemble dimensions and stronger reliability and adaptability. In the proposed model, the TCN model can well overcome the limitations of large amounts of data which leads to the problem of difficulty in learning temporal relationships; CNN-Bi-GRU model is used to extract important features to solve the problem of high-dimensional data. In addition, the multi-time window method is used to enhance the adaptability of the method and increase the information obtained by the model. Compared with the popular prediction methods, the prediction results of the proposed ensemble model have been improved by about 20 % on average on data sets. In addition, on the N-CMAPSS data set with larger data volume and more diverse states, the proposed model also achieves an average improvement of about 10% over comparison methods. It shows that the proposed method enhances the reliability and applicability of prediction.