Dual Siamese transformer-encoder-based network for remaining useful life prediction

Accurately predicting the capacity and remaining useful life (RUL) of lithium-ion batteries is crucial for their reliable and safe functioning. In this research, we propose a dual Siamese transformer-encoder-based network consisting of two subnetworks to improve the RUL prediction capability and for wider applications. The first subnetwork, autoTrans, adopts a transformer-encoder architecture to form an autoencoder structure for the feature extraction. The second subnetwork, regTrans, is a transformer-encoder-based regressor model which takes the featuring encodings from autoTrans as inputs and makes the RUL prediction. To enhance the model's robustness, both subnetworks employ the Siamese architecture for handling raw and noisy inputs. A joint training strategy is applied on autoTrans and regTrans to optimize the proposed approach. The experimental verification is conducted on the NASA battery, and our model achieves the best average results across different evaluation criteria. Furthermore, we also apply our model to the turbofan engine dataset and demonstrate promising performance as well.