This paper proposes a novel RUL estimation algorithm using the attention mechanism. The proposed method applies scaled dot product attention to encoder and decoder consisting of long short-term memory, convolutional neural network and fully connected layer. The encoder applies self-attention to extract the association between each time sequences, and the decoder extracts the association between the target RUL value and the time sequences using the representative vector of the RUL. Therefore, the proposed model better captures the long-term dependency between sequence data and outperforms other state-of-the-art models in the C-MAPSS dataset.