In the prognostics and health management (PHM) of industrial systems, prediction of remaining useful life (RUL) is a crucial task. RUL prediction is based on data collected from the industrial system, and involves learning underlying health indicator trends. As industrial systems are complex and can be monitored by different sensors, time alignment of multiple temporal data streams and extraction of their underlying characteristics are essential to perform an accurate prognostics. Hence, this paper aims to develop an efficient method to address the above issue. The proposed method is based on the attention and convolution mechanisms of deep neural networks. Its performance is highlighted when compared to other state of the art models such as RNN and LSTM using the C-MAPSS datasets. Numerous experiments demonstrate that our model provides better results in some situations, as well as an ability to capture both local short term contexts and long term associations.