  Accurately estimating the remaining useful life (RUL) of industrial machinery
is beneficial in many real-world applications. Estimation techniques have
mainly utilized linear models or neural network based approaches with a focus
on short term time dependencies. This paper, introduces a system model that
incorporates temporal convolutions with both long term and short term time
dependencies. The proposed network learns salient features and complex temporal
variations in sensor values, and predicts the RUL. A data augmentation method
is used for increased accuracy. The proposed method is compared with several
state-of-the-art algorithms on publicly available datasets. It demonstrates
promising results, with superior results for datasets obtained from complex
environments.
