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Accurately estimating the remaining useful life (RUL) of industrial machineryis beneficial in many real-world applications. Estimation techniques havemainly utilized linear models or neural network based approaches with a focuson short term time dependencies. This paper, introduces a system model thatincorporates temporal convolutions with both long term and short term timedependencies. The proposed network learns salient features and complex temporalvariations in sensor values, and predicts the RUL. A data augmentation methodis used for increased accuracy. The proposed method is compared with severalstate-of-the-art algorithms on publicly available datasets. It demonstratespromising results, with superior results for datasets obtained from complexenvironments.

Temporal Convolutional Memory Networks for Remaining Useful Life Estimation of Industrial Machinery