In the period of sector 4.0, safety, effectiveness and trustability of sector equipment is an abecedarian concern in marketing sectors. The accurate remaining useful life (RUL) vaticination of an outfit in due time allows us to effectively plan the conservation operation and alleviate the time-out to raise the profit of business. In the once decade, data driven grounded RUL prognostic styles had gained a lot of interest among the experimenters. There live colorful deep literacy- grounded ways which have been used for accurate RUL estimation. One of the extensively used methods in this regard is the long short- term memory( LSTM) networks. To further enhance the vaticination delicacy of LSTM networks, our paper proposes a model in which efficacious pre-processing ways are collaborated with LSTM network. C- MAPSS turbofan machine declination dataset released by NASA is used to corroborate the performance of the proposed model.

RUL, turbofan, C-MAPSS,LSTM.