Predicting machine failure using recurrent neural network-gated recurrent unit (RNN-GRU) through time series data

Time series data often involves big size environment that lead to high dimensionality problem. Many industries are generating time continuously update each second. The arising of machine learning may help in managing the data. It can forecast future instance while handling large issues. Forecasting is related predicting task an upcoming event avoid any circumstances happen current environment. helps those sectors such as production foresee state line with saving cost from sudden breakdown unplanned failure disrupt operation and loss up millions. Thus, this paper offers a deep algorithm named recurrent neural network-gated unit (RNN-GRU) machines producing oil gas sector. RNN-GRU affiliation network (RNN) control consecutive due existence reset gates. gates decided on necessary information be kept memory. simpler structure long short-term memory (RNN-LSTM) 87% accuracy prediction.