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Exploring LSTM based recurrent neural network for failure time series prediction

WANG Xin;WU Ji;LIU Chao;YANG Haiyan;DU Yanli;NIU Wensheng;School of Computer Science and Engineering,Beijing University of Aeronautics and Astronautics;Fengtai Vocational Education Central School;Aeronautical Computing Technique Research Institute,Aviation Industry Corporation of China;  
Effectively forecasting the failure data in the usage stage is essential to reasonably make reliability plans and carry out reliability maintaining activities. Beginning with the historical failure data of complex system,a long short-term memory(LSTM) based recurrent neural network for failure time series prediction is presented,in which the design of network structure,the procedures and algorithms of network training and forecasting are involved. Furthermore,a multilayer grid search algorithm is proposed to optimize the parameters of LSTM prediction model. The experimental results are compared with various typical time series prediction models,and validate that the proposed LSTM prediction model and the corresponding parameter optimization algorithm have strong adaptiveness and higher accuracy in failure time series prediction.
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