Machine failure prediction technique using recurrent neural network long short-term memory-particle swarm optimization algorithm

• 主要な著者: Rashid, N.A., Abdul Aziz, I., Hasan, M.H.B.
• フォーマット: 論文
• 出版事項: Springer Verlag 2019
• オンライン･アクセス: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85065925843&doi=10.1007%2f978-3-030-19810-7_24&partnerID=40&md5=f32eb9404f711e8e244ade6d877db179; http://eprints.utp.edu.my/23513/

This paper proposes a hybrid prediction technique based on Recurrent Neural Network Long-Short-Term Memory (RNN-LSTM) with the integration of Particle Swarm Optimization (PSO) algorithm to estimate the Remaining Useful Life (RUL) of machines. LSTM is an improvement of RNN as RNN faces issues with pr...