TY  - JOUR
EP  - 252
SN  - 21945357
PB  - Springer Verlag
N1  - cited By 5; Conference of 8th Computer Science On-line Conference, CSOC 2019 ; Conference Date: 24 April 2019 Through 27 April 2019; Conference Code:225859
SP  - 243
TI  - Machine failure prediction technique using recurrent neural network long short-term memory-particle swarm optimization algorithm
AV  - none
UR  - 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
JF  - Advances in Intelligent Systems and Computing
A1  - Rashid, N.A.
A1  - Abdul Aziz, I.
A1  - Hasan, M.H.B.
VL  - 985
Y1  - 2019///
N2  - 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 predicting long-term dependencies. Issues such as vanishing and exploding gradients are the results of backpropagating errors, taking place when the network is learning to store and relate information over extended time intervals. RNN-LSTM is a feasible technique for this research due to its effectiveness in resolving sequential long-term dependencies problems. However, the accuracy can still be enhanced to a satisfactory value considering the optimal network topology has not been discovered yet. Accuracy improvement can be achieved by resorting to hyperparameter tuning. A result of proof of concept validates that by increasing the number of epochs, the accuracy of prediction has improved but increases the execution time. To optimize between the accuracy and execution time, a population-inspired Particle Swarm Optimization (PSO) algorithm is employed. PSO will be utilized to select the optimal RNN-LSTM topology specifically the learning rate instead of using manual search. This optimized hybrid prediction technique is useful to be implemented in predictive maintenance to predict machine failure. © Springer Nature Switzerland AG 2019.
KW  - Brain; Forecasting; Learning systems; Particle swarm optimization (PSO); Topology
KW  -  Accuracy Improvement; Hybrid predictions; Long-term dependencies; Optimal network topology; Particle swarm optimization algorithm; Predictive maintenance; Proof of concept; Remaining useful lives
KW  -  Long short-term memory
ID  - scholars12165
ER  -